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 toedgeR::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