processOneAssay: Processing expression data from assay

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

Processing expression data from assay

Description

For raw counts, filter genes and samples, then estimate precision weights using linear mixed model weighting by number of cells observed for each sample. For normalized data, only weight by number of cells

Usage

processOneAssay(
  y,
  formula,
  data,
  n.cells,
  min.cells = 5,
  min.count = 2,
  min.samples = 4,
  min.prop = 0.4,
  min.total.count = 15,
  isCounts = TRUE,
  normalize.method = "TMM",
  span = "auto",
  quiet = TRUE,
  weights = NULL,
  rescaleWeightsAfter = FALSE,
  BPPARAM = SerialParam(),
  ...
)

Arguments

y	matrix of counts or log2 CPM
formula	regression formula for differential expression analysis
data	metadata used in regression formula
n.cells	array of cell count for each sample
min.cells	minimum number of observed cells for a sample to be included in the analysis
min.count	used to compute a CPM threshold of CPM.cutoff = min.count/median(lib.size)*1e6. 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 CPM > CPM.cutoff
min.total.count	minimum total count required per gene for inclusion
isCounts	logical, indicating if data is raw counts
normalize.method	normalization method to be used by calcNormFactors
span	Lowess smoothing parameter using by variancePartition::voomWithDreamWeights()
quiet	show messages
weights	matrix of precision weights
rescaleWeightsAfter	default = FALSE, should the output weights be scaled by the input weights
BPPARAM	parameters for parallel evaluation
...	other arguments passed to dream

Value

EList object storing log2 CPM and precision weights

See Also

processAssays()

GabrielHoffman/dreamlet documentation built on Nov. 8, 2024, 2:45 a.m.