processData: Pre-process the count matrix
In DavidKLim/FSCseq: Feature Selection and Clustering of RNA-seq Count Data

Description Usage Arguments Value Author(s) References Examples

Estimate size factors using DESeq2, and pre-filter based on low count and low variability genes using custom thresholds on median normalized count and median absolute deviation (MAD) values.

processData(
  y,
  geoMeans = NULL,
  estimateSFtype = "ratio",
  med_filt = TRUE,
  MAD_filt = TRUE,
  med_thresh = 100,
  MAD_quant_thresh = 50
)

`y`	integer, gene expression count matrix (output from simulateData)
`geoMeans`	(optional) numeric vector of length g (number of rows of y): custom geometric means of gene counts. Used to estimate prediction set size factors based on training set
`estimateSFtype`	string, input for estimateSizeFactors "type" argument. must be 'ratio', 'poscounts', or 'iterate'. See DESeq2 vignette.
`med_filt`	logical, TRUE to filter low-count genes via median threshold
`MAD_filt`	logical, TRUE to filter low-variable genes via MAD quantile threshold
`med_thresh`	numeric, median threshold for pre-filtering low-count genes (default 100, i.e. pre-filters genes with median normalized count below 100)
`MAD_quant_thresh`	numeric value between 0 to 100, quantile threshold imposed on MAD values for pre-filtering low-variable genes (default 50, i.e. pre-filters genes below 50th quantile of MAD values)

list containing the following objects: dds: DESeq2 output. size_factors: numeric vector, estimated size factors, from DESeq2 norm_y: numeric normalized count matrix, corrected for differences in sequencing depth, from DESeq2 idx: logical vector, TRUE for inclusion after pre-filtering low-count and low-variable genes row_medians: numeric vector, median normalized count for each gene row_MADs: numeric vector, median absolute deviation (MAD) value of log(norm_y+0.1) for each gene

David K. Lim, deelim@live.unc.edu

https://github.com/DavidKLim/FSCseq

1 2	sim.dat = simulateData(B=1, g=10000, K=2, n=50, LFCg=1, pDEg=0.05, beta0=12, phi0=0.35, nsims=1, save_file=F)[[1]] proc.dat = processData(sim.dat$cts)