ziaq: Zero-inflation adjusted quantile regression for single cell...

In gefeizhang/ZIAQ: A quantile regression method for differential expression analysis of single-cell RNA-seq data

Description

This function fits the zero-inflation adjusted quantile regression model for the differential expression analysis in single cell RNA sequencing data

Usage

ziaq(
  Y_matrix,
  colDat,
  formula = ~condition,
  group = "condition",
  probs = c(0.25, 0.5, 0.75),
  log_i = TRUE,
  parallel = FALSE,
  no.core = detectCores()
)

Arguments

Y_matrix	a matrix for expression values with row representing indiviudal genes and column representing cells
colDat	a dataframe including the individual cell information
formula	a formula with the predictors included in colDat. The default is ~condition.
group	the variable name in colDat for the factor used in group comparsion. The default is condition.
probs	the quantile levels for the quantile regressions. The default is c(0.25, 0.5, 0.75)
log_i	TRUE or FALSE indicate whether to apply log transformation. The default is TRUE.
parallel	TRUE or FALSE indicate whether to apply parallel computing. The default is TRUE.
no.core	The number of cores used in parallel computing. The default is all available cores detectCores()

Value

pvalue	The p-values of all genes for testing the signficance of the specified group variable.
res	The full results from function ziaq_fit for all genes

Examples

#Use simuluated data
ymatrix = matrix(round(100* runif(100*150)), ncol = 100)
rownames(ymatrix) = paste0('gene', 1:150)

colDat = data.frame(condition = rep(c(1, 0), e = 50))

res = ziaq(ymatrix, colDat, formula = ~ condition,
          group = 'condition', probs = c(0.25, 0.5, 0.75),
          log_i = TRUE, parallel = FALSE, no.core = 1)

print(res$pvalue)

gefeizhang/ZIAQ documentation built on Feb. 26, 2020, 6:15 p.m.