Title:	Analysis of Single-Cell Chromatin Data
Version:	1.14.0
Date:	2024-08-21
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>.
Depends:	R (≥ 4.1.0), methods
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
URL:	https://github.com/stuart-lab/signac, https://stuartlab.org/signac
BugReports:	https://github.com/stuart-lab/signac/issues
LinkingTo:	Rcpp
Imports:	GenomeInfoDb (≥ 1.29.3), GenomicRanges, IRanges, Matrix, Rsamtools, S4Vectors, SeuratObject (≥ 5.0.2), data.table, dplyr (≥ 1.0.0), future, future.apply, ggplot2, rlang, irlba, pbapply, tidyr, patchwork, stats, utils, BiocGenerics, stringi, fastmatch, RcppRoll, scales, Rcpp, grid, tidyselect, vctrs, lifecycle
Collate:	'RcppExports.R' 'data.R' 'differential_accessibility.R' 'generics.R' 'dimension_reduction.R' 'footprinting.R' 'fragments.R' 'genomeinfodb-methods.R' 'granges-methods.R' 'heatmaps.R' 'iranges-methods.R' 'links.R' 'mito.R' 'motifs.R' 'objects.R' 'peaks.R' 'preprocessing.R' 'quantification.R' 'region-enrichment.R' 'utilities.R' 'visualization.R' 'zzz.R'
Suggests:	Seurat (≥ 5.0.2), ggforce, ggrepel, ggseqlogo, testthat (≥ 2.1.0), chromVAR, SummarizedExperiment, TFBSTools, motifmatchr, BSgenome, shiny, miniUI, rtracklayer, biovizBase, Biostrings, lsa, MASS
NeedsCompilation:	yes
Packaged:	2024-08-21 01:13:53 UTC; stuartt
Author:	Tim Stuart [aut, cre], Avi Srivastava [aut], Paul Hoffman [ctb], Rahul Satija [ctb]
Maintainer:	Tim Stuart <stuartt@gis.a-star.edu.sg>
Repository:	CRAN
Date/Publication:	2024-08-21 07:40:02 UTC

Signac: 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.

Author(s)

Maintainer: Tim Stuart stuartt@gis.a-star.edu.sg (ORCID)

Authors:

• Avi Srivastava asrivastava@nygenome.org (ORCID)

Other contributors:

• Paul Hoffman phoffman@nygenome.org (ORCID) [contributor]

• Rahul Satija rsatija@nygenome.org (ORCID) [contributor]

See Also

Useful links:

• https://github.com/stuart-lab/signac

• https://stuartlab.org/signac

• Report bugs at https://github.com/stuart-lab/signac/issues

Accessible peaks

Description

Find accessible peaks in a set of cells

Usage

AccessiblePeaks(
  object,
  assay = NULL,
  idents = NULL,
  cells = NULL,
  min.cells = 10
)

Arguments

Value

Returns a vector of peak names

Add chromatin module

Description

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.

Usage

AddChromatinModule(object, features, genome, assay = NULL, verbose = TRUE, ...)

Arguments

Value

Returns a Seurat object

Add DNA sequence motif information

Description

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.

Usage

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, ...)

Arguments

Value

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.

See Also

motifmatchr

Quantify aggregated genome tiles

Description

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.

Usage

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,
  ...
)

Arguments

Value

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.

Compute allele frequencies per cell

Description

Collapses allele counts for each strand and normalize by the total number of counts at each nucleotide position.

Usage

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", ...)

Arguments

Value

Returns a Seurat object with a new assay containing the allele frequencies for the informative variants.

Annotation

Description

Get the annotation from a ChromatinAssay

Usage

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, ...) <- value

Arguments

Value

Returns a GRanges object if the annotation data is present, otherwise returns NULL

Examples

Annotation(atac_small[["peaks"]])


Annotation(atac_small)

genes <- Annotation(atac_small)
Annotation(atac_small[["peaks"]]) <- genes
genes <- Annotation(atac_small)
Annotation(atac_small) <- genes

Plot gene annotations

Description

Display gene annotations in a given region of the genome.

Usage

AnnotationPlot(
  object,
  region,
  assay = NULL,
  mode = "gene",
  sep = c("-", "-"),
  extend.upstream = 0,
  extend.downstream = 0
)

Arguments

Value

Returns a ggplot object

Examples

AnnotationPlot(object = atac_small, region = c("chr1-29554-39554"))

Average Counts

Description

Compute the mean counts per group of cells for a given assay

Usage

AverageCounts(object, assay = NULL, group.by = NULL, verbose = TRUE)

Arguments

Value

Returns a dataframe

Examples

AverageCounts(atac_small)

Plot data from BigWig files

Description

Create coverage tracks, heatmaps, or line plots from bigwig files.

Usage

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
)

Arguments

Details

Note that this function does not work on windows.

Value

Returns a ggplot object

Binarize counts

Description

Set counts >1 to 1 in a count matrix

Usage

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, ...)

Arguments

Value

Returns a Seurat object

Examples

x <- matrix(data = sample(0:3, size = 25, replace = TRUE), ncol = 5)
BinarizeCounts(x)
BinarizeCounts(atac_small[['peaks']])
BinarizeCounts(atac_small)

Call peaks

Description

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.

Usage

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,
  ...
)

Arguments

Details

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

Value

Returns a GRanges object

Set and get cell barcode information for a Fragment object

Description

Set and get cell barcode information for a Fragment object

Usage

Cells(x, ...) <- value

Arguments

Set and get cell barcode information for a Fragment object

Description

This 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.

Usage

## S3 method for class 'Fragment'
Cells(x, ...)

## S3 replacement method for class 'Fragment'
Cells(x, ...) <- value

Arguments

Details

To access the cell names that are stored in the fragment file itself, use GetFragmentData(object = x, name = "cells").

Cells per group

Description

Count the number of cells in each group

Usage

CellsPerGroup(object, group.by = NULL)

Arguments

Value

Returns a vector

Examples

CellsPerGroup(atac_small)

The ChromatinAssay class

Description

The ChromatinAssay object is an extended Assay for the storage and analysis of single-cell chromatin data.

Slots

ranges

A GRanges object describing the genomic location of features in the object

motifs

A Motif object

fragments

A list of Fragment objects.

seqinfo

A Seqinfo object containing basic information about the genome sequence used.

annotation

A 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")

bias

A vector containing Tn5 integration bias information (frequency of Tn5 integration at different kmers)

positionEnrichment

A named list of matrices containing positional enrichment scores for Tn5 integration (for example, enrichment at the TSS)

links

A 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.

Closest Feature

Description

Find the closest feature to a given set of genomic regions

Usage

ClosestFeature(object, regions, annotation = NULL, ...)

Arguments

Value

Returns a dataframe with the name of each region, the closest feature in the annotation, and the distance to the feature.

Examples

ClosestFeature(
  object = atac_small,
  regions = head(granges(atac_small))
)

Find relationships between clonotypes

Description

Perform hierarchical clustering on clonotype data

Usage

ClusterClonotypes(object, assay = NULL, group.by = NULL)

Arguments

Value

Returns a list containing two objects of class hclust, one for the cell clustering and one for the feature (allele) clustering

Combine genome region plots

Description

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.

Usage

CombineTracks(plotlist, expression.plot = NULL, heights = NULL, widths = NULL)

Arguments

Value

Returns a patchworked ggplot2 object

Examples

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))

Cicero connections to links

Description

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.

Usage

ConnectionsToLinks(conns, ccans = NULL, threshold = 0, sep = c("-", "-"))

Arguments

Details

See the Cicero package for more information: https://bioconductor.org/packages/cicero/

Value

Returns a GRanges object

Convert between motif name and motif ID

Description

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.

Usage

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, ...)

Arguments

Value

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 fragments

Description

Count total fragments per cell barcode present in a fragment file.

Usage

CountFragments(fragments, cells = NULL, max_lines = NULL, verbose = TRUE)

Arguments

Value

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

Examples

fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac")
counts <- CountFragments(fragments = fpath)

Counts in region

Description

Count reads per cell overlapping a given set of regions

Usage

CountsInRegion(object, assay, regions, ...)

Arguments

Value

Returns a numeric vector

Examples

CountsInRegion(
  object = atac_small,
  assay = 'bins',
  regions = blacklist_hg19
)

Genome browser

Description

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.

Usage

CoverageBrowser(object, region, assay = NULL, sep = c("-", "-"), ...)

Arguments

Value

Returns a list of ggplot objects

Plot Tn5 insertion frequency over a region

Description

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.

Usage

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,
  ...
)

Arguments

Details

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

Value

Returns a patchwork object

Examples

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 ChromatinAssay object

Description

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.

Usage

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,
  ...
)

Arguments

Create a Fragment object

Description

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.

Usage

CreateFragmentObject(
  path,
  cells = NULL,
  validate.fragments = TRUE,
  verbose = TRUE,
  ...
)

Arguments

Examples

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 motif matrix

Description

Create a motif x feature matrix from a set of genomic ranges, the genome, and a set of position weight matrices.

Usage

CreateMotifMatrix(
  features,
  pwm,
  genome,
  score = FALSE,
  use.counts = FALSE,
  sep = c("-", "-"),
  ...
)

Arguments

Details

Requires that motifmatchr is installed https://www.bioconductor.org/packages/motifmatchr/.

Value

Returns a sparse matrix

Examples

## 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 motif object

Description

Create a Motif-class object.

Usage

CreateMotifObject(
  data = NULL,
  pwm = NULL,
  motif.names = NULL,
  positions = NULL,
  meta.data = NULL
)

Arguments

Value

Returns a Motif object

Examples

motif.matrix <- matrix(
  data = sample(c(0,1),
    size = 100,
    replace = TRUE),
  ncol = 5
)
motif <- CreateMotifObject(data = motif.matrix)

Scatterplot colored by point density

Description

Create a scatterplot using variables in the object metadata and color cells by the density of points in the x-y space.

Usage

DensityScatter(object, x, y, log_x = FALSE, log_y = FALSE, quantiles = NULL)

Arguments

Value

Returns a ggplot object

Plot sequencing depth correlation

Description

Compute the correlation between total counts and each reduced dimension component.

Usage

DepthCor(object, assay = NULL, reduction = "lsi", n = 10, ...)

Arguments

Value

Returns a ggplot object

Examples

DepthCor(object = atac_small)

Downsample Features

Description

Randomly downsample features and assign to VariableFeatures for the object. This will select n features at random.

Usage

DownsampleFeatures(object, assay = NULL, n = 20000, verbose = TRUE)

Arguments

Value

Returns a Seurat object with VariableFeatures set to the randomly sampled features.

Examples

DownsampleFeatures(atac_small, n = 10)

Plot gene expression

Description

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.

Usage

ExpressionPlot(
  object,
  features,
  assay = NULL,
  group.by = NULL,
  idents = NULL,
  slot = "data"
)

Arguments

Examples

ExpressionPlot(atac_small, features = "TSPAN6", assay = "RNA")

Extend

Description

Resize GenomicRanges upstream and or downstream. From https://support.bioconductor.org/p/78652/

Usage

Extend(x, upstream = 0, downstream = 0, from.midpoint = FALSE)

Arguments

Value

Returns a GRanges object

Examples

Extend(x = blacklist_hg19, upstream = 100, downstream = 100)

Calculate fraction of reads in peaks per cell

Description

Calculate fraction of reads in peaks per cell

Usage

FRiP(object, assay, total.fragments, col.name = "FRiP", verbose = TRUE)

Arguments

Value

Returns a Seurat object

Examples

FRiP(object = atac_small, assay = 'peaks', total.fragments = "fragments")

Feature Matrix

Description

Construct a feature x cell matrix from a genomic fragments file

Usage

FeatureMatrix(
  fragments,
  features,
  cells = NULL,
  process_n = 2000,
  sep = c("-", "-"),
  verbose = TRUE
)

Arguments

Value

Returns a sparse matrix

Examples

fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac")
fragments <- CreateFragmentObject(fpath)
FeatureMatrix(
  fragments = fragments,
  features = granges(atac_small)
)

Filter cells from fragment file

Description

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.

Usage

FilterCells(
  fragments,
  cells,
  outfile = NULL,
  buffer_length = 256L,
  verbose = TRUE
)

Arguments

Examples

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)

Find clonotypes

Description

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.

Usage

FindClonotypes(
  object,
  assay = NULL,
  features = NULL,
  metric = "cosine",
  resolution = 1,
  k = 10,
  algorithm = 3
)

Arguments

Value

Returns a Seurat object

FindMotifs

Description

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.

Usage

FindMotifs(
  object,
  features,
  background = 40000,
  assay = NULL,
  verbose = TRUE,
  p.adjust.method = "BH",
  ...
)

Arguments

Value

Returns a data frame

Examples

de.motif <- head(rownames(atac_small))
bg.peaks <- tail(rownames(atac_small))
FindMotifs(
  object = atac_small,
  features = de.motif,
  background = bg.peaks
)

Find most frequently observed features

Description

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.

Usage

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, ...)

Arguments

Value

Returns a Seurat object

Examples

FindTopFeatures(object = atac_small[['peaks']]['data'])
FindTopFeatures(object = atac_small[['peaks']])
FindTopFeatures(object = atac_small[['peaks']])
FindTopFeatures(atac_small)

Transcription factor footprinting analysis

Description

Compute the normalized observed/expected Tn5 insertion frequency for each position surrounding a set of motif instances.

Usage

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,
  ...
)

Arguments

Value

Returns a Seurat object

Fraction of counts in a genomic region

Description

Find the fraction of counts per cell that overlap a given set of genomic ranges

Usage

FractionCountsInRegion(object, regions, assay = NULL, ...)

Arguments

Value

Returns a numeric vector

Examples

## Not run: 
FractionCountsInRegion(
  object = atac_small,
  assay = 'bins',
  regions = blacklist_hg19
)

## End(Not run)

The Fragment class

Description

The Fragment class is designed to hold information needed for working with fragment files.

Slots

path

Path to the fragment file on disk. See https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/output/fragments

hash

A vector of two md5sums: first element is the md5sum of the fragment file, the second element is the md5sum of the index.

cells

A 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 fragment length histogram

Description

Plot the frequency that fragments of different lengths are present for different groups of cells.

Usage

FragmentHistogram(
  object,
  assay = NULL,
  region = "chr1-1-2000000",
  group.by = NULL,
  cells = NULL,
  log.scale = FALSE,
  ...
)

Arguments

Value

Returns a ggplot object

Examples

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

Description

Get the Fragment objects

Usage

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, ...) <- value

Arguments

Value

Returns a list of Fragment objects. If there are no Fragment objects present, returns an empty list.

Examples

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) <- fragments

GRanges to String

Description

Convert GRanges object to a vector of strings

Usage

GRangesToString(grange, sep = c("-", "-"))

Arguments

Value

Returns a character vector

Examples

GRangesToString(grange = blacklist_hg19)

Create gene activity matrix

Description

Compute counts per cell in gene body and promoter region.

Usage

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
)

Arguments

Value

Returns a sparse matrix

Examples

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)

Genome bin matrix

Description

Construct a bin x cell matrix from a fragments file.

Usage

GenomeBinMatrix(
  fragments,
  genome,
  cells = NULL,
  binsize = 5000,
  process_n = 2000,
  sep = c("-", "-"),
  verbose = TRUE
)

Arguments

Details

This function bins the genome and calls FeatureMatrix to construct a bin x cell matrix.

Value

Returns a sparse matrix

Examples

genome <- 780007
names(genome) <- 'chr1'
fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac")
fragments <- CreateFragmentObject(fpath)
GenomeBinMatrix(
  fragments = fragments,
  genome = genome,
  binsize = 1000
)

Get cells in a region

Description

Extract cell names containing reads mapped within a given genomic region

Usage

GetCellsInRegion(tabix, region, cells = NULL)

Arguments

Value

Returns a list

Examples

fpath <- system.file("extdata", "fragments.tsv.gz", package="Signac")
GetCellsInRegion(tabix = fpath, region = "chr1-10245-762629")

Get footprinting data

Description

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.

Usage

GetFootprintData(
  object,
  features,
  assay = NULL,
  group.by = NULL,
  idents = NULL
)

Arguments

Value

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

Get Fragment object data

Description

Extract data from a Fragment-class object

Usage

GetFragmentData(object, slot = "path")

Arguments

Extract genomic ranges from EnsDb object

Description

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.

Usage

GetGRangesFromEnsDb(
  ensdb,
  standard.chromosomes = TRUE,
  biotypes = c("protein_coding", "lincRNA", "rRNA", "processed_transcript"),
  verbose = TRUE
)

Arguments

Find intersecting regions between two objects

Description

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.

Usage

GetIntersectingFeatures(
  object.1,
  object.2,
  assay.1 = NULL,
  assay.2 = NULL,
  distance = 0,
  verbose = TRUE
)

Arguments

Value

Returns a list of two character vectors containing the row names in each object that overlap each other.

Examples

GetIntersectingFeatures(
  object.1 = atac_small,
  object.2 = atac_small,
  assay.1 = 'peaks',
  assay.2 = 'bins'
)

Get genes linked to peaks

Description

Retrieve peak-gene links for a given set of genes. Links must be first obtained by running the LinkPeaks function.

Usage

GetLinkedGenes(object, features, assay = NULL, min.abs.score = 0)

Arguments

Details

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.

See Also

GetLinkedPeaks

Get peaks linked to genes

Description

Retrieve peak-gene links for a given set of genes. Links must be first obtained by running the LinkPeaks function.

Usage

GetLinkedPeaks(object, features, assay = NULL, min.abs.score = 0)

Arguments

Details

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.

See Also

GetLinkedGenes

Retrieve a motif matrix

Description

Get motif matrix for given assay

Usage

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", ...)

Arguments

Value

Returns a Seurat object

Examples

motif.obj <- SeuratObject::GetAssayData(
  object = atac_small[['peaks']], slot = "motifs"
)
GetMotifData(object = motif.obj)
GetMotifData(object = atac_small)

Find transcriptional start sites

Description

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.

Usage

GetTSSPositions(ranges, biotypes = "protein_coding")

Arguments

Identify mitochondrial variants

Description

Identify mitochondrial variants present in single cells.

Usage

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, ...)

Arguments

Value

Returns a dataframe

Examples

## 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)

Compute Tn5 insertion bias

Description

Counts the Tn5 insertion frequency for each DNA hexamer.

Usage

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,
  ...
)

Arguments

Value

Returns a Seurat object

Examples

## 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)

Intersect genomic coordinates with matrix rows

Description

Remove or retain matrix rows that intersect given genomic regions

Usage

IntersectMatrix(
  matrix,
  regions,
  invert = FALSE,
  sep = c("-", "-"),
  verbose = TRUE,
  ...
)

Arguments

Value

Returns a sparse matrix

Examples

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)

Calculate the Jaccard index between two matrices

Description

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.

Usage

Jaccard(x, y)

Arguments

Details

This will calculate the raw Jaccard index, without normalizing for the expected similarity between cells due to differences in sequencing depth.

Value

Returns a matrix

Examples

x <- matrix(data = sample(c(0, 1), size = 25, replace = TRUE), ncol = 5)
Jaccard(x = x, y = x)

Link peaks to genes

Description

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.

Usage

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
)

Arguments

Details

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

Value

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

Plot linked genomic elements

Description

Display links between pairs of genomic elements within a given region of the genome.

Usage

LinkPlot(
  object,
  region,
  assay = NULL,
  min.cutoff = 0,
  sep = c("-", "-"),
  extend.upstream = 0,
  extend.downstream = 0,
  scale.linewidth = FALSE
)

Arguments

Value

Returns a ggplot object

Get or set links information

Description

Get or set the genomic link information for a Seurat object or ChromatinAssay

Usage

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, ...) <- value

Arguments

Examples

Links(atac_small[["peaks"]])
Links(atac_small)
links <- Links(atac_small)
Links(atac_small[["peaks"]]) <- links
links <- Links(atac_small)
Links(atac_small) <- links

Get gene coordinates

Description

Extract the coordinates of the longest transcript for a gene stored in the annotations within an object.

Usage

LookupGeneCoords(object, gene, assay = NULL)

Arguments

Examples

LookupGeneCoords(atac_small, gene = "MIR1302-10")

Match DNA sequence characteristics

Description

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.

Usage

MatchRegionStats(
  meta.feature,
  query.feature,
  features.match = c("GC.percent"),
  n = 10000,
  verbose = TRUE,
  ...
)

Arguments

Details

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.

Value

Returns a character vector

Examples

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

Description

The Motif class is designed to store DNA sequence motif information, including motif PWMs or PFMs, motif positions, and metadata.

Slots

data

A 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.

pwm

A named list of position weight matrices

motif.names

A list containing the name of each motif

positions

A GRangesList object containing exact positions of each motif.

meta.data

A dataframe for storage of additional information related to each motif. This could include the names of proteins that bind the motif.

Count fragments surrounding motif sites

Description

Count the number of sequenced DNA fragments in a region surrounding each instance of a given DNA sequence motif.

Usage

MotifCounts(
  object,
  motifs,
  flanking.region = 1000,
  assay = NULL,
  verbose = TRUE,
  ...
)

Arguments

Value

Returns a list of sparse matrices

Plot DNA sequence motif

Description

Plot position weight matrix or position frequency matrix for different DNA sequence motifs.

Usage

MotifPlot(object, motifs, assay = NULL, use.names = TRUE, ...)

Arguments

Value

Returns a ggplot object

Examples

motif.obj <- Motifs(atac_small)
MotifPlot(atac_small, motifs = head(colnames(motif.obj)))

Get or set a motif information

Description

Get or set the Motif object for a Seurat object or ChromatinAssay.

Usage

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, ...) <- value

Arguments

Examples

Motifs(atac_small[["peaks"]])
Motifs(atac_small)
motifs <- Motifs(atac_small)
Motifs(atac_small[["peaks"]]) <- motifs
motifs <- Motifs(atac_small)
Motifs(atac_small) <- motifs

NucleosomeSignal

Description

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)

Usage

NucleosomeSignal(
  object,
  assay = NULL,
  n = ncol(object) * 5000,
  verbose = TRUE,
  ...
)

Arguments

Value

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.

Examples

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)

Plot peaks in a genomic region

Description

Display the genomic ranges in a ChromatinAssay object that fall in a given genomic region

Usage

PeakPlot(
  object,
  region,
  assay = NULL,
  peaks = NULL,
  group.by = NULL,
  color = "dimgrey",
  sep = c("-", "-"),
  extend.upstream = 0,
  extend.downstream = 0
)

Arguments

Value

Returns a ggplot object

Examples

# 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

Description

Plot motif footprinting results

Usage

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
)

Arguments

Read MGATK output

Description

Read output files from MGATK (https://github.com/caleblareau/mgatk).

Usage

ReadMGATK(dir, verbose = TRUE)

Arguments

Value

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.

Examples

## Not run: 
data.dir <- system.file("extdata", "test_mgatk", package="Signac")
mgatk <- ReadMGATK(dir = data.dir)

## End(Not run)

Region heatmap

Description

Plot fragment counts within a set of regions.

Usage

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
)

Arguments

Value

Returns a ggplot2 object

See Also

RegionMatrix

Region enrichment analysis

Description

Count fragments within a set of regions for different groups of cells.

Usage

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,
  ...
)

Arguments

Value

Returns a Seurat object

Region plot

Description

Plot fragment counts within a set of regions.

Usage

RegionPlot(
  object,
  key,
  assay = NULL,
  idents = NULL,
  normalize = TRUE,
  upstream = NULL,
  downstream = NULL,
  window = (upstream + downstream)/500,
  nrow = NULL
)

Arguments

Value

Returns a ggplot2 object

See Also

RegionMatrix

Compute base composition information for genomic ranges

Description

Compute the GC content, region lengths, and dinucleotide base frequencies for regions in the assay and add to the feature metadata.

Usage

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, ...)

Arguments

Value

Returns a dataframe

Examples

## 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)

Run chromVAR

Description

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.

Usage

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",
  ...
)

Arguments

Details

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

Value

Returns a Seurat object with a new assay

Examples

## 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 singular value decomposition

Description

Run partial singular value decomposition using irlba

Usage

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,
  ...
)

Arguments

Value

Returns a Seurat object

Examples

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)

Compute the term-frequency inverse-document-frequency

Description

Run term frequency inverse document frequency (TF-IDF) normalization on a matrix.

Usage

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,
  ...
)

Arguments

Details

Four different TF-IDF methods are implemented. We recommend using method 1 (the default).

Value

Returns a Seurat object

References

https://en.wikipedia.org/wiki/Latent_semantic_analysis#Latent_semantic_indexing

Examples

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)

Set motif data

Description

Set motif matrix for given assay

Usage

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, ...)

Arguments

Value

Returns a Seurat object

Examples

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
)

Sorts cell metadata variable by similarity using hierarchical clustering

Description

Compute distance matrix from a feature/variable matrix and perform hierarchical clustering to order variables (for example, cell types) according to their similarity.

Usage

SortIdents(
  object,
  layer = "data",
  assay = NULL,
  label = NULL,
  dendrogram = FALSE,
  method = "euclidean",
  verbose = TRUE
)

Arguments

Value

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.

Examples

atac_small$test <- sample(1:10, ncol(atac_small), replace = TRUE)
atac_small <- SortIdents(object = atac_small, label = 'test')
print(levels(atac_small$test))

Split fragment file by cell identities

Description

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.

Usage

SplitFragments(
  object,
  assay = NULL,
  group.by = NULL,
  idents = NULL,
  outdir = getwd(),
  file.suffix = "",
  append = TRUE,
  buffer_length = 256L,
  verbose = TRUE
)

Arguments

String to GRanges

Description

Convert a genomic coordinate string to a GRanges object

Usage

StringToGRanges(regions, sep = c("-", "-"), ...)

Arguments

Value

Returns a GRanges object

Examples

regions <- c('chr1-1-10', 'chr2-12-3121')
StringToGRanges(regions = regions)

Subset matrix rows and columns

Description

Subset the rows and columns of a matrix by removing rows and columns with less than the specified number of non-zero elements.

Usage

SubsetMatrix(
  mat,
  min.rows = 1,
  min.cols = 1,
  max.row.val = 10,
  max.col.val = NULL
)

Arguments

Value

Returns a matrix

Examples

SubsetMatrix(mat = volcano)

Compute TSS enrichment score per cell

Description

Compute the transcription start site (TSS) enrichment score for each cell, as defined by ENCODE: https://www.encodeproject.org/data-standards/terms/.

Usage

TSSEnrichment(
  object,
  tss.positions = NULL,
  n = NULL,
  fast = TRUE,
  assay = NULL,
  cells = NULL,
  process_n = 2000,
  verbose = TRUE,
  region_extension = 1000
)

Arguments

Details

The computed score will be added to the object metadata as "TSS.enrichment".

Value

Returns a Seurat object

Examples

## 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 signal enrichment around TSSs

Description

Plot the normalized TSS enrichment score at each position relative to the TSS. Requires that TSSEnrichment has already been run on the assay.

Usage

TSSPlot(object, assay = NULL, group.by = NULL, idents = NULL)

Arguments

Value

Returns a ggplot2 object

Plot integration sites per cell

Description

Plots the presence/absence of Tn5 integration sites for each cell within a genomic region.

Usage

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
)

Arguments

Value

Returns a ggplot object

Examples

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"))

Unify genomic ranges

Description

Create a unified set of non-overlapping genomic ranges from multiple Seurat objects containing single-cell chromatin data.

Usage

UnifyPeaks(object.list, mode = "reduce")

Arguments

Value

Returns a GRanges object

Examples

UnifyPeaks(object.list = list(atac_small, atac_small))

Update the file path for a Fragment object

Description

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.

Usage

UpdatePath(object, new.path, verbose = TRUE)

Arguments

Validate cells present in fragment file

Description

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.

Usage

ValidateCells(
  object,
  cells = NULL,
  tolerance = 0.5,
  max.lines = 5e+07,
  verbose = TRUE
)

Arguments

Validate Fragment object

Description

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.

Usage

ValidateFragments(object, verbose = TRUE, ...)

Arguments

Validate hashes for Fragment object

Description

Validate hashes for Fragment object

Usage

ValidateHash(object, verbose = TRUE)

Arguments

Plot strand concordance vs. VMR

Description

Plot the Pearson correlation between allele frequencies on each strand versus the log10 mean-variance ratio for the allele.

Usage

VariantPlot(
  variants,
  min.cells = 2,
  concordance.threshold = 0.65,
  vmr.threshold = 0.01
)

Arguments

Convert objects to a ChromatinAssay

Description

Convert objects to a ChromatinAssay

Usage

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("-", "-"),
  ...
)

Arguments

A small example scATAC-seq dataset

Description

A subsetted version of 10x Genomics 10k human (hg19) PBMC scATAC-seq dataset

Usage

atac_small

Format

A Seurat object with the following assays

peaks

A peak x cell dataset

bins

A 5 kb genome bin x cell dataset

RNA

A gene x cell dataset

Source

https://support.10xgenomics.com/single-cell-atac/datasets/1.1.0/atac_v1_pbmc_10k

Genomic blacklist regions for C. elegans ce10 (0-based)

Description

Genomic blacklist regions for C. elegans ce10 (0-based)

Usage

blacklist_ce10

Format

A GRanges object

Source

https://github.com/Boyle-Lab/Blacklist

doi:10.1038/s41598-019-45839-z

Genomic blacklist regions for C. elegans ce11 (0-based)

Description

Genomic blacklist regions for C. elegans ce11 (0-based)

Usage

blacklist_ce11

Format

A GRanges object

Source

https://github.com/Boyle-Lab/Blacklist

doi:10.1038/s41598-019-45839-z

Genomic blacklist regions for Drosophila dm3 (0-based)

Description

Genomic blacklist regions for Drosophila dm3 (0-based)

Usage

blacklist_dm3

Format

A GRanges object

Source

https://github.com/Boyle-Lab/Blacklist

doi:10.1038/s41598-019-45839-z

Genomic blacklist regions for Drosophila dm6 (0-based)

Description

Genomic blacklist regions for Drosophila dm6 (0-based)

Usage

blacklist_dm6

Format

A GRanges object

Source

https://github.com/Boyle-Lab/Blacklist

doi:10.1038/s41598-019-45839-z

Genomic blacklist regions for Human hg19 (0-based)

Description

Genomic blacklist regions for Human hg19 (0-based)

Usage

blacklist_hg19

Format

A GRanges object

Source

https://github.com/Boyle-Lab/Blacklist

doi:10.1038/s41598-019-45839-z

Genomic blacklist regions for Human GRCh38

Description

Genomic blacklist regions for Human GRCh38

Usage

blacklist_hg38

Format

A GRanges object

Source

https://github.com/Boyle-Lab/Blacklist

doi:10.1038/s41598-019-45839-z

Unified genomic blacklist regions for Human GRCh38

Description

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.

Usage

blacklist_hg38_unified

Format

A GRanges object

Author(s)

Anshul Kundaje

Anna Shcherbina

Source

https://www.encodeproject.org/files/ENCFF356LFX/

doi:10.1038/s41598-019-45839-z

Genomic blacklist regions for Mouse mm10 (0-based)

Description

Genomic blacklist regions for Mouse mm10 (0-based)

Usage

blacklist_mm10

Format

A GRanges object

Source

https://github.com/Boyle-Lab/Blacklist

doi:10.1038/s41598-019-45839-z

Sparse matrix correlation

Description

Compute the Pearson correlation matrix between columns of two sparse matrices.

Usage

corSparse(X, Y = NULL, cov = FALSE)

Arguments

Details

Originally from https://stackoverflow.com/questions/5888287/running-cor-or-any-variant-over-a-sparse-matrix-in-r and the qlcMatrix package.

Author(s)

Michael Cysouw, Karsten Looschen

Coverage of a ChromatinAssay object

Description

This is the coverage method for ChromatinAssay objects.

Usage

## 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")
)

Arguments

Functions

• coverage(ChromatinAssay): method for ChromatinAssay objects

• coverage(Seurat): method for Seurat objects

See Also

• coverage-methods in the IRanges package.

• coverage-methods in the GenomicRanges package

• ChromatinAssay-class

Find overlapping ranges for ChromatinAssay objects

Description

The findOverlaps, countOverlaps methods are available for ChromatinAssay objects. This allows finding overlaps between genomic ranges and the ranges stored in the ChromatinAssay.

Usage

## 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
)

Arguments

Details

If a ChromatinAssay is set as the default assay in a Seurat object, you can also call findOverlaps directly on the Seurat object.

Value

See findOverlaps

Functions

• 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

See Also

• findOverlaps-methods in the IRanges package.

• findOverlaps-methods in the GenomicRanges package

• ChromatinAssay-class

Access genomic ranges for ChromatinAssay objects

Description

Methods for accessing GRanges object information stored in a ChromatinAssay object.

Usage

## 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, ...)

Arguments

Value

Returns a GRanges object

Functions

• granges(Seurat): method for Seurat objects

See Also

• granges in the GenomicRanges package.

• ChromatinAssay-class

Examples

granges(atac_small)

Return the first rows of a fragment file

Description

Returns the first n rows of a fragment file. This allows the content of a fragment file to be inspected.

Usage

## S3 method for class 'Fragment'
head(x, n = 6L, ...)

Arguments

Value

The first n rows of a fragment file as a data.frame with the following columns: chrom, start, end, barcode, readCount.

Inter-range transformations for ChromatinAssay objects

Description

The range, reduce, gaps, disjoin, isDisjoint, disjointBins methods are available for ChromatinAssay objects.

Usage

## 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, ...)

Arguments

Functions

• 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

See Also

• inter-range-methods in the IRanges package.

• inter-range-methods in the GenomicRanges package

• ChromatinAssay-class

Find the nearest range neighbors for ChromatinAssay objects

Description

The precede, follow, nearest, distance, distanceToNearest methods are available for ChromatinAssay objects.

Usage

## 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, ...)

Arguments

Functions

• 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

See Also

• nearest-methods in the IRanges package.

• nearest-methods in the GenomicRanges package

• ChromatinAssay-class

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

SeuratObject

Cells

Access and modify sequence information for ChromatinAssay objects

Description

Methods for accessing and modifying Seqinfo object information stored in a ChromatinAssay object.

Usage

## 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

Arguments

Functions

• 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

See Also

• seqinfo in the GenomeInfoDb package.

• ChromatinAssay-class

Subset a Fragment object

Description

Returns a subset of a Fragment-class object.

Usage

## S3 method for class 'Fragment'
subset(x, cells = NULL, ...)

Arguments

Value

Returns a subsetted Fragment object

See Also

subset

Examples

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)))

Subset a Motif object

Description

Returns a subset of a Motif-class object.

Usage

## S3 method for class 'Motif'
subset(x, features = NULL, motifs = NULL, ...)

## S3 method for class 'Motif'
x[i, j, ...]

Arguments

Value

Returns a subsetted Motif object

See Also

subset

Examples

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]

Genome browser theme

Description

Theme applied to panels in the CoveragePlot function.

Usage

theme_browser(..., legend = TRUE, axis.text.y = FALSE)

Arguments

Examples

PeakPlot(atac_small, region = "chr1-710000-715000") + theme_browser()