| Title: | Chromatin Variation Across Regions |
|---|---|
| Description: | Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. |
| Authors: | Alicia Schep [aut, cre], Jason Buenrostro [ctb], Caleb Lareau [ctb], William Greenleaf [ths], Stanford University [cph] |
| Maintainer: | Alicia Schep <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.27.0 |
| Built: | 2024-09-22 05:21:43 UTC |
| Source: | https://github.com/bioc/chromVAR |
Computes GC content for peaks
addGCBias(object, ...) ## S4 method for signature 'RangedSummarizedExperiment' addGCBias(object, genome = GenomeInfoDb::genome(object)) ## S4 method for signature 'SummarizedExperiment' addGCBias(object, peaks, genome = GenomeInfoDb::genome(peaks))addGCBias(object, ...) ## S4 method for signature 'RangedSummarizedExperiment' addGCBias(object, genome = GenomeInfoDb::genome(object)) ## S4 method for signature 'SummarizedExperiment' addGCBias(object, peaks, genome = GenomeInfoDb::genome(peaks))
object |
(Ranged)SummarizedExperiment |
... |
additional arguments |
genome |
BSgenome object, by defualt hg19 |
peaks |
GenomicRanges with peaks, needed if object is SummarizedExperiment and not RangedSummarizedExperiment |
(Ranged)SummarizedExperiment object with new column in row metadata with the gc content of the peak in question
RangedSummarizedExperiment: method for RangedSummarizedExperiment
SummarizedExperiment: method for SummarizedExperiment
data(example_counts, package = "chromVAR") # show example on small part of data subset_counts <- example_counts[1:500,] library(BSgenome.Hsapiens.UCSC.hg19) example_counts <- addGCBias(subset_counts, genome = BSgenome.Hsapiens.UCSC.hg19)data(example_counts, package = "chromVAR") # show example on small part of data subset_counts <- example_counts[1:500,] library(BSgenome.Hsapiens.UCSC.hg19) example_counts <- addGCBias(subset_counts, genome = BSgenome.Hsapiens.UCSC.hg19)
annotationMatches
annotationMatches(object) annotationMatches(object) <- value ## S4 method for signature 'SummarizedExperiment' annotationMatches(object) ## S4 replacement method for signature 'SummarizedExperiment' annotationMatches(object) <- valueannotationMatches(object) annotationMatches(object) <- value ## S4 method for signature 'SummarizedExperiment' annotationMatches(object) ## S4 replacement method for signature 'SummarizedExperiment' annotationMatches(object) <- value
object |
SummarizedExperiment with matches slot, see details |
value |
logical Matrix with annotation matches |
Will extract matrix from the "matches", "annotationMatches", or "motif_matches" assay of a SummarizedExperiment
logical matrix of annotation matches
Alicia Schep
# load annotation matrix; result from matchMotifs data(mini_ix, package = "chromVAR") matches <- annotationMatches(mini_ix)# load annotation matrix; result from matchMotifs data(mini_ix, package = "chromVAR") matches <- annotationMatches(mini_ix)
function to create de novo motifs from kmers based on deviations
assembleKmers(object, threshold = 1.5, p = 0.01, progress = TRUE)assembleKmers(object, threshold = 1.5, p = 0.01, progress = TRUE)
object |
kmer chromVARDeviations object |
threshold |
variability threshold |
p |
p value threshold for inclusion of kmer |
progress |
show progress bar? |
function for assembling de novo kmers from kmer deviations
list with (1) motifs: de novo motif matrices, (2) seed: seed kmer for de novo motif
cbind returns an error when applied to chromVARDeviations because results for all cells or samples should originate from same computeDeviations computation
## S4 method for signature 'chromVARDeviations' cbind(..., deparse.level = 1)## S4 method for signature 'chromVARDeviations' cbind(..., deparse.level = 1)
... |
chromVARDeviations object to be combined |
deparse.level |
See ?base::rbind for a description of this argument. |
chromVARDeviations object
Alicia Schep
Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility, e.g. from scATAC-seq or sparse ATAC or DNAse-seq experiments.
theme for use with ggplot2, used by chromVAR plotting functions
chromVAR_theme(base_size = 12, base_family = "Helvetica")chromVAR_theme(base_size = 12, base_family = "Helvetica")
base_size |
base font size |
base_family |
base font family |
ggplot2 theme
Alicia Schep
p <- ggplot2::qplot(1:3,1:3) + chromVAR_theme(18)p <- ggplot2::qplot(1:3,1:3) + chromVAR_theme(18)
Class for storing results from computeDeviations function.
This class inherits from
SummarizedExperiment, and most methods
for that class should work for objects of this class as well. Additionally,
two accessor functions are defined for extracting bias corrected deviations
(deviations) and deviation Z-scores
(deviationScores)
Computes deviations in chromatin accessibility across sets of annotations
computeDeviations(object, annotations, ...) ## S4 method for signature 'SummarizedExperiment,SummarizedExperiment' computeDeviations(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object)) ## S4 method for signature 'SummarizedExperiment,MatrixOrmatrix' computeDeviations(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object)) ## S4 method for signature 'SummarizedExperiment,list' computeDeviations(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object)) ## S4 method for signature 'SummarizedExperiment,missingOrNULL' computeDeviations(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object)) ## S4 method for signature 'MatrixOrmatrix,SummarizedExperiment' computeDeviations(object, annotations, background_peaks, expectation = computeExpectations(object)) ## S4 method for signature 'MatrixOrmatrix,MatrixOrmatrix' computeDeviations(object, annotations, background_peaks, expectation = computeExpectations(object)) ## S4 method for signature 'MatrixOrmatrix,list' computeDeviations(object, annotations, background_peaks, expectation = computeExpectations(object)) ## S4 method for signature 'MatrixOrmatrix,missingOrNULL' computeDeviations(object, annotations, background_peaks, expectation = computeExpectations(object))computeDeviations(object, annotations, ...) ## S4 method for signature 'SummarizedExperiment,SummarizedExperiment' computeDeviations(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object)) ## S4 method for signature 'SummarizedExperiment,MatrixOrmatrix' computeDeviations(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object)) ## S4 method for signature 'SummarizedExperiment,list' computeDeviations(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object)) ## S4 method for signature 'SummarizedExperiment,missingOrNULL' computeDeviations(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object)) ## S4 method for signature 'MatrixOrmatrix,SummarizedExperiment' computeDeviations(object, annotations, background_peaks, expectation = computeExpectations(object)) ## S4 method for signature 'MatrixOrmatrix,MatrixOrmatrix' computeDeviations(object, annotations, background_peaks, expectation = computeExpectations(object)) ## S4 method for signature 'MatrixOrmatrix,list' computeDeviations(object, annotations, background_peaks, expectation = computeExpectations(object)) ## S4 method for signature 'MatrixOrmatrix,missingOrNULL' computeDeviations(object, annotations, background_peaks, expectation = computeExpectations(object))
object |
chromVARCounts object |
annotations |
chromVARAnnotations object |
... |
additional arguments |
background_peaks |
(optional) background peaks matrix computed using
|
expectation |
(optional) expectations computed using
|
multiprocessing using bplapply
chromVARDeviations-class, which inherits from
SummarizedExperiment, and has two assays: deviations and deviation scores.
object = SummarizedExperiment,annotations = SummarizedExperiment: object and annotations are SummarizedExperiment
object = SummarizedExperiment,annotations = MatrixOrmatrix: object is SummarizedExperiment,
annotations are Matrix
object = SummarizedExperiment,annotations = list: object is SummarizedExperiment,
annotations are list
object = SummarizedExperiment,annotations = missingOrNULL: object is SummarizedExperiment,
annotations are missing
object = MatrixOrmatrix,annotations = SummarizedExperiment: object and annotations are SummarizedExperiment
object = MatrixOrmatrix,annotations = MatrixOrmatrix: object is SummarizedExperiment,
annotations are Matrix
object = MatrixOrmatrix,annotations = list: object is SummarizedExperiment,
annotations are list
object = MatrixOrmatrix,annotations = missingOrNULL: object is SummarizedExperiment,
annotations are missing
Alicia Schep
computeVariability, plotVariability
# Register BiocParallel BiocParallel::register(BiocParallel::SerialParam()) # Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") # load annotation matrix; result from matchMotifs data(mini_ix, package = "chromVAR") # computing deviations dev <- computeDeviations(object = mini_counts, annotations = mini_ix)# Register BiocParallel BiocParallel::register(BiocParallel::SerialParam()) # Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") # load annotation matrix; result from matchMotifs data(mini_ix, package = "chromVAR") # computing deviations dev <- computeDeviations(object = mini_counts, annotations = mini_ix)
computeExpectations
computeExpectations(object, ...) ## S4 method for signature 'MatrixOrmatrix' computeExpectations(object, norm = FALSE, group = NULL) ## S4 method for signature 'SummarizedExperiment' computeExpectations(object, norm = FALSE, group = NULL)computeExpectations(object, ...) ## S4 method for signature 'MatrixOrmatrix' computeExpectations(object, norm = FALSE, group = NULL) ## S4 method for signature 'SummarizedExperiment' computeExpectations(object, norm = FALSE, group = NULL)
object |
SummarizedExperiment |
... |
additional arguments |
norm |
weight all samples equally? |
group |
an group vector, optional |
By default, this function will compute the expected fraction of reads per peak as the the total fragments per peak across all samples divided by total reads in peaks in all samples. Optionally, norm can be set to TRUE and then the expectation will be the average fraction of reads in a peak across the cells. This is not recommended for single cell applications as cells with very few reads will have a large impact. Another option is to give a vector of groups, in which case the expectation will be the average fraction of reads per peak within each group. If group vector is provided and norm is set to TRUE then within each group the fraction of reads per peak is the average fraction of reads per peak in each sample. Otherwise, the within group fraction of reads per peak is based on the reads per peak within the sample divided by the total reads within each sample. The group can also be given by a length 1 character vector representing the name of a column in the colData of the input object if the input is a SummarizedExperiment
vector with expected fraction of reads per peak.
MatrixOrmatrix: method for Matrix or matrix
SummarizedExperiment: method for SummarizedExperiment with counts
slot
Alicia Schep
# First get some data data(mini_counts, package = "chromVAR") # Compute expectations expectations <- computeExpectations(mini_counts)# First get some data data(mini_counts, package = "chromVAR") # Compute expectations expectations <- computeExpectations(mini_counts)
function to compute overall variability of motif sets across samples
computeVariability(object, bootstrap_error = TRUE, bootstrap_samples = 1000, bootstrap_quantiles = c(0.025, 0.975), na.rm = TRUE)computeVariability(object, bootstrap_error = TRUE, bootstrap_samples = 1000, bootstrap_quantiles = c(0.025, 0.975), na.rm = TRUE)
object |
output from |
bootstrap_error |
compute bootstrap confidence interval |
bootstrap_samples |
number of bootstrap samples to take |
bootstrap_quantiles |
quantiles for bootstrap |
na.rm |
remove NAs? default is true |
data.frame with columns for name, variability, bootstrap lower bound, bootstrap upper bound, raw p value, adjust p value.
# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") variability <- computeVariability(mini_dev)# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") variability <- computeVariability(mini_dev)
Accessors for the 'counts' slot of a SummarizedExperiment
## S4 method for signature 'SummarizedExperiment' counts(object) ## S4 replacement method for signature 'SummarizedExperiment,MatrixOrmatrix' counts(object) <- value## S4 method for signature 'SummarizedExperiment' counts(object) ## S4 replacement method for signature 'SummarizedExperiment,MatrixOrmatrix' counts(object) <- value
object |
SummarizedExperiment object |
value |
matrix of counts |
Matrix of counts
data(mini_counts, package = "chromVAR") fragment_counts <- counts(mini_counts)data(mini_counts, package = "chromVAR") fragment_counts <- counts(mini_counts)
Accessor for bias corrected deviations from
chromVARDeviations-class object
deviations(object) ## S4 method for signature 'chromVARDeviations' deviations(object)deviations(object) ## S4 method for signature 'chromVARDeviations' deviations(object)
object |
chromVARDeviations object |
matrix of bias corrected deviations
Alicia Schep
# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") bias_corrected_deviations <- deviations(mini_dev)# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") bias_corrected_deviations <- deviations(mini_dev)
Accessor for deviation Z-scores from
chromVARDeviations-class object
deviationScores(object) ## S4 method for signature 'chromVARDeviations' deviationScores(object)deviationScores(object) ## S4 method for signature 'chromVARDeviations' deviationScores(object)
object |
chromVARDeviations object |
The deviationScores and deviations accessors both return matrices.
matrix of deviation Z-scores
Alicia Schep
# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") scores <- deviationScores(mini_dev)# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") scores <- deviationScores(mini_dev)
deviationsCovariability
deviationsCovariability(object)deviationsCovariability(object)
object |
deviations result |
Returns the 'covariability' between motifs/kmers/peaksets. Covariability' is defined as covariance between Z-scores divided by variance of Z-scores for one motif/kmer/peakset (the row).
'covariability' matrix
# load very small example data data(mini_counts, package = "chromVAR") motifs <- getJasparMotifs() library(motifmatchr) motif_ix <- matchMotifs(motifs, mini_counts, genome = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19) # computing deviations dev <- computeDeviations(object = mini_counts, annotations = motif_ix) # get covariability for just first three motifs devcov <- deviationsCovariability(dev[1:3,])# load very small example data data(mini_counts, package = "chromVAR") motifs <- getJasparMotifs() library(motifmatchr) motif_ix <- matchMotifs(motifs, mini_counts, genome = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19) # computing deviations dev <- computeDeviations(object = mini_counts, annotations = motif_ix) # get covariability for just first three motifs devcov <- deviationsCovariability(dev[1:3,])
Perform tsne using bias corrected deviations to visualize either cell/sample similarity or motif/kmer/annotation similarity
deviationsTsne(object, threshold = 1.5, perplexity = if (what == "samples") 30 else 8, max_iter = 1000, theta = 0.5, what = c("samples", "annotations"), shiny = FALSE)deviationsTsne(object, threshold = 1.5, perplexity = if (what == "samples") 30 else 8, max_iter = 1000, theta = 0.5, what = c("samples", "annotations"), shiny = FALSE)
object |
deviations result |
threshold |
variability threshold – use only deviations with variability greater than threshold |
perplexity |
perplexity parameter for tsne |
max_iter |
max iterations parameter for tsne |
theta |
theta parameter for tsne |
what |
tsne for similarity of samples or annotations? |
shiny |
load a shiny widget that enable you to explore perplexity and variability threshold parameter? |
data.frame with two columns for the two dimensions of tSNE output
Alicia Schep
# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") tsne_res <- deviationsTsne(mini_dev, threshold = 0.8, shiny = FALSE) # setting very low variabilitiy threshold because this is mini data set # threshold should generally be above 1 # Use plotVariability to get a sense of an appropriate threshold# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") tsne_res <- deviationsTsne(mini_dev, threshold = 0.8, shiny = FALSE) # setting very low variabilitiy threshold because this is mini data set # threshold should generally be above 1 # Use plotVariability to get a sense of an appropriate threshold
Function to see whether deviations differ between groups
differentialDeviations(object, groups, alternative = c("two.sided", "less", "greater"), parametric = TRUE)differentialDeviations(object, groups, alternative = c("two.sided", "less", "greater"), parametric = TRUE)
object |
chromVARDeviations object |
groups |
either vector of groups or name of column in colData of object with group information |
alternative |
only used if there are two groups – two.sided or one sided test |
parametric |
use parametric test. alternatively will use kruskal wallace |
data.frame with p value and adjusted p value
Alicia Schep
# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") difdev <- differentialDeviations(mini_dev, "Cell_Type")# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") difdev <- differentialDeviations(mini_dev, "Cell_Type")
Function to determine whether groups differ in variability
differentialVariability(object, groups, parametric = TRUE)differentialVariability(object, groups, parametric = TRUE)
object |
chromVARDeviations object |
groups |
either vector of groups or name of column in colData of object with grouop information |
parametric |
use parametric test. alternatively will use kruskal wallace |
data.frame with p value and adjusted p value
Alicia Schep
# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") difvar <- differentialVariability(mini_dev, "Cell_Type")# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") difvar <- differentialVariability(mini_dev, "Cell_Type")
Very small sample data set for trying out chromVAR
data(example_counts)data(example_counts)
data(example_counts)data(example_counts)
function to get indices of peaks that pass filters
filterPeaks(object, min_fragments_per_peak = 1, non_overlapping = TRUE, ix_return = FALSE)filterPeaks(object, min_fragments_per_peak = 1, non_overlapping = TRUE, ix_return = FALSE)
object |
SummarizedExperiment with matrix of fragment counts per peak
per sample, as computed by |
min_fragments_per_peak |
minimum number of fragmints in peaks across all samples |
non_overlapping |
reduce peak set to non-overlapping peaks, see details |
ix_return |
return indices of peaks to keep instead of subsetted counts object |
if non_overlapping is set to true, when peaks overlap the overlapping peak with lower counts is removed
vector of indices, representing peaks that should be kept
Alicia Schep
getPeaks, filterSamples,
getCounts
data(example_counts, package = "chromVAR") counts_filtered <- filterSamples(example_counts, min_depth = 1500, min_in_peaks = 0.15, shiny = FALSE) counts_filtered <- filterPeaks(example_counts)data(example_counts, package = "chromVAR") counts_filtered <- filterSamples(example_counts, min_depth = 1500, min_in_peaks = 0.15, shiny = FALSE) counts_filtered <- filterPeaks(example_counts)
function to get indices of samples that pass filtters
filterSamples(object, min_in_peaks = NULL, min_depth = NULL, shiny = interactive(), ix_return = FALSE)filterSamples(object, min_in_peaks = NULL, min_depth = NULL, shiny = interactive(), ix_return = FALSE)
object |
SummarizedExperiment with matrix of fragment counts per peak
per sample, as computed by |
min_in_peaks |
minimum fraction of samples within peaks |
min_depth |
minimum library size |
shiny |
make shiny gadget? |
ix_return |
return indices of sample to keep instead of subsetted counts object |
If unspecified, min_in_peaks and min_depth cutoffs will be estimated based on data. min_in_peaks is set to 0.5 times the median proportion of fragments in peaks. min_depth is set to the maximum of 500 or 10 median library size.
indices of samples to keep
getCounts, getPeaks,
filterPeaks
data(example_counts, package = "chromVAR") counts_filtered <- filterSamples(example_counts, min_depth = 1500, min_in_peaks = 0.15, shiny = FALSE)data(example_counts, package = "chromVAR") counts_filtered <- filterSamples(example_counts, min_depth = 1500, min_in_peaks = 0.15, shiny = FALSE)
plot filtering of samples
filterSamplesPlot(object, min_in_peaks = NULL, min_depth = NULL, use_plotly = interactive())filterSamplesPlot(object, min_in_peaks = NULL, min_depth = NULL, use_plotly = interactive())
object |
SummarizedExperiment with matrix of fragment counts per peak
per sample, as computed by |
min_in_peaks |
minimum fraction of samples within peaks |
min_depth |
minimum library size |
use_plotly |
make interactive plot? |
If unspecified, min_in_peaks and min_depth cutoffs will be estimated based on data. min_in_peaks is set to 0.5 times the median proportion of fragments in peaks. min_depth is set to the maximum of 500 or 10 median library size.
indices of samples to keep
getCounts, getPeaks,
filterPeaks
data(example_counts, package = "chromVAR") counts_filtered <- filterSamples(example_counts, min_depth = 1500, min_in_peaks = 0.15, shiny = FALSE) counts_filtered_plot <- filterSamplesPlot(counts_filtered, min_in_peaks = 0.15, min_depth = 1500, use_plotly = FALSE)data(example_counts, package = "chromVAR") counts_filtered <- filterSamples(example_counts, min_depth = 1500, min_in_peaks = 0.15, shiny = FALSE) counts_filtered_plot <- filterSamplesPlot(counts_filtered, min_in_peaks = 0.15, min_depth = 1500, use_plotly = FALSE)
getAnnotationCorrelation
getAnnotationCorrelation(object, annotations, ...) ## S4 method for signature 'SummarizedExperiment,SummarizedExperiment' getAnnotationCorrelation(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object), variabilities = NULL) ## S4 method for signature 'SummarizedExperiment,MatrixOrmatrix' getAnnotationCorrelation(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object), variabilities = NULL) ## S4 method for signature 'SummarizedExperiment,list' getAnnotationCorrelation(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object), variabilities = NULL) ## S4 method for signature 'MatrixOrmatrix,SummarizedExperiment' getAnnotationCorrelation(object, annotations, background_peaks, expectation = computeExpectations(object), variabilities = NULL) ## S4 method for signature 'MatrixOrmatrix,MatrixOrmatrix' getAnnotationCorrelation(object, annotations, background_peaks, expectation = computeExpectations(object), variabilities = NULL) ## S4 method for signature 'MatrixOrmatrix,list' getAnnotationCorrelation(object, annotations, background_peaks, expectation = computeExpectations(object), variabilities = NULL)getAnnotationCorrelation(object, annotations, ...) ## S4 method for signature 'SummarizedExperiment,SummarizedExperiment' getAnnotationCorrelation(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object), variabilities = NULL) ## S4 method for signature 'SummarizedExperiment,MatrixOrmatrix' getAnnotationCorrelation(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object), variabilities = NULL) ## S4 method for signature 'SummarizedExperiment,list' getAnnotationCorrelation(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object), variabilities = NULL) ## S4 method for signature 'MatrixOrmatrix,SummarizedExperiment' getAnnotationCorrelation(object, annotations, background_peaks, expectation = computeExpectations(object), variabilities = NULL) ## S4 method for signature 'MatrixOrmatrix,MatrixOrmatrix' getAnnotationCorrelation(object, annotations, background_peaks, expectation = computeExpectations(object), variabilities = NULL) ## S4 method for signature 'MatrixOrmatrix,list' getAnnotationCorrelation(object, annotations, background_peaks, expectation = computeExpectations(object), variabilities = NULL)
object |
result from computeDeviations |
annotations |
SummarizedExperiment of annotation matches |
... |
additional arguments |
background_peaks |
optional, matrix of background peaks |
expectation |
optional, expected fraction of reads per peak, as computed by computeExpectations |
variabilities |
optional, variabilities computed from computeVariability |
should only be run on small number of motifs/kmers/peaksets (very slow!)
correlation matrix
object = SummarizedExperiment,annotations = SummarizedExperiment: object and annotations are
SummarizedExperiment
object = SummarizedExperiment,annotations = MatrixOrmatrix: object is SummarizedExperiment,
annotations are Matrix
object = SummarizedExperiment,annotations = list: object is SummarizedExperiment,
annotations are list
object = MatrixOrmatrix,annotations = SummarizedExperiment: object and annotations are
SummarizedExperiment
object = MatrixOrmatrix,annotations = MatrixOrmatrix: object is SummarizedExperiment,
annotations are Matrix
object = MatrixOrmatrix,annotations = list: object is SummarizedExperiment,
annotations are list
getAnnotations
getAnnotations(annotations, ...) ## S4 method for signature 'GRangesList' getAnnotations(annotations, rowRanges, ...) ## S4 method for signature 'MatrixOrmatrix' getAnnotations(annotations, ...) ## S4 method for signature 'data.frame' getAnnotations(annotations, ...) ## S4 method for signature 'list' getAnnotations(annotations, npeaks = NULL, ...) ## S4 method for signature 'character' getAnnotations(annotations, rowRanges, column = NULL, ...)getAnnotations(annotations, ...) ## S4 method for signature 'GRangesList' getAnnotations(annotations, rowRanges, ...) ## S4 method for signature 'MatrixOrmatrix' getAnnotations(annotations, ...) ## S4 method for signature 'data.frame' getAnnotations(annotations, ...) ## S4 method for signature 'list' getAnnotations(annotations, npeaks = NULL, ...) ## S4 method for signature 'character' getAnnotations(annotations, rowRanges, column = NULL, ...)
annotations |
matrix, Matrix, or data.frame of fragment counts, or SummarizedExperiment with counts assays, see details |
... |
additional arguments to pass to SummarizedExperiment |
rowRanges |
GenomicRanges or GenomicRangesList or RangedSummarizedExperiment |
npeaks |
number of peaks |
column |
column of bed file with annotation names, see details |
SummarizedExperiment object with 'matches' assay
GRangesList: get annotation matrix from GRangesList
MatrixOrmatrix: get annotation matrix from Matrix or matrix
data.frame: get annotation matrix from data.frame
list: get annotation matrix from list
character: get annotations from bed files
Alicia Schep
# First get example counts data(mini_counts, package = "chromVAR") # Get annotations from genomic ranges list library(GenomicRanges) library(SummarizedExperiment) my_annotation_granges <- GRangesList(GRanges("chr1", ranges = IRanges(start = c(566763,805090), width = 8)), GRanges("chr1", ranges = IRanges(start = c(566792,895798), width = 8))) anno_ix <- getAnnotations(my_annotation_granges, rowRanges = rowRanges(mini_counts))# First get example counts data(mini_counts, package = "chromVAR") # Get annotations from genomic ranges list library(GenomicRanges) library(SummarizedExperiment) my_annotation_granges <- GRangesList(GRanges("chr1", ranges = IRanges(start = c(566763,805090), width = 8)), GRanges("chr1", ranges = IRanges(start = c(566792,895798), width = 8))) anno_ix <- getAnnotations(my_annotation_granges, rowRanges = rowRanges(mini_counts))
getAnnotationSynergy
getAnnotationSynergy(object, annotations, ...) ## S4 method for signature 'SummarizedExperiment,SummarizedExperiment' getAnnotationSynergy(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object), variabilities = NULL, nbg = 25) ## S4 method for signature 'SummarizedExperiment,MatrixOrmatrix' getAnnotationSynergy(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object), variabilities = NULL, nbg = 25) ## S4 method for signature 'SummarizedExperiment,list' getAnnotationSynergy(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object), variabilities = NULL, nbg = 25) ## S4 method for signature 'MatrixOrmatrix,SummarizedExperiment' getAnnotationSynergy(object, annotations, background_peaks, expectation = computeExpectations(object), variabilities = NULL, nbg = 25) ## S4 method for signature 'MatrixOrmatrix,MatrixOrmatrix' getAnnotationSynergy(object, annotations, background_peaks, expectation = computeExpectations(object), variabilities = NULL, nbg = 25) ## S4 method for signature 'MatrixOrmatrix,list' getAnnotationSynergy(object, annotations, background_peaks, expectation = computeExpectations(object), variabilities = NULL, nbg = 25)getAnnotationSynergy(object, annotations, ...) ## S4 method for signature 'SummarizedExperiment,SummarizedExperiment' getAnnotationSynergy(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object), variabilities = NULL, nbg = 25) ## S4 method for signature 'SummarizedExperiment,MatrixOrmatrix' getAnnotationSynergy(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object), variabilities = NULL, nbg = 25) ## S4 method for signature 'SummarizedExperiment,list' getAnnotationSynergy(object, annotations, background_peaks = getBackgroundPeaks(object), expectation = computeExpectations(object), variabilities = NULL, nbg = 25) ## S4 method for signature 'MatrixOrmatrix,SummarizedExperiment' getAnnotationSynergy(object, annotations, background_peaks, expectation = computeExpectations(object), variabilities = NULL, nbg = 25) ## S4 method for signature 'MatrixOrmatrix,MatrixOrmatrix' getAnnotationSynergy(object, annotations, background_peaks, expectation = computeExpectations(object), variabilities = NULL, nbg = 25) ## S4 method for signature 'MatrixOrmatrix,list' getAnnotationSynergy(object, annotations, background_peaks, expectation = computeExpectations(object), variabilities = NULL, nbg = 25)
object |
result from computeDeviations |
annotations |
SummarizedExperiment of annotation matches |
... |
additional arguments |
background_peaks |
optional, matrix of background peaks |
expectation |
optional, expected fraction of reads per peak, as computed by computeExpectations |
variabilities |
optional, variabilities computed from computeVariability |
nbg |
number of background iterations |
should only be run on small number of motifs/kmers/peaksets (very slow!)
synergy matrix
object = SummarizedExperiment,annotations = SummarizedExperiment: object and annotations are
SummarizedExperiment
object = SummarizedExperiment,annotations = MatrixOrmatrix: object is SummarizedExperiment,
annotations are Matrix
object = SummarizedExperiment,annotations = list: object is SummarizedExperiment,
annotations are list
object = MatrixOrmatrix,annotations = SummarizedExperiment: object and annotations are
SummarizedExperiment
object = MatrixOrmatrix,annotations = MatrixOrmatrix: object is SummarizedExperiment,
annotations are Matrix
object = MatrixOrmatrix,annotations = list: object is SummarizedExperiment,
annotations are list
Function to get a set of background peaks for each peak based on GC content and # of fragments across all samples
getBackgroundPeaks(object, ...) ## S4 method for signature 'SummarizedExperiment' getBackgroundPeaks(object, bias = rowData(object)$bias, niterations = 50, w = 0.1, bs = 50) ## S4 method for signature 'RangedSummarizedExperiment' getBackgroundPeaks(object, bias = rowRanges(object)$bias, niterations = 50, w = 0.1, bs = 50) ## S4 method for signature 'MatrixOrmatrix' getBackgroundPeaks(object, bias, niterations = 50, w = 0.1, bs = 50)getBackgroundPeaks(object, ...) ## S4 method for signature 'SummarizedExperiment' getBackgroundPeaks(object, bias = rowData(object)$bias, niterations = 50, w = 0.1, bs = 50) ## S4 method for signature 'RangedSummarizedExperiment' getBackgroundPeaks(object, bias = rowRanges(object)$bias, niterations = 50, w = 0.1, bs = 50) ## S4 method for signature 'MatrixOrmatrix' getBackgroundPeaks(object, bias, niterations = 50, w = 0.1, bs = 50)
object |
fragment counts as SummarizedExperiment, RangedSummarized, Matrix, or matrix |
... |
addtional arguments |
bias |
vector of values for some bias signal for each row of object |
niterations |
number of background peaks to sample |
w |
parameter controlling similarity of background peaks |
bs |
bin size parameter |
Background peaks are chosen by sampling peaks based on similarity in GC content and # of fragments across samples using the Mahalanobis distance. The w paramter controls how similar background peaks should be. The bs parameter controls the precision with which the similarity is computed; increasing bs will make the function run slower. Sensible default parameters are chosen for both.
matrix with one row per peak and one column per iteration. values in a row represent indices of background peaks for the peak with that index
SummarizedExperiment: method for SummarizedExperiment
RangedSummarizedExperiment: method for RangedSummarizedExperiment
MatrixOrmatrix: method for Matrix or matrix
# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") # get background peaks bgpeaks <- getBackgroundPeaks(mini_counts)# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") # get background peaks bgpeaks <- getBackgroundPeaks(mini_counts)
Function for grouping peaks based on proximity along chromosomes
getCisGroups(object, ...) ## S4 method for signature 'RangedSummarizedExperiment' getCisGroups(object, grpsize = 25, stepsize = 10) ## S4 method for signature 'GenomicRanges' getCisGroups(object, grpsize = 25, stepsize = 10)getCisGroups(object, ...) ## S4 method for signature 'RangedSummarizedExperiment' getCisGroups(object, grpsize = 25, stepsize = 10) ## S4 method for signature 'GenomicRanges' getCisGroups(object, grpsize = 25, stepsize = 10)
object |
GenomicRanges or RangedSummarizedExperiment |
... |
additional arguments |
grpsize |
number of peaks to include in each grouop |
stepsize |
number of peaks between each new set of groups |
SummarizedExperiment with annotationMatches assay storing which peaks belong to which groups
RangedSummarizedExperiment: method for RangedSummarizedExperiment
GenomicRanges: method for GenomicRanges
Alicia Schep
# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") mini_counts <- sort(mini_counts) cisg <- getCisGroups(mini_counts)# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") mini_counts <- sort(mini_counts) cisg <- getCisGroups(mini_counts)
makes matrix of fragment counts in peaks using one or multiple bam or bed files
getCounts(alignment_files, peaks, paired, by_rg = FALSE, format = c("bam", "bed"), colData = NULL)getCounts(alignment_files, peaks, paired, by_rg = FALSE, format = c("bam", "bed"), colData = NULL)
alignment_files |
filenames for bam or bed files with aligned reads |
peaks |
GRanges object with peaks |
paired |
paired end data? |
by_rg |
use RG tags in bam to separate groups? |
format |
bam or bed? default is bam |
colData |
sample annotation DataFrame |
RangedSummarizedExperiment-class
object
getSampleDepths, getPeaks,
filterSamples
# First we'll read in some peaks peaks_file <- system.file("extdata", "test_bed.txt", package = "chromVAR") test_peaks <- getPeaks(peaks_file, sort = TRUE) # With single bam with RG tags (can also give multiple bams with RG) test_rg <- system.file("extdata", "test_RG.bam", package = "chromVAR") test_counts <- getCounts(test_rg, peaks = test_peaks, by_rg = TRUE, paired = TRUE, colData = S4Vectors::DataFrame(condition ="A")) # Multiple bams without RG tags test_bam1 <- system.file("extdata", "test_single1.bam", package = "chromVAR") test_bam2 <- system.file("extdata", "test_single2.bam", package = "chromVAR") test_bam3 <- system.file("extdata", "test_single3.bam", package = "chromVAR") test_counts2 <- getCounts(c(test_bam1, test_bam2,test_bam3), peaks = test_peaks, by_rg = FALSE, paired = TRUE, colData = S4Vectors::DataFrame(celltype = c("A","B","C"))) # Bed file with reads (can give multiple bed files, here we will just read 1) test_bed <- system.file("extdata", "test_reads.bed", package = "chromVAR") test_counts3 <- getCounts(test_bed, test_peaks, by_rg = FALSE, paired = FALSE, format = "bed")# First we'll read in some peaks peaks_file <- system.file("extdata", "test_bed.txt", package = "chromVAR") test_peaks <- getPeaks(peaks_file, sort = TRUE) # With single bam with RG tags (can also give multiple bams with RG) test_rg <- system.file("extdata", "test_RG.bam", package = "chromVAR") test_counts <- getCounts(test_rg, peaks = test_peaks, by_rg = TRUE, paired = TRUE, colData = S4Vectors::DataFrame(condition ="A")) # Multiple bams without RG tags test_bam1 <- system.file("extdata", "test_single1.bam", package = "chromVAR") test_bam2 <- system.file("extdata", "test_single2.bam", package = "chromVAR") test_bam3 <- system.file("extdata", "test_single3.bam", package = "chromVAR") test_counts2 <- getCounts(c(test_bam1, test_bam2,test_bam3), peaks = test_peaks, by_rg = FALSE, paired = TRUE, colData = S4Vectors::DataFrame(celltype = c("A","B","C"))) # Bed file with reads (can give multiple bed files, here we will just read 1) test_bed <- system.file("extdata", "test_reads.bed", package = "chromVAR") test_counts3 <- getCounts(test_bed, test_peaks, by_rg = FALSE, paired = FALSE, format = "bed")
getFragmentsPerPeak
getFragmentsPerPeak(object) ## S4 method for signature 'SummarizedExperiment' getFragmentsPerPeak(object) ## S4 method for signature 'MatrixOrmatrix' getFragmentsPerPeak(object)getFragmentsPerPeak(object) ## S4 method for signature 'SummarizedExperiment' getFragmentsPerPeak(object) ## S4 method for signature 'MatrixOrmatrix' getFragmentsPerPeak(object)
object |
SummarizedExperiment, matrix, or Matrix object |
vector with sum across rows of counts assay within chromVARCounts
SummarizedExperiment: method for SummarizedExperiment object
with counts assay
MatrixOrmatrix: method for Matrix or matrix object
getFragmentsPerSample,
getTotalFragments
# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") frags_per_peak <- getFragmentsPerPeak(mini_counts)# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") frags_per_peak <- getFragmentsPerPeak(mini_counts)
getFragmentsPerSample
getFragmentsPerSample(object) ## S4 method for signature 'SummarizedExperiment' getFragmentsPerSample(object) ## S4 method for signature 'MatrixOrmatrix' getFragmentsPerSample(object)getFragmentsPerSample(object) ## S4 method for signature 'SummarizedExperiment' getFragmentsPerSample(object) ## S4 method for signature 'MatrixOrmatrix' getFragmentsPerSample(object)
object |
SummarizedExperiment, matrix, or Matrix object |
vector with sum across columns of counts assay within chromVARCounts
SummarizedExperiment: method for SummarizedExperiment object
with counts assay
MatrixOrmatrix: method for Matrix or matrix object
getFragmentsPerPeak,
getTotalFragments
# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") frags_per_sample <- getFragmentsPerSample(mini_counts)# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") frags_per_sample <- getFragmentsPerSample(mini_counts)
Function to get motifs from JASPAR database
getJasparMotifs(species = "Homo sapiens", collection = "CORE", ...)getJasparMotifs(species = "Homo sapiens", collection = "CORE", ...)
species |
Which species? use eithe jaspar code or latin name. default is 'Homo sapiens' |
collection |
Which collection to use? default is 'CORE' |
... |
additional arguments to opts for
|
Simply a wrapper function for getMatrixSet
that calls JASPAR2016 database using JASPAR2016
motifs <- getJasparMotifs()motifs <- getJasparMotifs()
Read in peaks from a bed file.
getPeaks(filename, extra_cols = c(), sort_peaks = FALSE)getPeaks(filename, extra_cols = c(), sort_peaks = FALSE)
filename |
filename of bed file |
extra_cols |
extra columns to read in beyond first three |
sort_peaks |
sort the peaks? |
As in standard definition of bed file, first column is assumed to be chromosome, second is assumed to be start of peak (0-based), and third is assumed to be end of peak (1-based). Note that in output GenomicRanges output, start and end indices are both 1-based. Extra columns can be added as metadata or strand information if provided, but the user must indicate column index and name using named vector for extra_cols.
GenomicRanges containing peaks from file
getCounts, filterPeaks,
readNarrowpeaks
peaks_file <- system.file("extdata", "test_bed.txt", package = "chromVAR") peaks <- getPeaks(peaks_file, sort = TRUE)peaks_file <- system.file("extdata", "test_bed.txt", package = "chromVAR") peaks <- getPeaks(peaks_file, sort = TRUE)
Function to get permuted data while maintaining biases
getPermutedData(object, niterations = 10, w = 0.1, bs = 50)getPermutedData(object, niterations = 10, w = 0.1, bs = 50)
object |
SummarizedExperiment |
niterations |
number of background peaks to sample |
w |
parameter controlling similarity of background peaks |
bs |
bin size parameter |
Replaces the counts at a given peak with the count from another peak with similar GC content and average accessibility
new SummarizedExperiment with addition assays representing permuted version of counts
# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") # get background peaks perm_counts <- getPermutedData(mini_counts, niterations = 2)# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") # get background peaks perm_counts <- getPermutedData(mini_counts, niterations = 2)
Get correlation between samples based on bias corrected deviations
getSampleCorrelation(object, threshold = 1.5)getSampleCorrelation(object, threshold = 1.5)
object |
deviations result |
threshold |
threshold for variability |
This function will compute the correlation between samples based on the normalized deviations. It will first remove correlated motifs/peak sets. Then the pearson correlation coefficient will be computed and returned.
correlation matrix between samples
Alicia Schep
# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") sample_cor <- getSampleCorrelation(mini_dev, threshold = 0.8) # setting very low variabilitiy threshold because this is mini data set # threshold should generally be above 1 # Use plotVariability to get a sense of an appropriate threshold # As this is mini data set, results probably not meaningful!# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") sample_cor <- getSampleCorrelation(mini_dev, threshold = 0.8) # setting very low variabilitiy threshold because this is mini data set # threshold should generally be above 1 # Use plotVariability to get a sense of an appropriate threshold # As this is mini data set, results probably not meaningful!
makes vector of read depths in bam files or RG groups within bam files
getSampleDepths(alignment_files, paired = TRUE, by_rg = FALSE, format = c("bam", "bed"))getSampleDepths(alignment_files, paired = TRUE, by_rg = FALSE, format = c("bam", "bed"))
alignment_files |
filenames for bam or bed file(s) with aligned reads |
paired |
paired end data? |
by_rg |
use RG tags to separate groups? |
format |
bam or bed format? default is bam |
numeric vector
# With single bam with RG tags (can also give multiple bams with RG) test_rg <- system.file("extdata", "test_RG.bam", package = "chromVAR") test_counts <- getSampleDepths(test_rg, by_rg = TRUE, paired = TRUE) # Multiple bams without RG tags test_bam1 <- system.file("extdata", "test_single1.bam", package = "chromVAR") test_bam2 <- system.file("extdata", "test_single2.bam", package = "chromVAR") test_bam3 <- system.file("extdata", "test_single3.bam", package = "chromVAR") test_counts2 <- getSampleDepths(c(test_bam1, test_bam2,test_bam3), by_rg = FALSE, paired = TRUE)# With single bam with RG tags (can also give multiple bams with RG) test_rg <- system.file("extdata", "test_RG.bam", package = "chromVAR") test_counts <- getSampleDepths(test_rg, by_rg = TRUE, paired = TRUE) # Multiple bams without RG tags test_bam1 <- system.file("extdata", "test_single1.bam", package = "chromVAR") test_bam2 <- system.file("extdata", "test_single2.bam", package = "chromVAR") test_bam3 <- system.file("extdata", "test_single3.bam", package = "chromVAR") test_counts2 <- getSampleDepths(c(test_bam1, test_bam2,test_bam3), by_rg = FALSE, paired = TRUE)
Get distance between samples based on bias corrected deviations
getSampleDistance(object, threshold = 1.5, initial_dims = 50, distance_function = dist)getSampleDistance(object, threshold = 1.5, initial_dims = 50, distance_function = dist)
object |
deviations result |
threshold |
threshold for variability |
initial_dims |
initial dimentions for preliminary dimensionality reduction via pca |
distance_function |
distance function to use |
This function will compute the distance between samples based on the normalized deviations. It will first remove correlated motifs / peak sets. Then the dimensionality will be further reduced via PCA if the number of dimensions exceeds initial_dims. Then the supplied distance_function will be used.
dist object for distance between samples
Alicia Schep
# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") sample_dist <- getSampleDistance(mini_dev, threshold = 0.8) # setting very low variabilitiy threshold because this is mini data set # threshold should generally be above 1 # Use plotVariability to get a sense of an appropriate threshold # As this is mini data set, results not meaningful!# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") sample_dist <- getSampleDistance(mini_dev, threshold = 0.8) # setting very low variabilitiy threshold because this is mini data set # threshold should generally be above 1 # Use plotVariability to get a sense of an appropriate threshold # As this is mini data set, results not meaningful!
getTotalFragments
getTotalFragments(object) ## S4 method for signature 'SummarizedExperiment' getTotalFragments(object) ## S4 method for signature 'MatrixOrmatrix' getTotalFragments(object)getTotalFragments(object) ## S4 method for signature 'SummarizedExperiment' getTotalFragments(object) ## S4 method for signature 'MatrixOrmatrix' getTotalFragments(object)
object |
SummarizedExperiment, matrix, or Matrix object |
sum of all counts within object
SummarizedExperiment: method for SummarizedExperiment object
with counts assay
MatrixOrmatrix: method for Matrix or matrix object
getFragmentsPerSample,
getFragmentsPerPeak
# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") total_frags <- getTotalFragments(mini_counts)# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") total_frags <- getTotalFragments(mini_counts)
Makes bins based on fragment counts
makeBiasBins(object, ...) ## S4 method for signature 'SummarizedExperiment' makeBiasBins(object, bias = rowData(object)$bias, nbins = 25, frac = 0.3) ## S4 method for signature 'RangedSummarizedExperiment' makeBiasBins(object, bias = rowRanges(object)$bias, nbins = 25, frac = 0.3) ## S4 method for signature 'MatrixOrmatrix' makeBiasBins(object, bias, nbins = 25, frac = 0.3)makeBiasBins(object, ...) ## S4 method for signature 'SummarizedExperiment' makeBiasBins(object, bias = rowData(object)$bias, nbins = 25, frac = 0.3) ## S4 method for signature 'RangedSummarizedExperiment' makeBiasBins(object, bias = rowRanges(object)$bias, nbins = 25, frac = 0.3) ## S4 method for signature 'MatrixOrmatrix' makeBiasBins(object, bias, nbins = 25, frac = 0.3)
object |
fragment counts stored as RangedSummarizedExperiment, SummarizedExperiment, matrix, or Matrix |
... |
addtional arguments |
bias |
vector of some bias signal (usually gc content) for each row of object |
nbins |
number of bins for each category, see Details |
frac |
fraction of peaks within given bin to select randomly |
Will create nbins * 3 annotations based on sampling from peaks with a certain fragment count, fragment count, or fragment count & bias.
SummarizedExperiment storing bias bins annotation
SummarizedExperiment: method for SummarizedExperiment
RangedSummarizedExperiment: method for RangedSummarizedExperiment
MatrixOrmatrix: method for Matrix or matrix
Alicia Schep
# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") bb <- makeBiasBins(mini_counts)# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") bb <- makeBiasBins(mini_counts)
Makes annotations sets with similar bias to input sets
makePermutedSets(object, annotations, ...) ## S4 method for signature 'SummarizedExperiment,SummarizedExperiment' makePermutedSets(object, annotations, bias = rowData(object)$bias, window = 10) ## S4 method for signature 'RangedSummarizedExperiment,SummarizedExperiment' makePermutedSets(object, annotations, bias = rowRanges(object)$bias, window = 10) ## S4 method for signature 'MatrixOrmatrix,SummarizedExperiment' makePermutedSets(object, annotations, bias, window = 10) ## S4 method for signature 'SummarizedExperiment,MatrixOrmatrix' makePermutedSets(object, annotations, bias = rowData(object)$bias, window = 10) ## S4 method for signature 'RangedSummarizedExperiment,MatrixOrmatrix' makePermutedSets(object, annotations, bias = rowRanges(object)$bias, window = 10) ## S4 method for signature 'MatrixOrmatrix,MatrixOrmatrix' makePermutedSets(object, annotations, bias, window = 10) ## S4 method for signature 'SummarizedExperiment,list' makePermutedSets(object, annotations, bias = rowData(object)$bias, window = 10) ## S4 method for signature 'RangedSummarizedExperiment,list' makePermutedSets(object, annotations, bias = rowRanges(object)$bias, window = 10) ## S4 method for signature 'MatrixOrmatrix,list' makePermutedSets(object, annotations, bias, window = 10)makePermutedSets(object, annotations, ...) ## S4 method for signature 'SummarizedExperiment,SummarizedExperiment' makePermutedSets(object, annotations, bias = rowData(object)$bias, window = 10) ## S4 method for signature 'RangedSummarizedExperiment,SummarizedExperiment' makePermutedSets(object, annotations, bias = rowRanges(object)$bias, window = 10) ## S4 method for signature 'MatrixOrmatrix,SummarizedExperiment' makePermutedSets(object, annotations, bias, window = 10) ## S4 method for signature 'SummarizedExperiment,MatrixOrmatrix' makePermutedSets(object, annotations, bias = rowData(object)$bias, window = 10) ## S4 method for signature 'RangedSummarizedExperiment,MatrixOrmatrix' makePermutedSets(object, annotations, bias = rowRanges(object)$bias, window = 10) ## S4 method for signature 'MatrixOrmatrix,MatrixOrmatrix' makePermutedSets(object, annotations, bias, window = 10) ## S4 method for signature 'SummarizedExperiment,list' makePermutedSets(object, annotations, bias = rowData(object)$bias, window = 10) ## S4 method for signature 'RangedSummarizedExperiment,list' makePermutedSets(object, annotations, bias = rowRanges(object)$bias, window = 10) ## S4 method for signature 'MatrixOrmatrix,list' makePermutedSets(object, annotations, bias, window = 10)
object |
fragment counts stored as RangedSummarizedExperiment, SummarizedExperiment, matrix, or Matrix |
annotations |
annotations as SummarizedExperiment, matrix, or list |
... |
additional arguments |
bias |
vector of some bias signal (usually gc content) for each row of object |
window |
number of nearest neighbors to consider |
Will create nbins * 3 annotations based on sampling from peaks with a certain fragment count, fragment count, or fragment count & bias.
SummarizedExperiment storing bias bins annotation
object = SummarizedExperiment,annotations = SummarizedExperiment: method for SummarizedExperiment and
SummarizedExperiment
object = RangedSummarizedExperiment,annotations = SummarizedExperiment: method for RangedSummarizedExperiment and
SummarizedExperiment
object = MatrixOrmatrix,annotations = SummarizedExperiment: method for Matrix or matrix and
SummarizedExperiment
object = SummarizedExperiment,annotations = MatrixOrmatrix: method for SummarizedExperiment and
MatrixOrmatrix
object = RangedSummarizedExperiment,annotations = MatrixOrmatrix: method for RangedSummarizedExperiment and
MatrixOrmatrix
object = MatrixOrmatrix,annotations = MatrixOrmatrix: method for Matrix/matrix and Matrix/matrix
object = SummarizedExperiment,annotations = list: method for SummarizedExperiment and list
object = RangedSummarizedExperiment,annotations = list: method for RangedSummarizedExperiment and list
object = MatrixOrmatrix,annotations = list: method for Matrix or matrix and list
Alicia Schep
# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") data(example_motifs, package = "motifmatchr") library(motifmatchr) library(BSgenome.Hsapiens.UCSC.hg19) motif_ix <- matchMotifs(example_motifs, mini_counts, genome = BSgenome.Hsapiens.UCSC.hg19) perm_sets <- makePermutedSets(mini_counts, motif_ix)# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") data(example_motifs, package = "motifmatchr") library(motifmatchr) library(BSgenome.Hsapiens.UCSC.hg19) motif_ix <- matchMotifs(example_motifs, mini_counts, genome = BSgenome.Hsapiens.UCSC.hg19) perm_sets <- makePermutedSets(mini_counts, motif_ix)
Find kmer matches in the DNA string-based subject
matchKmers(k, subject, ...) ## S4 method for signature 'character,DNAStringSet' matchKmers(k, subject, out = c("matches", "positions"), ranges = NULL) ## S4 method for signature 'character,character' matchKmers(k, subject, out = c("matches", "positions"), ranges = NULL) ## S4 method for signature 'character,DNAString' matchKmers(k, subject, out = c("matches", "positions"), ranges = NULL) ## S4 method for signature 'character,GenomicRanges' matchKmers(k, subject, genome = GenomeInfoDb::genome(subject), out = c("matches", "positions")) ## S4 method for signature 'character,RangedSummarizedExperiment' matchKmers(k, subject, ...) ## S4 method for signature 'numeric,ANY' matchKmers(k, subject, ...) ## S4 method for signature 'DNAStringSet,ANY' matchKmers(k, subject, ...) ## S4 method for signature 'DNAString,ANY' matchKmers(k, subject, ...)matchKmers(k, subject, ...) ## S4 method for signature 'character,DNAStringSet' matchKmers(k, subject, out = c("matches", "positions"), ranges = NULL) ## S4 method for signature 'character,character' matchKmers(k, subject, out = c("matches", "positions"), ranges = NULL) ## S4 method for signature 'character,DNAString' matchKmers(k, subject, out = c("matches", "positions"), ranges = NULL) ## S4 method for signature 'character,GenomicRanges' matchKmers(k, subject, genome = GenomeInfoDb::genome(subject), out = c("matches", "positions")) ## S4 method for signature 'character,RangedSummarizedExperiment' matchKmers(k, subject, ...) ## S4 method for signature 'numeric,ANY' matchKmers(k, subject, ...) ## S4 method for signature 'DNAStringSet,ANY' matchKmers(k, subject, ...) ## S4 method for signature 'DNAString,ANY' matchKmers(k, subject, ...)
k |
k |
subject |
either |
... |
additional arguments |
out |
what to return? see details |
ranges |
if subject is not GenomicRanges, ranges to use when out is positions |
genome |
BSgenome object, only used if subect is
|
Can either return a SummarizedExperiment with just sparse matrix with values set to 1 for a match (if return == 'matches'), or a GenomicRanges object with all the positions of matches
SummarizedExperiment with matches assay storing which peaks contain which kmers
k = character,subject = DNAStringSet: For DNAStringSet Objects
k = character,subject = character: For character strings
k = character,subject = DNAString: For DNA String objects
k = character,subject = GenomicRanges: For GenomicRanges
k = character,subject = RangedSummarizedExperiment: For RangedSummarizedExperiment (containing GRanges in
rowRanges)
k = numeric,subject = ANY: Catch-all for other un-documented types
k = DNAStringSet,subject = ANY: Catch-all for other un-documented types with
DNAStringSet
k = DNAString,subject = ANY: Catch-all for other un-documented types with DNAString
getAnnotations, computeDeviations
# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") # Get peak-kmer annotation matrix for 6mers library(BSgenome.Hsapiens.UCSC.hg19) kmer_ix <- matchKmers(6, mini_counts, genome = BSgenome.Hsapiens.UCSC.hg19)# Load very small example counts (already filtered) data(mini_counts, package = "chromVAR") # Get peak-kmer annotation matrix for 6mers library(BSgenome.Hsapiens.UCSC.hg19) kmer_ix <- matchKmers(6, mini_counts, genome = BSgenome.Hsapiens.UCSC.hg19)
Tiny sample data set for chromVAR funtion examples
data(mini_counts)data(mini_counts)
data(mini_counts)data(mini_counts)
Tiny sample chromVARDeviations object resulting from computeDeviations Result from running computeDeviations(mini_counts, mini_ix) on mini_ix and mini_counts data from this package
data(mini_dev)data(mini_dev)
computeDeviations, mini_counts,
mini_ix
data(mini_dev)data(mini_dev)
Tiny sample annotation object for use in chromVAR examples Result from running matchMotifs(example_motifs,mini_counts,"hg19) on example_motifs from motifmatchr package and mini_counts from this package
data(mini_ix)data(mini_ix)
data(mini_ix)data(mini_ix)
plots sample similarity tsne
plotDeviationsTsne(object, tsne, var_df = NULL, sample_column = NULL, annotation_name = NULL, shiny = interactive())plotDeviationsTsne(object, tsne, var_df = NULL, sample_column = NULL, annotation_name = NULL, shiny = interactive())
object |
deviations result object |
tsne |
result from |
var_df |
variability result |
sample_column |
column name for sample data – colData(object) – to be used for coloring points |
annotation_name |
name of chromVAR annotation for coloring points |
shiny |
return shiny app? otherwise return static plots |
shiny app or plots
Alicia Schep
plotKmerMismatch
plotKmerMismatch(kmer, cov_mat, pval = 0.01)plotKmerMismatch(kmer, cov_mat, pval = 0.01)
kmer |
kmer, e.g. 'AAAAAAA' |
cov_mat |
result from |
pval |
p value threshold |
A plot
plot variability of motifs/etc
plotVariability(variability, xlab = "Sorted TFs", n = 3, labels = variability$name, use_plotly = interactive())plotVariability(variability, xlab = "Sorted TFs", n = 3, labels = variability$name, use_plotly = interactive())
variability |
output from |
xlab |
label for x-axis (default is 'Sorted TFs') |
n |
number of toppoints to label? |
labels |
names of sets. if not given, uses rownames of variability |
use_plotly |
make plot interactive (using plotly) |
ggplot or plotly object, depending on whether use_plotly is TRUE
Alicia Schep
# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") variability <- computeVariability(mini_dev) var_plot <- plotVariability(variability, use_plotly = FALSE)# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") variability <- computeVariability(mini_dev) var_plot <- plotVariability(variability, use_plotly = FALSE)
computes distance between every pwm in a list or between pwms in one list with pwms in another
pwmDistance(x, y = NULL, min_overlap = 5)pwmDistance(x, y = NULL, min_overlap = 5)
x |
list of pwms or pfms, see Details |
y |
list of pwms or pfms, see Details |
min_overlap |
minimum number of basepairs overlapping between motifs |
The format of x and y should be a
PWMatrixList
or PFMatrixList or a list of matrices with rows
corresponding to "A","C","G","T" and columns summing to 1.
a list with three matrices- 'dist' has the distance between each pair of motifs, 'strand' has the strand of the motif for the match, and 'offset' has the offset between the motifs.
motifs <- getJasparMotifs() library(TFBSTools) pwm_dists <- pwmDistance(toPWM(motifs[[1]]), toPWM(motifs[[2]]))motifs <- getJasparMotifs() library(TFBSTools) pwm_dists <- pwmDistance(toPWM(motifs[[1]]), toPWM(motifs[[2]]))
Concatenates chromVARDeviations results for different sets of annotations
## S4 method for signature 'chromVARDeviations' rbind(..., deparse.level = 1)## S4 method for signature 'chromVARDeviations' rbind(..., deparse.level = 1)
... |
chromVARDeviations object to be combined |
deparse.level |
See ?base::rbind for a description of this argument. |
chromVARDeviations object
Alicia Schep
# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") doubledev <- rbind(mini_dev, mini_dev) #concatenate two of the same tother# Load very small example results from computeDeviations data(mini_dev, package = "chromVAR") doubledev <- rbind(mini_dev, mini_dev) #concatenate two of the same tother
Reads in peaks in narrowpeaks format, as output by macs2. Uses summit as center of peak, and makes peak the given 'width'. By default removes overlapping peaks to get set of peaks with no overlaps
readNarrowpeaks(filename, width = 500, non_overlapping = TRUE)readNarrowpeaks(filename, width = 500, non_overlapping = TRUE)
filename |
filename |
width |
desired width of peaks |
non_overlapping |
remove overlapping peaks |