diffExp: Compute differential expression
In Bin-Chen-Lab/octad_desktop: Open Cancer TherApeutic Discovery (OCTAD)

Description Usage Arguments Value See Also Examples

View source: R/diffExp.R

Compute differential expression for case vs control samples. Will produce the file computedEmpGenes.csv listing empiricaly differentially expressed genes used for RNA-Seq normalization.

  diffExp(case_id='',control_id='',source='octad.small',
  file='octad.counts.and.tpm.h5',normalize_samples=TRUE,k=1,
  expSet=NULL,n_topGenes=500,DE_method='edgeR',
  parallel_cores = 2,output=TRUE,outputFolder='', annotate=TRUE)

`case_id`	vector of cases used for differential expression.
`control_id`	vector of controls used for differential expression.
`source`	the file for the octad expression matrix. By default, set to `octad.small` to use only 978 landmark genes profiled in LINCS database. Use `octad.whole` option to compute DE on the whole transcriptome `octad.counts.and.tpm.h5` file. The file should be present in the working directory or the whole path should be included. If `source` is set to `'side'`, the expSet matrix is estimated.
`expSet`	input expression matrix. By default set to `NULL` since the expSet is created based on cases, controls and source file.
`file`	if `expSet='octad.whole'`, source path to `expSet='octad.counts.and.tpm.h5'` file is required if it is not in working directory. By default function seeks for the .h5 file in the working directory.
`normalize_samples`	if TRUE, RUVSeq normalization is applied to either EdgeR or DESeq. No normalization needed for limma+voom.
`k`	eiter k=1 (by default), k=2 or k=3, number of factors used in model matrix construction in RUVSeq normalization if `normalize_samples=TRUE`.
`n_topGenes`	number of empiricaly differentially expressed genes estimated for RUVSeq normalization. Default is 5000.
`DE_method`	edgeR, DESeq2 or limma DE analysis.
`parallel_cores`	number of cores to be used for parallel computing in DESeq2.
`output`	if `TRUE`, output files is produced.
`outputFolder`	path to output folder. By default, the function produces result files in working directory.
`annotate`	if `TRUE`, annotation by ENSEMBL gene is performed. If `TRUE`, make sure row.names of the custom input contain ensembl gene ids.

`res`	`data.frame` with list of differentially expressed genes.
`computedEmpGenes.csv`	`data.frame` listing empiricaly differentially expressed genes used for RNA-Seq normalization.

computeRefTissue,runsRGES,geneEnrich.

HCC_primary=subset(phenoDF,cancer=='liver hepatocellular carcinoma'&sample.type == 'primary') #select data
case_id=HCC_primary$sample.id #select cases
HCC_adjacent=subset(phenoDF,cancer=='liver hepatocellular carcinoma'&sample.type == 'adjacent'&data.source == 'TCGA') #select data
control_id=HCC_adjacent$sample.id #select cases
res=diffExp(case_id,control_id,source='octad.small',output=TRUE)