getExprGeneNames: Get list of expressed genes for each assay

In GabrielHoffman/dreamlet: Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs

Get list of expressed genes for each assay

Description

Get list of expressed genes for each assay using same filters as processAssays().

Usage

getExprGeneNames(
  sceObj,
  assays = assayNames(sceObj),
  min.cells = 5,
  min.count = 5,
  min.samples = 4,
  min.prop = 0.4,
  min.total.count = 15,
  normalize.method = "TMM"
)

Arguments

sceObj	SingleCellExperiment object
assays	array of assay names to include in analysis. Defaults to assayNames(sceObj)
min.cells	minimum number of observed cells for a sample to be included in the analysis
min.count	minimum number of reads for a gene to be considered expressed in a sample. Passed to edgeR::filterByExpr
min.samples	minimum number of samples passing cutoffs for cell cluster to be retained
min.prop	minimum proportion of retained samples with non-zero counts for a gene to be retained
min.total.count	minimum total count required per gene for inclusion
normalize.method	normalization method to be used by calcNormFactors

Examples

library(muscat)

data(example_sce)

# create pseudobulk for each sample and cell cluster
pb <- aggregateToPseudoBulk(example_sce,
  assay = "counts",
  sample_id = "sample_id",
  cluster_id = "cluster_id",
  verbose = FALSE
)

# Gene expressed genes for each cell type
geneList = getExprGeneNames(pb)

# Create precision weights for pseudobulk
# By default, weights are set to cell count,
# which is the default in processAssays()
# even when no weights are specified
weightsList <- pbWeights(example_sce,
  sample_id = "sample_id",
  cluster_id = "cluster_id",
  geneList = geneList
)

# voom-style normalization using initial weights
res.proc <- processAssays(pb, ~group_id, weightsList = weightsList)

GabrielHoffman/dreamlet documentation built on May 20, 2024, 2:05 p.m.