Hi, I am trying to remove (filter out) low expressed exons / genes to speed up DEXSeq 1.34.0 analysis.

1) What would be the best way to do that? I am not sure how to do that starting from dexseq object created by DEXSeqDataSetFromHTSeq() function. Can you please post some example code for filtering?

2) What would be the suggested minimal exon / gene expression? Can you please give me some starting directions for filtering?

Here is my code:

library(argparser)
library(dplyr)
library(readr)
library(DEXSeq)

p <- arg_parser("inputs")
p <- add_argument(p, "--countFiles", nargs=Inf)
p <- add_argument(p, "--flattenedGTF", nargs=1)
p <- add_argument(p, "--sampleTable", nargs=1)
p <- add_argument(p, "--factor", nargs=1)

args <- parse_args(p)

count_files = args$countFiles
flattened_gtf = args$flattenedGTF
sampleTable = read.csv(args$sampleTable) 
factor_ = args$factor

# Number of cores / workers
BPPARAM = MulticoreParam(workers = 18)

# Create null and full formula
formula_null = as.formula("~ sample + exon")
formula_full = as.formula(paste0("~ sample + exon + ", factor_, ":exon"))

#DEXSeq
dxd = DEXSeqDataSetFromHTSeq(
      countfiles = count_files,
      sampleData = sampleTable,
      design = formula_full,
      flattenedfile = flattened_gtf)

dxd_norm = estimateSizeFactors( dxd )

dxd_esf = estimateDispersions(dxd_norm, 
                          formula=formula_full,
                          BPPARAM = BPPARAM)

dxd_test = testForDEU(dxd_esf,
                  reducedModel=formula_null,
                  fullModel=formula_full,
                  BPPARAM = BPPARAM)  

dxd_foldchange = estimateExonFoldChanges(dxd_test,
                                     fitExpToVar=factor_,
                                     denominator="normal",
                                     BPPARAM = BPPARAM) 
dxr1 = DEXSeqResults( dxd_foldchange )

Thanks!