Modeling for Single-Cell Open Chromatin Analysis
MotifEnrichment
addAccessibilityShift
addMotifSet
annotateTiles
bulkDimReduction
bulkUMAP
callOpenTiles Perform peak-calling on a set of fragments or an ArchR...
combineSampleTileMatrix
differentialsToGRanges Converts a data.frame matrix to a GRanges, pr...
Default ggplot theme for counts plot
Common theme for gene plots
exportCoverage
exportDifferentials
exportMotifs
exportOpenTiles
exportSmoothedInsertions
extractErrorFromConsensusTiles
extractRegion
filterCoAccessibleLinks
Annotate Peaks falling in Transcription Start Sites (TSS) and identify...
getAnnotationDbFromInstalledPkgname Loads and attaches an installed ...
getCellPopMatrix
getCellTypes Extract cell type names from a Tile Results or Sample T...
getCellTypeTiles Extract the GRanges for a particular cell type
getCoAccessibleLinks
Get sample-specific coverage files for each sample-cell population.
getDifferentialAccessibleTiles
getIntensityThreshold
getModelValues from runZIGLMM output.
Extract fragments by populations from an ArchR Project
getPromoterGenes
getSampleCellTypeMetadata Extract Sample-celltype specific metadata
getSampleTileMatrix
getSequencingBias
GRangesToString Converts a GRanges object to a string in the format ...
mergeTileResults
MotifSetEnrichmentAnalysis
packMOCHA
Execute a pilot run of single linear model on a subset of data
Execute a pilot run of model on a subset of data
plotConsensus
plotIntensityDistribution
plotRegion
renameCellTypes
Run Linear Mixed-Effects Modeling for continuous, non-zero inflated da...
Run Zero-inflated Generalized Linear Mixed Modeling on pseudobulked sc...
simplifiedConsensusTiles
singlePopulationConsensusTiles
singlePopulationSampleTileMatrix
StringsToGRanges
subsetMOCHAObject
testCoAccessibility
testCoAccessibilityChromVar
testCoAccessibilityRandom
unpackMOCHA
Zero-inflated Variance Decomposition for pseudobulked scATAC data
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/>.