erssa_edger_parallel: Run edgeR for computed sample combinations with parallel...
In ERSSA: Empirical RNA-seq Sample Size Analysis

Description Usage Arguments Details Value Author(s) References Examples

erssa_edger_parallel function performs the same calculation as erssa_edger except now employs BiocParallel to perform parallel edgeR calculations. This function runs classic edgeR method to identify differentially expressed (DE) genes for each sample combination computed by comb_gen function. A gene is considered to be differentially expressed by defined FDR (Default=0.05) and logFC (Default=1) values. As an option, the function can also save the edgeR topTags tables as csv files to the drive.

erssa_edger_parallel(
  count_table.filtered = NULL,
  combinations = NULL,
  condition_table = NULL,
  control = NULL,
  cutoff_stat = 0.05,
  cutoff_Abs_logFC = 1,
  save_table = FALSE,
  path = ".",
  num_workers = 1
)

`count_table.filtered`	Count table pre-filtered to remove non- to low- expressing genes. Can be the output of `count_filter` function.
`combinations`	List of combinations that is produced by `comb_gen` function.
`condition_table`	A condition table with two columns and each sample as a row. Column 1 contains sample names and Column 2 contains sample condition (e.g. Control, Treatment).
`control`	One of the condition names that will serve as control.
`cutoff_stat`	The cutoff in FDR for DE consideration. Genes with lower FDR pass the cutoff. Default = 0.05.
`cutoff_Abs_logFC`	The cutoff in abs(logFC) for differential expression consideration. Genes with higher abs(logFC) pass the cutoff. Default = 1.
`save_table`	Boolean. When set to TRUE, function will, in addition, save the generated edgeR TopTags table as csv files. The files are saved on the drive in the working directory in a new folder named "ERSSA_edgeR_table". Tables are saved separately by the replicate level. Default = FALSE.
`path`	Path to which the files will be saved. Default to current working directory.
`num_workers`	Number of workers for parallel computing. Default=1.

The main function calls edgeR functions to perform exact test for each computed combinations generated by comb_gen. In all tests, the pair-wise test sets the condition defined in the object "control" as the control condition.

In typical usage, after each test, the list of differentially expressed genes are filtered by FDR and log2FC values and only the filtered gene names are saved for further analysis. However, it is also possible to save all of the generated TopTags table to the drive for additional analysis that is outside the scope of this package.

A list of list of vectors. Top list contains elements corresponding to replicate levels. Each child list contains elements corresponding to each combination at the respective replicate level. The child vectors contain differentially expressed gene names.

Zixuan Shao, Zixuanshao.zach@gmail.com

Morgan M, Obenchain V, Lang M, Thompson R, Turaga N (2018). BiocParallel: Bioconductor facilities for parallel evaluation. R package version 1.14.1, https://github.com/Bioconductor/BiocParallel.

Robinson MD, McCarthy DJ, Smyth GK (2010). <e2><80><9c>edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.<e2><80><9d> Bioinformatics, 26(1), 139-140.

# load example filtered count_table, condition_table and combinations
# generated by comb_gen function
# example dataset containing 1000 genes, 4 replicates and 5 comb. per rep.
# level
data(count_table.filtered.partial, package = "ERSSA")
data(combinations.partial, package = "ERSSA")
data(condition_table.partial, package = "ERSSA")

# run erssa_edger_parallel with heart condition as control
deg.partial = erssa_edger_parallel(count_table.filtered.partial,
combinations.partial, condition_table.partial,
control='heart', num_workers=1)