,
Avi Srivastava [aut] ,
Paul Hoffman [ctb] ,
Rahul Satija [ctb] | Title: | Analysis of Single-Cell Chromatin Data |
|---|---|
| Description: | A framework for the analysis and exploration of single-cell chromatin data. The 'Signac' package contains functions for quantifying single-cell chromatin data, computing per-cell quality control metrics, dimension reduction and normalization, visualization, and DNA sequence motif analysis. Reference: Stuart et al. (2021) <doi:10.1038/s41592-021-01282-5>. |
| Authors: | Tim Stuart [aut, cre] ,
Avi Srivastava [aut] ,
Paul Hoffman [ctb] ,
Rahul Satija [ctb] |
| Maintainer: | Tim Stuart <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.14.0 |
| Built: | 2024-11-20 03:24:11 UTC |
| Source: | https://github.com/stuart-lab/signac |
A framework for the analysis and exploration of single-cell chromatin data. The 'Signac' package contains functions for quantifying single-cell chromatin data, computing per-cell quality control metrics, dimension reduction and normalization, visualization, and DNA sequence motif analysis. Reference: Stuart et al. (2021) doi:10.1038/s41592-021-01282-5.
Maintainer: Tim Stuart [email protected] (ORCID)
Authors:
Avi Srivastava [email protected] (ORCID)
Other contributors:
Paul Hoffman [email protected] (ORCID) [contributor]
Rahul Satija [email protected] (ORCID) [contributor]
Useful links:
Report bugs at https://github.com/stuart-lab/signac/issues
Find accessible peaks in a set of cells
AccessiblePeaks( object, assay = NULL, idents = NULL, cells = NULL, min.cells = 10 )AccessiblePeaks( object, assay = NULL, idents = NULL, cells = NULL, min.cells = 10 )
object |
A Seurat object |
assay |
Name of assay to use |
idents |
A set of identity classes to find accessible peaks for |
cells |
A vector of cells to find accessible peaks for |
min.cells |
Minimum number of cells with the peak accessible (>0 counts) for the peak to be called accessible |
Returns a vector of peak names
Compute chromVAR deviations for groups of peaks. The goal of this function is
similar to that of AddModuleScore except that it is
designed for single-cell chromatin data. The chromVAR deviations for each
group of peaks will be added to the object metadata.
AddChromatinModule(object, features, genome, assay = NULL, verbose = TRUE, ...)AddChromatinModule(object, features, genome, assay = NULL, verbose = TRUE, ...)
object |
A Seurat object |
features |
A named list of features to include in each module. The name of each element in the list will be used to name the modules computed, which will be stored in the object metadata. |
genome |
A BSgenome object |
assay |
Name of assay to use. If NULL, use the default assay. |
verbose |
Display messages |
... |
Additional arguments passed to |
Returns a Seurat object
Construct a Motif object containing DNA sequence motif
information and add it to an existing Seurat object or ChromatinAssay.
If running on a Seurat object, AddMotifs will also run
RegionStats to compute the GC content of each peak and store
the results in the feature metadata. PFMs or PWMs are matched to the genome
sequence using the matchMotifs function with
default parameters to construct a matrix of motif positions in genomic
regions.
AddMotifs(object, ...) ## Default S3 method: AddMotifs(object, genome, pfm, verbose = TRUE, ...) ## S3 method for class 'ChromatinAssay' AddMotifs(object, genome, pfm, verbose = TRUE, ...) ## S3 method for class 'Assay' AddMotifs(object, genome, pfm, verbose = TRUE, ...) ## S3 method for class 'StdAssay' AddMotifs(object, genome, pfm, verbose = TRUE, ...) ## S3 method for class 'Seurat' AddMotifs(object, genome, pfm, assay = NULL, verbose = TRUE, ...)AddMotifs(object, ...) ## Default S3 method: AddMotifs(object, genome, pfm, verbose = TRUE, ...) ## S3 method for class 'ChromatinAssay' AddMotifs(object, genome, pfm, verbose = TRUE, ...) ## S3 method for class 'Assay' AddMotifs(object, genome, pfm, verbose = TRUE, ...) ## S3 method for class 'StdAssay' AddMotifs(object, genome, pfm, verbose = TRUE, ...) ## S3 method for class 'Seurat' AddMotifs(object, genome, pfm, assay = NULL, verbose = TRUE, ...)
object |
A Seurat object or ChromatinAssay object |
... |
Additional arguments passed to other methods |
genome |
A |
pfm |
A |
verbose |
Display messages |
assay |
Name of assay to use. If NULL, use the default assay |
When running on a ChromatinAssay or Seurat object,
returns a modified version of the input object. When running on a matrix,
returns a Motif object.
motifmatchr
Quantifies fragment counts per cell in fixed-size genome bins across the whole genome, then removes bins with less than a desired minimum number of counts in the bin, then merges adjacent tiles into a single region.
AggregateTiles(object, ...) ## S3 method for class 'Seurat' AggregateTiles( object, genome, assay = NULL, new.assay.name = "tiles", min_counts = 5, binsize = 5000, verbose = TRUE, ... ) ## S3 method for class 'ChromatinAssay' AggregateTiles( object, genome, min_counts = 5, binsize = 5000, verbose = TRUE, ... ) ## Default S3 method: AggregateTiles( object, genome, cells = NULL, min_counts = 5, binsize = 5000, verbose = TRUE, ... )AggregateTiles(object, ...) ## S3 method for class 'Seurat' AggregateTiles( object, genome, assay = NULL, new.assay.name = "tiles", min_counts = 5, binsize = 5000, verbose = TRUE, ... ) ## S3 method for class 'ChromatinAssay' AggregateTiles( object, genome, min_counts = 5, binsize = 5000, verbose = TRUE, ... ) ## Default S3 method: AggregateTiles( object, genome, cells = NULL, min_counts = 5, binsize = 5000, verbose = TRUE, ... )
object |
A Seurat object or ChromatinAssay object |
... |
Additional arguments passed to other methods |
genome |
genome A vector of chromosome sizes for the genome. This is used to construct the genome bin coordinates. The can be obtained by calling seqlengths on a BSgenome-class object. |
assay |
Name of assay to use |
new.assay.name |
Name of new assay to create containing aggregated genome tiles |
min_counts |
Minimum number of counts for a tile to be retained prior to aggregation |
binsize |
Size of the genome bins (tiles) in base pairs |
verbose |
Display messages |
cells |
Cells to include |
When running on a Seurat object, returns the Seurat object with a new
ChromatinAssay added.
When running on a ChromatinAssay, returns a new
ChromatinAssay containing the aggregated genome tiles.
When running on a fragment file, returns a sparse region x cell matrix.
Collapses allele counts for each strand and normalize by the total number of counts at each nucleotide position.
AlleleFreq(object, ...) ## Default S3 method: AlleleFreq(object, variants, ...) ## S3 method for class 'Assay' AlleleFreq(object, variants, ...) ## S3 method for class 'StdAssay' AlleleFreq(object, variants, ...) ## S3 method for class 'Seurat' AlleleFreq(object, variants, assay = NULL, new.assay.name = "alleles", ...)AlleleFreq(object, ...) ## Default S3 method: AlleleFreq(object, variants, ...) ## S3 method for class 'Assay' AlleleFreq(object, variants, ...) ## S3 method for class 'StdAssay' AlleleFreq(object, variants, ...) ## S3 method for class 'Seurat' AlleleFreq(object, variants, assay = NULL, new.assay.name = "alleles", ...)
object |
A Seurat object, Assay, or matrix |
... |
Arguments passed to other methods |
variants |
A character vector of informative variants to keep. For
example, |
assay |
Name of assay to use |
new.assay.name |
Name of new assay to store variant data in |
Returns a Seurat object with a new assay
containing the allele frequencies for the informative variants.
Get the annotation from a ChromatinAssay
Annotation(object, ...) Annotation(object, ...) <- value ## S3 method for class 'ChromatinAssay' Annotation(object, ...) ## S3 method for class 'Seurat' Annotation(object, ...) ## S3 replacement method for class 'ChromatinAssay' Annotation(object, ...) <- value ## S3 replacement method for class 'Seurat' Annotation(object, ...) <- valueAnnotation(object, ...) Annotation(object, ...) <- value ## S3 method for class 'ChromatinAssay' Annotation(object, ...) ## S3 method for class 'Seurat' Annotation(object, ...) ## S3 replacement method for class 'ChromatinAssay' Annotation(object, ...) <- value ## S3 replacement method for class 'Seurat' Annotation(object, ...) <- value
object |
A Seurat object or ChromatinAssay object |
... |
Arguments passed to other methods |
value |
A value to set. Can be NULL, to remove the current annotation
information, or a |
Returns a GRanges object
if the annotation data is present, otherwise returns NULL
Annotation(atac_small[["peaks"]]) Annotation(atac_small) genes <- Annotation(atac_small) Annotation(atac_small[["peaks"]]) <- genes genes <- Annotation(atac_small) Annotation(atac_small) <- genesAnnotation(atac_small[["peaks"]]) Annotation(atac_small) genes <- Annotation(atac_small) Annotation(atac_small[["peaks"]]) <- genes genes <- Annotation(atac_small) Annotation(atac_small) <- genes
Display gene annotations in a given region of the genome.
AnnotationPlot( object, region, assay = NULL, mode = "gene", sep = c("-", "-"), extend.upstream = 0, extend.downstream = 0 )AnnotationPlot( object, region, assay = NULL, mode = "gene", sep = c("-", "-"), extend.upstream = 0, extend.downstream = 0 )
object |
A |
region |
A genomic region to plot |
assay |
Name of assay to use. If NULL, use the default assay. |
mode |
Display mode. Choose either "gene" or "transcript" to determine whether genes or transcripts are plotted. |
sep |
Separators to use for strings encoding genomic coordinates. First element is used to separate the chromosome from the coordinates, second element is used to separate the start from end coordinate. |
extend.upstream |
Number of bases to extend the region upstream. |
extend.downstream |
Number of bases to extend the region downstream. |
Returns a ggplot object
AnnotationPlot(object = atac_small, region = c("chr1-29554-39554"))AnnotationPlot(object = atac_small, region = c("chr1-29554-39554"))
Convert objects to a ChromatinAssay
as.ChromatinAssay(x, ...) ## S3 method for class 'Assay' as.ChromatinAssay( x, ranges = NULL, seqinfo = NULL, annotation = NULL, motifs = NULL, fragments = NULL, bias = NULL, positionEnrichment = NULL, sep = c("-", "-"), ... )as.ChromatinAssay(x, ...) ## S3 method for class 'Assay' as.ChromatinAssay( x, ranges = NULL, seqinfo = NULL, annotation = NULL, motifs = NULL, fragments = NULL, bias = NULL, positionEnrichment = NULL, sep = c("-", "-"), ... )
x |
An object to convert to class |
... |
Arguments passed to other methods |
ranges |
A GRanges object |
seqinfo |
A |
annotation |
Genomic annotation |
motifs |
A |
fragments |
A list of |
bias |
Tn5 integration bias matrix |
positionEnrichment |
A named list of position enrichment matrices. |
sep |
Characters used to separate the chromosome, start, and end coordinates in the row names of the data matrix |
A subsetted version of 10x Genomics 10k human (hg19) PBMC scATAC-seq dataset
atac_smallatac_small
A Seurat object with the following assays
A peak x cell dataset
A 5 kb genome bin x cell dataset
A gene x cell dataset
https://support.10xgenomics.com/single-cell-atac/datasets/1.1.0/atac_v1_pbmc_10k
Compute the mean counts per group of cells for a given assay
AverageCounts(object, assay = NULL, group.by = NULL, verbose = TRUE)AverageCounts(object, assay = NULL, group.by = NULL, verbose = TRUE)
object |
A Seurat object |
assay |
Name of assay to use. Default is the active assay |
group.by |
Grouping variable to use. Default is the active identities |
verbose |
Display messages |
Returns a dataframe
AverageCounts(atac_small)AverageCounts(atac_small)
Create coverage tracks, heatmaps, or line plots from bigwig files.
BigwigTrack( region, bigwig, smooth = 200, extend.upstream = 0, extend.downstream = 0, type = "coverage", y_label = "bigWig", bigwig.scale = "common", ymax = NULL, max.downsample = 3000, downsample.rate = 0.1 )BigwigTrack( region, bigwig, smooth = 200, extend.upstream = 0, extend.downstream = 0, type = "coverage", y_label = "bigWig", bigwig.scale = "common", ymax = NULL, max.downsample = 3000, downsample.rate = 0.1 )
region |
GRanges object specifying region to plot |
bigwig |
List of bigwig file paths. List should be named, and the name of each element in the list of files will be displayed alongside the track in the final plot. |
smooth |
Number of bases to smooth data over (rolling mean). If NULL, do not apply smoothing. |
extend.upstream |
Number of bases to extend the region upstream. |
extend.downstream |
Number of bases to extend the region downstream. |
type |
Plot type. Can be one of "line", "heatmap", or "coverage" |
y_label |
Y-axis label |
bigwig.scale |
Scaling to apply to data from different bigwig files. Can be:
|
ymax |
Maximum value for Y axis. Can be one of:
|
max.downsample |
Minimum number of positions kept when downsampling. Downsampling rate is adaptive to the window size, but this parameter will set the minimum possible number of positions to include so that plots do not become too sparse when the window size is small. |
downsample.rate |
Fraction of positions to retain when downsampling. Retaining more positions can give a higher-resolution plot but can make the number of points large, resulting in larger file sizes when saving the plot and a longer period of time needed to draw the plot. |
Note that this function does not work on windows.
Returns a ggplot object
Set counts >1 to 1 in a count matrix
BinarizeCounts(object, ...) ## Default S3 method: BinarizeCounts(object, assay = NULL, verbose = TRUE, ...) ## S3 method for class 'Assay' BinarizeCounts(object, assay = NULL, verbose = TRUE, ...) ## S3 method for class 'Seurat' BinarizeCounts(object, assay = NULL, verbose = TRUE, ...)BinarizeCounts(object, ...) ## Default S3 method: BinarizeCounts(object, assay = NULL, verbose = TRUE, ...) ## S3 method for class 'Assay' BinarizeCounts(object, assay = NULL, verbose = TRUE, ...) ## S3 method for class 'Seurat' BinarizeCounts(object, assay = NULL, verbose = TRUE, ...)
object |
A Seurat object |
... |
Arguments passed to other methods |
assay |
Name of assay to use. Can be a list of assays, and binarization will be applied to each. |
verbose |
Display messages |
Returns a Seurat object
x <- matrix(data = sample(0:3, size = 25, replace = TRUE), ncol = 5) BinarizeCounts(x) BinarizeCounts(atac_small[['peaks']]) BinarizeCounts(atac_small)x <- matrix(data = sample(0:3, size = 25, replace = TRUE), ncol = 5) BinarizeCounts(x) BinarizeCounts(atac_small[['peaks']]) BinarizeCounts(atac_small)
Genomic blacklist regions for C. elegans ce10 (0-based)
blacklist_ce10blacklist_ce10
A GRanges object
https://github.com/Boyle-Lab/Blacklist
doi:10.1038/s41598-019-45839-z
Genomic blacklist regions for C. elegans ce11 (0-based)
blacklist_ce11blacklist_ce11
A GRanges object
https://github.com/Boyle-Lab/Blacklist
doi:10.1038/s41598-019-45839-z
Genomic blacklist regions for Drosophila dm3 (0-based)
blacklist_dm3blacklist_dm3
A GRanges object
https://github.com/Boyle-Lab/Blacklist
doi:10.1038/s41598-019-45839-z
Genomic blacklist regions for Drosophila dm6 (0-based)
blacklist_dm6blacklist_dm6
A GRanges object
https://github.com/Boyle-Lab/Blacklist
doi:10.1038/s41598-019-45839-z
Genomic blacklist regions for Human hg19 (0-based)
blacklist_hg19blacklist_hg19
A GRanges object
https://github.com/Boyle-Lab/Blacklist
doi:10.1038/s41598-019-45839-z
Genomic blacklist regions for Human GRCh38
blacklist_hg38blacklist_hg38
A GRanges object
https://github.com/Boyle-Lab/Blacklist
doi:10.1038/s41598-019-45839-z
Manually curated genomic blacklist regions for the hg38 genome by Anshul Kundaje and Anna Shcherbina. See https://www.encodeproject.org/files/ENCFF356LFX/ for a description of how this blacklist was curated.
blacklist_hg38_unifiedblacklist_hg38_unified
A GRanges object
Anshul Kundaje
Anna Shcherbina
https://www.encodeproject.org/files/ENCFF356LFX/
doi:10.1038/s41598-019-45839-z
Genomic blacklist regions for Mouse mm10 (0-based)
blacklist_mm10blacklist_mm10
A GRanges object
https://github.com/Boyle-Lab/Blacklist
doi:10.1038/s41598-019-45839-z
Call peaks using MACS. Fragment files linked to the specified assay will be
used to call peaks. If multiple fragment files are present, all will be used
in a single MACS invocation. Returns the .narrowPeak MACS output as a
GRanges object.
CallPeaks(object, ...) ## S3 method for class 'Seurat' CallPeaks( object, assay = NULL, group.by = NULL, idents = NULL, macs2.path = NULL, broad = FALSE, format = "BED", outdir = tempdir(), fragment.tempdir = tempdir(), combine.peaks = TRUE, effective.genome.size = 2.7e+09, extsize = 200, shift = -extsize/2, additional.args = NULL, name = Project(object), cleanup = TRUE, verbose = TRUE, ... ) ## S3 method for class 'ChromatinAssay' CallPeaks( object, macs2.path = NULL, outdir = tempdir(), broad = FALSE, format = "BED", effective.genome.size = 2.7e+09, extsize = 200, shift = -extsize/2, additional.args = NULL, name = "macs2", cleanup = TRUE, verbose = TRUE, ... ) ## S3 method for class 'Fragment' CallPeaks( object, macs2.path = NULL, outdir = tempdir(), broad = FALSE, format = "BED", effective.genome.size = 2.7e+09, extsize = 200, shift = -extsize/2, additional.args = NULL, name = "macs2", cleanup = TRUE, verbose = TRUE, ... ) ## Default S3 method: CallPeaks( object, macs2.path = NULL, outdir = tempdir(), broad = FALSE, format = "BED", effective.genome.size = 2.7e+09, extsize = 200, shift = -extsize/2, additional.args = NULL, name = "macs2", cleanup = TRUE, verbose = TRUE, ... )CallPeaks(object, ...) ## S3 method for class 'Seurat' CallPeaks( object, assay = NULL, group.by = NULL, idents = NULL, macs2.path = NULL, broad = FALSE, format = "BED", outdir = tempdir(), fragment.tempdir = tempdir(), combine.peaks = TRUE, effective.genome.size = 2.7e+09, extsize = 200, shift = -extsize/2, additional.args = NULL, name = Project(object), cleanup = TRUE, verbose = TRUE, ... ) ## S3 method for class 'ChromatinAssay' CallPeaks( object, macs2.path = NULL, outdir = tempdir(), broad = FALSE, format = "BED", effective.genome.size = 2.7e+09, extsize = 200, shift = -extsize/2, additional.args = NULL, name = "macs2", cleanup = TRUE, verbose = TRUE, ... ) ## S3 method for class 'Fragment' CallPeaks( object, macs2.path = NULL, outdir = tempdir(), broad = FALSE, format = "BED", effective.genome.size = 2.7e+09, extsize = 200, shift = -extsize/2, additional.args = NULL, name = "macs2", cleanup = TRUE, verbose = TRUE, ... ) ## Default S3 method: CallPeaks( object, macs2.path = NULL, outdir = tempdir(), broad = FALSE, format = "BED", effective.genome.size = 2.7e+09, extsize = 200, shift = -extsize/2, additional.args = NULL, name = "macs2", cleanup = TRUE, verbose = TRUE, ... )
object |
A Seurat object, ChromatinAssay object, Fragment object, or the path to fragment file/s. |
... |
Arguments passed to other methods |
assay |
Name of assay to use |
group.by |
Grouping variable to use. If set, peaks will be called
independently on each group of cells and then combined. Note that to call
peaks using subsets of cells we first split the fragment file/s used, so
using a grouping variable will require extra time to split the files and
perform multiple MACS peak calls, and will store additional files on-disk
that may be large. Note that we store split fragment files in the temp
directory ( |
idents |
List of identities to include if grouping cells (only valid if
also setting the |
macs2.path |
Path to MACS program. If NULL, try to find MACS automatically. |
broad |
Call broad peaks ( |
format |
File format to use. Should be either "BED" or "BEDPE" (see MACS documentation). |
outdir |
Path for output files |
fragment.tempdir |
Path to write temporary fragment files. Only used if
|
combine.peaks |
Controls whether peak calls from different groups of
cells are combined using |
effective.genome.size |
Effective genome size parameter for MACS
( |
extsize |
|
shift |
|
additional.args |
Additional arguments passed to MACS. This should be a single character string |
name |
Name for output MACS files. This will also be placed in the
|
cleanup |
Remove MACS output files |
verbose |
Display messages |
See https://macs3-project.github.io/MACS/ for MACS documentation.
If you call peaks using MACS2 please cite: doi:10.1186/gb-2008-9-9-r137
Returns a GRanges object
Fragment objectThis returns the names of cells in the object that are contained in the
fragment file. These cell barcodes may not match the barcodes present in the
fragment file. The Fragment object contains an internal mapping
of the cell names in the ChromatinAssay object to the cell
names in the fragment file, so that cell names can be changed in the
assay without needing to change the cell names on disk.
## S3 method for class 'Fragment' Cells(x, ...) ## S3 replacement method for class 'Fragment' Cells(x, ...) <- value## S3 method for class 'Fragment' Cells(x, ...) ## S3 replacement method for class 'Fragment' Cells(x, ...) <- value
x |
A Fragment object |
... |
Arguments passed to other methods |
value |
A vector of cell names to store in the |
To access the cell names that are stored in the fragment file itself, use
GetFragmentData(object = x, name = "cells").
Set and get cell barcode information for a Fragment object
Cells(x, ...) <- valueCells(x, ...) <- value
x |
A Seurat object |
... |
Arguments passed to other methods |
value |
A character vector of cell barcodes |
Count the number of cells in each group
CellsPerGroup(object, group.by = NULL)CellsPerGroup(object, group.by = NULL)
object |
A Seurat object |
group.by |
A grouping variable. Default is the active identities |
Returns a vector
CellsPerGroup(atac_small)CellsPerGroup(atac_small)
The ChromatinAssay object is an extended Assay
for the storage and analysis of single-cell chromatin data.
rangesA GRanges object describing the
genomic location of features in the object
motifsA Motif object
fragmentsA list of Fragment objects.
seqinfoA Seqinfo object containing basic
information about the genome sequence used.
annotationA GRanges object containing
genomic annotations. This should be a GRanges object with the following
columns:
tx_id: Transcript ID
gene_name: Gene name
gene_id: Gene ID
gene_biotype: Gene biotype (e.g. "protein_coding", "lincRNA")
type: Annotation type (e.g. "exon", "gap")
biasA vector containing Tn5 integration bias information (frequency of Tn5 integration at different kmers)
positionEnrichmentA named list of matrices containing positional enrichment scores for Tn5 integration (for example, enrichment at the TSS)
linksA GRanges object describing linked
genomic positions, such as co-accessible sites or enhancer-gene regulatory
relationships. This should be a GRanges object, where the start and
end coordinates are the two linked genomic positions, and must contain a
"score" metadata column.
Find the closest feature to a given set of genomic regions
ClosestFeature(object, regions, annotation = NULL, ...)ClosestFeature(object, regions, annotation = NULL, ...)
object |
A Seurat object |
regions |
A set of genomic regions to query |
annotation |
A GRanges object containing annotation information. If NULL, use the annotations stored in the object. |
... |
Additional arguments passed to |
Returns a dataframe with the name of each region, the closest feature in the annotation, and the distance to the feature.
ClosestFeature( object = atac_small, regions = head(granges(atac_small)) )ClosestFeature( object = atac_small, regions = head(granges(atac_small)) )
Perform hierarchical clustering on clonotype data
ClusterClonotypes(object, assay = NULL, group.by = NULL)ClusterClonotypes(object, assay = NULL, group.by = NULL)
object |
A Seurat object |
assay |
Name of assay to use |
group.by |
Grouping variable for cells |
Returns a list containing two objects of class
hclust, one for the cell clustering and one for the
feature (allele) clustering
This can be used to combine coverage plots, peak region plots, gene annotation plots, and linked element plots. The different tracks are stacked on top of each other and the x-axis combined.
CombineTracks(plotlist, expression.plot = NULL, heights = NULL, widths = NULL)CombineTracks(plotlist, expression.plot = NULL, heights = NULL, widths = NULL)
plotlist |
A list of plots to combine. Must be from the same genomic region. |
expression.plot |
Plot containing gene expression information. If supplied, this will be placed to the left of the coverage tracks and aligned with each track |
heights |
Relative heights for each plot. If NULL, the first plot will be 8x the height of the other tracks. |
widths |
Relative widths for each plot. Only required if adding a gene expression panel. If NULL, main plots will be 8x the width of the gene expression panel |
Returns a patchworked ggplot2 object
p1 <- PeakPlot(atac_small, region = "chr1-29554-39554") p2 <- AnnotationPlot(atac_small, region = "chr1-29554-39554") CombineTracks(plotlist = list(p1, p2), heights = c(1, 1))p1 <- PeakPlot(atac_small, region = "chr1-29554-39554") p2 <- AnnotationPlot(atac_small, region = "chr1-29554-39554") CombineTracks(plotlist = list(p1, p2), heights = c(1, 1))
Convert the output of Cicero connections to a set of genomic ranges where the start and end coordinates of the range are the midpoints of the linked elements. Only elements on the same chromosome are included in the output.
ConnectionsToLinks(conns, ccans = NULL, threshold = 0, sep = c("-", "-"))ConnectionsToLinks(conns, ccans = NULL, threshold = 0, sep = c("-", "-"))
conns |
A dataframe containing co-accessible elements. This would
usually be the output of |
ccans |
This is optional, but if supplied should be a dataframe
containing the cis-co-accessibility network (CCAN) information generated
by |
threshold |
Threshold for retaining a coaccessible site. Links with a value less than or equal to this threshold will be discarded. |
sep |
Separators to use for strings encoding genomic coordinates. First element is used to separate the chromosome from the coordinates, second element is used to separate the start from end coordinate. |
See the Cicero package for more information: https://bioconductor.org/packages/cicero/
Returns a GRanges object
Converts from motif name to motif ID or vice versa. To convert common names
to IDs, use the name parameter. To convert IDs to common names, use
the id parameter.
ConvertMotifID(object, ...) ## Default S3 method: ConvertMotifID(object, name, id, ...) ## S3 method for class 'Motif' ConvertMotifID(object, ...) ## S3 method for class 'ChromatinAssay' ConvertMotifID(object, ...) ## S3 method for class 'Assay' ConvertMotifID(object, ...) ## S3 method for class 'StdAssay' ConvertMotifID(object, ...) ## S3 method for class 'Seurat' ConvertMotifID(object, assay = NULL, ...)ConvertMotifID(object, ...) ## Default S3 method: ConvertMotifID(object, name, id, ...) ## S3 method for class 'Motif' ConvertMotifID(object, ...) ## S3 method for class 'ChromatinAssay' ConvertMotifID(object, ...) ## S3 method for class 'Assay' ConvertMotifID(object, ...) ## S3 method for class 'StdAssay' ConvertMotifID(object, ...) ## S3 method for class 'Seurat' ConvertMotifID(object, assay = NULL, ...)
object |
A Seurat, ChromatinAssay, or Motif object |
... |
Arguments passed to other methods |
name |
A vector of motif names |
id |
A vector of motif IDs. Only one of |
assay |
For |
Returns a character vector with the same length and order as the
input. Any names or IDs that were not found will be stored as NA.
Count total fragments per cell barcode present in a fragment file.
CountFragments(fragments, cells = NULL, max_lines = NULL, verbose = TRUE)CountFragments(fragments, cells = NULL, max_lines = NULL, verbose = TRUE)
fragments |
Path to a fragment file. If a list of fragment files is provided, the total fragments for each cell barcode across all files will be returned |
cells |
Cells to include. If NULL, include all cells |
max_lines |
Maximum number of lines to read from the fragment file. If NULL, read all lines in the file. |
verbose |
Display messages |
Returns a data.frame with the following columns:
CB: the cell barcode
frequency_count: total number of fragments sequenced for the cell
mononucleosome: total number of fragments with length between 147 bp and 294 bp
nucleosome_free: total number of fragments with length <147 bp
reads_count: total number of reads sequenced for the cell
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") counts <- CountFragments(fragments = fpath)fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") counts <- CountFragments(fragments = fpath)
Count reads per cell overlapping a given set of regions
CountsInRegion(object, assay, regions, ...)CountsInRegion(object, assay, regions, ...)
object |
A Seurat object |
assay |
Name of a chromatin assay in the object to use |
regions |
A GRanges object |
... |
Additional arguments passed to |
Returns a numeric vector
CountsInRegion( object = atac_small, assay = 'bins', regions = blacklist_hg19 )CountsInRegion( object = atac_small, assay = 'bins', regions = blacklist_hg19 )
This is the coverage method for ChromatinAssay objects.
## S4 method for signature 'ChromatinAssay' coverage( x, shift = 0L, width = NULL, weight = 1L, method = c("auto", "sort", "hash") ) ## S4 method for signature 'Seurat' coverage( x, shift = 0L, width = NULL, weight = 1L, method = c("auto", "sort", "hash") )## S4 method for signature 'ChromatinAssay' coverage( x, shift = 0L, width = NULL, weight = 1L, method = c("auto", "sort", "hash") ) ## S4 method for signature 'Seurat' coverage( x, shift = 0L, width = NULL, weight = 1L, method = c("auto", "sort", "hash") )
x |
A |
shift |
How much each range should be shifted before coverage is
computed. See |
width |
Specifies the length of the returned coverage vectors.
See |
weight |
Assigns weight to each range in |
method |
See |
coverage(ChromatinAssay): method for ChromatinAssay objects
coverage(Seurat): method for Seurat objects
coverage-methods in the IRanges package.
coverage-methods in the GenomicRanges package
Interactive version of the CoveragePlot function. Allows
altering the genome position interactively. The current view at any time can
be saved to a list of ggplot objects using the "Save
plot" button, and this list of plots will be returned after ending the
browser by pressing the "Done" button.
CoverageBrowser(object, region, assay = NULL, sep = c("-", "-"), ...)CoverageBrowser(object, region, assay = NULL, sep = c("-", "-"), ...)
object |
A Seurat object |
region |
A set of genomic coordinates |
assay |
Name of assay to use |
sep |
Separators for genomic coordinates if region supplied as a string rather than GRanges object |
... |
Parameters passed to |
Returns a list of ggplot objects
Plot frequency of Tn5 insertion events for different groups of cells within given regions of the genome. Tracks are normalized using a per-group scaling factor computed as the number of cells in the group multiplied by the mean sequencing depth for that group of cells. This accounts for differences in number of cells and potential differences in sequencing depth between groups.
CoveragePlot( object, region, features = NULL, assay = NULL, split.assays = FALSE, assay.scale = "common", show.bulk = FALSE, expression.assay = "RNA", expression.slot = "data", annotation = TRUE, peaks = TRUE, peaks.group.by = NULL, ranges = NULL, ranges.group.by = NULL, ranges.title = "Ranges", region.highlight = NULL, links = TRUE, tile = FALSE, tile.size = 100, tile.cells = 100, bigwig = NULL, bigwig.type = "coverage", bigwig.scale = "common", heights = NULL, group.by = NULL, split.by = NULL, window = 100, extend.upstream = 0, extend.downstream = 0, scale.factor = NULL, ymax = NULL, cells = NULL, idents = NULL, sep = c("-", "-"), max.downsample = 3000, downsample.rate = 0.1, ... )CoveragePlot( object, region, features = NULL, assay = NULL, split.assays = FALSE, assay.scale = "common", show.bulk = FALSE, expression.assay = "RNA", expression.slot = "data", annotation = TRUE, peaks = TRUE, peaks.group.by = NULL, ranges = NULL, ranges.group.by = NULL, ranges.title = "Ranges", region.highlight = NULL, links = TRUE, tile = FALSE, tile.size = 100, tile.cells = 100, bigwig = NULL, bigwig.type = "coverage", bigwig.scale = "common", heights = NULL, group.by = NULL, split.by = NULL, window = 100, extend.upstream = 0, extend.downstream = 0, scale.factor = NULL, ymax = NULL, cells = NULL, idents = NULL, sep = c("-", "-"), max.downsample = 3000, downsample.rate = 0.1, ... )
object |
A Seurat object |
region |
A set of genomic coordinates to show. Can be a GRanges object, a string encoding a genomic position, a gene name, or a vector of strings describing the genomic coordinates or gene names to plot. If a gene name is supplied, annotations must be present in the assay. |
features |
A vector of features present in another assay to plot alongside accessibility tracks (for example, gene names). |
assay |
Name of the assay to plot. If a list of assays is provided, data from each assay will be shown overlaid on each track. The first assay in the list will define the assay used for gene annotations, links, and peaks (if shown). The order of assays given defines the plotting order. |
split.assays |
When plotting data from multiple assays, display each assay as a separate track. If FALSE, data from different assays are overlaid on a single track with transparancy applied. |
assay.scale |
Scaling to apply to data from different assays. Can be:
|
show.bulk |
Include coverage track for all cells combined (pseudo-bulk). Note that this will plot the combined accessibility for all cells included in the plot (rather than all cells in the object). |
expression.assay |
Name of the assay containing expression data to plot
alongside accessibility tracks. Only needed if supplying |
expression.slot |
Name of slot to pull expression data from. Only needed
if supplying the |
annotation |
Display gene annotations. Set to TRUE or FALSE to control whether genes models are displayed, or choose "transcript" to display all transcript isoforms, or "gene" to display gene models only (same as setting TRUE). |
peaks |
Display peaks |
peaks.group.by |
Grouping variable to color peaks by. Must be a variable present in the feature metadata. If NULL, do not color peaks by any variable. |
ranges |
Additional genomic ranges to plot |
ranges.group.by |
Grouping variable to color ranges by. Must be a
variable present in the metadata stored in the |
ranges.title |
Y-axis title for ranges track. Only relevant if
|
region.highlight |
Region to highlight on the plot. Should be a GRanges object containing the coordinates to highlight. By default, regions will be highlighted in grey. To change the color of the highlighting, include a metadata column in the GRanges object named "color" containing the color to use for each region. |
links |
Display links. This can be a TRUE/FALSE value which will determine whether a links track is displayed, and if TRUE links for all genes in the plotted region will be shown. Alternatively, a character vector can be provided, giving a list of gene names to plot links for. If this is provided, only links for those genes will be displayed in the plot. |
tile |
Display per-cell fragment information in sliding windows. If plotting multi-assay data, only the first assay is shown in the tile plot. |
tile.size |
Size of the sliding window for per-cell fragment tile plot |
tile.cells |
Number of cells to display fragment information for in tile plot. |
bigwig |
List of bigWig file paths to plot data from. Files can be remotely hosted. The name of each element in the list will determine the y-axis label given to the track. |
bigwig.type |
Type of track to use for bigWig files ("line", "heatmap", or "coverage"). Should either be a single value, or a list of values giving the type for each individual track in the provided list of bigwig files. |
bigwig.scale |
Same as |
heights |
Relative heights for each track (accessibility, gene annotations, peaks, links). |
group.by |
Name of one or more metadata columns to group (color) the cells by. Default is the current cell identities |
split.by |
A metadata variable to split the tracks by. For example, grouping by "celltype" and splitting by "batch" will create separate tracks for each combination of celltype and batch. |
window |
Smoothing window size |
extend.upstream |
Number of bases to extend the region upstream. |
extend.downstream |
Number of bases to extend the region downstream. |
scale.factor |
Scaling factor for track height. If NULL (default), use the median group scaling factor determined by total number of fragments sequences in each group. |
ymax |
Maximum value for Y axis. Can be one of:
|
cells |
Which cells to plot. Default all cells |
idents |
Which identities to include in the plot. Default is all identities. |
sep |
Separators to use for strings encoding genomic coordinates. First element is used to separate the chromosome from the coordinates, second element is used to separate the start from end coordinate. |
max.downsample |
Minimum number of positions kept when downsampling. Downsampling rate is adaptive to the window size, but this parameter will set the minimum possible number of positions to include so that plots do not become too sparse when the window size is small. |
downsample.rate |
Fraction of positions to retain when downsampling. Retaining more positions can give a higher-resolution plot but can make the number of points large, resulting in larger file sizes when saving the plot and a longer period of time needed to draw the plot. |
... |
Additional arguments passed to |
Additional information can be layered on the coverage plot by setting several different options in the CoveragePlot function. This includes showing:
gene annotations
peak positions
additional genomic ranges
additional data stored in a bigWig file, which may be hosted remotely
gene or protein expression data alongside coverage tracks
peak-gene links
the position of individual sequenced fragments as a heatmap
data for multiple chromatin assays simultaneously
a pseudobulk for all cells combined
Returns a patchwork object
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), validate.fragments = FALSE ) Fragments(atac_small) <- fragments # Basic coverage plot CoveragePlot(object = atac_small, region = c("chr1-713500-714500")) # Show additional ranges ranges.show <- StringToGRanges("chr1-713750-714000") CoveragePlot(object = atac_small, region = c("chr1-713500-714500"), ranges = ranges.show) # Highlight region CoveragePlot(object = atac_small, region = c("chr1-713500-714500"), region.highlight = ranges.show) # Change highlight color ranges.show$color <- "orange" CoveragePlot(object = atac_small, region = c("chr1-713500-714500"), region.highlight = ranges.show) # Show expression data CoveragePlot(object = atac_small, region = c("chr1-713500-714500"), features = "ELK1")fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), validate.fragments = FALSE ) Fragments(atac_small) <- fragments # Basic coverage plot CoveragePlot(object = atac_small, region = c("chr1-713500-714500")) # Show additional ranges ranges.show <- StringToGRanges("chr1-713750-714000") CoveragePlot(object = atac_small, region = c("chr1-713500-714500"), ranges = ranges.show) # Highlight region CoveragePlot(object = atac_small, region = c("chr1-713500-714500"), region.highlight = ranges.show) # Change highlight color ranges.show$color <- "orange" CoveragePlot(object = atac_small, region = c("chr1-713500-714500"), region.highlight = ranges.show) # Show expression data CoveragePlot(object = atac_small, region = c("chr1-713500-714500"), features = "ELK1")
Create a ChromatinAssay object from a count matrix or
normalized data matrix. The expected format of the input matrix is features x
cells. A set of genomic ranges must be supplied along with the matrix, with
the length of the ranges equal to the number of rows in the matrix. If a set
of genomic ranges are not supplied, they will be extracted from the
row names of the matrix.
CreateChromatinAssay( counts, data, min.cells = 0, min.features = 0, max.cells = NULL, ranges = NULL, motifs = NULL, fragments = NULL, genome = NULL, annotation = NULL, bias = NULL, positionEnrichment = NULL, sep = c("-", "-"), validate.fragments = TRUE, verbose = TRUE, ... )CreateChromatinAssay( counts, data, min.cells = 0, min.features = 0, max.cells = NULL, ranges = NULL, motifs = NULL, fragments = NULL, genome = NULL, annotation = NULL, bias = NULL, positionEnrichment = NULL, sep = c("-", "-"), validate.fragments = TRUE, verbose = TRUE, ... )
counts |
Unnormalized data (raw counts) |
data |
Normalized data; if provided, do not pass counts |
min.cells |
Include features detected in at least this many cells. Will subset the counts matrix as well. To reintroduce excluded features, create a new object with a lower cutoff. |
min.features |
Include cells where at least this many features are detected. |
max.cells |
Include features detected in less than this many cells. Will subset the counts matrix as well. To reintroduce excluded features, create a new object with a higher cutoff. This can be useful for chromatin assays where certain artefactual loci accumulate reads in all cells. A percentage cutoff can also be set using 'q' followed by the percentage of cells, for example 'q90' will discard features detected in 90 percent of cells. If NULL (default), do not apply any maximum value. |
ranges |
A set of |
motifs |
A Motif object (not required) |
fragments |
Path to a tabix-indexed fragments file for the data
contained in the input matrix. If multiple fragment files are required,
you can add additional |
genome |
A |
annotation |
A set of |
bias |
A Tn5 integration bias matrix |
positionEnrichment |
A named list of matrices containing positional signal enrichment information for each cell. Should be a cell x position matrix, centered on an element of interest (for example, TSS sites). |
sep |
Separators to use for strings encoding genomic coordinates.
First element is used to separate the chromosome from the coordinates,
second element is used to separate the start from end coordinate. Only
used if |
validate.fragments |
Check that cells in the assay are present in the fragment file. |
verbose |
Display messages |
... |
Additional arguments passed to |
Create a Fragment object to store fragment file information.
This object stores a 32-bit MD5 hash of the fragment file and the fragment
file index so that any changes to the files on-disk can be detected. A check
is also performed to ensure that the expected cells are present in the
fragment file.
CreateFragmentObject( path, cells = NULL, validate.fragments = TRUE, verbose = TRUE, ... )CreateFragmentObject( path, cells = NULL, validate.fragments = TRUE, verbose = TRUE, ... )
path |
A path to the fragment file. The file should contain a tabix index in the same directory. |
cells |
A named character vector containing cell barcodes contained in the fragment file. This does not need to be all cells in the fragment file, but there should be no cells in the vector that are not present in the fragment file. A search of the file will be performed until at least one fragment from each cell is found. If NULL, don't check for expected cells. Each element of the vector should be a cell barcode that appears in the fragment file, and the name of each element should be the corresponding cell name in the object. |
validate.fragments |
Check that expected cells are present in the fragment file. |
verbose |
Display messages |
... |
Additional arguments passed to |
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") cells <- colnames(x = atac_small) names(x = cells) <- paste0("test_", cells) frags <- CreateFragmentObject(path = fpath, cells = cells, verbose = FALSE, tolerance = 0.5)fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") cells <- colnames(x = atac_small) names(x = cells) <- paste0("test_", cells) frags <- CreateFragmentObject(path = fpath, cells = cells, verbose = FALSE, tolerance = 0.5)
Create a motif x feature matrix from a set of genomic ranges, the genome, and a set of position weight matrices.
CreateMotifMatrix( features, pwm, genome, score = FALSE, use.counts = FALSE, sep = c("-", "-"), ... )CreateMotifMatrix( features, pwm, genome, score = FALSE, use.counts = FALSE, sep = c("-", "-"), ... )
features |
A GRanges object containing a set of genomic features |
pwm |
A |
genome |
Any object compatible with the |
score |
Record the motif match score, rather than presence/absence (default FALSE) |
use.counts |
Record motif counts per region. If FALSE (default),
record presence/absence of motif. Only applicable if |
sep |
A length-2 character vector containing the separators to be used when constructing matrix rownames from the GRanges |
... |
Additional arguments passed to
|
Requires that motifmatchr is installed https://www.bioconductor.org/packages/motifmatchr/.
Returns a sparse matrix
## Not run: library(JASPAR2018) library(TFBSTools) library(BSgenome.Hsapiens.UCSC.hg19) pwm <- getMatrixSet( x = JASPAR2018, opts = list(species = 9606, all_versions = FALSE) ) motif.matrix <- CreateMotifMatrix( features = granges(atac_small), pwm = pwm, genome = BSgenome.Hsapiens.UCSC.hg19 ) ## End(Not run)## Not run: library(JASPAR2018) library(TFBSTools) library(BSgenome.Hsapiens.UCSC.hg19) pwm <- getMatrixSet( x = JASPAR2018, opts = list(species = 9606, all_versions = FALSE) ) motif.matrix <- CreateMotifMatrix( features = granges(atac_small), pwm = pwm, genome = BSgenome.Hsapiens.UCSC.hg19 ) ## End(Not run)
Create a Motif-class object.
CreateMotifObject( data = NULL, pwm = NULL, motif.names = NULL, positions = NULL, meta.data = NULL )CreateMotifObject( data = NULL, pwm = NULL, motif.names = NULL, positions = NULL, meta.data = NULL )
data |
A motif x region matrix |
pwm |
A named list of position weight matrices or position frequency
matrices matching the motif names in |
motif.names |
A named list of motif names. List element names
must match the names given in |
positions |
A |
meta.data |
A data.frame containing metadata |
Returns a Motif object
motif.matrix <- matrix( data = sample(c(0,1), size = 100, replace = TRUE), ncol = 5 ) motif <- CreateMotifObject(data = motif.matrix)motif.matrix <- matrix( data = sample(c(0,1), size = 100, replace = TRUE), ncol = 5 ) motif <- CreateMotifObject(data = motif.matrix)
Create a scatterplot using variables in the object metadata and color cells by the density of points in the x-y space.
DensityScatter(object, x, y, log_x = FALSE, log_y = FALSE, quantiles = NULL)DensityScatter(object, x, y, log_x = FALSE, log_y = FALSE, quantiles = NULL)
object |
A Seurat object |
x |
Name of metadata variable to plot on x axis |
y |
Name of metadata variable to plot on y axis |
log_x |
log10 transform x values |
log_y |
log10 transform y values |
quantiles |
Vector of quantiles to display
for x and y data distribution. Must be integer values
between 0 and 100.
TRUE can be passed as a shorthand way to set
|
Returns a ggplot object
Compute the correlation between total counts and each reduced dimension component.
DepthCor(object, assay = NULL, reduction = "lsi", n = 10, ...)DepthCor(object, assay = NULL, reduction = "lsi", n = 10, ...)
object |
A |
assay |
Name of assay to use for sequencing depth. If NULL, use the default assay. |
reduction |
Name of a dimension reduction stored in the input object |
n |
Number of components to use. If |
... |
Additional arguments passed to |
Returns a ggplot object
DepthCor(object = atac_small)DepthCor(object = atac_small)
Randomly downsample features and assign to VariableFeatures for the object. This will select n features at random.
DownsampleFeatures(object, assay = NULL, n = 20000, verbose = TRUE)DownsampleFeatures(object, assay = NULL, n = 20000, verbose = TRUE)
object |
A Seurat object |
assay |
Name of assay to use. Default is the active assay. |
n |
Number of features to retain (default 20000). |
verbose |
Display messages |
Returns a Seurat object with
VariableFeatures set to the randomly sampled features.
DownsampleFeatures(atac_small, n = 10)DownsampleFeatures(atac_small, n = 10)
Display gene expression values for different groups of cells and different genes. Genes will be arranged on the x-axis and different groups stacked on the y-axis, with expression value distribution for each group shown as a violin plot. This is designed to work alongside a genomic coverage track, and the plot will be able to be aligned with coverage tracks for the same groups of cells.
ExpressionPlot( object, features, assay = NULL, group.by = NULL, idents = NULL, slot = "data" )ExpressionPlot( object, features, assay = NULL, group.by = NULL, idents = NULL, slot = "data" )
object |
A Seurat object |
features |
A list of features to plot |
assay |
Name of the assay storing expression information |
group.by |
A grouping variable to group cells by. If NULL, use the current cell identities |
idents |
A list of identities to include in the plot. If NULL, include all identities |
slot |
Which slot to pull expression data from |
ExpressionPlot(atac_small, features = "TSPAN6", assay = "RNA")ExpressionPlot(atac_small, features = "TSPAN6", assay = "RNA")
Resize GenomicRanges upstream and or downstream. From https://support.bioconductor.org/p/78652/
Extend(x, upstream = 0, downstream = 0, from.midpoint = FALSE)Extend(x, upstream = 0, downstream = 0, from.midpoint = FALSE)
x |
A range |
upstream |
Length to extend upstream |
downstream |
Length to extend downstream |
from.midpoint |
Count bases from region midpoint, rather than the 5' or 3' end for upstream and downstream respectively. |
Returns a GRanges object
Extend(x = blacklist_hg19, upstream = 100, downstream = 100)Extend(x = blacklist_hg19, upstream = 100, downstream = 100)
Construct a feature x cell matrix from a genomic fragments file
FeatureMatrix( fragments, features, cells = NULL, process_n = 2000, sep = c("-", "-"), verbose = TRUE )FeatureMatrix( fragments, features, cells = NULL, process_n = 2000, sep = c("-", "-"), verbose = TRUE )
fragments |
A list of |
features |
A GRanges object containing a set of genomic intervals. These will form the rows of the matrix, with each entry recording the number of unique reads falling in the genomic region for each cell. If a genomic region provided is on a chromosome that is not present in the fragment file, it will not be included in the returned matrix. |
cells |
Vector of cells to include. If NULL, include all cells found in the fragments file |
process_n |
Number of regions to load into memory at a time, per thread. Processing more regions at once can be faster but uses more memory. |
sep |
Vector of separators to use for genomic string. First element is used to separate chromosome and coordinates, second separator is used to separate start and end coordinates. |
verbose |
Display messages |
Returns a sparse matrix
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject(fpath) FeatureMatrix( fragments = fragments, features = granges(atac_small) )fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject(fpath) FeatureMatrix( fragments = fragments, features = granges(atac_small) )
Remove all fragments that are not from an allowed set of cell barcodes from
the fragment file. This will create a new file on disk that only contains
fragments from cells specified in the cells argument. The output file
is block gzip-compressed and indexed, ready for use with Signac functions.
FilterCells( fragments, cells, outfile = NULL, buffer_length = 256L, verbose = TRUE )FilterCells( fragments, cells, outfile = NULL, buffer_length = 256L, verbose = TRUE )
fragments |
Path to a fragment file |
cells |
A vector of cells to keep |
outfile |
Name for output file |
buffer_length |
Size of buffer to be read from the fragment file. This must be longer than the longest line in the file. |
verbose |
Display messages |
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") tmpf <- tempfile(fileext = ".gz") FilterCells( fragments = fpath, cells = head(colnames(atac_small)), outfile = tmpf ) file.remove(tmpf)fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") tmpf <- tempfile(fileext = ".gz") FilterCells( fragments = fpath, cells = head(colnames(atac_small)), outfile = tmpf ) file.remove(tmpf)
Identify groups of related cells from allele frequency data. This will cluster the cells based on their allele frequencies, reorder the factor levels for the cluster identities by hierarchical clustering the collapsed (pseudobulk) cluster allele frequencies, and set the variable features for the allele frequency assay to the order of features defined by hierarchical clustering.
FindClonotypes( object, assay = NULL, features = NULL, metric = "cosine", resolution = 1, k = 10, algorithm = 3 )FindClonotypes( object, assay = NULL, features = NULL, metric = "cosine", resolution = 1, k = 10, algorithm = 3 )
object |
A Seurat object |
assay |
Name of assay to use |
features |
Features to include when constructing neighbor graph |
metric |
Distance metric to use |
resolution |
Clustering resolution to use. See
|
k |
Passed to |
algorithm |
Community detection algorithm to use. See
|
Returns a Seurat object
Find motifs over-represented in a given set of genomic features. Computes the number of features containing the motif (observed) and compares this to the total number of features containing the motif (background) using the hypergeometric test.
FindMotifs( object, features, background = 40000, assay = NULL, verbose = TRUE, p.adjust.method = "BH", ... )FindMotifs( object, features, background = 40000, assay = NULL, verbose = TRUE, p.adjust.method = "BH", ... )
object |
A Seurat object |
features |
A vector of features to test for enrichments over background |
background |
Either a vector of features to use as the background set,
or a number specify the number of features to randomly select as a background
set. If a number is provided, regions will be selected to match the sequence
characteristics of the query features. To match the sequence characteristics,
these characteristics must be stored in the feature metadata for the assay.
This can be added using the
|
assay |
Which assay to use. Default is the active assay |
verbose |
Display messages |
p.adjust.method |
Multiple testing correction method to be applied.
Passed to |
... |
Arguments passed to |
Returns a data frame
de.motif <- head(rownames(atac_small)) bg.peaks <- tail(rownames(atac_small)) FindMotifs( object = atac_small, features = de.motif, background = bg.peaks )de.motif <- head(rownames(atac_small)) bg.peaks <- tail(rownames(atac_small)) FindMotifs( object = atac_small, features = de.motif, background = bg.peaks )
The findOverlaps, countOverlaps methods are available for
ChromatinAssay objects. This allows finding overlaps between
genomic ranges and the ranges stored in the ChromatinAssay.
## S4 method for signature 'Vector,ChromatinAssay' findOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE ) ## S4 method for signature 'ChromatinAssay,Vector' findOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE ) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' findOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE ) ## S4 method for signature 'Vector,Seurat' findOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE ) ## S4 method for signature 'Seurat,Vector' findOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE ) ## S4 method for signature 'Seurat,Seurat' findOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE ) ## S4 method for signature 'Vector,ChromatinAssay' countOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), ignore.strand = FALSE ) ## S4 method for signature 'ChromatinAssay,Vector' countOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), ignore.strand = FALSE ) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' countOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), ignore.strand = FALSE ) ## S4 method for signature 'Seurat,Vector' countOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), ignore.strand = FALSE ) ## S4 method for signature 'Vector,Seurat' countOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), ignore.strand = FALSE ) ## S4 method for signature 'Seurat,Seurat' countOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), ignore.strand = FALSE )## S4 method for signature 'Vector,ChromatinAssay' findOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE ) ## S4 method for signature 'ChromatinAssay,Vector' findOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE ) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' findOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE ) ## S4 method for signature 'Vector,Seurat' findOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE ) ## S4 method for signature 'Seurat,Vector' findOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE ) ## S4 method for signature 'Seurat,Seurat' findOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), select = c("all", "first", "last", "arbitrary"), ignore.strand = FALSE ) ## S4 method for signature 'Vector,ChromatinAssay' countOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), ignore.strand = FALSE ) ## S4 method for signature 'ChromatinAssay,Vector' countOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), ignore.strand = FALSE ) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' countOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), ignore.strand = FALSE ) ## S4 method for signature 'Seurat,Vector' countOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), ignore.strand = FALSE ) ## S4 method for signature 'Vector,Seurat' countOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), ignore.strand = FALSE ) ## S4 method for signature 'Seurat,Seurat' countOverlaps( query, subject, maxgap = -1L, minoverlap = 0L, type = c("any", "start", "end", "within", "equal"), ignore.strand = FALSE )
query, subject
|
A |
maxgap, minoverlap, type, select, ignore.strand
|
See
|
If a ChromatinAssay is set as the default assay in a
Seurat object, you can also call findOverlaps
directly on the Seurat object.
See findOverlaps
findOverlaps(query = ChromatinAssay, subject = Vector): method for ChromatinAssay, Vector
findOverlaps(query = ChromatinAssay, subject = ChromatinAssay): method for ChromatinAssay, ChromatinAssay
findOverlaps(query = Vector, subject = Seurat): method for Vector, Seurat
findOverlaps(query = Seurat, subject = Vector): method for Seurat, Vector
findOverlaps(query = Seurat, subject = Seurat): method for Seurat, Seurat
countOverlaps(query = Vector, subject = ChromatinAssay): method for Vector, ChromatinAssay
countOverlaps(query = ChromatinAssay, subject = Vector): method for ChromatinAssay, Vector
countOverlaps(query = ChromatinAssay, subject = ChromatinAssay): method for ChromatinAssay, ChromatinAssay
countOverlaps(query = Seurat, subject = Vector): method for Seurat, Vector
countOverlaps(query = Vector, subject = Seurat): method for Vector, Seurat
countOverlaps(query = Seurat, subject = Seurat): method for Seurat, Seurat
findOverlaps-methods in the IRanges package.
findOverlaps-methods in the GenomicRanges package
Find top features for a given assay based on total number of counts for the
feature. Can specify a minimum cell count, or a lower percentile
bound to determine the set of variable features. Running this function will
store the total counts and percentile rank for each feature in the feature
metadata for the assay. To only compute the feature metadata, without
changing the variable features for the assay, set min.cutoff=NA.
FindTopFeatures(object, ...) ## Default S3 method: FindTopFeatures(object, assay = NULL, min.cutoff = "q5", verbose = TRUE, ...) ## S3 method for class 'Assay' FindTopFeatures(object, assay = NULL, min.cutoff = "q5", verbose = TRUE, ...) ## S3 method for class 'StdAssay' FindTopFeatures(object, assay = NULL, min.cutoff = "q5", verbose = TRUE, ...) ## S3 method for class 'Seurat' FindTopFeatures(object, assay = NULL, min.cutoff = "q5", verbose = TRUE, ...)FindTopFeatures(object, ...) ## Default S3 method: FindTopFeatures(object, assay = NULL, min.cutoff = "q5", verbose = TRUE, ...) ## S3 method for class 'Assay' FindTopFeatures(object, assay = NULL, min.cutoff = "q5", verbose = TRUE, ...) ## S3 method for class 'StdAssay' FindTopFeatures(object, assay = NULL, min.cutoff = "q5", verbose = TRUE, ...) ## S3 method for class 'Seurat' FindTopFeatures(object, assay = NULL, min.cutoff = "q5", verbose = TRUE, ...)
object |
A Seurat object |
... |
Arguments passed to other methods |
assay |
Name of assay to use |
min.cutoff |
Cutoff for feature to be included in the VariableFeatures for the object. This can be a percentile specified as 'q' followed by the minimum percentile, for example 'q5' to set the top 95% most common features as the VariableFeatures for the object. Alternatively, this can be an integer specifying the minimum number of counts for the feature to be included in the set of VariableFeatures. For example, setting to 10 will include features with >10 total counts in the set of VariableFeatures. If NULL, include all features in VariableFeatures. If NA, VariableFeatures will not be altered, and only the feature metadata will be updated with the total counts and percentile rank for each feature. |
verbose |
Display messages |
Returns a Seurat object
FindTopFeatures(object = atac_small[['peaks']]['data']) FindTopFeatures(object = atac_small[['peaks']]) FindTopFeatures(object = atac_small[['peaks']]) FindTopFeatures(atac_small)FindTopFeatures(object = atac_small[['peaks']]['data']) FindTopFeatures(object = atac_small[['peaks']]) FindTopFeatures(object = atac_small[['peaks']]) FindTopFeatures(atac_small)
Compute the normalized observed/expected Tn5 insertion frequency for each position surrounding a set of motif instances.
Footprint(object, ...) ## S3 method for class 'ChromatinAssay' Footprint( object, genome, motif.name = NULL, key = motif.name, regions = NULL, assay = NULL, upstream = 250, downstream = 250, compute.expected = TRUE, in.peaks = FALSE, verbose = TRUE, ... ) ## S3 method for class 'Seurat' Footprint( object, genome, regions = NULL, motif.name = NULL, assay = NULL, upstream = 250, downstream = 250, in.peaks = FALSE, verbose = TRUE, ... )Footprint(object, ...) ## S3 method for class 'ChromatinAssay' Footprint( object, genome, motif.name = NULL, key = motif.name, regions = NULL, assay = NULL, upstream = 250, downstream = 250, compute.expected = TRUE, in.peaks = FALSE, verbose = TRUE, ... ) ## S3 method for class 'Seurat' Footprint( object, genome, regions = NULL, motif.name = NULL, assay = NULL, upstream = 250, downstream = 250, in.peaks = FALSE, verbose = TRUE, ... )
object |
A Seurat or ChromatinAssay object |
... |
Arguments passed to other methods |
genome |
A |
motif.name |
Name of a motif stored in the assay to footprint. If not supplied, must supply a set of regions. |
key |
Key to store positional enrichment information under. |
regions |
A set of genomic ranges containing the motif instances. These should all be the same width. |
assay |
Name of assay to use |
upstream |
Number of bases to extend upstream |
downstream |
Number of bases to extend downstream |
compute.expected |
Find the expected number of insertions at each position given the local DNA sequence context and the insertion bias of Tn5 |
in.peaks |
Restrict motifs to those that fall in peaks |
verbose |
Display messages |
Returns a Seurat object
Find the fraction of counts per cell that overlap a given set of genomic ranges
FractionCountsInRegion(object, regions, assay = NULL, ...)FractionCountsInRegion(object, regions, assay = NULL, ...)
object |
A Seurat object |
regions |
A GRanges object containing a set of genomic regions |
assay |
Name of assay to use |
... |
Additional arguments passed to |
Returns a numeric vector
## Not run: FractionCountsInRegion( object = atac_small, assay = 'bins', regions = blacklist_hg19 ) ## End(Not run)## Not run: FractionCountsInRegion( object = atac_small, assay = 'bins', regions = blacklist_hg19 ) ## End(Not run)
The Fragment class is designed to hold information needed for working with fragment files.
pathPath to the fragment file on disk. See https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/output/fragments
hashA vector of two md5sums: first element is the md5sum of the fragment file, the second element is the md5sum of the index.
cellsA named vector of cells where each element is the cell barcode as it appears in the fragment file, and the name of each element is the corresponding cell barcode as stored in the ChromatinAssay object.
Plot the frequency that fragments of different lengths are present for different groups of cells.
FragmentHistogram( object, assay = NULL, region = "chr1-1-2000000", group.by = NULL, cells = NULL, log.scale = FALSE, ... )FragmentHistogram( object, assay = NULL, region = "chr1-1-2000000", group.by = NULL, cells = NULL, log.scale = FALSE, ... )
object |
A Seurat object |
assay |
Which assay to use. Default is the active assay. |
region |
Genomic range to use. Default is fist two megabases of chromosome 1. Can be a GRanges object, a string, or a vector of strings. |
group.by |
Name of one or more metadata columns to group (color) the cells by. Default is the current cell identities |
cells |
Which cells to plot. Default all cells |
log.scale |
Display Y-axis on log scale. Default is FALSE. |
... |
Arguments passed to other functions |
Returns a ggplot object
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") Fragments(atac_small) <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), validate.fragments = FALSE ) FragmentHistogram(object = atac_small, region = "chr1-10245-780007")fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") Fragments(atac_small) <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), validate.fragments = FALSE ) FragmentHistogram(object = atac_small, region = "chr1-10245-780007")
Get the Fragment objects
Fragments(object, ...) Fragments(object, ...) <- value ## S3 method for class 'ChromatinAssay' Fragments(object, ...) ## S3 method for class 'Seurat' Fragments(object, ...) ## S3 replacement method for class 'ChromatinAssay' Fragments(object, ...) <- value ## S3 replacement method for class 'Seurat' Fragments(object, ...) <- valueFragments(object, ...) Fragments(object, ...) <- value ## S3 method for class 'ChromatinAssay' Fragments(object, ...) ## S3 method for class 'Seurat' Fragments(object, ...) ## S3 replacement method for class 'ChromatinAssay' Fragments(object, ...) <- value ## S3 replacement method for class 'Seurat' Fragments(object, ...) <- value
object |
A Seurat object or ChromatinAssay object |
... |
Arguments passed to other methods |
value |
A |
Returns a list of Fragment objects. If there are
no Fragment objects present, returns an empty list.
Fragments(atac_small[["peaks"]]) Fragments(atac_small) fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), validate.fragments = FALSE ) Fragments(atac_small[["bins"]]) <- fragments fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), validate.fragments = FALSE ) Fragments(atac_small) <- fragmentsFragments(atac_small[["peaks"]]) Fragments(atac_small) fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), validate.fragments = FALSE ) Fragments(atac_small[["bins"]]) <- fragments fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), validate.fragments = FALSE ) Fragments(atac_small) <- fragments
Calculate fraction of reads in peaks per cell
FRiP(object, assay, total.fragments, col.name = "FRiP", verbose = TRUE)FRiP(object, assay, total.fragments, col.name = "FRiP", verbose = TRUE)
object |
A Seurat object |
assay |
Name of the assay containing a peak x cell matrix |
total.fragments |
Name of a metadata column containing the total number
of sequenced fragments for each cell. This can be computed using the
|
col.name |
Name of column in metadata to store the FRiP information. |
verbose |
Display messages |
Returns a Seurat object
FRiP(object = atac_small, assay = 'peaks', total.fragments = "fragments")FRiP(object = atac_small, assay = 'peaks', total.fragments = "fragments")
Compute counts per cell in gene body and promoter region.
GeneActivity( object, assay = NULL, features = NULL, extend.upstream = 2000, extend.downstream = 0, biotypes = "protein_coding", max.width = 5e+05, process_n = 2000, gene.id = FALSE, verbose = TRUE )GeneActivity( object, assay = NULL, features = NULL, extend.upstream = 2000, extend.downstream = 0, biotypes = "protein_coding", max.width = 5e+05, process_n = 2000, gene.id = FALSE, verbose = TRUE )
object |
A Seurat object |
assay |
Name of assay to use. If NULL, use the default assay |
features |
Genes to include. If NULL, use all protein-coding genes in the annotations stored in the object |
extend.upstream |
Number of bases to extend upstream of the TSS |
extend.downstream |
Number of bases to extend downstream of the TTS |
biotypes |
Gene biotypes to include. If NULL, use all biotypes in the gene annotation. |
max.width |
Maximum allowed gene width for a gene to be quantified. Setting this parameter can avoid quantifying extremely long transcripts that can add a relatively long amount of time. If NULL, do not filter genes based on width. |
process_n |
Number of regions to load into memory at a time, per thread. Processing more regions at once can be faster but uses more memory. |
gene.id |
Record gene IDs in output matrix rather than gene name. |
verbose |
Display messages |
Returns a sparse matrix
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), validate.fragments = FALSE ) Fragments(atac_small) <- fragments GeneActivity(atac_small)fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), validate.fragments = FALSE ) Fragments(atac_small) <- fragments GeneActivity(atac_small)
Construct a bin x cell matrix from a fragments file.
GenomeBinMatrix( fragments, genome, cells = NULL, binsize = 5000, process_n = 2000, sep = c("-", "-"), verbose = TRUE )GenomeBinMatrix( fragments, genome, cells = NULL, binsize = 5000, process_n = 2000, sep = c("-", "-"), verbose = TRUE )
fragments |
Path to tabix-indexed fragments file or a list of
|
genome |
A vector of chromosome sizes for the genome. This is used to
construct the genome bin coordinates. The can be obtained by calling
|
cells |
Vector of cells to include. If NULL, include all cells found in the fragments file |
binsize |
Size of the genome bins to use |
process_n |
Number of regions to load into memory at a time, per thread. Processing more regions at once can be faster but uses more memory. |
sep |
Vector of separators to use for genomic string. First element is used to separate chromosome and coordinates, second separator is used to separate start and end coordinates. |
verbose |
Display messages |
This function bins the genome and calls FeatureMatrix to
construct a bin x cell matrix.
Returns a sparse matrix
genome <- 780007 names(genome) <- 'chr1' fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject(fpath) GenomeBinMatrix( fragments = fragments, genome = genome, binsize = 1000 )genome <- 780007 names(genome) <- 'chr1' fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject(fpath) GenomeBinMatrix( fragments = fragments, genome = genome, binsize = 1000 )
Extract cell names containing reads mapped within a given genomic region
GetCellsInRegion(tabix, region, cells = NULL)GetCellsInRegion(tabix, region, cells = NULL)
tabix |
Tabix object |
region |
A string giving the region to extract from the fragments file |
cells |
Vector of cells to include in output. If NULL, include all cells |
Returns a list
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") GetCellsInRegion(tabix = fpath, region = "chr1-10245-762629")fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") GetCellsInRegion(tabix = fpath, region = "chr1-10245-762629")
Extract footprint data for a set of transcription factors or metafeatures.
This function will pull accessibility data for a given feature (eg, a TF),
and perform background normalization for each identity class. This is the
data that's used to create TF footprinting plots with the
PlotFootprint function.
GetFootprintData( object, features, assay = NULL, group.by = NULL, idents = NULL )GetFootprintData( object, features, assay = NULL, group.by = NULL, idents = NULL )
object |
A Seurat object |
features |
A vector of features to extract data for |
assay |
Name of assay to use |
group.by |
A grouping variable |
idents |
Set of identities to group cells by |
Returns a data.frame with the following columns:
group: Cell group (determined by group.by parameter
position: Position relative to motif center
count: Normalized Tn5 insertion counts at each position
norm.value: Normalized Tn5 insertion counts at each position (same as count)
feature: Name of the footprinted motif
class: observed or expected
Extract data from a Fragment-class object
GetFragmentData(object, slot = "path")GetFragmentData(object, slot = "path")
object |
A |
slot |
Information to pull from object (path, hash, cells, prefix, suffix) |
Pulls the transcript information for all chromosomes from an EnsDb object.
This wraps crunch and applies the extractor
function to all chromosomes present in the EnsDb object.
GetGRangesFromEnsDb( ensdb, standard.chromosomes = TRUE, biotypes = c("protein_coding", "lincRNA", "rRNA", "processed_transcript"), verbose = TRUE )GetGRangesFromEnsDb( ensdb, standard.chromosomes = TRUE, biotypes = c("protein_coding", "lincRNA", "rRNA", "processed_transcript"), verbose = TRUE )
ensdb |
An EnsDb object |
standard.chromosomes |
Keep only standard chromosomes |
biotypes |
Biotypes to keep |
verbose |
Display messages |
Intersects the regions stored in the rownames of two objects and returns a vector containing the names of rows that intersect for each object. The order of the row names return corresponds to the intersecting regions, i.e. the nth feature of the first vector will intersect the nth feature in the second vector. A distance parameter can be given, in which case features within the given distance will be called as intersecting.
GetIntersectingFeatures( object.1, object.2, assay.1 = NULL, assay.2 = NULL, distance = 0, verbose = TRUE )GetIntersectingFeatures( object.1, object.2, assay.1 = NULL, assay.2 = NULL, distance = 0, verbose = TRUE )
object.1 |
The first Seurat object |
object.2 |
The second Seurat object |
assay.1 |
Name of the assay to use in the first object. If NULL, use the default assay |
assay.2 |
Name of the assay to use in the second object. If NULL, use the default assay |
distance |
Maximum distance between regions allowed for an intersection to be recorded. Default is 0. |
verbose |
Display messages |
Returns a list of two character vectors containing the row names in each object that overlap each other.
GetIntersectingFeatures( object.1 = atac_small, object.2 = atac_small, assay.1 = 'peaks', assay.2 = 'bins' )GetIntersectingFeatures( object.1 = atac_small, object.2 = atac_small, assay.1 = 'peaks', assay.2 = 'bins' )
Retrieve peak-gene links for a given set of genes. Links must be first
obtained by running the LinkPeaks function.
GetLinkedGenes(object, features, assay = NULL, min.abs.score = 0)GetLinkedGenes(object, features, assay = NULL, min.abs.score = 0)
object |
A Seurat object |
features |
A list of peaks to find linked genes for |
assay |
Name of assay to use. If NULL, use the default assay |
min.abs.score |
Minimum absolute value of the link score for a link to be returned |
This function is designed to obtain the stored results from running the
LinkPeaks function. Alternatively, custom peak-gene linkage methods
can be used as long as they store the gene name, peak name, and a peak-gene
score information as metadata columns named "gene," "peak," and "score"
respectively.
GetLinkedPeaks
Retrieve peak-gene links for a given set of genes. Links must be first
obtained by running the LinkPeaks function.
GetLinkedPeaks(object, features, assay = NULL, min.abs.score = 0)GetLinkedPeaks(object, features, assay = NULL, min.abs.score = 0)
object |
A Seurat object |
features |
A list of genes to find linked peaks for |
assay |
Name of assay to use. If NULL, use the default assay |
min.abs.score |
Minimum absolute value of the link score for a link to be returned |
This function is designed to obtain the stored results from running the
LinkPeaks function. Alternatively, custom peak-gene linkage methods
can be used as long as they store the gene name, peak name, and a peak-gene
score information as metadata columns named "gene," "peak," and "score"
respectively.
GetLinkedGenes
Get motif matrix for given assay
GetMotifData(object, ...) ## S3 method for class 'Motif' GetMotifData(object, slot = "data", ...) ## S3 method for class 'ChromatinAssay' GetMotifData(object, slot = "data", ...) ## S3 method for class 'Seurat' GetMotifData(object, assay = NULL, slot = "data", ...)GetMotifData(object, ...) ## S3 method for class 'Motif' GetMotifData(object, slot = "data", ...) ## S3 method for class 'ChromatinAssay' GetMotifData(object, slot = "data", ...) ## S3 method for class 'Seurat' GetMotifData(object, assay = NULL, slot = "data", ...)
object |
A Seurat object |
... |
Arguments passed to other methods |
slot |
Information to pull from object (data, pwm, meta.data) |
assay |
Which assay to use. Default is the current active assay |
Returns a Seurat object
motif.obj <- SeuratObject::GetAssayData( object = atac_small[['peaks']], slot = "motifs" ) GetMotifData(object = motif.obj) GetMotifData(object = atac_small)motif.obj <- SeuratObject::GetAssayData( object = atac_small[['peaks']], slot = "motifs" ) GetMotifData(object = motif.obj) GetMotifData(object = atac_small)
Get the TSS positions from a set of genomic ranges containing gene positions. Ranges can contain exons, introns, UTRs, etc, rather than the whole transcript. Only protein coding gene biotypes are included in output.
GetTSSPositions(ranges, biotypes = "protein_coding")GetTSSPositions(ranges, biotypes = "protein_coding")
ranges |
A GRanges object containing gene annotations. |
biotypes |
Gene biotypes to include. If NULL, use all biotypes in the supplied gene annotation. |
Methods for accessing GRanges object
information stored in a ChromatinAssay object.
## S4 method for signature 'ChromatinAssay' granges(x, use.names = TRUE, use.mcols = FALSE, ...) ## S4 method for signature 'Seurat' granges(x, use.names = TRUE, use.mcols = FALSE, ...)## S4 method for signature 'ChromatinAssay' granges(x, use.names = TRUE, use.mcols = FALSE, ...) ## S4 method for signature 'Seurat' granges(x, use.names = TRUE, use.mcols = FALSE, ...)
x |
A |
use.names |
Whether the names on the genomic ranges should be propagated to the returned object. |
use.mcols |
Not supported for |
... |
Additional arguments |
Returns a GRanges object
granges(Seurat): method for Seurat objects
granges in the GenomicRanges package.
granges(atac_small)granges(atac_small)
Convert GRanges object to a vector of strings
GRangesToString(grange, sep = c("-", "-"))GRangesToString(grange, sep = c("-", "-"))
grange |
A GRanges object |
sep |
Vector of separators to use for genomic string. First element is used to separate chromosome and coordinates, second separator is used to separate start and end coordinates. |
Returns a character vector
GRangesToString(grange = blacklist_hg19)GRangesToString(grange = blacklist_hg19)
Returns the first n rows of a fragment file. This allows the content
of a fragment file to be inspected.
## S3 method for class 'Fragment' head(x, n = 6L, ...)## S3 method for class 'Fragment' head(x, n = 6L, ...)
x |
a |
n |
an integer specifying the number of rows to return from the fragment file |
... |
additional arguments passed to |
The first n rows of a fragment file as a data.frame
with the following columns: chrom, start, end, barcode, readCount.
Identify mitochondrial variants present in single cells.
IdentifyVariants(object, ...) ## Default S3 method: IdentifyVariants( object, refallele, stabilize_variance = TRUE, low_coverage_threshold = 10, verbose = TRUE, ... ) ## S3 method for class 'Assay' IdentifyVariants(object, refallele, ...) ## S3 method for class 'StdAssay' IdentifyVariants(object, refallele, ...) ## S3 method for class 'Seurat' IdentifyVariants(object, refallele, assay = NULL, ...)IdentifyVariants(object, ...) ## Default S3 method: IdentifyVariants( object, refallele, stabilize_variance = TRUE, low_coverage_threshold = 10, verbose = TRUE, ... ) ## S3 method for class 'Assay' IdentifyVariants(object, refallele, ...) ## S3 method for class 'StdAssay' IdentifyVariants(object, refallele, ...) ## S3 method for class 'Seurat' IdentifyVariants(object, refallele, assay = NULL, ...)
object |
A Seurat object |
... |
Arguments passed to other methods |
refallele |
A dataframe containing reference alleles for the mitochondrial genome. |
stabilize_variance |
Stabilize variance |
low_coverage_threshold |
Low coverage threshold |
verbose |
Display messages |
assay |
Name of assay to use. If NULL, use the default assay. |
Returns a dataframe
## Not run: data.dir <- "path/to/data/directory" mgatk <- ReadMGATK(dir = data.dir) variant.df <- IdentifyVariants( object = mgatk$counts, refallele = mgatk$refallele ) ## End(Not run)## Not run: data.dir <- "path/to/data/directory" mgatk <- ReadMGATK(dir = data.dir) variant.df <- IdentifyVariants( object = mgatk$counts, refallele = mgatk$refallele ) ## End(Not run)
Counts the Tn5 insertion frequency for each DNA hexamer.
InsertionBias(object, ...) ## S3 method for class 'ChromatinAssay' InsertionBias(object, genome, region = "chr1-1-249250621", verbose = TRUE, ...) ## S3 method for class 'Seurat' InsertionBias( object, genome, assay = NULL, region = "chr1-1-249250621", verbose = TRUE, ... )InsertionBias(object, ...) ## S3 method for class 'ChromatinAssay' InsertionBias(object, genome, region = "chr1-1-249250621", verbose = TRUE, ...) ## S3 method for class 'Seurat' InsertionBias( object, genome, assay = NULL, region = "chr1-1-249250621", verbose = TRUE, ... )
object |
A Seurat or ChromatinAssay object |
... |
Additional arguments passed to |
genome |
A |
region |
Genomic region to use when assessing bias. |
verbose |
Display messages |
assay |
Name of assay to use |
Returns a Seurat object
## Not run: library(BSgenome.Mmusculus.UCSC.mm10) region.use <- GRanges( seqnames = c('chr1', 'chr2'), IRanges(start = c(1,1), end = c(195471971, 182113224)) ) InsertionBias( object = atac_small, genome = BSgenome.Mmusculus.UCSC.mm10, region = region.use ) ## End(Not run)## Not run: library(BSgenome.Mmusculus.UCSC.mm10) region.use <- GRanges( seqnames = c('chr1', 'chr2'), IRanges(start = c(1,1), end = c(195471971, 182113224)) ) InsertionBias( object = atac_small, genome = BSgenome.Mmusculus.UCSC.mm10, region = region.use ) ## End(Not run)
The range, reduce, gaps, disjoin, isDisjoint, disjointBins methods
are available for ChromatinAssay objects.
## S4 method for signature 'ChromatinAssay' range(x, ..., with.revmap = FALSE, na.rm = FALSE) ## S4 method for signature 'Seurat' range(x, ..., with.revmap = FALSE, na.rm = FALSE) ## S4 method for signature 'ChromatinAssay' reduce(x, drop.empty.ranges = FALSE, ...) ## S4 method for signature 'Seurat' reduce(x, drop.empty.ranges = FALSE, ...) ## S4 method for signature 'ChromatinAssay' gaps(x, start = NA, end = NA) ## S4 method for signature 'Seurat' gaps(x, start = NA, end = NA) ## S4 method for signature 'ChromatinAssay' disjoin(x, ...) ## S4 method for signature 'Seurat' disjoin(x, ...) ## S4 method for signature 'ChromatinAssay' isDisjoint(x, ...) ## S4 method for signature 'Seurat' isDisjoint(x, ...) ## S4 method for signature 'ChromatinAssay' disjointBins(x, ...) ## S4 method for signature 'Seurat' disjointBins(x, ...)## S4 method for signature 'ChromatinAssay' range(x, ..., with.revmap = FALSE, na.rm = FALSE) ## S4 method for signature 'Seurat' range(x, ..., with.revmap = FALSE, na.rm = FALSE) ## S4 method for signature 'ChromatinAssay' reduce(x, drop.empty.ranges = FALSE, ...) ## S4 method for signature 'Seurat' reduce(x, drop.empty.ranges = FALSE, ...) ## S4 method for signature 'ChromatinAssay' gaps(x, start = NA, end = NA) ## S4 method for signature 'Seurat' gaps(x, start = NA, end = NA) ## S4 method for signature 'ChromatinAssay' disjoin(x, ...) ## S4 method for signature 'Seurat' disjoin(x, ...) ## S4 method for signature 'ChromatinAssay' isDisjoint(x, ...) ## S4 method for signature 'Seurat' isDisjoint(x, ...) ## S4 method for signature 'ChromatinAssay' disjointBins(x, ...) ## S4 method for signature 'Seurat' disjointBins(x, ...)
x |
A |
... |
Additional arguments |
with.revmap |
See |
na.rm |
Ignored |
drop.empty.ranges |
See |
start, end
|
See |
range(Seurat): method for Seurat objects
reduce(ChromatinAssay): method for ChromatinAssay objects
reduce(Seurat): method for Seurat objects
gaps(ChromatinAssay): method for ChromatinAssay objects
gaps(Seurat): method for Seurat objects
disjoin(ChromatinAssay): method for ChromatinAssay objects
disjoin(Seurat): method for Seurat objects
isDisjoint(ChromatinAssay): method for ChromatinAssay objects
isDisjoint(Seurat): method for Seurat objects
disjointBins(ChromatinAssay): method for ChromatinAssay objects
disjointBins(Seurat): method for Seurat objects
inter-range-methods in the IRanges package.
inter-range-methods in the GenomicRanges package
Remove or retain matrix rows that intersect given genomic regions
IntersectMatrix( matrix, regions, invert = FALSE, sep = c("-", "-"), verbose = TRUE, ... )IntersectMatrix( matrix, regions, invert = FALSE, sep = c("-", "-"), verbose = TRUE, ... )
matrix |
A matrix with genomic regions in the rows |
regions |
A set of genomic regions to intersect with regions in the matrix. Either a vector of strings encoding the genomic coordinates, or a GRanges object. |
invert |
Discard rows intersecting the genomic regions supplied, rather than retain. |
sep |
A length-2 character vector containing the separators to be used for extracting genomic coordinates from a string. The first element will be used to separate the chromosome name from coordinates, and the second element used to separate start and end coordinates. |
verbose |
Display messages |
... |
Additional arguments passed to |
Returns a sparse matrix
counts <- matrix(data = rep(0, 12), ncol = 2) rownames(counts) <- c("chr1-565107-565550","chr1-569174-569639", "chr1-713460-714823","chr1-752422-753038", "chr1-762106-763359","chr1-779589-780271") IntersectMatrix(matrix = counts, regions = blacklist_hg19)counts <- matrix(data = rep(0, 12), ncol = 2) rownames(counts) <- c("chr1-565107-565550","chr1-569174-569639", "chr1-713460-714823","chr1-752422-753038", "chr1-762106-763359","chr1-779589-780271") IntersectMatrix(matrix = counts, regions = blacklist_hg19)
Finds the Jaccard similarity between rows of the two matrices. Note that the matrices must be binary, and any rows with zero total counts will result in an NaN entry that could cause problems in downstream analyses.
Jaccard(x, y)Jaccard(x, y)
x |
The first matrix |
y |
The second matrix |
This will calculate the raw Jaccard index, without normalizing for the expected similarity between cells due to differences in sequencing depth.
Returns a matrix
x <- matrix(data = sample(c(0, 1), size = 25, replace = TRUE), ncol = 5) Jaccard(x = x, y = x)x <- matrix(data = sample(c(0, 1), size = 25, replace = TRUE), ncol = 5) Jaccard(x = x, y = x)
Find peaks that are correlated with the expression of nearby genes. For each gene, this function computes the correlation coefficient between the gene expression and accessibility of each peak within a given distance from the gene TSS, and computes an expected correlation coefficient for each peak given the GC content, accessibility, and length of the peak. The expected coefficient values for the peak are then used to compute a z-score and p-value.
LinkPeaks( object, peak.assay, expression.assay, peak.slot = "counts", expression.slot = "data", method = "pearson", gene.coords = NULL, distance = 5e+05, min.distance = NULL, min.cells = 10, genes.use = NULL, n_sample = 200, pvalue_cutoff = 0.05, score_cutoff = 0.05, gene.id = FALSE, verbose = TRUE )LinkPeaks( object, peak.assay, expression.assay, peak.slot = "counts", expression.slot = "data", method = "pearson", gene.coords = NULL, distance = 5e+05, min.distance = NULL, min.cells = 10, genes.use = NULL, n_sample = 200, pvalue_cutoff = 0.05, score_cutoff = 0.05, gene.id = FALSE, verbose = TRUE )
object |
A Seurat object |
peak.assay |
Name of assay containing peak information |
expression.assay |
Name of assay containing gene expression information |
peak.slot |
Name of slot to pull chromatin data from |
expression.slot |
Name of slot to pull expression data from |
method |
Correlation method to use. One of "pearson" or "spearman" |
gene.coords |
GRanges object containing coordinates of genes in the expression assay. If NULL, extract from gene annotations stored in the assay. |
distance |
Distance threshold for peaks to include in regression model |
min.distance |
Minimum distance between peak and TSS to include in regression model. If NULL (default), no minimum distance is used. |
min.cells |
Minimum number of cells positive for the peak and gene needed to include in the results. |
genes.use |
Genes to test. If NULL, determine from expression assay. |
n_sample |
Number of peaks to sample at random when computing the null distribution. |
pvalue_cutoff |
Minimum p-value required to retain a link. Links with a p-value equal or greater than this value will be removed from the output. |
score_cutoff |
Minimum absolute value correlation coefficient for a link to be retained |
gene.id |
Set to TRUE if genes in the expression assay are named using gene IDs rather than gene names. |
verbose |
Display messages |
This function was inspired by the method originally described by SHARE-seq (Sai Ma et al. 2020, Cell). Please consider citing the original SHARE-seq work if using this function: doi:10.1016/j.cell.2020.09.056
Returns a Seurat object with the Links information set. This is
a granges object accessible via the Links
function, with the following information:
score: the correlation coefficient between the accessibility of the peak and expression of the gene
zscore: the z-score of the correlation coefficient, computed based on the distribution of correlation coefficients from a set of background peaks
pvalue: the p-value associated with the z-score for the link
gene: name of the linked gene
peak: name of the linked peak
Display links between pairs of genomic elements within a given region of the genome.
LinkPlot( object, region, assay = NULL, min.cutoff = 0, sep = c("-", "-"), extend.upstream = 0, extend.downstream = 0, scale.linewidth = FALSE )LinkPlot( object, region, assay = NULL, min.cutoff = 0, sep = c("-", "-"), extend.upstream = 0, extend.downstream = 0, scale.linewidth = FALSE )
object |
A |
region |
A genomic region to plot |
assay |
Name of assay to use. If NULL, use the default assay. |
min.cutoff |
Minimum absolute score for link to be plotted. |
sep |
Separators to use for strings encoding genomic coordinates. First element is used to separate the chromosome from the coordinates, second element is used to separate the start from end coordinate. |
extend.upstream |
Number of bases to extend the region upstream. |
extend.downstream |
Number of bases to extend the region downstream. |
scale.linewidth |
Scale thickness of the line according to link score. |
Returns a ggplot object
Get or set the genomic link information for a Seurat object or ChromatinAssay
Links(object, ...) Links(object, ...) <- value ## S3 method for class 'ChromatinAssay' Links(object, ...) ## S3 method for class 'Seurat' Links(object, ...) ## S3 replacement method for class 'ChromatinAssay' Links(object, ...) <- value ## S3 replacement method for class 'Seurat' Links(object, ...) <- valueLinks(object, ...) Links(object, ...) <- value ## S3 method for class 'ChromatinAssay' Links(object, ...) ## S3 method for class 'Seurat' Links(object, ...) ## S3 replacement method for class 'ChromatinAssay' Links(object, ...) <- value ## S3 replacement method for class 'Seurat' Links(object, ...) <- value
object |
A Seurat object |
... |
Arguments passed to other methods |
value |
A |
Links(atac_small[["peaks"]]) Links(atac_small) links <- Links(atac_small) Links(atac_small[["peaks"]]) <- links links <- Links(atac_small) Links(atac_small) <- linksLinks(atac_small[["peaks"]]) Links(atac_small) links <- Links(atac_small) Links(atac_small[["peaks"]]) <- links links <- Links(atac_small) Links(atac_small) <- links
Extract the coordinates of the longest transcript for a gene stored in the annotations within an object.
LookupGeneCoords(object, gene, assay = NULL)LookupGeneCoords(object, gene, assay = NULL)
object |
A Seurat object |
gene |
Name of a gene to extract |
assay |
Name of assay to use |
LookupGeneCoords(atac_small, gene = "MIR1302-10")LookupGeneCoords(atac_small, gene = "MIR1302-10")
Return a vector if genomic regions that match the distribution of a set of
query regions for any given set of characteristics, specified in the input
meta.feature dataframe.
MatchRegionStats( meta.feature, query.feature, features.match = c("GC.percent"), n = 10000, verbose = TRUE, ... )MatchRegionStats( meta.feature, query.feature, features.match = c("GC.percent"), n = 10000, verbose = TRUE, ... )
meta.feature |
A dataframe containing DNA sequence information for features to choose from |
query.feature |
A dataframe containing DNA sequence information for features to match. |
features.match |
Which features of the query to match when selecting a set of regions. A vector of column names present in the feature metadata can be supplied to match multiple characteristics at once. Default is GC content. |
n |
Number of regions to select, with characteristics matching the query |
verbose |
Display messages |
... |
Arguments passed to other functions |
For each requested feature to match, a density
distribution is estimated using the density function,
and a set of weights for each feature in the dataset estimated based on the
density distribution. If multiple features are to be matched (for example,
GC content and overall accessibility), a joint density distribution is then
computed by multiplying the individual feature weights. A set of features
with characteristics matching the query regions is then selected using the
sample function, with the probability of randomly
selecting each feature equal to the joint density distribution weight.
Returns a character vector
metafeatures <- SeuratObject::GetAssayData( object = atac_small[['peaks']], layer = 'meta.features' ) query.feature <- metafeatures[1:10, ] features.choose <- metafeatures[11:nrow(metafeatures), ] MatchRegionStats( meta.feature = features.choose, query.feature = query.feature, features.match = "percentile", n = 10 )metafeatures <- SeuratObject::GetAssayData( object = atac_small[['peaks']], layer = 'meta.features' ) query.feature <- metafeatures[1:10, ] features.choose <- metafeatures[11:nrow(metafeatures), ] MatchRegionStats( meta.feature = features.choose, query.feature = query.feature, features.match = "percentile", n = 10 )
The Motif class is designed to store DNA sequence motif information, including motif PWMs or PFMs, motif positions, and metadata.
dataA sparse, binary, feature x motif matrix. Columns correspond to motif IDs, rows correspond to genomic features (peaks or bins). Entries in the matrix should be 1 if the genomic feature contains the motif, and 0 otherwise.
pwmA named list of position weight matrices
motif.namesA list containing the name of each motif
positionsA GRangesList object containing
exact positions of each motif.
meta.dataA dataframe for storage of additional information related to each motif. This could include the names of proteins that bind the motif.
Count the number of sequenced DNA fragments in a region surrounding each instance of a given DNA sequence motif.
MotifCounts( object, motifs, flanking.region = 1000, assay = NULL, verbose = TRUE, ... )MotifCounts( object, motifs, flanking.region = 1000, assay = NULL, verbose = TRUE, ... )
object |
A Seurat object |
motifs |
A list of DNA sequence motif names. One matrix will be generated for each motif |
flanking.region |
Amount of sequence to include surrounding the motif itself |
assay |
Name of assay to use. Must be a ChromatinAssay |
verbose |
Display messages |
... |
Additional arguments passed to |
Returns a list of sparse matrices
Plot position weight matrix or position frequency matrix for different DNA sequence motifs.
MotifPlot(object, motifs, assay = NULL, use.names = TRUE, ...)MotifPlot(object, motifs, assay = NULL, use.names = TRUE, ...)
object |
A Seurat object |
motifs |
A list of motif IDs or motif names to plot |
assay |
Name of the assay to use |
use.names |
Use motif names stored in the motif object |
... |
Additional parameters passed to |
Returns a ggplot object
motif.obj <- Motifs(atac_small) MotifPlot(atac_small, motifs = head(colnames(motif.obj)))motif.obj <- Motifs(atac_small) MotifPlot(atac_small, motifs = head(colnames(motif.obj)))
Get or set the Motif object for a Seurat object or ChromatinAssay.
Motifs(object, ...) Motifs(object, ...) <- value ## S3 method for class 'ChromatinAssay' Motifs(object, ...) ## S3 method for class 'Seurat' Motifs(object, ...) ## S3 replacement method for class 'ChromatinAssay' Motifs(object, ...) <- value ## S3 replacement method for class 'Seurat' Motifs(object, ...) <- valueMotifs(object, ...) Motifs(object, ...) <- value ## S3 method for class 'ChromatinAssay' Motifs(object, ...) ## S3 method for class 'Seurat' Motifs(object, ...) ## S3 replacement method for class 'ChromatinAssay' Motifs(object, ...) <- value ## S3 replacement method for class 'Seurat' Motifs(object, ...) <- value
object |
A Seurat object |
... |
Arguments passed to other methods |
value |
A |
Motifs(atac_small[["peaks"]]) Motifs(atac_small) motifs <- Motifs(atac_small) Motifs(atac_small[["peaks"]]) <- motifs motifs <- Motifs(atac_small) Motifs(atac_small) <- motifsMotifs(atac_small[["peaks"]]) Motifs(atac_small) motifs <- Motifs(atac_small) Motifs(atac_small[["peaks"]]) <- motifs motifs <- Motifs(atac_small) Motifs(atac_small) <- motifs
The precede, follow, nearest, distance, distanceToNearest methods
are available for ChromatinAssay objects.
## S4 method for signature 'ANY,ChromatinAssay' precede(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ChromatinAssay,ANY' precede(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' precede(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ANY,Seurat' precede(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'Seurat,ANY' precede(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'Seurat,Seurat' precede(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ANY,ChromatinAssay' follow(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ChromatinAssay,ANY' follow(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' follow(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ANY,Seurat' follow(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'Seurat,ANY' follow(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'Seurat,Seurat' follow(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ANY,ChromatinAssay' nearest(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ChromatinAssay,ANY' nearest(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' nearest(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ANY,Seurat' nearest(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'Seurat,ANY' nearest(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'Seurat,Seurat' nearest(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ANY,ChromatinAssay' distance(x, y, ignore.strand = FALSE, ...) ## S4 method for signature 'ChromatinAssay,ANY' distance(x, y, ignore.strand = FALSE, ...) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' distance(x, y, ignore.strand = FALSE, ...) ## S4 method for signature 'ANY,Seurat' distance(x, y, ignore.strand = FALSE, ...) ## S4 method for signature 'Seurat,ANY' distance(x, y, ignore.strand = FALSE, ...) ## S4 method for signature 'Seurat,Seurat' distance(x, y, ignore.strand = FALSE, ...) ## S4 method for signature 'ANY,ChromatinAssay' distanceToNearest(x, subject, ignore.strand = FALSE, ...) ## S4 method for signature 'ChromatinAssay,ANY' distanceToNearest(x, subject, ignore.strand = FALSE, ...) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' distanceToNearest(x, subject, ignore.strand = FALSE, ...) ## S4 method for signature 'ANY,Seurat' distanceToNearest(x, subject, ignore.strand = FALSE, ...) ## S4 method for signature 'Seurat,ANY' distanceToNearest(x, subject, ignore.strand = FALSE, ...) ## S4 method for signature 'Seurat,Seurat' distanceToNearest(x, subject, ignore.strand = FALSE, ...)## S4 method for signature 'ANY,ChromatinAssay' precede(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ChromatinAssay,ANY' precede(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' precede(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ANY,Seurat' precede(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'Seurat,ANY' precede(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'Seurat,Seurat' precede(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ANY,ChromatinAssay' follow(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ChromatinAssay,ANY' follow(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' follow(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ANY,Seurat' follow(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'Seurat,ANY' follow(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'Seurat,Seurat' follow(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ANY,ChromatinAssay' nearest(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ChromatinAssay,ANY' nearest(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' nearest(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ANY,Seurat' nearest(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'Seurat,ANY' nearest(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'Seurat,Seurat' nearest(x, subject, select = c("arbitrary", "all"), ignore.strand = FALSE) ## S4 method for signature 'ANY,ChromatinAssay' distance(x, y, ignore.strand = FALSE, ...) ## S4 method for signature 'ChromatinAssay,ANY' distance(x, y, ignore.strand = FALSE, ...) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' distance(x, y, ignore.strand = FALSE, ...) ## S4 method for signature 'ANY,Seurat' distance(x, y, ignore.strand = FALSE, ...) ## S4 method for signature 'Seurat,ANY' distance(x, y, ignore.strand = FALSE, ...) ## S4 method for signature 'Seurat,Seurat' distance(x, y, ignore.strand = FALSE, ...) ## S4 method for signature 'ANY,ChromatinAssay' distanceToNearest(x, subject, ignore.strand = FALSE, ...) ## S4 method for signature 'ChromatinAssay,ANY' distanceToNearest(x, subject, ignore.strand = FALSE, ...) ## S4 method for signature 'ChromatinAssay,ChromatinAssay' distanceToNearest(x, subject, ignore.strand = FALSE, ...) ## S4 method for signature 'ANY,Seurat' distanceToNearest(x, subject, ignore.strand = FALSE, ...) ## S4 method for signature 'Seurat,ANY' distanceToNearest(x, subject, ignore.strand = FALSE, ...) ## S4 method for signature 'Seurat,Seurat' distanceToNearest(x, subject, ignore.strand = FALSE, ...)
x |
A query |
subject |
The subject |
select |
Logic for handling ties.
See |
ignore.strand |
Logical argument controlling whether strand information should be ignored. |
y |
For the |
... |
Additional arguments for methods |
precede(x = ChromatinAssay, subject = ANY): method for ChromatinAssay, ANY
precede(x = ChromatinAssay, subject = ChromatinAssay): method for ChromatinAssay, ChromatinAssay
precede(x = ANY, subject = Seurat): method for ANY, Seurat
precede(x = Seurat, subject = ANY): method for Seurat, ANY
precede(x = Seurat, subject = Seurat): method for Seurat, Seurat
follow(x = ANY, subject = ChromatinAssay): method for ANY, ChromatinAssay
follow(x = ChromatinAssay, subject = ANY): method for ChromatinAssay, ANY
follow(x = ChromatinAssay, subject = ChromatinAssay): method for ChromatinAssay, ChromatinAssay
follow(x = ANY, subject = Seurat): method for ANY, Seurat
follow(x = Seurat, subject = ANY): method for Seurat, ANY
follow(x = Seurat, subject = Seurat): method for Seurat, Seurat
nearest(x = ANY, subject = ChromatinAssay): method for ANY, ChromatinAssay
nearest(x = ChromatinAssay, subject = ANY): method for ChromatinAssay, ANY
nearest(x = ChromatinAssay, subject = ChromatinAssay): method for ChromatinAssay, ChromatinAssay
nearest(x = ANY, subject = Seurat): method for ANY, Seurat
nearest(x = Seurat, subject = ANY): method for Seurat, ANY
nearest(x = Seurat, subject = Seurat): method for Seurat, Seurat
distance(x = ANY, y = ChromatinAssay): method for ANY, ChromatinAssay
distance(x = ChromatinAssay, y = ANY): method for ChromatinAssay, ANY
distance(x = ChromatinAssay, y = ChromatinAssay): method for ChromatinAssay, ChromatinAssay
distance(x = ANY, y = Seurat): method for ANY, Seurat
distance(x = Seurat, y = ANY): method for Seurat, ANY
distance(x = Seurat, y = Seurat): method for Seurat, Seurat
distanceToNearest(x = ANY, subject = ChromatinAssay): method for ANY, ChromatinAssay
distanceToNearest(x = ChromatinAssay, subject = ANY): method for ChromatinAssay, ANY
distanceToNearest(x = ChromatinAssay, subject = ChromatinAssay): method for ChromatinAssay, ChromatinAssay
distanceToNearest(x = ANY, subject = Seurat): method for ANY, Seurat
distanceToNearest(x = Seurat, subject = ANY): method for Seurat, ANY
distanceToNearest(x = Seurat, subject = Seurat): method for Seurat, Seurat
nearest-methods in the IRanges package.
nearest-methods in the GenomicRanges package
Calculate the strength of the nucleosome signal per cell. Computes the ratio of fragments between 147 bp and 294 bp (mononucleosome) to fragments < 147 bp (nucleosome-free)
NucleosomeSignal( object, assay = NULL, n = ncol(object) * 5000, verbose = TRUE, ... )NucleosomeSignal( object, assay = NULL, n = ncol(object) * 5000, verbose = TRUE, ... )
object |
A Seurat object |
assay |
Name of assay to use. Only required if a fragment path is not provided. If NULL, use the active assay. |
n |
Number of lines to read from the fragment file. If NULL, read all lines. Default scales with the number of cells in the object. |
verbose |
Display messages |
... |
Arguments passed to other functions |
Returns a Seurat object with
added metadata for the ratio of mononucleosomal to nucleosome-free fragments
per cell, and the percentile rank of each ratio.
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") Fragments(atac_small) <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), tolerance = 0.5 ) NucleosomeSignal(object = atac_small)fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") Fragments(atac_small) <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), tolerance = 0.5 ) NucleosomeSignal(object = atac_small)
Display the genomic ranges in a ChromatinAssay object that fall
in a given genomic region
PeakPlot( object, region, assay = NULL, peaks = NULL, group.by = NULL, color = "dimgrey", sep = c("-", "-"), extend.upstream = 0, extend.downstream = 0 )PeakPlot( object, region, assay = NULL, peaks = NULL, group.by = NULL, color = "dimgrey", sep = c("-", "-"), extend.upstream = 0, extend.downstream = 0 )
object |
A |
region |
A genomic region to plot |
assay |
Name of assay to use. If NULL, use the default assay. |
peaks |
A GRanges object containing peak coordinates. If NULL, use coordinates stored in the Seurat object. |
group.by |
Name of variable in feature metadata (if using ranges in the Seurat object) or genomic ranges metadata (if using supplied ranges) to color ranges by. If NULL, do not color by any metadata variable. |
color |
Color to use. If |
sep |
Separators to use for strings encoding genomic coordinates. First element is used to separate the chromosome from the coordinates, second element is used to separate the start from end coordinate. |
extend.upstream |
Number of bases to extend the region upstream. |
extend.downstream |
Number of bases to extend the region downstream. |
Returns a ggplot object
# plot peaks in assay PeakPlot(atac_small, region = "chr1-710000-715000") # manually set color PeakPlot(atac_small, region = "chr1-710000-715000", color = "red") # color by a variable in the feature metadata PeakPlot(atac_small, region = "chr1-710000-715000", group.by = "count")# plot peaks in assay PeakPlot(atac_small, region = "chr1-710000-715000") # manually set color PeakPlot(atac_small, region = "chr1-710000-715000", color = "red") # color by a variable in the feature metadata PeakPlot(atac_small, region = "chr1-710000-715000", group.by = "count")
Plot motif footprinting results
PlotFootprint( object, features, assay = NULL, group.by = NULL, split.by = NULL, idents = NULL, label = TRUE, repel = TRUE, show.expected = TRUE, normalization = "subtract", label.top = 3, label.idents = NULL )PlotFootprint( object, features, assay = NULL, group.by = NULL, split.by = NULL, idents = NULL, label = TRUE, repel = TRUE, show.expected = TRUE, normalization = "subtract", label.top = 3, label.idents = NULL )
object |
A Seurat object |
features |
A vector of features to plot |
assay |
Name of assay to use |
group.by |
A grouping variable |
split.by |
A metadata variable to split the plot by. For example, grouping by "celltype" and splitting by "batch" will create separate plots for each celltype and batch. |
idents |
Set of identities to include in the plot |
label |
TRUE/FALSE value to control whether groups are labeled. |
repel |
Repel labels from each other |
show.expected |
Plot the expected Tn5 integration frequency below the main footprint plot |
normalization |
Method to normalize for Tn5 DNA sequence bias. Options are "subtract", "divide", or NULL to perform no bias correction. |
label.top |
Number of groups to label based on highest accessibility in motif flanking region. |
label.idents |
Vector of identities to label. If supplied,
|
Read output files from MGATK (https://github.com/caleblareau/mgatk).
ReadMGATK(dir, verbose = TRUE)ReadMGATK(dir, verbose = TRUE)
dir |
Path to directory containing MGATK output files |
verbose |
Display messages |
Returns a list containing a sparse matrix (counts) and two dataframes (depth and refallele).
The sparse matrix contains read counts for each base at each position and strand.
The depth dataframe contains the total depth for each cell. The refallele dataframe contains the reference genome allele at each position.
## Not run: data.dir <- system.file("extdata", "test_mgatk", package="Signac") mgatk <- ReadMGATK(dir = data.dir) ## End(Not run)## Not run: data.dir <- system.file("extdata", "test_mgatk", package="Signac") mgatk <- ReadMGATK(dir = data.dir) ## End(Not run)
Plot fragment counts within a set of regions.
RegionHeatmap( object, key, assay = NULL, idents = NULL, normalize = TRUE, upstream = 3000, downstream = 3000, max.cutoff = "q95", cols = NULL, min.counts = 1, window = (upstream + downstream)/30, order = TRUE, nrow = NULL )RegionHeatmap( object, key, assay = NULL, idents = NULL, normalize = TRUE, upstream = 3000, downstream = 3000, max.cutoff = "q95", cols = NULL, min.counts = 1, window = (upstream + downstream)/30, order = TRUE, nrow = NULL )
object |
A Seurat object |
key |
Name of key to pull data from. Stores the results from
|
assay |
Name of assay to use. If a list or vector of assay names is
given, data will be plotted from each assay. Note that all assays must
contain |
idents |
Cell identities to include. Note that cells cannot be
regrouped, this will require re-running |
normalize |
Normalize by number of cells in each group |
upstream |
Number of bases to include upstream of region. If NULL, use
all bases that were included in the |
downstream |
Number of bases to include downstream of region. See
documentation for |
max.cutoff |
Maximum cutoff value. Data above this value will be clipped to the maximum value. A quantile maximum can be specified in the form of "q##" where "##" is the quantile (eg, "q90" for 90th quantile). If NULL, no cutoff will be set |
cols |
Vector of colors to use as the maximum value of the color scale. One color must be supplied for each assay. If NULL, the default ggplot2 colors are used. |
min.counts |
Minimum total counts to display region in plot |
window |
Smoothing window to apply |
order |
Order regions by the total number of fragments in the region across all included identities |
nrow |
Number of rows to use when creating plot. If NULL, chosen automatically by ggplot2 |
Returns a ggplot2 object
RegionMatrix
Count fragments within a set of regions for different groups of cells.
RegionMatrix(object, ...) ## S3 method for class 'Seurat' RegionMatrix( object, regions, key, assay = NULL, group.by = NULL, idents = NULL, upstream = 3000, downstream = 3000, verbose = TRUE, ... ) ## S3 method for class 'ChromatinAssay' RegionMatrix( object, regions, key, assay = NULL, group.by = NULL, idents = NULL, upstream = 3000, downstream = 3000, verbose = TRUE, ... ) ## Default S3 method: RegionMatrix( object, regions, key, assay = NULL, group.by = NULL, idents = NULL, upstream = 3000, downstream = 3000, verbose = TRUE, ... )RegionMatrix(object, ...) ## S3 method for class 'Seurat' RegionMatrix( object, regions, key, assay = NULL, group.by = NULL, idents = NULL, upstream = 3000, downstream = 3000, verbose = TRUE, ... ) ## S3 method for class 'ChromatinAssay' RegionMatrix( object, regions, key, assay = NULL, group.by = NULL, idents = NULL, upstream = 3000, downstream = 3000, verbose = TRUE, ... ) ## Default S3 method: RegionMatrix( object, regions, key, assay = NULL, group.by = NULL, idents = NULL, upstream = 3000, downstream = 3000, verbose = TRUE, ... )
object |
A Seurat or ChromatinAssay object |
... |
Arguments passed to other methods |
regions |
A |
key |
Name to store resulting matrices under |
assay |
Name of assay to use. If NULL, use the default assay |
group.by |
Grouping variable to use when aggregating data across cells. If NULL, use the active cell identities |
idents |
Cell identities to include. If NULL, include all identities |
upstream |
Number of bases to extend regions upstream |
downstream |
Number of bases to extend regions downstream |
verbose |
Display messages |
Returns a Seurat object
Plot fragment counts within a set of regions.
RegionPlot( object, key, assay = NULL, idents = NULL, normalize = TRUE, upstream = NULL, downstream = NULL, window = (upstream + downstream)/500, nrow = NULL )RegionPlot( object, key, assay = NULL, idents = NULL, normalize = TRUE, upstream = NULL, downstream = NULL, window = (upstream + downstream)/500, nrow = NULL )
object |
A Seurat object |
key |
Name of key to pull data from. Stores the results from
|
assay |
Name of assay to use. If a list or vector of assay names is
given, data will be plotted from each assay. Note that all assays must
contain |
idents |
Cell identities to include. Note that cells cannot be
regrouped, this will require re-running |
normalize |
Normalize by number of cells in each group |
upstream |
Number of bases to include upstream of region. If NULL, use
all bases that were included in the |
downstream |
Number of bases to include downstream of region. See
documentation for |
window |
Smoothing window to apply |
nrow |
Number of rows to use when creating plot. If NULL, chosen automatically by ggplot2 |
Returns a ggplot2 object
RegionMatrix
Compute the GC content, region lengths, and dinucleotide base frequencies for regions in the assay and add to the feature metadata.
RegionStats(object, ...) ## Default S3 method: RegionStats(object, genome, verbose = TRUE, ...) ## S3 method for class 'ChromatinAssay' RegionStats(object, genome, verbose = TRUE, ...) ## S3 method for class 'Seurat' RegionStats(object, genome, assay = NULL, verbose = TRUE, ...)RegionStats(object, ...) ## Default S3 method: RegionStats(object, genome, verbose = TRUE, ...) ## S3 method for class 'ChromatinAssay' RegionStats(object, genome, verbose = TRUE, ...) ## S3 method for class 'Seurat' RegionStats(object, genome, assay = NULL, verbose = TRUE, ...)
object |
A Seurat object, Assay object, or set of genomic ranges |
... |
Arguments passed to other methods |
genome |
A |
verbose |
Display messages |
assay |
Name of assay to use |
Returns a dataframe
## Not run: library(BSgenome.Hsapiens.UCSC.hg19) RegionStats( object = rownames(atac_small), genome = BSgenome.Hsapiens.UCSC.hg19 ) ## End(Not run) ## Not run: library(BSgenome.Hsapiens.UCSC.hg19) RegionStats( object = atac_small[['peaks']], genome = BSgenome.Hsapiens.UCSC.hg19 ) ## End(Not run) ## Not run: library(BSgenome.Hsapiens.UCSC.hg19) RegionStats( object = atac_small, assay = 'bins', genome = BSgenome.Hsapiens.UCSC.hg19 ) ## End(Not run)## Not run: library(BSgenome.Hsapiens.UCSC.hg19) RegionStats( object = rownames(atac_small), genome = BSgenome.Hsapiens.UCSC.hg19 ) ## End(Not run) ## Not run: library(BSgenome.Hsapiens.UCSC.hg19) RegionStats( object = atac_small[['peaks']], genome = BSgenome.Hsapiens.UCSC.hg19 ) ## End(Not run) ## Not run: library(BSgenome.Hsapiens.UCSC.hg19) RegionStats( object = atac_small, assay = 'bins', genome = BSgenome.Hsapiens.UCSC.hg19 ) ## End(Not run)
Wrapper to run chromVAR on an assay with a motif
object present. Will return a new Seurat assay with the motif activities
(the deviations in chromatin accessibility across the set of regions) as
a new assay.
RunChromVAR(object, ...) ## S3 method for class 'ChromatinAssay' RunChromVAR(object, genome, motif.matrix = NULL, verbose = TRUE, ...) ## S3 method for class 'Seurat' RunChromVAR( object, genome, motif.matrix = NULL, assay = NULL, new.assay.name = "chromvar", ... )RunChromVAR(object, ...) ## S3 method for class 'ChromatinAssay' RunChromVAR(object, genome, motif.matrix = NULL, verbose = TRUE, ...) ## S3 method for class 'Seurat' RunChromVAR( object, genome, motif.matrix = NULL, assay = NULL, new.assay.name = "chromvar", ... )
object |
A Seurat object |
... |
Additional arguments passed to
|
genome |
A |
motif.matrix |
A peak x motif matrix. If NULL, pull the peak x motif matrix from a Motif object stored in the assay. |
verbose |
Display messages |
assay |
Name of assay to use |
new.assay.name |
Name of new assay used to store the chromVAR results. Default is "chromvar". |
See the chromVAR documentation for more information: https://greenleaflab.github.io/chromVAR/index.html
See the chromVAR paper: https://www.nature.com/articles/nmeth.4401
Returns a Seurat object with a new assay
## Not run: library(BSgenome.Hsapiens.UCSC.hg19) RunChromVAR(object = atac_small[["peaks"]], genome = BSgenome.Hsapiens.UCSC.hg19) ## End(Not run) ## Not run: library(BSgenome.Hsapiens.UCSC.hg19) RunChromVAR(object = atac_small, genome = BSgenome.Hsapiens.UCSC.hg19) ## End(Not run)## Not run: library(BSgenome.Hsapiens.UCSC.hg19) RunChromVAR(object = atac_small[["peaks"]], genome = BSgenome.Hsapiens.UCSC.hg19) ## End(Not run) ## Not run: library(BSgenome.Hsapiens.UCSC.hg19) RunChromVAR(object = atac_small, genome = BSgenome.Hsapiens.UCSC.hg19) ## End(Not run)
Run partial singular value decomposition using irlba
RunSVD(object, ...) ## Default S3 method: RunSVD( object, assay = NULL, n = 50, scale.embeddings = TRUE, reduction.key = "LSI_", scale.max = NULL, verbose = TRUE, irlba.work = n * 3, tol = 1e-05, ... ) ## S3 method for class 'Assay' RunSVD( object, assay = NULL, features = NULL, n = 50, reduction.key = "LSI_", scale.max = NULL, verbose = TRUE, ... ) ## S3 method for class 'StdAssay' RunSVD( object, assay = NULL, features = NULL, n = 50, reduction.key = "LSI_", scale.max = NULL, verbose = TRUE, ... ) ## S3 method for class 'Seurat' RunSVD( object, assay = NULL, features = NULL, n = 50, reduction.key = "LSI_", reduction.name = "lsi", scale.max = NULL, verbose = TRUE, ... )RunSVD(object, ...) ## Default S3 method: RunSVD( object, assay = NULL, n = 50, scale.embeddings = TRUE, reduction.key = "LSI_", scale.max = NULL, verbose = TRUE, irlba.work = n * 3, tol = 1e-05, ... ) ## S3 method for class 'Assay' RunSVD( object, assay = NULL, features = NULL, n = 50, reduction.key = "LSI_", scale.max = NULL, verbose = TRUE, ... ) ## S3 method for class 'StdAssay' RunSVD( object, assay = NULL, features = NULL, n = 50, reduction.key = "LSI_", scale.max = NULL, verbose = TRUE, ... ) ## S3 method for class 'Seurat' RunSVD( object, assay = NULL, features = NULL, n = 50, reduction.key = "LSI_", reduction.name = "lsi", scale.max = NULL, verbose = TRUE, ... )
object |
A Seurat object |
... |
Arguments passed to other methods |
assay |
Which assay to use. If NULL, use the default assay |
n |
Number of singular values to compute |
scale.embeddings |
Scale cell embeddings within each component to mean 0 and SD 1 (default TRUE). |
reduction.key |
Key for dimension reduction object |
scale.max |
Clipping value for cell embeddings. Default (NULL) is no clipping. |
verbose |
Print messages |
irlba.work |
work parameter for |
tol |
Tolerance (tol) parameter for |
features |
Which features to use. If NULL, use variable features |
reduction.name |
Name for stored dimension reduction object. Default 'svd' |
Returns a Seurat object
x <- matrix(data = rnorm(100), ncol = 10) RunSVD(x) ## Not run: RunSVD(atac_small[['peaks']]) ## End(Not run) ## Not run: RunSVD(atac_small[['peaks']]) ## End(Not run) ## Not run: RunSVD(atac_small) ## End(Not run)x <- matrix(data = rnorm(100), ncol = 10) RunSVD(x) ## Not run: RunSVD(atac_small[['peaks']]) ## End(Not run) ## Not run: RunSVD(atac_small[['peaks']]) ## End(Not run) ## Not run: RunSVD(atac_small) ## End(Not run)
Run term frequency inverse document frequency (TF-IDF) normalization on a matrix.
RunTFIDF(object, ...) ## Default S3 method: RunTFIDF( object, assay = NULL, method = 1, scale.factor = 10000, idf = NULL, verbose = TRUE, ... ) ## S3 method for class 'Assay' RunTFIDF( object, assay = NULL, method = 1, scale.factor = 10000, idf = NULL, verbose = TRUE, ... ) ## S3 method for class 'StdAssay' RunTFIDF( object, assay = NULL, method = 1, scale.factor = 10000, idf = NULL, verbose = TRUE, ... ) ## S3 method for class 'Seurat' RunTFIDF( object, assay = NULL, method = 1, scale.factor = 10000, idf = NULL, verbose = TRUE, ... )RunTFIDF(object, ...) ## Default S3 method: RunTFIDF( object, assay = NULL, method = 1, scale.factor = 10000, idf = NULL, verbose = TRUE, ... ) ## S3 method for class 'Assay' RunTFIDF( object, assay = NULL, method = 1, scale.factor = 10000, idf = NULL, verbose = TRUE, ... ) ## S3 method for class 'StdAssay' RunTFIDF( object, assay = NULL, method = 1, scale.factor = 10000, idf = NULL, verbose = TRUE, ... ) ## S3 method for class 'Seurat' RunTFIDF( object, assay = NULL, method = 1, scale.factor = 10000, idf = NULL, verbose = TRUE, ... )
object |
A Seurat object |
... |
Arguments passed to other methods |
assay |
Name of assay to use |
method |
Which TF-IDF implementation to use. Choice of:
|
scale.factor |
Which scale factor to use. Default is 10000. |
idf |
A precomputed IDF vector to use. If NULL, compute based on the input data matrix. |
verbose |
Print progress |
Four different TF-IDF methods are implemented. We recommend using method 1 (the default).
Returns a Seurat object
https://en.wikipedia.org/wiki/Latent_semantic_analysis#Latent_semantic_indexing
mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5) RunTFIDF(object = mat) RunTFIDF(atac_small[['peaks']]) RunTFIDF(atac_small[['peaks']]) RunTFIDF(object = atac_small)mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5) RunTFIDF(object = mat) RunTFIDF(atac_small[['peaks']]) RunTFIDF(atac_small[['peaks']]) RunTFIDF(object = atac_small)
Methods for accessing and modifying
Seqinfo object information stored in a
ChromatinAssay object.
## S4 method for signature 'ChromatinAssay' seqinfo(x) ## S4 replacement method for signature 'ChromatinAssay' seqinfo(x) <- value ## S4 method for signature 'ChromatinAssay' seqlevels(x) ## S4 replacement method for signature 'ChromatinAssay' seqlevels(x) <- value ## S4 method for signature 'ChromatinAssay' seqnames(x) ## S4 replacement method for signature 'ChromatinAssay' seqnames(x) <- value ## S4 method for signature 'ChromatinAssay' seqlengths(x) ## S4 replacement method for signature 'ChromatinAssay' seqlengths(x) <- value ## S4 method for signature 'ChromatinAssay' genome(x) ## S4 replacement method for signature 'ChromatinAssay' genome(x) <- value ## S4 method for signature 'ChromatinAssay' isCircular(x) ## S4 replacement method for signature 'ChromatinAssay' isCircular(x) <- value ## S4 method for signature 'Seurat' seqinfo(x) ## S4 replacement method for signature 'Seurat' seqinfo(x) <- value ## S4 method for signature 'Seurat' seqlevels(x) ## S4 replacement method for signature 'Seurat' seqlevels(x) <- value ## S4 method for signature 'Seurat' seqnames(x) ## S4 replacement method for signature 'Seurat' seqnames(x) <- value ## S4 method for signature 'Seurat' seqlengths(x) ## S4 replacement method for signature 'Seurat' seqlengths(x) <- value ## S4 method for signature 'Seurat' genome(x) ## S4 replacement method for signature 'Seurat' genome(x) <- value ## S4 method for signature 'Seurat' isCircular(x) ## S4 replacement method for signature 'Seurat' isCircular(x) <- value## S4 method for signature 'ChromatinAssay' seqinfo(x) ## S4 replacement method for signature 'ChromatinAssay' seqinfo(x) <- value ## S4 method for signature 'ChromatinAssay' seqlevels(x) ## S4 replacement method for signature 'ChromatinAssay' seqlevels(x) <- value ## S4 method for signature 'ChromatinAssay' seqnames(x) ## S4 replacement method for signature 'ChromatinAssay' seqnames(x) <- value ## S4 method for signature 'ChromatinAssay' seqlengths(x) ## S4 replacement method for signature 'ChromatinAssay' seqlengths(x) <- value ## S4 method for signature 'ChromatinAssay' genome(x) ## S4 replacement method for signature 'ChromatinAssay' genome(x) <- value ## S4 method for signature 'ChromatinAssay' isCircular(x) ## S4 replacement method for signature 'ChromatinAssay' isCircular(x) <- value ## S4 method for signature 'Seurat' seqinfo(x) ## S4 replacement method for signature 'Seurat' seqinfo(x) <- value ## S4 method for signature 'Seurat' seqlevels(x) ## S4 replacement method for signature 'Seurat' seqlevels(x) <- value ## S4 method for signature 'Seurat' seqnames(x) ## S4 replacement method for signature 'Seurat' seqnames(x) <- value ## S4 method for signature 'Seurat' seqlengths(x) ## S4 replacement method for signature 'Seurat' seqlengths(x) <- value ## S4 method for signature 'Seurat' genome(x) ## S4 replacement method for signature 'Seurat' genome(x) <- value ## S4 method for signature 'Seurat' isCircular(x) ## S4 replacement method for signature 'Seurat' isCircular(x) <- value
x |
A |
value |
A |
seqinfo(ChromatinAssay) <- value: set method for ChromatinAssay objects
seqlevels(ChromatinAssay): get method for ChromatinAssay objects
seqlevels(ChromatinAssay) <- value: set method for ChromatinAssay objects
seqnames(ChromatinAssay): get method for ChromatinAssay objects
seqnames(ChromatinAssay) <- value: set method for ChromatinAssay objects
seqlengths(ChromatinAssay): get method for ChromatinAssay objects
seqlengths(ChromatinAssay) <- value: set method for ChromatinAssay objects
genome(ChromatinAssay): get method for ChromatinAssay objects
genome(ChromatinAssay) <- value: set method for ChromatinAssay objects
isCircular(ChromatinAssay): get method for ChromatinAssay objects
isCircular(ChromatinAssay) <- value: set method for ChromatinAssay objects
seqinfo(Seurat): get method for Seurat objects
seqinfo(Seurat) <- value: set method for Seurat objects
seqlevels(Seurat): get method for Seurat objects
seqlevels(Seurat) <- value: set method for Seurat objects
seqnames(Seurat): get method for Seurat objects
seqnames(Seurat) <- value: set method for Seurat objects
seqlengths(Seurat): get method for Seurat objects
seqlengths(Seurat) <- value: set method for Seurat objects
genome(Seurat): get method for Seurat objects
genome(Seurat) <- value: set method for Seurat objects
isCircular(Seurat): get method for Seurat objects
isCircular(Seurat) <- value: set method for Seurat objects
seqinfo in the GenomeInfoDb package.
Set motif matrix for given assay
SetMotifData(object, ...) ## S3 method for class 'Motif' SetMotifData(object, slot, new.data, ...) ## S3 method for class 'ChromatinAssay' SetMotifData(object, slot, new.data, ...) ## S3 method for class 'Seurat' SetMotifData(object, assay = NULL, ...)SetMotifData(object, ...) ## S3 method for class 'Motif' SetMotifData(object, slot, new.data, ...) ## S3 method for class 'ChromatinAssay' SetMotifData(object, slot, new.data, ...) ## S3 method for class 'Seurat' SetMotifData(object, assay = NULL, ...)
object |
A Seurat object |
... |
Arguments passed to other methods |
slot |
Name of slot to use |
new.data |
motif matrix to add. Should be matrix or sparse matrix class |
assay |
Name of assay whose data should be set |
Returns a Seurat object
motif.obj <- SeuratObject::GetAssayData( object = atac_small[['peaks']], slot = "motifs" ) SetMotifData(object = motif.obj, slot = 'data', new.data = matrix(1:2)) SetMotifData( object = atac_small[['peaks']], slot = 'data', new.data = matrix(1:2) ) motif.matrix <- GetMotifData(object = atac_small) SetMotifData( object = atac_small, assay = 'peaks', slot = 'data', new.data = motif.matrix )motif.obj <- SeuratObject::GetAssayData( object = atac_small[['peaks']], slot = "motifs" ) SetMotifData(object = motif.obj, slot = 'data', new.data = matrix(1:2)) SetMotifData( object = atac_small[['peaks']], slot = 'data', new.data = matrix(1:2) ) motif.matrix <- GetMotifData(object = atac_small) SetMotifData( object = atac_small, assay = 'peaks', slot = 'data', new.data = motif.matrix )
Compute distance matrix from a feature/variable matrix and perform hierarchical clustering to order variables (for example, cell types) according to their similarity.
SortIdents( object, layer = "data", assay = NULL, label = NULL, dendrogram = FALSE, method = "euclidean", verbose = TRUE )SortIdents( object, layer = "data", assay = NULL, label = NULL, dendrogram = FALSE, method = "euclidean", verbose = TRUE )
object |
A Seurat object containing single-cell data. |
layer |
The layer of the data to use (default is "data"). |
assay |
Name of assay to use. If NULL, use the default assay |
label |
Metadata attribute to sort. If NULL, uses the active identities. |
dendrogram |
Logical, whether to plot the dendrogram (default is FALSE). |
method |
The distance method to use for hierarchical clustering
(default is 'euclidean', other options from |
verbose |
Display messages |
The Seurat object with metadata variable reordered by similarity. If the metadata variable was a character vector, it will be converted to a factor and the factor levels set according to the similarity ordering. If active identities were used (label=NULL), the levels will be updated according to similarity ordering.
atac_small$test <- sample(1:10, ncol(atac_small), replace = TRUE) atac_small <- SortIdents(object = atac_small, label = 'test') print(levels(atac_small$test))atac_small$test <- sample(1:10, ncol(atac_small), replace = TRUE) atac_small <- SortIdents(object = atac_small, label = 'test') print(levels(atac_small$test))
Splits a fragment file into separate files for each group of cells. If splitting multiple fragment files containing common cell types, fragments originating from different files will be appended to the same file for one group of cell identities.
SplitFragments( object, assay = NULL, group.by = NULL, idents = NULL, outdir = getwd(), file.suffix = "", append = TRUE, buffer_length = 256L, verbose = TRUE )SplitFragments( object, assay = NULL, group.by = NULL, idents = NULL, outdir = getwd(), file.suffix = "", append = TRUE, buffer_length = 256L, verbose = TRUE )
object |
A Seurat object |
assay |
Name of assay to use |
group.by |
Name of grouping variable to group cells by |
idents |
List of identities to include |
outdir |
Directory to write output files |
file.suffix |
Suffix to add to all file names (before file extension).
If splitting multiple fragment files without the |
append |
If splitting multiple fragment files, append cells from the same group (eg cluster) to the same file. Note that this can cause the output file to be unsorted. |
buffer_length |
Size of buffer to be read from the fragment file. This must be longer than the longest line in the file. |
verbose |
Display messages |
Convert a genomic coordinate string to a GRanges object
StringToGRanges(regions, sep = c("-", "-"), ...)StringToGRanges(regions, sep = c("-", "-"), ...)
regions |
Vector of genomic region strings |
sep |
Vector of separators to use for genomic string. First element is used to separate chromosome and coordinates, second separator is used to separate start and end coordinates. |
... |
Additional arguments passed to
|
Returns a GRanges object
regions <- c('chr1-1-10', 'chr2-12-3121') StringToGRanges(regions = regions)regions <- c('chr1-1-10', 'chr2-12-3121') StringToGRanges(regions = regions)
Returns a subset of a Fragment-class object.
## S3 method for class 'Fragment' subset(x, cells = NULL, ...)## S3 method for class 'Fragment' subset(x, cells = NULL, ...)
x |
A Fragment object |
cells |
Vector of cells to retain |
... |
Arguments passed to other methods |
Returns a subsetted Fragment object
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") cells <- colnames(x = atac_small) names(x = cells) <- paste0("test_", cells) frags <- CreateFragmentObject(path = fpath, cells = cells, verbose = FALSE, tolerance = 0.5) subset(frags, head(names(cells)))fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") cells <- colnames(x = atac_small) names(x = cells) <- paste0("test_", cells) frags <- CreateFragmentObject(path = fpath, cells = cells, verbose = FALSE, tolerance = 0.5) subset(frags, head(names(cells)))
Returns a subset of a Motif-class object.
## S3 method for class 'Motif' subset(x, features = NULL, motifs = NULL, ...) ## S3 method for class 'Motif' x[i, j, ...]## S3 method for class 'Motif' subset(x, features = NULL, motifs = NULL, ...) ## S3 method for class 'Motif' x[i, j, ...]
x |
A Motif object |
features |
Which features to retain |
motifs |
Which motifs to retain |
... |
Arguments passed to other methods |
i |
Which columns to retain |
j |
Which rows to retain |
Returns a subsetted Motif object
motif.obj <- SeuratObject::GetAssayData( object = atac_small[['peaks']], layer = "motifs" ) subset(x = motif.obj, features = head(rownames(motif.obj), 10)) motif.obj <- SeuratObject::GetAssayData( object = atac_small, assay = 'peaks', layer = 'motifs' ) motif.obj[1:10,1:10]motif.obj <- SeuratObject::GetAssayData( object = atac_small[['peaks']], layer = "motifs" ) subset(x = motif.obj, features = head(rownames(motif.obj), 10)) motif.obj <- SeuratObject::GetAssayData( object = atac_small, assay = 'peaks', layer = 'motifs' ) motif.obj[1:10,1:10]
Subset the rows and columns of a matrix by removing rows and columns with less than the specified number of non-zero elements.
SubsetMatrix( mat, min.rows = 1, min.cols = 1, max.row.val = 10, max.col.val = NULL )SubsetMatrix( mat, min.rows = 1, min.cols = 1, max.row.val = 10, max.col.val = NULL )
mat |
A matrix |
min.rows |
Minimum number of non-zero elements for the row to be retained |
min.cols |
Minimum number of non-zero elements for the column to be retained |
max.row.val |
Maximum allowed value in a row for the row to be retained. If NULL, don't set any limit. |
max.col.val |
Maximum allowed value in a column for the column to be retained. If NULL, don't set any limit. |
Returns a matrix
SubsetMatrix(mat = volcano)SubsetMatrix(mat = volcano)
Theme applied to panels in the CoveragePlot function.
theme_browser(..., legend = TRUE, axis.text.y = FALSE)theme_browser(..., legend = TRUE, axis.text.y = FALSE)
... |
Additional arguments |
legend |
Display plot legend |
axis.text.y |
Display y-axis text |
PeakPlot(atac_small, region = "chr1-710000-715000") + theme_browser()PeakPlot(atac_small, region = "chr1-710000-715000") + theme_browser()
Plots the presence/absence of Tn5 integration sites for each cell within a genomic region.
TilePlot( object, region, sep = c("-", "-"), tile.size = 100, tile.cells = 100, extend.upstream = 0, extend.downstream = 0, assay = NULL, cells = NULL, group.by = NULL, order.by = "total", idents = NULL )TilePlot( object, region, sep = c("-", "-"), tile.size = 100, tile.cells = 100, extend.upstream = 0, extend.downstream = 0, assay = NULL, cells = NULL, group.by = NULL, order.by = "total", idents = NULL )
object |
A Seurat object |
region |
A set of genomic coordinates to show. Can be a GRanges object, a string encoding a genomic position, a gene name, or a vector of strings describing the genomic coordinates or gene names to plot. If a gene name is supplied, annotations must be present in the assay. |
sep |
Separators to use for strings encoding genomic coordinates. First element is used to separate the chromosome from the coordinates, second element is used to separate the start from end coordinate. |
tile.size |
Size of the sliding window for per-cell fragment tile plot |
tile.cells |
Number of cells to display fragment information for in tile plot. |
extend.upstream |
Number of bases to extend the region upstream. |
extend.downstream |
Number of bases to extend the region downstream. |
assay |
Name of assay to use |
cells |
Which cells to plot. Default all cells |
group.by |
Name of grouping variable to group cells by. If NULL, use the current cell identities |
order.by |
Option for determining how cells are chosen from each group. Options are "total" or "random". "total" will select the top cells based on total number of fragments in the region, "random" will select randomly. |
idents |
List of cell identities to include in the plot. If NULL, use all identities. |
Returns a ggplot object
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), validate.fragments = FALSE ) Fragments(atac_small) <- fragments TilePlot(object = atac_small, region = c("chr1-713500-714500"))fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") fragments <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), validate.fragments = FALSE ) Fragments(atac_small) <- fragments TilePlot(object = atac_small, region = c("chr1-713500-714500"))
Compute the transcription start site (TSS) enrichment score for each cell, as defined by ENCODE: https://www.encodeproject.org/data-standards/terms/.
TSSEnrichment( object, tss.positions = NULL, n = NULL, fast = TRUE, assay = NULL, cells = NULL, process_n = 2000, verbose = TRUE, region_extension = 1000 )TSSEnrichment( object, tss.positions = NULL, n = NULL, fast = TRUE, assay = NULL, cells = NULL, process_n = 2000, verbose = TRUE, region_extension = 1000 )
object |
A Seurat object |
tss.positions |
A GRanges object containing the TSS positions. If NULL, use the genomic annotations stored in the assay. |
n |
Number of TSS positions to use. This will select the first _n_ TSSs from the set. If NULL, use all TSSs (slower). |
fast |
Just compute the TSS enrichment score, without storing the base-resolution matrix of integration counts at each site. This reduces the memory required to store the object but does not allow plotting the accessibility profile at the TSS. |
assay |
Name of assay to use |
cells |
A vector of cells to include. If NULL (default), use all cells in the object |
process_n |
Number of regions to process at a time if using |
verbose |
Display messages |
region_extension |
Distance extended upstream and downstream from TSS in which to calculate enrichment and background. |
The computed score will be added to the object metadata as "TSS.enrichment".
Returns a Seurat object
## Not run: fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") Fragments(atac_small) <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), tolerance = 0.5 ) TSSEnrichment(object = atac_small) ## End(Not run)## Not run: fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac") Fragments(atac_small) <- CreateFragmentObject( path = fpath, cells = colnames(atac_small), tolerance = 0.5 ) TSSEnrichment(object = atac_small) ## End(Not run)
Plot the normalized TSS enrichment score at each position relative to the
TSS. Requires that TSSEnrichment has already been run on the
assay.
TSSPlot(object, assay = NULL, group.by = NULL, idents = NULL)TSSPlot(object, assay = NULL, group.by = NULL, idents = NULL)
object |
A Seurat object |
assay |
Name of the assay to use. Should have the TSS enrichment
information for each cell
already computed by running |
group.by |
Set of identities to group cells by |
idents |
Set of identities to include in the plot |
Returns a ggplot2 object
Create a unified set of non-overlapping genomic ranges from multiple Seurat objects containing single-cell chromatin data.
UnifyPeaks(object.list, mode = "reduce")UnifyPeaks(object.list, mode = "reduce")
object.list |
A list of Seurat objects or ChromatinAssay objects |
mode |
Function to use when combining genomic ranges. Can be "reduce"
(default) or "disjoin".
See |
Returns a GRanges object
UnifyPeaks(object.list = list(atac_small, atac_small))UnifyPeaks(object.list = list(atac_small, atac_small))
Change the path to a fragment file store in a Fragment
object. Path must be to the same file that was used to create the fragment
object. An MD5 hash will be computed using the new path and compared to the
hash stored in the Fragment object to verify that the files are the same.
UpdatePath(object, new.path, verbose = TRUE)UpdatePath(object, new.path, verbose = TRUE)
object |
A |
new.path |
Path to the fragment file |
verbose |
Display messages |
Search for a fragment from each cell that should exist in the fragment file. Will iterate through chunks of the fragment file until at least one fragment from each cell barcode requested is found.
ValidateCells( object, cells = NULL, tolerance = 0.5, max.lines = 5e+07, verbose = TRUE )ValidateCells( object, cells = NULL, tolerance = 0.5, max.lines = 5e+07, verbose = TRUE )
object |
A |
cells |
A character vector containing cell barcodes to search for. If NULL, use the cells stored in the Fragment object. |
tolerance |
Fraction of input cells that can be unseen before returning
TRUE. For example, |
max.lines |
Maximum number of lines to read in without finding the required number of cells before returning FALSE. Setting this value avoids having to search the whole file if it becomes clear that the expected cells are not present. Setting this value to NULL will enable an exhaustive search of the entire file. |
verbose |
Display messages |
Verify that the cells listed in the object exist in the fragment file and that the fragment file or index have not changed since creating the fragment object.
ValidateFragments(object, verbose = TRUE, ...)ValidateFragments(object, verbose = TRUE, ...)
object |
A |
verbose |
Display messages |
... |
Additional parameters passed to |
Validate hashes for Fragment object
ValidateHash(object, verbose = TRUE)ValidateHash(object, verbose = TRUE)
object |
A |
verbose |
Display messages |
Plot the Pearson correlation between allele frequencies on each strand versus the log10 mean-variance ratio for the allele.
VariantPlot( variants, min.cells = 2, concordance.threshold = 0.65, vmr.threshold = 0.01 )VariantPlot( variants, min.cells = 2, concordance.threshold = 0.65, vmr.threshold = 0.01 )
variants |
A dataframe containing variant information. This should be
computed using |
min.cells |
Minimum number of high-confidence cells detected with the variant for the variant to be displayed. |
concordance.threshold |
Strand concordance threshold |
vmr.threshold |
Mean-variance ratio threshold |