normWrapper: Iteratively fit group regression and evaluate to choose...
In rhondabacher/SCnorm: Normalization of single cell RNA-seq data

normWrapper

R Documentation

Iteratively fit group regression and evaluate to choose optimal K

Description

This function iteratively normalizes using K groups and then evaluates whether K is sufficient. If the maximum mode received from the GetK() function is larger than .1, K is increased to K + 1. Uses params sent from SCnorm.

Usage

normWrapper(
  Data,
  SeqDepth = NULL,
  Slopes = NULL,
  CondNum = NULL,
  PrintProgressPlots,
  PropToUse,
  Tau,
  Thresh,
  ditherCounts
)

Arguments

`Data`	can be a matrix of single-cell expression with cells where rows are genes and columns are samples. Gene names should not be a column in this matrix, but should be assigned to rownames(Data). Data can also be an object of class `SummarizedExperiment` that contains the single-cell expression matrix and other metadata. The `assays` slot contains the expression matrix and is named `"Counts"`. This matrix should have one row for each gene and one sample for each column. The `colData` slot should contain a data.frame with one row per sample and columns that contain metadata for each sample. This data.frame should contain a variable that represents biological condition in the same order as the columns of `NormCounts`). Additional information about the experiment can be contained in the `metadata` slot as a list.
`SeqDepth`	sequencing depth for each cell/sample.
`Slopes`	per gene estimates of the count-depth relationship.
`CondNum`	name of group being normalized, just for printing messages.
`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.
`PropToUse`	proportion of genes closest to the slope mode used for the group fitting, default is set at .25. This number #' mainly affects speed.
`Tau`	value of quantile for the quantile regression used to estimate gene-specific slopes (default is median, Tau = .5 ).
`Thresh`	threshold to use in evaluating the sufficiency of K, default is .1.
`ditherCounts`	whether to dither/jitter the counts, may be used for data with many ties, default is FALSE.