normalize.scnorm: SCnorm 1.1.0

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

Description

The method forces to output diagnoistic plots. The PLOT = T/F option is obsolete.

Usage

normalize.scnorm(counts, condition, control = list(save_modelFit = FALSE,
  FilterCellNum = 10, K = NULL, NCores = 4, useSpikes = FALSE, reportSF = TRUE))

Arguments

counts	Gene by sample expression count matrix (G by N).
condition	Binary vector of length N indicating sample biological condition.
control	A list with control arguments, including save_modelFit TRUE to output the complete SCnorm output. FilterCellNum SCnorm argument. Minimum number of cells required to estimate the count-depth relationship. K SCnorm argument. Number of quantile groups. Default NULL: letting SCnorm find the optimal K. Ncores SCnorm argument. Number of cores to use. Default 4. useSpikes SCnorm argument. Whether to use spike-ins to perform the between-condition scaling using spike-ins. Assume that the spike-in labels start with ERCC reportSF SCnorm argument. Whehter to provide a matrix of scaling factors in the output.

Value

List of the following objects count_normed numeric vector of the scale factors for library size. model_output SCnorm complete output.

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