Package 'ATACCoGAPS'

Find Enrichment of GO Terms in PatternMarker Peaks using GREAT

Description

Use the rGREAT package to find enrichment of GO terms or genes for the peaks found to be most pattern differentiating using the PatternMarker statistic.

Usage

applyGREAT(
  cogapsResult,
  granges,
  genome,
  scoreThreshold = NULL,
  GREATCategory = "GO"
)

Arguments

cogapsResult	result object from CoGAPS
granges	GRanges object corresponding to the peaks of the scATAC-seq data CoGAPS was applied to
genome	UCSC genome designation for input to the sumbitGreatJob function from the rGREAT package (e.g. "hg19")
scoreThreshold	threshold of PatternMarker score to take peaks for analysis, higher values return more peaks. Defaults to use all PatternMarker genes with value NULL
GREATCategory	input to the category argument of the rGREAT getEnrichmentTables function. Usually "GO" or "Genes"

Value

list containing enrichment results for each pattern

Examples

data("schepCogapsResult")
data(schepGranges)

GOenrichment <- applyGREAT(cogapsResult = schepCogapsResult,
 granges = schepGranges, genome = "hg19")

---

Transfer Learning between ATACseq data sets using projectR

Description

Wrapper function for projectR which finds overlaps between the peaks of the atac data CoGAPS was run on and maps them to new data set the user wishes to project learned patterns into.

Usage

ATACTransferLearning(
  newData,
  CoGAPSResult,
  originalPeaks,
  originalGranges,
  newGranges
)

Arguments

newData	the ATAC data to project into
CoGAPSResult	result from CoGAPS run on original ATAC data
originalPeaks	peaks from the ATAC data Cogaps was run on
originalGranges	granges of the peaks for the data set Cogaps was run on
newGranges	granges of the peaks for the new data set

Value

A matrix of the projected patterns in the input data as well as p-values for each element of that matrix.

---

Plot Individual CoGAPS Patterns

Description

Function to plot each pattern of the pattern matrix from a cogapsResult and color by cell classifier information to identify which patterns identify which cell classes.

Usage

cgapsPlot(
  cgaps_result,
  sample.classifier,
  cols = NULL,
  sort = TRUE,
  patterns = NULL,
  matrix = FALSE,
  ...
)

Arguments

cgaps_result	CoGAPSResult object from a CoGAPS run or the pattern matrix (matrix must be set equal to TRUE in the latter case)
sample.classifier	factor of sample classifications for all cells for the data to be plotted by (e.g. celltypes)
cols	vector of colors to be used for the cell classes; should have the same number of colors as levels of the sample.classifier factor. If left null a list of colors is produced
sort	TRUE if samples will be sorted according to sample.classifier prior to plotting
patterns	numerical vector of patterns to be plotted; if null all patterns are plotted
matrix	if false cgaps_result is interpreted as a CoGAPSResult object, if true it is interpreted as the pattern matrix
...	addition arguments to plot function

Value

Series of plots of pattern matrix patterns colored by cell classifications

Examples

data("schepCogapsResult")
data(schepCellTypes)

cgapsPlot(schepCogapsResult, schepCellTypes)

---

Filter scATACseq by sparsity

Description

Function to filter a set of scATACseq data by sparsity and return a subset of filtered data, as well as list of the remaining cells and peaks.

Usage

dataSubsetBySparsity(
  data,
  cell_list,
  peak_list,
  cell_cut = 0.99,
  peak_cut = 0.99
)

Arguments

data	matrix of read counts peaks x cells
cell_list	list of cell names/identifiers for the data
peak_list	list of peaks from the data
cell_cut	threshold of sparsity to filter at (eg. 0.99 filters all cells with more than 99 percent zero values)
peak_cut	threshold of sparsity to filter at for peaks

Value

nested list containing the subset data, a list of peaks, and list of cells

Examples

data("subsetSchepData")
data("schepPeaks")
data("schepCellTypes")

outData = dataSubsetBySparsity(subsetSchepData, schepCellTypes, schepPeaks)

---

Example list of motifs for examples

Description

PWMMatrixList used for examples with functions based on DNA motifs. Each entry contains the motif ID and the probability of each nucleotide at each position, as a matrix.

Usage

exampleMotifList

Format

PWMMatrixList of length 100

---

Estimate fold Accessibility of a Gene Relative to Average

Description

Compares the accessibility of peaks overlapping with a gene, as returned by the geneAccessibility function to the average accessibility of peaks within a given cell population. Meant to provide a rough estimate of how accessible a gene is with values higher than 1 providing evidence of differential accessibility (and thus implying possible transcription), with values lower than 1 indicating the opposite.

Usage

foldAccessibility(peaksAccessibility, cellTypeList, cellType, binaryMatrix)

Arguments

peaksAccessibility	the binarized accessibility of a set of peaks; one value returned from the geneAccessibility function
cellTypeList	list of celltypes grouping cells in the data
cellType	the particular cell type of interest from within cellTypeList
binaryMatrix	binarized scATAC data matrix

Value

Fold accessibility value as compared to average peaks for a given cell type

Examples

data("subsetSchepData")
data(schepCellTypes)
library(Homo.sapiens)
geneList <- c("TAL1", "IRF1")
data(schepGranges)
binarizedData <- (subsetSchepData > 0) + 0
accessiblePeaks <- geneAccessibility(geneList = geneList, peakGranges = schepGranges,
 atacData = subsetSchepData, genome = Homo.sapiens)
foldAccessibility(peaksAccessibility = accessiblePeaks$TAL1, cellTypeList = schepCellTypes,
 cellType = "K562 Erythroleukemia", binaryMatrix = binarizedData)

---

Find the accessibility of the peaks overlapping a set of genes and their promoters

Description

The accessibility of a particular set of interest genes is checked by testing overlap of peaks with the genes and gene promoters and then returning the binarized accesibility data for those peaks

Usage

geneAccessibility(geneList, peakGranges, atacData, genome)

Arguments

geneList	vector of HGNC gene symbols to find overlapping peaks for in the data
peakGranges	a GRanges object corresponding to the peaks in the atacData matrix, in the same order as the rows of the atacData matrix
atacData	a single-cell ATAC-seq count matrix peaks by cells
genome	TxDb object to produce gene GRanges from

Value

List of matrices corresponding to the accessible peaks overlapping with each gene across all cells in the data

Examples

library(Homo.sapiens)
geneList <- c("TAL1", "IRF1")
data(schepGranges)
data("subsetSchepData")
accessiblePeaks <- geneAccessibility(geneList = geneList, peakGranges = schepGranges,
 atacData = subsetSchepData, genome = Homo.sapiens)

---

Match genes to pattern differentiating peaks

Description

Function to take as input CoGAPS results for ATAC-seq data and find genes within the most "pattern-defining" regions (as identified by cut thresholded pattern Marker statistic from the CoGAPS package), as well as the nearest gene and the nearest gene following the region. Note: a TxDb object for the genome of interest must be loaded prior to running this function.

Usage

genePatternMatch(cogapsResult, generanges, genome, scoreThreshold = NULL)

Arguments

cogapsResult	the CogapsResult object produced by a CoGAPS run
generanges	GRanges object corresponding to the genomic regions identified as peaks for the ATAC-seq data that CoGAPS was run on
genome	A TxDb object for the genome of interest, it must be loaded prior to calling this function
scoreThreshold	threshold for the most pattern defining peaks as per the PatternMarker statistic from the CoGAPS package. Default is NULL, returning all PatternMarker peaks. Useful to reduce computational time, as top results are reasonably robust to using more stringent thresholds

Value

double nested list containing lists of the genes in, nearest, and following the peaks matched each pattern

Examples

data("schepCogapsResult")
data(schepGranges)

library(Homo.sapiens)

genes = genePatternMatch(cogapsResult = schepCogapsResult,
 generanges = schepGranges, genome = Homo.sapiens)

---

Heatmap Gene Accessibility

Description

Use the output from geneAccessibility function to plot a heatmap of the accessible peaks for a particular gene.

Usage

heatmapGeneAccessibility(
  genePeaks,
  celltypes,
  colColors = NULL,
  order = TRUE,
  ...
)

Arguments

genePeaks	The peaks corresponding to a singular gene; one element of the list ouptut by geneAccessibility()
celltypes	List or factor of celltypes corresponding to the cells in the scATAC-seq data set the peaks were found in
colColors	A vector of colors to color the celltypes by, if NULL a random vector of colors is generated
order	should the data be ordered by the celltype classifier? TRUE by default
...	additional arguments to the heatmap.2 function from the gplots package

Value

A plot of the peaks overlapping with a particular gene of interest

Examples

library(Homo.sapiens)
geneList <- c("TAL1", "EGR1")
data(schepGranges)
data("subsetSchepData")
data(schepCellTypes)
accessiblePeaks <- geneAccessibility(geneList = geneList,
 peakGranges = schepGranges, atacData = subsetSchepData, genome = Homo.sapiens)
heatmapGeneAccessibility(genePeaks = accessiblePeaks$EGR1, celltypes = schepCellTypes)

---

Create Heatmap of PatternMarker Peaks

Description

Function to make a heatmap of the accessibility of the most differentially accessible regions as discovered by CoGAPS.

Usage

heatmapPatternMarkers(
  cgaps_result,
  atac_data,
  celltypes,
  numregions = 50,
  colColors = NULL,
  rowColors = NULL,
  patterns = NULL,
  order = TRUE,
  ...
)

Arguments

cgaps_result	CogapsResult object from CoGAPS run
atac_data	a numeric matrix of the ATAC data input to CoGAPS
celltypes	a list or factor of celltypes corresponding to the positions of those cells in the atac_data matrix
numregions	number of chromosomal regions/peaks to plot for each CoGAPS pattern. Default is 50. Plotting very large numbers of regions can cause significant slowdown in runtime
colColors	column-wise colors for distinguishing celltypes. If NULL, will be generated randomly
rowColors	row-wise colors for distinguishing patterns. If NULL will be generated randomly
patterns	which patterns should be plotted, if NULL all will be plotted
order	option whether to sort the data by celltype before plotting, TRUE by default
...	additional arguments to the heatmap.2 function

Value

heatmap of the accessibility for numregions for each pattern

Note

If you get the error: "Error in plot.new() : figure margins too large" while using this function in RStudio just make the plotting pane in Rstudio larger and run the code again; this error only means the legend is being cut off in any case, the main plot will still appear correctly

Examples

data("schepCogapsResult")
data(schepCellTypes)
data("subsetSchepData")

heatmapPatternMarkers(schepCogapsResult, atac_data = subsetSchepData,
                      celltypes = schepCellTypes, numregions = 50)

---

Plot the patternMatrix as a heatmap

Description

Selects the pattermMatrix (patterns by cells) from the CoGAPSResult and plots the data as a heatmap. Intended to visualize the celltypes distinguished by the patterns found by CoGAPS.

Usage

heatmapPatternMatrix(
  cgaps_result,
  sample.classifier,
  cellCols = NULL,
  sort = TRUE,
  patterns = NULL,
  matrix = FALSE,
  rowColors = NULL,
  ...
)

Arguments

cgaps_result	CoGAPSResult object from a CoGAPS run or the pattern matrix (matrix must be set equal to TRUE in the latter case)
sample.classifier	factor of sample classifications for all cells for the data to be plotted by (e.g. celltypes)
cellCols	vector of colors to be used for the cell classes; should have the same number of colors as levels of the sample.classifier factor. If left null a list of colors is produced
sort	TRUE if samples will be sorted according to sample.classifier prior to plotting
patterns	numerical vector of patterns to be plotted; if null all patterns are plotted
matrix	if false cgaps_result is interpreted as a CoGAPSResult object, if true it is interpreted as the pattern matrix being input directly
rowColors	vector of colors to plot along patterns, if NULL generated automatically
...	additional arguments to the heatmap.2 function

Value

Heatmap of patternMatrix with color labels for samples

Examples

data("schepCogapsResult")
data(schepCellTypes)

heatmapPatternMatrix(schepCogapsResult, sample.classifier = schepCellTypes)

---

Find Motifs and TFs from PatternMarker Peaks

Description

Function that takes CoGAPS result and list of DNA motifs as input and returns motifs which match to the most pattern-defining peaks for each pattern.

Usage

motifPatternMatch(
  cogapsResult,
  generanges,
  motiflist,
  genome,
  scoreThreshold = NULL,
  motifsPerRegion = 1
)

getTFs(motifList, tfData)

findRegulatoryNetworks(TFs, networks)

getTFDescriptions(TFs)

Arguments

cogapsResult	the result object from a CoGAPS run
generanges	GRanges objects corresponding to the genomic regions which form the rows of the ATAC-seq data that CoGAPS was run on
motiflist	a PWMlist of motifs to search the regions for
genome	the ucsc genome version to use e.g. "hg19", "mm10"
scoreThreshold	threshold for the most pattern defining peaks as per the PatternMarker statistic from the CoGAPS package. By default is NULL, in which case all Pattern defining peaks will be used for motif matching. Used to reduce compute time, as results are quite robust across thresholds
motifsPerRegion	number of top motifs to return from each peak
motifList	list produced by the motifPatternMatch function
tfData	dataframe of motifs and TFs from cisBP database
TFs	object of TF info returned from the getTFs function
networks	a list of regulatory networks of genes corresponding to TFs; we include humanRegNets and mouseRegNets, downloaded from the TTrust database (Han et al Nucleic Acid Res. 2018)

Value

motifPatternMatch: nested list of the top motif for each region for x number of regions for each pattern

getTFs: list containing list of dataframes of tfData subset to matched TFs and list of how many times each TF was matched to a motif/peak

findRegulatoryNetworks: list of TFs for which we have annotations and the corresponding gene networks for each pattern

getTFDescriptions: list of functional annotations for all TFs in each pattern

Functions

• getTFs: Match motifs to TFs based on the list of motifs returned by motifPatternMatch

• findRegulatoryNetworks: function to match TFs identified by getTFs function to a list of regulatory networks of genes known for those TFs

• getTFDescriptions: function to match functional annotation to a list of TFs from the getTFs function

Examples

data(exampleMotifList)
data(schepGranges)
data(schepCogapsResult)

motifsByPattern = motifPatternMatch(schepCogapsResult, schepGranges,
 exampleMotifList, "hg19")
data(exampleMotifList)
data(schepGranges)
data(schepCogapsResult)
data(tfData)

motifsByPattern = motifPatternMatch(schepCogapsResult, schepGranges, exampleMotifList, "hg19")
motifTFs = getTFs(motifsByPattern, tfData)
data(exampleMotifList)
data(schepGranges)
data(schepCogapsResult)
data(tfData)

motifsByPattern = motifPatternMatch(schepCogapsResult, schepGranges, exampleMotifList, "hg19")
motifTFs = getTFs(motifsByPattern, tfData)

regNets = findRegulatoryNetworks(motifTFs, ATACCoGAPS:::humanRegNets)
data(exampleMotifList)
data(schepGranges)
data(schepCogapsResult)
data(tfData)

motifsByPattern = motifPatternMatch(schepCogapsResult, schepGranges, exampleMotifList, "hg19")
motifTFs = getTFs(motifsByPattern, tfData)

tfDesc = getTFDescriptions(motifTFs)

---

Map Peaks to DNA motifs in scATAC-seq Data

Description

Provides functionality to summarize scATAC-seq data by motifs from peak summary. Uses motifmatchr to prepare data for CoGAPS run using motif summarization

Usage

motifSummarization(
  motifList,
  scATACData,
  granges,
  genome,
  cellNames,
  pCutoff = 5e-09
)

Arguments

motifList	PWMatrixList object of motifs (from the TFBS tools package)
scATACData	matrix of scATACseq data, peaks (rows) by cells (columns)
granges	GenomicRanges object corresponding to all peaks used to summarize scATACData
genome	The UCSC Genome to use for input to motifmatchr (e.g "hg19")
cellNames	List of cellnames corresponding to the cells in scATACData
pCutoff	p-value cutoff for motifmatchr, 5e-09 by default to identify only matches with high confidence

Value

matrix for input to CoGAPS with summary to motifs; motifs by cells

Examples

## Not run: 
motifSummTest = motifSummarization(motifList = motifs, scATACData = scatac,
 granges = peakGranges, genome = "hg19", cellNames = cells, pCutoff = 5e-09)

## End(Not run)

---

Matches list of genes to pathways

Description

Takes the result of the genePatternMatch function and finds significantly enriched pathways for each pattern.

Usage

pathwayMatch(gene_list, pathways, p_threshold = 0.05, pAdjustMethod = "BH")

Arguments

gene_list	Result from the genePatternMatch function, a list of genes for each pattern
pathways	List of pathways to perform gene enrichment on. Recommended to download using msigdbr (see examples)
p_threshold	significance level to use in enrichment analysis
pAdjustMethod	multiple testing correction method to apply using the p.adjust options (e.g. "BH")

Value

List of gene overlap objects, pathways with significant overlap and pathway names for each pattern

Examples

data(schepCogapsResult)
data(schepGranges)
library(Homo.sapiens)

genes <- genePatternMatch(cogapsResult = schepCogapsResult,
 generanges = schepGranges, genome = Homo.sapiens)

library(dplyr)
pathways = msigdbr::msigdbr(species = "Homo sapiens", category ="H") %>% 
dplyr::select(gs_name, gene_symbol) %>% as.data.frame()

matchedPathways = pathwayMatch(genes, pathways, p_threshold = 0.001)

---

Match cells to patterns

Description

Use the patternMarker statistic to determine which cells belong to each pattern in the data

Usage

patternMarkerCellClassifier(cgapsResult)

Arguments

cgapsResult

a CoGAPSResult object

Value

list containing a prediction matrix and vector classifying cells to patterns

Examples

data("schepCogapsResult")
pClass <- patternMarkerCellClassifier(schepCogapsResult)

---

List of peaks to GRanges

Description

Wrapper function for makeGrangesFromDataFrame() from the GenomicRanges package to build GRanges objects from character list of chromosomal regions because this is a common format to receive peak information.

Usage

peaksToGRanges(region_list, sep)

Arguments

region_list	character list or vector of chromosomal regions/peaks in form chromosomenumber(sep)start(sep)end eg. Chr1-345678-398744
sep	separator between information pieces of string (conventionally "-" or ".")

Value

GRanges corrsponding to input list of region information

Note

If region_list is a dataframe you should use the GenomicRanges function makeGRangesFromDataFrame which this function applies

Examples

data(schepPeaks)

schepGranges = peaksToGRanges(schepPeaks, sep = "-")

---

Validate TF Findings with RNA-seq CoGAPS

Description

Use results from CoGAPS run on matched RNA-seq data to verify TF activity suggested by motif matching analysis of ATAC CoGAPS output. Uses the fgsea package to find enrichment of PatternMarker genes among genes regulated by identified candidate TFs

Usage

RNAseqTFValidation(
  TFGenes,
  RNACoGAPSResult,
  ATACPatternSet,
  RNAPatternSet,
  matrix = FALSE
)

Arguments

TFGenes	genes regulated by the TFs as returned by simpleMotifTFMatch() or findRegulatoryNetworks()
RNACoGAPSResult	CoGAPSResult object from matched RNA-seq data, or, if matrix = TRUE, a matrix containing patternMarker gene ranks. Must contain gene names
ATACPatternSet	vector of patterns found by CoGAPS in the ATAC data to match against patterns found in RNA
RNAPatternSet	vector of patterns found by CoGAPS in RNA to match against those found in ATAC
matrix	TRUE if inputting matrix of PatternMarker genes, FALSE if inputting CoGAPS result object. FALSE by default

Value

Result matrices from the fgsea function for each pattern comparison

Examples

## Not run: 
gseaList = RNAseqTFValidation(TFMatchResult$RegulatoryNetworks, RNACoGAPS,
 c(1,3), c(2,7), matrix = FALSE)

## End(Not run)

---

Cell types corresponding to subsetSchepData

Description

Factor of cell types in the order of the subsetSchepData object from the Schep et al, 2017, Nature Methods paper.

Usage

schepCellTypes

Format

Factor of length 600 with 12 levels

Source

10.1038/nmeth.4401

---

CogapsResult from the subsetSchepData object

Description

Output from applying the CoGAPS algorithm to the subsetSchepData object.

Usage

schepCogapsResult

Format

Large CogapsResult

---

GRanges corresponding to subsetSchepData

Description

GRanges in the order of the peaks of the subsetSchepData object from the Schep et al, 2017, Nature Methods paper.

Usage

schepGranges

Format

GRanges of length 5036

Source

10.1038/nmeth.4401

---

Peaks corresponding to subsetSchepData

Description

Character vector of peaks in the order of the peaks of the subsetSchepData object from the Schep et al, 2017, Nature Methods paper.

Usage

schepPeaks

Format

Character vector of length 5036

Source

10.1038/nmeth.4401

---

Motif/TF Matching in a Single Function

Description

If the user does not have a specific set of motifs, transcription factors, or regulatory networks that they want to match against, simply uses the core motifs from the JASPAR database to find motifs and TFs in the most Pattern differentiating peaks, as well as regulatory networks from TTrust database corresponding to the identified TFs. This is used to provide transcription factors with functional annotation which may suggest plausible unknown regulatory mechanisms operating in the cell types of interest within the data.

Usage

simpleMotifTFMatch(
  cogapsResult,
  generanges,
  organism,
  genome,
  scoreThreshold = NULL,
  motifsPerRegion = 1
)

Arguments

cogapsResult	result object from CoGAPS run
generanges	GRanges object corresponding to peaks in ATACseq data CoGAPS was run on
organism	organism name (e.g. "Homo sapiens")
genome	genome version to use (e.g. hg19, mm10)
scoreThreshold	threshold for the most pattern defining peaks as per the PatternMarker statistic from the CoGAPS package. By default is NULL, in which case all Pattern defining peaks will be used for motif matching. Used to reduce compute time, as results are quite robust across thresholds
motifsPerRegion	number of motifs to attempt to find within each peak

Value

list containing list of matched motifs, list of transciption factors, regulatory gene networks known for those TFs, functional annotations, summary showing how many times each TF was matched to a peak, and the downloaded set of motifs for the user to save for reproducibility

Examples

data("schepCogapsResult")
data(schepGranges)

motifResults = simpleMotifTFMatch(cogapsResult = schepCogapsResult,
 generanges = schepGranges, organism = "Homo sapiens",
 genome = "hg19", motifsPerRegion = 1)

---

Small subset of the scATAC-seq data from Schep et al, 2017, Nature Methods paper.

Description

Subset from the Schep et al data, used for examples in this package.

Usage

subsetSchepData

Format

A matrix with 5036 peaks and 600 cells in the order of the schepPeaks, schepCellTypes, and schepGranges data objects.

Source

10.1038/nmeth.4401

---

List of human TFs and motifs from cisBP database

Description

Information on human TFs and their correpsonding DNA motifs

Usage

tfData

Format

Data frame with 95413 rows and 28 columns.

Source

http://cisbp.ccbr.utoronto.ca/

---