detectLowQualityCells.housekeeping: detectLowQualityCells.housekeeping

In jasonserviss/sp.scRNAseqData: Data and processing functions for the CIMseq.data package

Description

It is often the case that some samples from sequencing experiments are of low quality, in many cases due to issues during the sample preperation stage. Due to the fact that these samples represent a high level of technical noise, it is often desirable to remove these before downstream analysis which is facilitated by this function. The function achieves this by utilizing a house keeping gene and assuming its log2 expression to be normally distributed. We then detect samples where the probability of the expression for the house keeping gene in that sample is greater than the quantile.cut argument.

Arguments

counts	data.frame; A data frame with counts data with gene names as rownames and sample names as colnames.
geneName	character; The gene name to use for the quantile cutoff. This must be present in the rownames of the counts argument. Default is ACTB.
quantileCut	numeric; This indicates probability at which the quantile cutoff will be calculated using the normal distribution. Default = 0.01.

Value

A logical vector with length = ncol(counts) that is TRUE when the counts data.frame column contains a sample with meeting the criteria specified by the arguments.

Author(s)

Jason Serviss

Examples

set.seed(8292)
x <- runif(2e4)
y <- runif(2e4, 1.5, 100)
names <- paste0(letters, 1:2e4)
counts <- data.frame(a = x, b = y, c = y, row.names = names)
detectLowQualityCells.housekeeping(counts, geneName = "a1")

jasonserviss/sp.scRNAseqData documentation built on Jan. 8, 2020, 11:46 a.m.