SCnormFit: Fit group quantile regression for K groups
In SCnorm: Normalization of single cell RNA-seq data

Description Usage Arguments Value Author(s)

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.

1	SCnormFit(Data, SeqDepth, Slopes, K, PropToUse = 0.25, Tau = 0.5, ditherCounts)

`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.