| Title: | Modeling for Single-Cell Open Chromatin Analysis |
|---|---|
| Description: | A statistical framework and analysis tool for open chromatin analysis designed specifically for single cell ATAC-seq (Assay for Transposase-Accessible Chromatin) data, after cell type/cluster identification. These novel modules remove unwanted technical variation, identify open chromatin, robustly models repeated measures in single cell data, implement advanced statistical frameworks to model zero-inflation for differential and co-accessibility analyses, and integrate with existing databases and modules for downstream analyses to reveal biological insights. MOCHA provides a statistical foundation for complex downstream analysis to help advance the potential of single cell ATAC-seq for applied studies. Methods for zero-inflated statistics are as described in: Ghazanfar, S., Lin, Y., Su, X. et al. (2020) <doi:10.1038/s41592-020-0885-x>. Pimentel, Ronald Silva, "Kendall's Tau and Spearman's Rho for Zero-Inflated Data" (2009) <https://scholarworks.wmich.edu/dissertations/721/>. |
| Authors: | Samir Rachid Zaim [aut, ctb], Mark-Phillip Pebworth [aut, ctb], Imran McGrath [aut, cre], Lauren Okada [aut, ctb], Xiaojun Li [aut, ctb] |
| Maintainer: | Imran McGrath <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.1.0 |
| Built: | 2024-12-21 07:01:53 UTC |
| Source: | CRAN |
Default ggplot theme for counts plot
.counts_plot_default_theme.counts_plot_default_theme
An object of class list of length 10.
Common theme for gene plots
.gene_plot_theme.gene_plot_theme
An object of class list of length 5.
addAccessibilityShiftaddAccessibilityShift will add a new condition to the SummarizedExperiment output of extractRegion,
which will contain the difference in accessibility between two conditions
addAccessibilityShift(CountSE, foreground, background, assayName = NULL)addAccessibilityShift(CountSE, foreground, background, assayName = NULL)
CountSE |
The SummarizedExperiment object output from extractRegion |
foreground |
Group that will be used as the foreground for the subtraction of accessibility |
background |
Group that will be used as the background for the subtraction of accessibility |
assayName |
The name given to the new assay that is difference in accessibility between foreground and background. |
countSE a SummarizedExperiment containing coverage for the given input cell populations.
## Not run: # CountSE is a SummarizedExperiment generated by extractRegion() countSE <- MOCHA::addAccessibilityShift( CountSE = CountSE, foreground = "Condition1", background = "Condition2", assayName = "AccessibilityChanges" ) ## End(Not run)## Not run: # CountSE is a SummarizedExperiment generated by extractRegion() countSE <- MOCHA::addAccessibilityShift( CountSE = CountSE, foreground = "Condition1", background = "Condition2", assayName = "AccessibilityChanges" ) ## End(Not run)
addMotifSetaddMotifSet Identify motifs within your peakset.
addMotifSet( SampleTileObj, motifPWMs, w = 7, returnSTM = TRUE, motifSetName = "Motifs" )addMotifSet( SampleTileObj, motifPWMs, w = 7, returnSTM = TRUE, motifSetName = "Motifs" )
SampleTileObj |
A SummarizedExperiment, specifically the output of getSampleTileMatrix |
motifPWMs |
A pwms object for the motif database. Either PFMatrix, PFMatrixList, PWMatrix, or PWMatrixList |
w |
Parameter for motifmatchr controlling size in basepairs of window for filtration. Default is 7. |
returnSTM |
If TRUE, return the modified SampleTileObj with motif set added to metadata (default). If FALSE, return the motifs from motifmatchr as a GRanges. |
motifSetName |
Name to give motifList in the SampleTileObj's metadata if ‘returnSTM=TRUE'. Default is ’Motifs'. |
the modified SampleTileObj with motifs added to the metadata
## Not run: # load a curated motif set from library(chromVARmotifs) # included with ArchR installation data(human_pwms_v2) SE_with_motifs <- addMotifSet( SampleTileObj, motifPWMs = human_pwms_v2, returnSTM = TRUE, motifSetName = "Motifs", w = 7 ) ## End(Not run)## Not run: # load a curated motif set from library(chromVARmotifs) # included with ArchR installation data(human_pwms_v2) SE_with_motifs <- addMotifSet( SampleTileObj, motifPWMs = human_pwms_v2, returnSTM = TRUE, motifSetName = "Motifs", w = 7 ) ## End(Not run)
annotateTilesannotateTiles annotates a set of sample-tile matrices
given with gene annotations. Details on TxDb and Org annotation packages
and available annotations can be found at Bioconductor:
https://bioconductor.org/packages/3.15/data/annotation/
annotateTiles(Obj, TxDb = NULL, Org = NULL, promoterRegion = c(2000, 100))annotateTiles(Obj, TxDb = NULL, Org = NULL, promoterRegion = c(2000, 100))
Obj |
A RangedSummarizedExperiment generated from getSampleTileMatrix, containing TxDb and Org in the metadata. This may also be a GRanges object. |
TxDb |
The annotation package for TxDb object for your genome. Optional, only required if Obj is a GRanges. |
Org |
The genome-wide annotation for your organism. Optional, only required if Obj is a GRanges. |
promoterRegion |
Optional list containing the window size in basepairs defining the promoter region. The format is (upstream, downstream). Default is (2000, 100). |
Obj, the input data structure with added gene annotations (whether GRanges or SampleTileObj)
## Not run: library(TxDb.Hsapiens.UCSC.hg38.refGene) library(org.Hs.eg.db) SampleTileMatricesAnnotated <- MOCHA::annotateTiles( SampleTileMatrices, TxDb = TxDb.Hsapiens.UCSC.hg38.refGene, Org = org.Hs.eg.db ) ## End(Not run)## Not run: library(TxDb.Hsapiens.UCSC.hg38.refGene) library(org.Hs.eg.db) SampleTileMatricesAnnotated <- MOCHA::annotateTiles( SampleTileMatrices, TxDb = TxDb.Hsapiens.UCSC.hg38.refGene, Org = org.Hs.eg.db ) ## End(Not run)
bulkDimReductionbulkDimReduction runs dimensionality reduction (either PCA or LSI). We adapt Signac's
bulkDimReduction( SampleTileObj, cellType = "All", componentNumber = 30, method = "LSI", verbose = FALSE )bulkDimReduction( SampleTileObj, cellType = "All", componentNumber = 30, method = "LSI", verbose = FALSE )
SampleTileObj |
The SummarizedExperiment object output from getSampleTileMatrix |
cellType |
vector of strings. Cell subsets for which to call peaks. This list of group names must be identical to names that appear in the SampleTileObj. Optional, if cellPopulations='ALL', then peak calling is done on all cell populations. Default is 'ALL'. |
componentNumber |
integer. Number of components to include in LSI, or PCA This must be strictly less than |
method |
a string. Represents the method to use. Includes LSI or PCA, but we do not recommend PCA for scATAC pseudobulk. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
SEObj a SummarizedExperiment containing PC components from dimensionality reduction and metadata from the SampleTileObj
LSI method adapted from Andrew Hill: http://andrewjohnhill.com/blog/2019/05/06/dimensionality-reduction-for-scatac-data/
## Not run: LSIObj <- MOCHA::bulkDimReduction(SampleTileObj, cellType = "CD16_Mono") ## End(Not run)## Not run: LSIObj <- MOCHA::bulkDimReduction(SampleTileObj, cellType = "CD16_Mono") ## End(Not run)
bulkUMAPbulkUMAP generates UMAP from pseudobulk LSIObj object, and merges in metadata.
bulkUMAP( SEObj, assay = "LSI", components = c(1:30), nNeighbors = 15, returnModel = FALSE, seed = 1, ... )bulkUMAP( SEObj, assay = "LSI", components = c(1:30), nNeighbors = 15, returnModel = FALSE, seed = 1, ... )
SEObj |
The SummarizedExperiment object output from bulkDimReduction, or an STM, subsetted down to just one cell type. |
assay |
A string, describing the name of the assay within SEObj to run UMAP ('PCA', 'LSI', or 'counts'). |
components |
A vector of integers. Number of components to include in LSI (1:30 typically). |
nNeighbors |
See umap. The size of local neighborhood (in terms of number of neighboring sample points) used for manifold approximation. Default is 15. |
returnModel |
A boolean. Default is FALSE. If set to true, it will return a list, where the first is the UMAP coordinates with metadata for plotting, and the second is the full UMAP model so further projection can occur. |
seed |
an integer. Represents the random seed to pass to the UMAP. Default seed is 1. |
... |
Additional arguments to be passed to umap. |
fullUMAP data.frame of UMAP values with metadata attached.
## Not run: UMAPvalues <- MOCHA::bulkUMAP(LSIObj) ## End(Not run)## Not run: UMAPvalues <- MOCHA::bulkUMAP(LSIObj) ## End(Not run)
callOpenTiles Perform peak-calling on a set of fragments or an
ArchR Project.callOpenTiles is the main peak-calling function in MOCHA
that serves as a wrapper function to call peaks provided a set of fragment
files and an ArchR Project for meta-data purposes
callOpenTiles( ATACFragments, cellColData, blackList, genome, cellPopLabel, cellPopulations = "ALL", studySignal = NULL, generalizeStudySignal = FALSE, cellCol = "RG", TxDb, OrgDb, outDir, numCores = 30, verbose = FALSE, force = FALSE ) ## S4 method for signature 'GRangesList' callOpenTiles( ATACFragments, cellColData, blackList, genome, cellPopLabel, cellPopulations = "ALL", studySignal = NULL, generalizeStudySignal = FALSE, cellCol = "RG", TxDb, OrgDb, outDir, numCores = 30, verbose = FALSE, force = FALSE ) ## S4 method for signature 'list' callOpenTiles( ATACFragments, cellColData, blackList, genome, cellPopLabel, cellPopulations = "ALL", studySignal = NULL, generalizeStudySignal = FALSE, cellCol = "RG", TxDb, OrgDb, outDir, numCores = 30, verbose = FALSE, force = FALSE ) .callOpenTiles_ArchR( ATACFragments, cellPopLabel, cellPopulations = "ALL", studySignal = NULL, generalizeStudySignal = FALSE, TxDb, OrgDb, outDir = NULL, numCores = 30, verbose = FALSE, force = FALSE )callOpenTiles( ATACFragments, cellColData, blackList, genome, cellPopLabel, cellPopulations = "ALL", studySignal = NULL, generalizeStudySignal = FALSE, cellCol = "RG", TxDb, OrgDb, outDir, numCores = 30, verbose = FALSE, force = FALSE ) ## S4 method for signature 'GRangesList' callOpenTiles( ATACFragments, cellColData, blackList, genome, cellPopLabel, cellPopulations = "ALL", studySignal = NULL, generalizeStudySignal = FALSE, cellCol = "RG", TxDb, OrgDb, outDir, numCores = 30, verbose = FALSE, force = FALSE ) ## S4 method for signature 'list' callOpenTiles( ATACFragments, cellColData, blackList, genome, cellPopLabel, cellPopulations = "ALL", studySignal = NULL, generalizeStudySignal = FALSE, cellCol = "RG", TxDb, OrgDb, outDir, numCores = 30, verbose = FALSE, force = FALSE ) .callOpenTiles_ArchR( ATACFragments, cellPopLabel, cellPopulations = "ALL", studySignal = NULL, generalizeStudySignal = FALSE, TxDb, OrgDb, outDir = NULL, numCores = 30, verbose = FALSE, force = FALSE )
ATACFragments |
an ArchR Project, or a GRangesList of fragments. Each GRanges in the GRanges list must have unique cell IDs in the column given by 'cellCol'. |
cellColData |
A DataFrame containing cell-level metadata. This must contain both a column 'Sample' with unique sample IDs and the column specified by 'cellPopLabel'. |
blackList |
A GRanges of blacklisted regions |
genome |
A BSgenome object, or the full name of an installed BSgenome data package, or a short string specifying the name of an NCBI assembly (e.g. "GRCh38", "TAIR10.1", etc...) or UCSC genome (e.g. "hg38", "bosTau9", "galGal6", "ce11", etc...). The supplied short string must refer unambiguously to an installed BSgenome data package. See getBSgenome. |
cellPopLabel |
string indicating which column in the ArchRProject metadata contains the cell population label. |
cellPopulations |
vector of strings. Cell subsets for which to call peaks. This list of group names must be identical to names that appear in the ArchRProject metadata. Optional, if cellPopulations='ALL', then peak calling is done on all cell populations in the ArchR project metadata. Default is 'ALL'. |
studySignal |
The median signal (number of fragments) in your study. If not set, this will be calculated using the input ArchR project but relies on the assumption that the ArchR project encompasses your whole study (i.e. is not a subset). |
generalizeStudySignal |
If 'studySignal' is not provided, calculate the signal as the mean of the mean & median number of fragments for of individual samples within each cell population. This may improve MOCHA's ability to generalize to datasets with XXXXXX #TODO. Default is FALSE, use the median number of fragments. |
cellCol |
The column in cellColData specifying unique cell ids or barcodes. Default is "RG", the unique cell identifier used by ArchR. |
TxDb |
The exact package name of a TxDb-class transcript annotation package for your organism (e.g. "TxDb.Hsapiens.UCSC.hg38.refGene"). This must be installed. See Bioconductor AnnotationData Packages. |
OrgDb |
The exact package name of a OrgDb-class genome wide annotation package for your organism (e.g. "org.Hs.eg.db"). This must be installed. See Bioconductor AnnotationData Packages |
outDir |
is a string describing the output directory for coverage files. Must be a complete directory string. With ArchR input, set outDir to NULL to create a directory within the input ArchR project directory named MOCHA for saving files. |
numCores |
integer. Number of cores to parallelize peak-calling across multiple cell populations. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
force |
Optional, whether to force creation of coverage files if they already exist. Default is FALSE. |
tileResults A MultiAssayExperiment object containing ranged data for each tile
## Not run: # Starting from an ArchR Project: tileResults <- MOCHA::callOpenTiles( ArchRProj = myArchRProj, cellPopLabel = "celltype_labeling", cellPopulations = "CD4", TxDb = "TxDb.Hsapiens.UCSC.hg38.refGene", OrgDb = "org.Hs.eg.db", numCores = 1 ) ## End(Not run) # Starting from GRangesList if ( requireNamespace("BSgenome.Hsapiens.UCSC.hg19") && requireNamespace("TxDb.Hsapiens.UCSC.hg38.refGene") && requireNamespace("org.Hs.eg.db") ) { tiles <- MOCHA::callOpenTiles( ATACFragments = MOCHA::exampleFragments, cellColData = MOCHA::exampleCellColData, blackList = MOCHA::exampleBlackList, genome = "BSgenome.Hsapiens.UCSC.hg19", TxDb = "TxDb.Hsapiens.UCSC.hg38.refGene", OrgDb = "org.Hs.eg.db", outDir = tempdir(), cellPopLabel = "Clusters", cellPopulations = c("C2", "C5"), numCores = 1 ) }## Not run: # Starting from an ArchR Project: tileResults <- MOCHA::callOpenTiles( ArchRProj = myArchRProj, cellPopLabel = "celltype_labeling", cellPopulations = "CD4", TxDb = "TxDb.Hsapiens.UCSC.hg38.refGene", OrgDb = "org.Hs.eg.db", numCores = 1 ) ## End(Not run) # Starting from GRangesList if ( requireNamespace("BSgenome.Hsapiens.UCSC.hg19") && requireNamespace("TxDb.Hsapiens.UCSC.hg38.refGene") && requireNamespace("org.Hs.eg.db") ) { tiles <- MOCHA::callOpenTiles( ATACFragments = MOCHA::exampleFragments, cellColData = MOCHA::exampleCellColData, blackList = MOCHA::exampleBlackList, genome = "BSgenome.Hsapiens.UCSC.hg19", TxDb = "TxDb.Hsapiens.UCSC.hg38.refGene", OrgDb = "org.Hs.eg.db", outDir = tempdir(), cellPopLabel = "Clusters", cellPopulations = c("C2", "C5"), numCores = 1 ) }
combineSampleTileMatrixcombineSampleTileMatrix combines all celltypes in a
SampleTileMatrix object into a SummarizedExperiment with one single matrix
across all cell types and samples,
combineSampleTileMatrix(SampleTileObj, NAtoZero = TRUE, verbose = FALSE)combineSampleTileMatrix(SampleTileObj, NAtoZero = TRUE, verbose = FALSE)
SampleTileObj |
The SummarizedExperiment object output from getSampleTileMatrix containing your sample-tile matrices |
NAtoZero |
Set NA values in the sample-tile matrix to zero |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
TileCorr A data.table correlation matrix
differentialsToGRanges Converts a data.frame matrix to a GRanges,
preserving additional columns as GRanges metadatadifferentialsToGRanges Converts a data.frame matrix to a GRanges,
preserving additional columns as GRanges metadata
differentialsToGRanges(differentials, tileColumn = "Tile")differentialsToGRanges(differentials, tileColumn = "Tile")
differentials |
a matrix/data.frame with a column tileColumn containing region strings in the format "chr:start-end" |
tileColumn |
name of column containing region strings. Default is "Tile". |
a GRanges containing all original information
Example input of a blackList extracted from the PBMC_Small dataset consisting of 2k cells and spanning chr1 and 2 (~2-300MB). The data is publicly available with the ArchR package at <https://www.archrproject.com/reference/getTestProject.html>
exampleBlackListexampleBlackList
A GRanges object with 210 ranges and 2 metadata columns
Example input of cellColData extracted from the PBMC_Small dataset consisting of 2k cells and spanning chr1 and 2 (~2-300MB). The data is publicly available with the ArchR package at <https://www.archrproject.com/reference/getTestProject.html>
exampleCellColDataexampleCellColData
A DataFrame with 2217 rows and 3 columns
Example input of ATAC fragments extracted from the PBMC_Small dataset consisting of 2k cells and spanning chr1 and 2 (~2-300MB). This subset consists of two cell populations: Clusters C2 and C5. The data is publicly available with the ArchR package at <https://www.archrproject.com/reference/getTestProject.html>
exampleFragmentsexampleFragments
A list of 2 GRanges objects
exportCoverageexportCoverage will export normalized coverage files to
BigWig files, either as sample-specific or sample-averaged files, for
visualization in genome browsers.
exportCoverage( SampleTileObject, dir = getwd(), type = TRUE, cellPopulations = "ALL", groupColumn = NULL, subGroups = NULL, sampleSpecific = FALSE, saveFile = TRUE, numCores = 1, verbose = FALSE )exportCoverage( SampleTileObject, dir = getwd(), type = TRUE, cellPopulations = "ALL", groupColumn = NULL, subGroups = NULL, sampleSpecific = FALSE, saveFile = TRUE, numCores = 1, verbose = FALSE )
SampleTileObject |
The SummarizedExperiment object output from getSampleTileMatrix |
dir |
string. Directory to save files to. |
type |
Boolean. Default is TRUE, and exports Coverage. If set to FALSE, exports Insertions. |
cellPopulations |
vector of strings. Cell subsets for which to call peaks. This list of group names must be identical to names that appear in the SampleTileObject. Optional, if cellPopulations='ALL', then peak calling is done on all cell populations. Default is 'ALL'. |
groupColumn |
Optional, the column containing sample group labels for returning coverage within sample groups. Default is NULL, all samples will be used. |
subGroups |
a list of subgroup(s) within the groupColumn from the metadata. Optional, default is NULL, all labels within groupColumn will be used. |
sampleSpecific |
If TRUE, a BigWig will export for each sample-cell type combination. |
saveFile |
Boolean. If TRUE, it will save to a BigWig. If FALSE, it will return the GRangesList without writing a BigWig. |
numCores |
integer. Number of cores to parallelize peak-calling across multiple cell populations |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
countSE a SummarizedExperiment containing coverage for the given input cell populations.
## Not run: MOCHA::exportCoverage( SampleTileObject = SampleTileMatrices, cellPopulations = "ALL", numCores = 30, sampleSpecific = FALSE ) ## End(Not run)## Not run: MOCHA::exportCoverage( SampleTileObject = SampleTileMatrices, cellPopulations = "ALL", numCores = 30, sampleSpecific = FALSE ) ## End(Not run)
exportDifferentialsexportDifferentials exports the differential peaks
output GRangesList output from getDifferentialAccessibleTiles to
bigBed format for visualization in genome browsers.
exportDifferentials( SampleTileObject, DifferentialsGRList, outDir, verbose = FALSE )exportDifferentials( SampleTileObject, DifferentialsGRList, outDir, verbose = FALSE )
SampleTileObject |
The SummarizedExperiment object output from
|
DifferentialsGRList |
GRangesList output from
|
outDir |
Desired output directory where bigBed files will be saved |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
outList A List of output filepaths
## Not run: MOCHA::exportDifferentials( SampleTileObject = SampleTileMatrices, DifferentialsGRList, outDir = tempdir(), verbose = TRUE ) ## End(Not run)## Not run: MOCHA::exportDifferentials( SampleTileObject = SampleTileMatrices, DifferentialsGRList, outDir = tempdir(), verbose = TRUE ) ## End(Not run)
exportMotifsexportMotifs exports a motif set GRanges from running
addMotifSet(returnSTM=FALSE) to bigBed file files for visualization
in genome browsers.
exportMotifs( SampleTileObject, motifsGRanges, motifSetName = "motifs", filterByOpenTiles = FALSE, outDir, verbose = FALSE )exportMotifs( SampleTileObject, motifsGRanges, motifSetName = "motifs", filterByOpenTiles = FALSE, outDir, verbose = FALSE )
SampleTileObject |
The SummarizedExperiment object output from
|
motifsGRanges |
A GRanges containing motif annotations, typically from
|
motifSetName |
Optional, a name indicating the motif set. Used to name
files in the specified |
filterByOpenTiles |
Boolean. If TRUE, a bigBed file will be exported for each cell population with motifs filtered to those occurring only in open tiles. |
outDir |
Desired output directory where bigBed files will be saved |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
outList A List of output filepaths
## Not run: MOCHA::exportMotifs( SampleTileObject = SampleTileMatrices, motifsGRanges, motifSetName = "CISBP", filterByOpenTiles = FALSE, outDir = tempdir(), verbose = TRUE ) ## End(Not run)## Not run: MOCHA::exportMotifs( SampleTileObject = SampleTileMatrices, motifsGRanges, motifSetName = "CISBP", filterByOpenTiles = FALSE, outDir = tempdir(), verbose = TRUE ) ## End(Not run)
exportOpenTilesexportOpenTiles exports the open tiles of a given cell
population to bigBed file for visualization in genome browsers.
exportOpenTiles(SampleTileObject, cellPopulation, outDir, verbose = FALSE)exportOpenTiles(SampleTileObject, cellPopulation, outDir, verbose = FALSE)
SampleTileObject |
The SummarizedExperiment object output from
|
cellPopulation |
The name of the cell population to export |
outDir |
Desired output directory where bigBed files will be saved |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
outList A List of output filepaths
## Not run: MOCHA::exportOpenTiles( SampleTileObject = SampleTileObject, cellPopulation, outDir = tempdir(), verbose = TRUE ) ## End(Not run)## Not run: MOCHA::exportOpenTiles( SampleTileObject = SampleTileObject, cellPopulation, outDir = tempdir(), verbose = TRUE ) ## End(Not run)
exportSmoothedInsertionsexportSmoothedInsertions Takes a SampleTileMatrix with
linked insertion files and applies a smoothing filter (a rolling sum then
rolling median) to the insertions, finally exporting the smoothed insertion
files to bigwig format.
exportSmoothedInsertions( SampleTileObj, cellPopulation, outDir = NULL, sumWidth = 10, medianWidth = 11, force = FALSE, slow = FALSE, verbose = FALSE )exportSmoothedInsertions( SampleTileObj, cellPopulation, outDir = NULL, sumWidth = 10, medianWidth = 11, force = FALSE, slow = FALSE, verbose = FALSE )
SampleTileObj |
A MultiAssayExperiment or RangedSummarizedExperiment from MOCHA |
cellPopulation |
A string denoting the cell population of interest |
outDir |
Directory to write output bigwig files. Default is NULL, where the directory in 'SampleTileObj@metadata$Directory' will be used. |
sumWidth |
Window size for rolling sum in basepairs. Default is 10. |
medianWidth |
Window size for rolling median in basepairs. Must be odd. Default is 11. |
force |
Set TRUE to overwrite existing files. Default is FALSE. |
slow |
Set TRUE to bypass optimisations and compute smoothing filter directly on the whole genome. May run slower and consume more RAM. Default is FALSE. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
outPaths List of paths of exported insertion files
## Not run: # Depends on and manipulates files on filesystem outPath <- MOCHA::exportSmoothedInsertions( SampleTileObj, cellPopulation = "CD4 Naive", sumWidth = 10, medianWidth = 11, verbose = FALSE ) ## End(Not run)## Not run: # Depends on and manipulates files on filesystem outPath <- MOCHA::exportSmoothedInsertions( SampleTileObj, cellPopulation = "CD4 Naive", sumWidth = 10, medianWidth = 11, verbose = FALSE ) ## End(Not run)
extractRegionextractRegion will extract the coverage files created by
callOpenTiles and return a specific region's coverage
extractRegion( SampleTileObj, type = TRUE, region, cellPopulations = "ALL", groupColumn = NULL, subGroups = NULL, sampleSpecific = FALSE, approxLimit = 1e+05, binSize = 250, sliding = NULL, numCores = 1, verbose = FALSE )extractRegion( SampleTileObj, type = TRUE, region, cellPopulations = "ALL", groupColumn = NULL, subGroups = NULL, sampleSpecific = FALSE, approxLimit = 1e+05, binSize = 250, sliding = NULL, numCores = 1, verbose = FALSE )
SampleTileObj |
The SummarizedExperiment object output from getSampleTileMatrix |
type |
Boolean. Default is true, and exports Coverage. If set to FALSE, exports Insertions. |
region |
a GRanges object or vector or strings containing the regions of interest. Strings must be in the format "chr:start-end", e.g. "chr4:1300-2222". |
cellPopulations |
vector of strings. Cell subsets for which to call peaks. This list of group names must be identical to names that appear in the SampleTileObj. Optional, if cellPopulations='ALL', then peak calling is done on all cell populations. Default is 'ALL'. |
groupColumn |
Optional, the column containing sample group labels for returning coverage within sample groups. Default is NULL, all samples will be used. |
subGroups |
a list of subgroup(s) within the groupColumn from the metadata. Optional, default is NULL, all labels within groupColumn will be used. |
sampleSpecific |
If TRUE, get a sample-specific count dataframe out. Default is FALSE, average across samples and get a dataframe out. |
approxLimit |
Optional limit to region size, where if region is larger than approxLimit basepairs, binning will be used. Default is 100000. |
binSize |
Optional numeric, size of bins in basepairs when binning is used. Default is 250. |
sliding |
Optional numeric. Default is NULL. This number is the size of the sliding window for generating average intensities. |
numCores |
integer. Number of cores to parallelize peak-calling across multiple cell populations |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
countSE a SummarizedExperiment containing coverage for the given input cell populations.
## Not run: countSE <- MOCHA::extractRegion( SampleTileObj = SampleTileMatrices, cellPopulations = "ALL", region = "chr1:18137866-38139912", numCores = 30, sampleSpecific = FALSE ) ## End(Not run)## Not run: countSE <- MOCHA::extractRegion( SampleTileObj = SampleTileMatrices, cellPopulations = "ALL", region = "chr1:18137866-38139912", numCores = 30, sampleSpecific = FALSE ) ## End(Not run)
filterCoAccessibleLinksfilterCoAccessibleLinks will filter the output from
getCoAccessibleLinks by a threshold, retaining links with a
absolute correlation greater than the threshold.
This function also adds the chr, start, and end site of each link to
the output table.
filterCoAccessibleLinks(TileCorr, threshold = 0.5)filterCoAccessibleLinks(TileCorr, threshold = 0.5)
TileCorr |
The correlation table output from getCoAccessibleLinks |
threshold |
Keep |
FilteredTileCorr The filtered correlation table with chr, start, and end site of each link
## Not run: # links is the output of MOCHA::getCoAccessibleLinks MOCHA::filterCoAccessibleLinks(links, threshold = 0.5) ## End(Not run)## Not run: # links is the output of MOCHA::getCoAccessibleLinks MOCHA::filterCoAccessibleLinks(links, threshold = 0.5) ## End(Not run)
Trained MOCHA models - LOESS and linear regression
finalModelObjectfinalModelObject
A list of lists containing 2 items: "Loess" and "Linear" each with "Total" "Max" and "Intercept"
LOESS model
Linear model
getAltTSS Pulls out all peaks that fall in TSS,
annotates them with the name of gene, and identifies genes that have
evidence for alternatively regulated TSSs, including both type i (only some
of the open TSSs for a gene are significantly more (or less) accessible),
and type ii (multiple TSSs are significant different, with some being more
accessible and others less). Alternatively, this function will return all
open TSSs with differential measurements if the returnAllTSS flag is set to
TRUE.
getAltTSS( completeDAPs, returnAllTSS = FALSE, nuancedTSS = TRUE, nuancedTSSGap = 150, threshold = 0.2, TxDb, OrgDb )getAltTSS( completeDAPs, returnAllTSS = FALSE, nuancedTSS = TRUE, nuancedTSSGap = 150, threshold = 0.2, TxDb, OrgDb )
completeDAPs |
GRanges object that contains the differential measurements across all peaks (unfiltered DAPs). Will also work with data.frame or data.table version of a GRanges object. If you want alternatively regulated TSSs, the object must include a column names 'FDR', and 'Log2FC_C', which is standard for MOCHA differentials. |
returnAllTSS |
Flag to return all TSSs with DAPs measurements, without filtering for alternative TSS usage. If multiple TSSs fall within the same tile, then that tile will be repeated for each TSS. |
nuancedTSS |
True/False flag to determine if alternative TSS genes should be filtered out if all their differential TSS usage falls within too small of a range. Default is TRUE |
nuancedTSSGap |
Minimum distance betweeen TSSs needed for them to considered distinctly regulated TSSs. If two TSSs are too close, it is unclear and highly unlikely that ATAC data can distinguish between them. Default is 150 bp. |
threshold |
FDR Threshold for determining significant vs non-significant changes in accessibility. Following MOCHA's standards, default is 0.2. |
TxDb |
The TxDb-class transcript annotation package for your organism (e.g. "TxDb.Hsapiens.UCSC.hg38.refGene"). This must be installed. See Bioconductor AnnotationData Packages. |
OrgDb |
The OrgDb-class genome wide annotation package for your organism (e.g. "org.Hs.eg.db"). This must be installed. See Bioconductor AnnotationData Packages |
tpeaks A GRanges containing annotated peaks falling in TSS
getAnnotationDbFromInstalledPkgname Loads and attaches an installed TxDb or
OrgDb-class Annotation database package.See getBSgenome
getAnnotationDbFromInstalledPkgname(dbName, type)getAnnotationDbFromInstalledPkgname(dbName, type)
dbName |
Exact name of installed annotation data package. |
type |
Expected class of the annotation data package, must be either "OrgDb" or "TxDb". |
the loaded Annotation database object.#' @noRd
getCellPopMatrixgetCellPopMatrix pulls out the SampleTileMatrix of tiles
called in one given cell population.
getCellPopMatrix( SampleTileObj, cellPopulation, dropSamples = TRUE, NAtoZero = TRUE )getCellPopMatrix( SampleTileObj, cellPopulation, dropSamples = TRUE, NAtoZero = TRUE )
SampleTileObj |
The output from getSampleTileMatrix, a SummarizedExperiment of pseudobulk intensities across all tiles & cell types. |
cellPopulation |
The cell population you want to pull out. |
dropSamples |
Boolean flag to determine whether to drop samples that were too small for peak calling. |
NAtoZero |
Boolean flag to determine whether to replace NAs with zero |
sampleTileMatrix a matrix of samples by called tiles for a given cell population.
getCellTypes Extract cell type names from a Tile Results or Sample Tile object.getCellTypes Returns a vector of cell names from a Tile Results or Sample Tile object.
getCellTypes(object)getCellTypes(object)
object |
tileResults object from callOpenTiles or SummarizedExperiment from getSampleTileMatrix |
a vector of cell type names.
getCellTypeTiles Extract the GRanges for a particular cell typegetCellTypeTiles Returns a GRanges object of all tiles called for a certain cell type
getCellTypeTiles(object, cellType)getCellTypeTiles(object, cellType)
object |
A SampleTileObject. |
cellType |
A string describing one cell type. |
a vector of cell type names.
getCoAccessibleLinksgetCoAccessibleLinks takes an input set of regions (tiles) and finds co-accessible neighboring regions within a window. Co-accessibility is defined as the correlation between two region intensity (openness) across samples.
getCoAccessibleLinks( SampleTileObj, cellPopulation = "All", regions, chrChunks = 1, windowSize = 1 * 10^6, numCores = 1, ZI = TRUE, approximateTile = FALSE, verbose = FALSE )getCoAccessibleLinks( SampleTileObj, cellPopulation = "All", regions, chrChunks = 1, windowSize = 1 * 10^6, numCores = 1, ZI = TRUE, approximateTile = FALSE, verbose = FALSE )
SampleTileObj |
The SummarizedExperiment object output from getSampleTileMatrix containing your sample-tile matrices |
cellPopulation |
A string denoting the cell population of interest, which must be present in SampleTileObj |
regions |
a GRanges object or vector or strings containing the regions on which to compute co-accessible links. Strings must be in the format "chr:start-end", e.g. "chr4:1300-2222". Can be the output from getDifferentialAccessibleTiles. |
chrChunks |
This functions subsets by groups of chromosome, and then parallelizes within each group of chromosomes when running correlations. This method keeps memory low. To speed things up on high performing platforms, you can chunk out more than one chromosome at a time. Default is chrChunks = 1, so only one chromosome at a time. |
windowSize |
the size of the window, in basepairs, around each input region to search for co-accessible links |
numCores |
Optional, the number of cores to use with multiprocessing. Default is 1. |
ZI |
boolean flag that enables zero-inflated (ZI) Spearman correlations to be used. Default is TRUE. If FALSE, skip zero-inflation and calculate the normal Spearman. |
approximateTile |
If set to TRUE, it will use all tiles that overlap with the regions given, instead of finding an exact match to the regions variable. Default is FALSE. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
The technical details of the zero-inflated correlation can be found here:
Pimentel, Ronald Silva, "Kendall's Tau and Spearman's Rho for Zero-Inflated Data" (2009). Dissertations.
while the implementation (scHOT R package), can be found here: http://www.bioconductor.org/packages/release/bioc/html/scHOT.html
TileCorr A data.table correlation matrix
getCoverage takes the output of MOCHA::getPopFrags and returns a GRanges of singe-basepair resolution coverage.
getCoverage( popFrags, normFactor, TxDb, cl, filterEmpty = FALSE, verbose = FALSE )getCoverage( popFrags, normFactor, TxDb, cl, filterEmpty = FALSE, verbose = FALSE )
popFrags |
GRangesList of fragments for all sample/cell populations |
normFactor |
Normalization factor. Can be either be one, in which case all coverage files will be normalized by the same value, or the same length as the GRangesList |
TxDb |
The TxDb-class transcript annotation package for your organism (e.g. "TxDb.Hsapiens.UCSC.hg38.refGene"). This must be installed. See Bioconductor AnnotationData Packages. |
cl |
cl argument to |
filterEmpty |
True/False flag on whether or not to carry forward regions without coverage. |
verbose |
Boolean variable to determine verbosity of output. |
popCounts A GRangesList of coverage for each sample and cell population
getDifferentialAccessibleTilesgetDifferentialAccessibleTiles allows you to
determine whether regions of chromatin are differentially accessible
between groups by conducting a test
getDifferentialAccessibleTiles( SampleTileObj, cellPopulation, groupColumn, foreground, background, signalThreshold = 12, minZeroDiff = 0.5, fdrToDisplay = 0.2, outputGRanges = TRUE, numCores = 1, verbose = FALSE )getDifferentialAccessibleTiles( SampleTileObj, cellPopulation, groupColumn, foreground, background, signalThreshold = 12, minZeroDiff = 0.5, fdrToDisplay = 0.2, outputGRanges = TRUE, numCores = 1, verbose = FALSE )
SampleTileObj |
The SummarizedExperiment object output from getSampleTileMatrix |
cellPopulation |
A string denoting the cell population of interest |
groupColumn |
The column containing sample group labels |
foreground |
The foreground group of samples for differential comparison |
background |
The background group of samples for differential comparison |
signalThreshold |
Minimum median intensity required to keep tiles for differential testing to increase statistical power in small sample cohorts. Default is 12. |
minZeroDiff |
Minimum difference in average dropout rates across groups require to keep tiles for differential testing. Default is 0.5 (50%). |
fdrToDisplay |
False-discovery rate used only for standard output messaging. Default is 0.2. |
outputGRanges |
Outputs a GRanges if TRUE and a data.frame if FALSE. Default is TRUE. |
numCores |
The number of cores to use with multiprocessing. Default is 1. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
full_results The differential accessibility results as a GRanges or matrix data.frame depending on the flag 'outputGRanges'.
## Not run: cellPopulation <- "MAIT" foreground <- "Positive" background <- "Negative" # Standard output will display the number of tiles found below a false-discovery rate threshold. # This parameter does not filter results and only affects the aforementioned message. fdrToDisplay <- 0.2 # Choose to output a GRanges or data.frame. # Default is TRUE outputGRanges <- TRUE # SampleTileMatrices is the output of MOCHA::getSampleTileMatrix differentials <- MOCHA::getDifferentialAccessibleTiles( SampleTileObj = SampleTileMatrices, cellPopulation = cellPopulation, groupColumn = groupColumn, foreground = foreground, background = background, fdrToDisplay = fdrToDisplay, outputGRanges = outputGRanges, numCores = numCores ) ## End(Not run)## Not run: cellPopulation <- "MAIT" foreground <- "Positive" background <- "Negative" # Standard output will display the number of tiles found below a false-discovery rate threshold. # This parameter does not filter results and only affects the aforementioned message. fdrToDisplay <- 0.2 # Choose to output a GRanges or data.frame. # Default is TRUE outputGRanges <- TRUE # SampleTileMatrices is the output of MOCHA::getSampleTileMatrix differentials <- MOCHA::getDifferentialAccessibleTiles( SampleTileObj = SampleTileMatrices, cellPopulation = cellPopulation, groupColumn = groupColumn, foreground = foreground, background = background, fdrToDisplay = fdrToDisplay, outputGRanges = outputGRanges, numCores = numCores ) ## End(Not run)
getIntensityThresholdgetIntensityThreshold takes the output of peak calling with
callOpenTiles and creates sample-tile matrices containing the signal
intensity at each tile.
getIntensityThreshold( TSAM, cellPopulations = "all", type = "mean", returnPlots = TRUE, verbose = FALSE )getIntensityThreshold( TSAM, cellPopulations = "all", type = "mean", returnPlots = TRUE, verbose = FALSE )
TSAM |
a SummarizedExperiment object generated by MOCHA |
cellPopulations |
vector of strings. Cell subsets found in the TSAM, or the word 'All' if all should be used. |
type |
string. Describes the type of metric to be used. Options include median or mean. |
returnPlots |
Boolean. Default is TRUE and returns a plot of |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
plot object
'r lifecycle::badge("deprecated")'
This function is deprecated - improved modeling functions can be found in
the package "ChAI" at https://github.com/aifimmunology/ChAI
getModelValues Pull out a data.frame of model values (slope, significance, and std.error) for a given factor from the SummarizedExperiment output of runZIGLMM.
getModelValues(object, specificVariable)getModelValues(object, specificVariable)
object |
A SummarizedExperiment object generated from runZIGLMM. |
specificVariable |
A string, describing the factor of influence. |
A data.frame of slopes, significance, and standard error for one factor.
## Not run: age_df <- getModelValues(runZIGLMM_output, "Age") ## End(Not run)## Not run: age_df <- getModelValues(runZIGLMM_output, "Age") ## End(Not run)
getPopFrags returns a list of sample-specific fragments per cell population as a GRangesList.
getPopFrags( ArchRProj, cellPopLabel, cellSubsets = "ALL", poolSamples = FALSE, numCores = 1, verbose = FALSE )getPopFrags( ArchRProj, cellPopLabel, cellSubsets = "ALL", poolSamples = FALSE, numCores = 1, verbose = FALSE )
ArchRProj |
The ArchR Project. |
cellPopLabel |
The name of the metadata column of the ArchR Project that contains the populations of cells you want to extract fragments from. |
cellSubsets |
Default is 'ALL'. If you want to export only some populations, then give it a list of group names. This needs to be unique - no duplicated names. This list of group names must be identical to names that appear in the given cellPopLabel metadata column of the ArchR Project. |
poolSamples |
Set TRUE to pool sample-specific fragments by cell population. By default this is FALSE and sample-specific fragments are returned. |
numCores |
Number of cores to use. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
A list of GRanges containing fragments. Each GRanges corresponds to a population defined by cellSubsets and sample.
getPromoterGenesgetPromoterGenes Takes a rowRanges from annotateTiles and extracts a unique list of genes.
getPromoterGenes(GRangesObj)getPromoterGenes(GRangesObj)
GRangesObj |
a GRanges object with a metadata column for tileType and Gene. |
vector of strings with gene names.
getSampleCellTypeMetadata Extract Sample-celltype specific metadatagetSampleCellTypeMetadata Extract Sample-celltype specific metadata like fragment number, cell counts, and
getSampleCellTypeMetadata(object)getSampleCellTypeMetadata(object)
object |
tileResults object from callOpenTiles or SummarizedExperiment from getSampleTileMatrix |
a SummarizedExperiment where each assay is a different type of metadata.
getSampleTileMatrixgetSampleTileMatrix takes the output of peak calling with
callOpenTiles and creates sample-tile matrices containing the signal
intensity at each tile.
getSampleTileMatrix( tileResults, cellPopulations = "ALL", groupColumn = NULL, threshold = 0.2, numCores = 1, verbose = FALSE )getSampleTileMatrix( tileResults, cellPopulations = "ALL", groupColumn = NULL, threshold = 0.2, numCores = 1, verbose = FALSE )
tileResults |
a MultiAssayExperiment returned by callOpenTiles containing containing peak calling results. |
cellPopulations |
vector of strings. Cell subsets in TileResults for which to generate sample-tile matrices. This list of group names must be identical to names that appear in the ArchRProject metadata. If cellPopulations='ALL', then peak calling is done on all cell populations in the ArchR project metadata. Default is 'ALL'. |
groupColumn |
Optional, the column containing sample group labels for determining consensus tiles within sample groups. Default is NULL, all samples will be used for determining consensus tiles. |
threshold |
Threshold for consensus tiles, the minimum % of samples (within a sample group, if groupColumn is set) that a peak must be called in to be retained. If set to 0, retain the union of all samples' peaks (this is equivalent to a threshold of 1/numSamples). It is recommended to tune this parameter to omit potentially spurious peaks. |
numCores |
Optional, the number of cores to use with multiprocessing. Default is 1. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
SampleTileMatrices a MultiAssayExperiment containing a sample-tile intensity matrix for each cell population
# Starting from GRangesList if ( require(BSgenome.Hsapiens.UCSC.hg19) && require(TxDb.Hsapiens.UCSC.hg38.refGene) && require(org.Hs.eg.db) ) { tiles <- MOCHA::callOpenTiles( ATACFragments = MOCHA::exampleFragments, cellColData = MOCHA::exampleCellColData, blackList = MOCHA::exampleBlackList, genome = "BSgenome.Hsapiens.UCSC.hg19", TxDb = "TxDb.Hsapiens.UCSC.hg38.refGene", Org = "org.Hs.eg.db", outDir = tempdir(), cellPopLabel = "Clusters", cellPopulations = c("C2", "C5"), numCores = 1 ) SampleTileMatrices <- MOCHA::getSampleTileMatrix( tiles, cellPopulations = c("C2", "C5"), threshold = 0 # Take union of all samples' open tiles ) }# Starting from GRangesList if ( require(BSgenome.Hsapiens.UCSC.hg19) && require(TxDb.Hsapiens.UCSC.hg38.refGene) && require(org.Hs.eg.db) ) { tiles <- MOCHA::callOpenTiles( ATACFragments = MOCHA::exampleFragments, cellColData = MOCHA::exampleCellColData, blackList = MOCHA::exampleBlackList, genome = "BSgenome.Hsapiens.UCSC.hg19", TxDb = "TxDb.Hsapiens.UCSC.hg38.refGene", Org = "org.Hs.eg.db", outDir = tempdir(), cellPopLabel = "Clusters", cellPopulations = c("C2", "C5"), numCores = 1 ) SampleTileMatrices <- MOCHA::getSampleTileMatrix( tiles, cellPopulations = c("C2", "C5"), threshold = 0 # Take union of all samples' open tiles ) }
getSequencingBiasgetSequencingBias takes the output of peak calling with
callOpenTiles and creates sample-tile matrices containing the signal
intensity at each tile.
getSequencingBias( SampleTileObj, cellPopulations = "all", cellPopulation, groupColumn, foreground, background, verbose = TRUE )getSequencingBias( SampleTileObj, cellPopulations = "all", cellPopulation, groupColumn, foreground, background, verbose = TRUE )
SampleTileObj |
a SummarizedExperiment object generated by MOCHA |
cellPopulations |
vector of strings. Cell subsets found in the TSAM, or the word 'All' if all should be used. |
cellPopulation |
A string denoting the cell population of interest |
groupColumn |
The column containing sample group labels |
foreground |
The foreground group of samples for differential comparison |
background |
The background group of samples for differential comparison |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
plot object
GRangesToString Converts a GRanges object to a string in the format 'chr1:100-200'GRangesToString Turns a GRanges Object into
a list of strings in the format chr1:100-200
GRangesToString(GR_obj)GRangesToString(GR_obj)
GR_obj |
the GRanges object to convert to a string |
A string or list of strings in the format 'chr1:100-200' representing ranges in the input GRanges
mergeTileResultsmergeTileResults merges a list of tileResults that
each contain unique samples into a single object encompassing all samples.
Only cell populations shared among all input tileResults will be retained.
This function can merge MultiAssayExperiment objects from callOpenTiles
that are created with the same TxDb, OrgDb, and Genome assembly.
mergeTileResults(tileResultsList, numCores = 1, verbose = TRUE)mergeTileResults(tileResultsList, numCores = 1, verbose = TRUE)
tileResultsList |
List of MultiAssayExperiments objects returned by callOpenTiles containing containing peak calling results. |
numCores |
Optional, the number of cores to use with multiprocessing. Default is 1. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
tileResults a single MultiAssayExperiment containing a sample-tile intensity matrix for each sample and common cell population in the input tileResultsList.
## Not run: # Depends on local MOCHA tileResults MOCHA::mergeTileResults( list(tileResultsCelltypesABC, tileResultsCelltypesBCD) ) ## End(Not run)## Not run: # Depends on local MOCHA tileResults MOCHA::mergeTileResults( list(tileResultsCelltypesABC, tileResultsCelltypesBCD) ) ## End(Not run)
MotifEnrichmentTest for enrichment of motifs within Group1 against a background Group2 using a hypergeometric t-test.
MotifEnrichment(Group1, Group2, motifPosList, type = NULL)MotifEnrichment(Group1, Group2, motifPosList, type = NULL)
Group1 |
A GRanges object, such as a set of significant differential tiles. |
Group2 |
A GRanges object containing background regions, non-overlapping with Group1 |
motifPosList |
A GRangesList of motifs and positions for each motif. Must be named for each motif. |
type |
Optional, name of a metadata column in Group1 and Group2 to test for enrichment the number of unique entries in column given by 'type'. Default is NULL, which tests the number of Ranges. |
A data.frame containing enrichment for each group
MotifSetEnrichmentAnalysisThis analogous to Gene Set Enrichment Analysis. Instead of testing for enrichment of a geneset with a given gene set in a pathway, we are testing the enrichment of a given TF motif set against a motif set downstream of a multiple ligands. If there is enrichment, it's a sign that that ligand could drive that set of motifs.
MotifSetEnrichmentAnalysis( ligandTFMatrix, motifEnrichmentDF, motifColumn, ligands, statColumn, statThreshold, annotationName = "CellType", annotation = "none", numCores = 1, verbose = FALSE )MotifSetEnrichmentAnalysis( ligandTFMatrix, motifEnrichmentDF, motifColumn, ligands, statColumn, statThreshold, annotationName = "CellType", annotation = "none", numCores = 1, verbose = FALSE )
ligandTFMatrix |
NicheNet Ligand-TF matrix |
motifEnrichmentDF |
Dataframe (unfiltered) from ArchR's peakAnnoEnrich step. Expected to have a column with motif names, and a column with the -log10 adjusted p-values. |
motifColumn |
Column name within the motifEnrichmentDF that has motif names. |
ligands |
Vector of ligands to test |
statColumn |
Column name in motifEnrichmentDF containing the statistic to test |
statThreshold |
Significance threshold used to select significant motif set |
annotationName |
Optional column name for the annotation. Default is "CellType". |
annotation |
Optional annotation value added to all rows of the output motif dataframe. Can be character vector or numeric. Default is "none". |
numCores |
The number of cores to use with multiprocessing. Default is 1. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
specDF A dataframe containing enrichment analysis results
packMOCHApackMOCHA combines a MOCHA object (Sample-Tile
Matrix or tileResults) with its saved coverage tracks into a single zip
archive. This allows MOCHA objects and the necessary coverage files for
plotting to be shared to other file systems. See also:
unpackMOCHA
packMOCHA(MOCHAObj, zipfile, verbose = FALSE)packMOCHA(MOCHAObj, zipfile, verbose = FALSE)
MOCHAObj |
A MultiAssayExperiment or RangedSummarizedExperiment, from MOCHA |
zipfile |
Filename and path of the zip archive. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
zipfile Path to zip archive.
## Not run: # Depends on and manipulates files on filesystem myOutputDir <- "/home/documents/MOCHA_out" zipPath <- MOCHA::packMOCHA( tileResults, zipfile = file.path(myOutputDir, "testzip.zip") ) ## End(Not run)## Not run: # Depends on and manipulates files on filesystem myOutputDir <- "/home/documents/MOCHA_out" zipPath <- MOCHA::packMOCHA( tileResults, zipfile = file.path(myOutputDir, "testzip.zip") ) ## End(Not run)
'r lifecycle::badge("deprecated")'
This function is deprecated - improved modeling functions can be found in
the package "ChAI" at https://github.com/aifimmunology/ChAI
pilotLMEM Runs linear mixed-effects modeling for
continuous, non-zero inflated data using lmer
pilotLMEM( ExperimentObj, assayName, modelFormula, pilotIndices = 1:10, verbose = FALSE )pilotLMEM( ExperimentObj, assayName, modelFormula, pilotIndices = 1:10, verbose = FALSE )
ExperimentObj |
A SummarizedExperiment-type object generated from chromVAR, makePseudobulkRNA, or other. Objects from getSampleTileMatrix can work, but we recommend runZIGLMM for those objects, not runLMEM> |
assayName |
a character string, matching the name of an assay within the SummarizedExperiment. The assay named will be used for modeling. |
modelFormula |
The formula to use with lmerTest::lmer, in the format (exp ~ factors). All factors must be found in column names of the ExperimentObj metadata. |
pilotIndices |
A vector of integers defining the subset of the ExperimentObj matrix. Default is 1:10. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
modelList a list of outputs from lmerTest::lmer
'r lifecycle::badge("deprecated")'
This function is deprecated - improved modeling functions can be found in
the package "ChAI" at https://github.com/aifimmunology/ChAI
pilotLMEM Runs linear mixed-effects modeling for zero
inflated data using glmmTMB. TryCatch will catch
errors, and return the error and dataframe for troubleshooting.
pilotZIGLMM( TSAM_Object, cellPopulation = NULL, continuousFormula = NULL, ziformula = NULL, zi_threshold = 0, verbose = FALSE, pilotIndices = 1:10 )pilotZIGLMM( TSAM_Object, cellPopulation = NULL, continuousFormula = NULL, ziformula = NULL, zi_threshold = 0, verbose = FALSE, pilotIndices = 1:10 )
TSAM_Object |
A SummarizedExperiment object generated from getSampleTileMatrix, chromVAR, or other. |
cellPopulation |
A single cell population on which to run this pilot model |
continuousFormula |
The formula, see |
ziformula |
The zero-inflated formula, see
|
zi_threshold |
Zero-inflated threshold (range = 0-1), representing the fraction of samples with zeros. When the percentage of zeros in the tile is between 0 and zi_threshold, samples with zeroes are dropped and only the continous formula is used. Use this parameter at your own risk. Default is 0. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
pilotIndices |
A vector of integers defining the subset of the ExperimentObj matrix. Default is 1:10. |
modelList a list of outputs from glmmTMB::glmmTMB
plotConsensusplotConsensus Extracts the peak reproducibility and generates a
heuristic plots that can be used to determine the reproducibility threshold
used within getSampleTileMatrix.
plotConsensus( tileObject, cellPopulations = "All", groupColumn = NULL, returnPlotList = FALSE, returnDFs = FALSE, numCores = 1 )plotConsensus( tileObject, cellPopulations = "All", groupColumn = NULL, returnPlotList = FALSE, returnDFs = FALSE, numCores = 1 )
tileObject |
A MultiAssayExperiment object from callOpenTiles, |
cellPopulations |
the cell populations you want to visualize. |
groupColumn |
Optional parameter, same as in getSampleTileMatrix, which defines whether you want to plot reproducibility within each |
returnPlotList |
Instead of one plot with all celltypes/conditions, it returns a list of plots for each cell types |
returnDFs |
Instead of a plot, returns a data.frame of the reproducibility across samples. If set to false, then it plots the data.frame instead of returning it. |
numCores |
Number of cores to multithread over. |
SampleTileObj the input data structure with added gene annotations.
plotIntensityDistributionplotIntensityDistribution Plots the distribution of sample-tile intensities for a give cell type
plotIntensityDistribution Plots the distribution of sample-tile intensities for a give cell type
plotIntensityDistribution( TSAM_object, cellPopulation, returnDF = FALSE, density = TRUE ) plotIntensityDistribution( TSAM_object, cellPopulation, returnDF = FALSE, density = TRUE )plotIntensityDistribution( TSAM_object, cellPopulation, returnDF = FALSE, density = TRUE ) plotIntensityDistribution( TSAM_object, cellPopulation, returnDF = FALSE, density = TRUE )
TSAM_object |
SummarizedExperiment from getSampleTileMatrix |
cellPopulation |
Cell type names (assay name) within the TSAM_object |
returnDF |
If TRUE, return the data frame without plotting. Default is FALSE. |
density |
Boolean to determine whether to plot density or histogram. Default is TRUE (plots density). |
data.frame or ggplot histogram.
data.frame or ggplot histogram.
plotRegionplotRegion Plots the region that you've summarized across
all cell groupings (groups=initial getPopFrags() split) with optional motif
overlay, chromosome position ideogram, and additional GRanges tracks. If
plotting motif overlay, ensure that motif annotations have been added to
your counts SummarizedExperiment. A basic plot can be rendered with just a
counts SummarizedExperiment, but additional formatting arguments allow for
further customization. Note that to show specific genes with the option
'whichGenes' the RMariaDB package must be installed.
plotRegion( countSE, plotType = "area", base_size = 12, counts_color = NULL, range_label_size = 2, legend.position = NULL, legendRatio = 0.25, facet_label_side = "top", counts_color_var = "Groups", counts_group_colors = NULL, counts_theme_ls = NULL, motifSetName = NULL, motif_y_space_factor = 4, motif_stagger_labels_y = FALSE, motif_weights = NULL, motif_weight_name = "Motif Weight", motif_weight_colors = c(darkblue = -10, gray = 0, darkred = 10), motif_lab_size = 1, motif_lab_alpha = 0.25, motif_line_alpha = 0.25, motif_line_size = 0.75, showGene = TRUE, whichGenes = NULL, monotoneGenes = FALSE, db_id_col = "REFSEQ", collapseGenes = FALSE, gene_theme_ls = NULL, additionalGRangesTrack = NULL, linkdf = NULL, showIdeogram = TRUE, ideogram_genome = "hg19", relativeHeights = c(Chr = 0.9, `Normalized Counts` = 7, Links = 1.5, Genes = 2, AdditionalGRanges = 4.5), verbose = FALSE )plotRegion( countSE, plotType = "area", base_size = 12, counts_color = NULL, range_label_size = 2, legend.position = NULL, legendRatio = 0.25, facet_label_side = "top", counts_color_var = "Groups", counts_group_colors = NULL, counts_theme_ls = NULL, motifSetName = NULL, motif_y_space_factor = 4, motif_stagger_labels_y = FALSE, motif_weights = NULL, motif_weight_name = "Motif Weight", motif_weight_colors = c(darkblue = -10, gray = 0, darkred = 10), motif_lab_size = 1, motif_lab_alpha = 0.25, motif_line_alpha = 0.25, motif_line_size = 0.75, showGene = TRUE, whichGenes = NULL, monotoneGenes = FALSE, db_id_col = "REFSEQ", collapseGenes = FALSE, gene_theme_ls = NULL, additionalGRangesTrack = NULL, linkdf = NULL, showIdeogram = TRUE, ideogram_genome = "hg19", relativeHeights = c(Chr = 0.9, `Normalized Counts` = 7, Links = 1.5, Genes = 2, AdditionalGRanges = 4.5), verbose = FALSE )
countSE |
A SummarizedExperiment from MOCHA::getCoverage |
plotType |
Options include 'overlaid','area', 'line', or 'RidgePlot'. default is 'area', which will plot a separate track for each group with the area filled in under the curve. Setting plotType to 'overlaid' will overlay count plot histograms across samples, instead of faceting out separately. Setting plotType to 'RidgePlot' will generate a RidgePlot across all groups. |
base_size |
Numeric, default 12. Global plot base text size parameter |
counts_color |
Optional color palette. A named vector of color values where names are unique values in the 'color_var' column |
range_label_size |
Numeric value, default 4. Text size for the y-axis range label |
legend.position |
Any acceptable 'legend.position' argument to theme(). Default NULL will place legend for overlaid plots at (0.8,0.8), or to the "right" for faceted plots. |
legendRatio |
Ratio of width or height of the main plot to the legend. Useful if the legend is to large. If only used when legend.position is set to top, bottom, left, or right. |
facet_label_side |
Direction character value, default "top". Can also be "right", "left", or "bottom". Position of facet label. |
counts_color_var |
Character value, default "Groups". Column name from countdf to use to color counts plots. Only used if counts_group_colors provided |
counts_group_colors |
Optional named color vector. Values as colors, names are levels of 'counts_color_var'. If provided, will color the plots specifically using 'scale_color_manual()' |
counts_theme_ls |
A list of named theme arguments passed to theme(). For example, 'list(axis.ticks = element_blank())'. Default NULL will use '.counts_plot_default_theme'. |
motifSetName |
The name of the motif set in ArchRProj to use for annotation. Example: 'JasparMotifs' |
motif_y_space_factor |
A factor for vertical spacing between motif labels. Default 4. Increase to make labels farther apart, decrease to make labels closer. |
motif_stagger_labels_y |
= FALSE Logical value, default FALSE. If TRUE, will stagger motif labels in adjacent columns in the vertical direction |
motif_weights |
Optional numeric vector, default NULL. If provided will be used to color motif labels by the weighted values |
motif_weight_name |
Character value, default "Motif Weight". Used to label the legend for motif colors |
motif_weight_colors |
Named numeric vector. Names should be color values and breaks should be the corresponding values of motif_weights. Values outside the highest and lowest value will appear as max or min defined color value. |
motif_lab_size |
Numeric value, default 1. Size of motif labels. |
motif_lab_alpha |
Numeric value, default 0.25. Alpha for motif labels. |
motif_line_alpha |
Numeric value, default 0.25. Alpha for motif lines. |
motif_line_size |
Numeric value, default 1. Size of motif lines. |
showGene |
Logical value, default TRUE. Whether or not the gene track should be plotted. |
whichGenes |
Name of gene for plotting this specific gene in region. |
monotoneGenes |
Boolean. Determines whether to color-code genes by gene name, or to set them all to dark gray. |
db_id_col |
Character value. Column in 'OrgDb' containing the output id for 'whichGenes' plotting. Default "REFSEQ". |
collapseGenes |
Options include 'collapseAll', 'longestTx', or 'None' Default 'None' will plot the expanded view of the reference genes, 'collapseAll' if you want collapse the gene tracks into one, and 'longestTx' will only plot the longest transcript of each gene. |
gene_theme_ls |
Named list of parameters passed to 'theme()' for the gene plot. Default NULL will use '.gene_plot_theme' |
additionalGRangesTrack |
A GRanges object containing additional track plot data |
linkdf |
A dataframe with co-accessible links to display as an additional track |
showIdeogram |
Logical value, default TRUE. If TRUE plots the chromosome ideogram at the top of the multi-track plot |
ideogram_genome |
Character value, a genome name for the ideogram plot. Default 'hg19'. |
relativeHeights |
Named numeric vector of relative heights for each of the 4 track plots to enable clean visualization when there are many tracks. Unused tracks will be ignored. Default value = c('Chr' = 0.9, 'Normalized Counts' = 7, 'Genes'= 2, 'AdditionalGRanges' = 4.5) |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
The input ggplot object with motif labels overlaid
## Not run: # my_count_SE is a counts data frame generated by extractRegion() # Simple counts + ideogram + all genes: plotRegion(countSE = my_count_SE) # Motif overlay for a project my_proj containing "JasparMotifs" annotations: plotRegion( countSE = my_count_SE, motifSetName = "JasparMotifs", motif_lab_alpha = 1, motif_line_alpha = 1 ) # Motif overlay w/ weights: plotRegion( countSE = my_count_SE, motifSetName = "JasparMotifs", motif_lab_alpha = 1, motif_line_alpha = 1, motif_weights = my_enrichment_weights ) ## End(Not run)## Not run: # my_count_SE is a counts data frame generated by extractRegion() # Simple counts + ideogram + all genes: plotRegion(countSE = my_count_SE) # Motif overlay for a project my_proj containing "JasparMotifs" annotations: plotRegion( countSE = my_count_SE, motifSetName = "JasparMotifs", motif_lab_alpha = 1, motif_line_alpha = 1 ) # Motif overlay w/ weights: plotRegion( countSE = my_count_SE, motifSetName = "JasparMotifs", motif_lab_alpha = 1, motif_line_alpha = 1, motif_weights = my_enrichment_weights ) ## End(Not run)
renameCellTypesrenameCellTypes Allows you to modify the cell type names for a MOCHA SampleTileObject, from
the assay names, GRanges column names, and summarizedData (within the metadata), all at once.
renameCellTypes(MOCHAObject, oldNames, newNames)renameCellTypes(MOCHAObject, oldNames, newNames)
MOCHAObject |
A RangedSummarizedExperiment, |
oldNames |
A list of cell type names that you want to change. |
newNames |
A list of new cell type names to replace the old names with. |
A MOCHA SampleTile object with new cell types.
runLMEM Runs linear mixed-effects modeling for
continuous, non-zero inflated data using lmer
runLMEM( ExperimentObj, assayName, modelFormula, initialSampling = 5, verbose = FALSE, numCores = 2 )runLMEM( ExperimentObj, assayName, modelFormula, initialSampling = 5, verbose = FALSE, numCores = 2 )
ExperimentObj |
A SummarizedExperiment object generated from getSampleTileMatrix, chromVAR, or other. It is expected to contain only one assay, or only the first assay will be used for the model. Data should not be zero-inflated. |
assayName |
The name of the assay to model within the SummarizedExperiment. |
modelFormula |
The formula to use with lmerTest::lmer, in the format (exp ~ factors). All factors must be found in column names of the ExperimentObj metadata. modelFormula must start with 'exp' as the response. See lmer. |
initialSampling |
Size of data to use for pilot |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
numCores |
integer. Number of cores to parallelize across. |
results a SummarizedExperiment containing LMEM results. Assays are metrics related to the model coefficients, including the Estimate, Std_Error, df, t_value, p_value. Within each assay, each row corresponds to each row of the SummarizedExperiment and columns correspond to each fixed effect variable within the model. Any row metadata from the ExperimentObject (see rowData(ExperimentObj)) is preserved in the output. The Residual matrix and the variance of the random effects are saved in the metadata slot of the output.
## Not run: modelList <- runLMEM(ExperimentObj, assayName = names(ExperimentObj)[[1]] modelFormula = NULL, initialSampling = 5, verbose = FALSE, numCores = 1 ) ## End(Not run)## Not run: modelList <- runLMEM(ExperimentObj, assayName = names(ExperimentObj)[[1]] modelFormula = NULL, initialSampling = 5, verbose = FALSE, numCores = 1 ) ## End(Not run)
'r lifecycle::badge("deprecated")'
This function is deprecated - improved modeling functions can be found in
the package "ChAI" at https://github.com/aifimmunology/ChAI
runZIGLMM Runs linear mixed-effects modeling for
zero-inflated data using glmmTMB.
runZIGLMM( TSAM_Object, cellPopulation = "all", continuousFormula = NULL, ziformula = NULL, zi_threshold = 0, initialSampling = 5, verbose = FALSE, numCores = 1 )runZIGLMM( TSAM_Object, cellPopulation = "all", continuousFormula = NULL, ziformula = NULL, zi_threshold = 0, initialSampling = 5, verbose = FALSE, numCores = 1 )
TSAM_Object |
A SummarizedExperiment object generated from getSampleTileMatrix. |
cellPopulation |
Name of a cell type(s), or 'all'. The function will combine the cell types mentioned into one matrix before running the model. |
continuousFormula |
The formula for the continuous data that should be used within glmmTMB. It should be in the format (exp ~ factors). All factors must be found in column names of the TSAM_Object metadata, except for CellType, FragNumber and CellCount, which will be extracted from the TSAM_Object. modelFormula must start with 'exp' as the response. See glmmTMB. |
ziformula |
The formula for the zero-inflated data that should be used within glmmTMB. It should be in the format ( ~ factors). All factors must be found in column names of the TSAM_Object colData metadata, except for CellType, FragNumber and CellCount, which will be extracted from the TSAM_Object. |
zi_threshold |
Zero-inflated threshold ( range = 0-1), representing the fraction of samples with zeros. When the percentage of zeros in the tile is between 0 and zi_threshold, samples with zeroes are dropped and only the continous formula is used. Use this parameter at your own risk. Default is 0. |
initialSampling |
Size of data to use for pilot |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
numCores |
integer. Number of cores to parallelize across. |
results a SummarizedExperiment containing LMEM results
## Not run: modelList <- runZIGLMM(STM[c(1:1000), ], cellPopulation = "CD16 Mono", continuousFormula = exp ~ Age + Sex + days_since_symptoms + (1 | PTID), ziformula = ~ FragNumber + Age, verbose = TRUE, numCores = 35 ) ## End(Not run)## Not run: modelList <- runZIGLMM(STM[c(1:1000), ], cellPopulation = "CD16 Mono", continuousFormula = exp ~ Age + Sex + days_since_symptoms + (1 | PTID), ziformula = ~ FragNumber + Age, verbose = TRUE, numCores = 35 ) ## End(Not run)
StringsToGRangesStringsToGRanges Turns a list of strings in the format chr1:100-200
into a GRanges object
StringsToGRanges(regionString)StringsToGRanges(regionString)
regionString |
A string or list of strings each in the format chr1:100-200 |
a GRanges object with ranges representing the input string(s)
subsetMOCHAObjectsubsetMOCHAObject subsets a tileResults-type object (from
callOpenTiles), or a SummarizedExperiment-type object (from
getSampleTileMatrix), either by cell type or sample metadata.
subsetMOCHAObject( Object, subsetBy, groupList, removeNA = TRUE, subsetPeaks = TRUE, verbose = FALSE )subsetMOCHAObject( Object, subsetBy, groupList, removeNA = TRUE, subsetPeaks = TRUE, verbose = FALSE )
Object |
A MultiAssayExperiment or RangedSummarizedExperiment, |
subsetBy |
The variable to subset by. Can either be 'celltype', or a column from the sample metadata (see 'colData(Object)'). |
groupList |
the list of cell type names or sample-associated data that should be used to subset the Object |
removeNA |
If TRUE, removes groups in groupList that are NA. If FALSE, keep groups that are NA. |
subsetPeaks |
If ‘subsetBy' = ’celltype', subset the tile set to tiles only called in those cell types. Default is TRUE. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
Object the input Object, filtered down to either the cell type or samples desired.
testCoAccessibilitytestCoAccessibility takes an input set of tile
pairs and tests whether they are significantly different compared to
random, non-overlapping background set.
testCoAccessibility( SampleTileObj, tile1, tile2, numCores = 1, ZI = TRUE, backNumber = 1000, calcPValue = TRUE, returnBackGround = FALSE, verbose = TRUE )testCoAccessibility( SampleTileObj, tile1, tile2, numCores = 1, ZI = TRUE, backNumber = 1000, calcPValue = TRUE, returnBackGround = FALSE, verbose = TRUE )
SampleTileObj |
The SummarizedExperiment object output from getSampleTileMatrix containing your sample-tile matrices |
tile1 |
vector of indices or tile names (chrX:100-2000) for tile pairs to test (first tile in each pair) |
tile2 |
vector of indices or tile names (chrX:100-2000) for tile pairs to test (second tile in each pair) |
numCores |
Optional, the number of cores to use with multiprocessing. Default is 1. |
ZI |
boolean flag that enables zero-inflated (ZI) Spearman correlations to be used. Default is TRUE. If FALSE, skip zero-inflation and calculate the normal Spearman. |
backNumber |
number of background pairs. Default is 1000. |
calcPValue |
Boolean, if TRUE calculate p-values. Default is TRUE. |
returnBackGround |
Boolean, if TRUE return the background correlations as well as foreground. Default is FALSE. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
foreGround A data.frame with Tile1, Tile2, Correlation, and p-value for that correlation compared to the background
unpackMOCHAunpackMOCHA will unpack a zip archive created by
unpackMOCHA, setting the stored MOCHA object's stored
directory path to the new location. See also: packMOCHA
unpackMOCHA(zipfile, exdir, verbose = FALSE)unpackMOCHA(zipfile, exdir, verbose = FALSE)
zipfile |
Filepath to the packed MOCHA object. |
exdir |
The path to the external directory where you want to unpack the MOCHA object. |
verbose |
Display additional messages. Default is FALSE. |
MOCHAObj the MOCHA object (tileResults or Sample-Tile Matrix)
## Not run: # Depends on files existing on your system MOCHA::unpackMOCHA(zipfile = "./mochaobj.zip", exdir = "./newMOCHAdir") ## End(Not run)## Not run: # Depends on files existing on your system MOCHA::unpackMOCHA(zipfile = "./mochaobj.zip", exdir = "./newMOCHAdir") ## End(Not run)
'r lifecycle::badge("deprecated")'
This function is deprecated - improved modeling functions can be found in
the package "ChAI" at https://github.com/aifimmunology/ChAI
varZIGLMM Identified variance decomposition on a given
cell type across both zero-inflated and continuous space using a
zero-inflated general linear mixed model glmmTMB
varZIGLMM( TSAM_Object, cellPopulation = NULL, continuousRandom = NULL, ziRandom = NULL, zi_threshold = 0.1, verbose = FALSE, numCores = 1 )varZIGLMM( TSAM_Object, cellPopulation = NULL, continuousRandom = NULL, ziRandom = NULL, zi_threshold = 0.1, verbose = FALSE, numCores = 1 )
TSAM_Object |
A SummarizedExperiment object generated from getSampleTileMatrix. |
cellPopulation |
Name of a cell type(s), or 'all'. The function will combine the cell types mentioned into one matrix before running the model. |
continuousRandom |
Random effects to test in the continuous portion. All factors must be found in column names of the TSAM_Object metadata, except for FragNumber and CellCount, which will be extracted from the TSAM_Object's metadata. |
ziRandom |
Random effects to test in the zero-inflated portion. All factors must be found in column names of the TSAM_Object colData metadata, except for FragNumber and CellCount, which will be extracted from the TSAM_Object's metadata. |
zi_threshold |
Zero-inflated threshold ( range = 0-1), representing the fraction of samples with zeros. When the percentage of zeros in the tile is between 0 and zi_threshold, samples with zeroes are dropped and only the continous formula is used. Use this parameter at your own risk. Default is 0. |
verbose |
Set TRUE to display additional messages. Default is FALSE. |
numCores |
integer. Number of cores to parallelize across. |
results a SummarizedExperiment containing results from ZIGLMM (Fixed effect estiamtes, P-values, and Std Error)
## Not run: modelList <- runZIGLMM(STM[c(1:1000), ], cellPopulation = "CD16 Mono", continuousRandom = c("Age", "Sex", "Days"), ziRandom = c("FragNumber", "Days"), verbose = TRUE, numCores = 35 ) ## End(Not run)## Not run: modelList <- runZIGLMM(STM[c(1:1000), ], cellPopulation = "CD16 Mono", continuousRandom = c("Age", "Sex", "Days"), ziRandom = c("FragNumber", "Days"), verbose = TRUE, numCores = 35 ) ## End(Not run)
Trained regression model for predicting a cutoff threshold for peak calling. Call: loess(formula = OptimalCutpoint ~ Ncells, data = thresh_df)
youden_thresholdyouden_threshold
A list of 18 regression variables
Number of Observations: 27 Equivalent Number of Parameters: 5.98 Residual Standard Error: 0.02121