evaluateK {SCnorm} | R 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

[Package SCnorm version 1.4.7 Index]