evaluateK: Evaluate normalization using K slope groups

View source: R/evaluateK.R

evaluateKR Documentation

Evaluate normalization using K slope groups

Description

Median quantile regression is fit for each gene using the normalized gene expression values. A slope near zero indicate the sequencing depth effect has been successfully removed. Genes are divided into ten equally sized groups based on their non-zero median expression. Slope densities are plot for each group and estimated modes are calculated. If any of the ten group modes is larger than .1, the K is not sufficient to normalize the data.

Usage

evaluateK(
  Data,
  SeqDepth,
  OrigData,
  Slopes,
  Name,
  Tau,
  PrintProgressPlots,
  ditherCounts
)

Arguments

Data

matrix of normalized expression counts. Rows are genes and columns are samples.

SeqDepth

vector of sequencing depths estimated as columns sums of un-normalized expression matrix.

OrigData

matrix of un-normalized expression counts. Rows are genes and columns are samples.

Slopes

vector of slopes estimated in the GetSlopes() function. Only used here to obtain the names of genes considered in the normalization.

Name

plot title

Tau

value of quantile for the quantile regression used to estimate gene-specific slopes (default is median, Tau = .5 ).

PrintProgressPlots

whether to automatically produce plot as SCnorm determines the optimal number of groups (default is FALSE, highly suggest using TRUE). Plots will be printed to the current device.

ditherCounts

whether to dither/jitter the counts, may be used for data with many ties, default is FALSE.

Value

value of largest mode and a plot of the ten normalized slope densities.

Author(s)

Rhonda Bacher


rhondabacher/SCnorm documentation built on July 8, 2023, 11:36 p.m.