SCnormFit: Fit group quantile regression for K groups

In rhondabacher/SCnorm: Normalization of single cell RNA-seq data

Fit group quantile regression for K groups

Description

For each group K, a quantile regression is fit over all genes (PropToUse) for a grid of possible degree's d and quantile's tau. For each value of tau and d, the predicted expression values are obtained and regressed against the original sequencing depths. The optimal tau and d combination is chosen as that closest to the mode of the gene slopes.

Usage

SCnormFit(Data, SeqDepth, Slopes, K, PropToUse = 0.25, Tau = 0.5, 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.
K	the number of groups for normalizing. If left unspecified, an evaluation procedure will determine the optimal value of K (recommended).
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 ).
ditherCounts	whether to dither/jitter the counts, may be used for data with many ties, default is FALSE.

Value

normalized expression matrix and matrix of scaling factors.

Author(s)

Rhonda Bacher

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