query.pipeline: Run multiple normalization methods and multiple DE methods

In jhsiao999/ashbun: Methods and evaluation tools for single cell RNA-seq count data

Usage

query.pipeline(counts, condition, is_nullgene = NULL,
  methodsNormalize = c("LIB", "TMM", "RLE", "census", "SCnorm", "scran"),
  methodsMeanExpression = c("DESeq2", "limmaVoom", "edgeR", "BPSC", "MAST",
  "ROTS", "scde"))

Arguments

counts	Gene by sample expression count matrix (G by N). Use raw count data before filtering.
condition	Binary vector of length N indicating sample biological condition.
methodsNormalize	Chararacter vector of evaluted methods. To run all methods, use c("normalize.cpm", "normalize.tmm", "normalize.rle", "normalize.census", "normalize.scnorm", "normalize.scran")
methodsMeanExpression	Chararacter vector of evaluted methods. To run all methods, use c("DESeq2", "limmaVoom", "edgeR","BPSC", "MAST", "ROTS")
null	binary indicator of true/false. True = Non-null gene and FALSE = Null gene.

Value

data List of filtered data, including count matrix, sample condition vector, and logical vector for null gene status (TRUE if null). pvals_longformat data.frame of pvals.

Examples

ipsc_eset <- get(load(system.file("testdata", "HumanTungiPSC.rda", package = "ashbun")))
counts <- exprs(ipsc_eset)[sample(nrow(exprs(ipsc_eset)), 500), ]
condition <- pData(ipsc_eset)$replicate

results <- query.pipeline(counts = counts,
                          condition = condition,
                          is_nullgene = NULL,
                          methodsNormalize = c("TMM", "RLE", "census","scran"),
                          methodsMeanExpression = c("DESeq2", "limmaVoom"))

jhsiao999/ashbun documentation built on May 8, 2019, 11:17 p.m.