DEGordering: Variable ordering according to the p-values returned by...

Description Usage Arguments Details Value Author(s) Examples

View source: R/deg_ordering.R

Description

Order the variables (columns) of a count table in preparation for supervised classification, by running differential expression analysis with either DESeq2 or edgeR.

Usage

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DEGordering(
  countDataset,
  method = "DESeq2",
  permuteLabels = FALSE,
  alpha = 0.05
)

Arguments

countDataset

an object of class DataTableWithClasses containing counts of reads per features. Importantly, edgeR and DESeq2 require non-normalized counts as input.

permuteLabels=FALSE

if TRUE, class labels are permuted before running the differential analysis, in order to run a negative control (empirical approximation of the null hypothesis)

method="DESeq2"

choice of method for differential expression analysis. Supported: "DESeq2" , "edgeR".

alpha=0.05

threshold on adjusted p-value (FDR) to call genes positive

...

all additional parmeters are passed to the differential expression method (DEseq2 or edgeR).

Details

###################################################

Value

a list with the following fields.

Note that genes are ordered according to nominal p-value (pvalue) rather than adjusted p-value (padj) because DESeq2 produces NA values for the padj.

Author(s)

Mustafa AbuElQumsan and Jacques van Helden

Examples

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###################################################
## loading required packages

recountID <- "SRP048759"
recountID <- "SRP042620"
studyCases <- loadCounts(recountID = recountID, parameters = project.parameters[[recountID]])
filteredCounts <- studyCases[[recountID]]$datasetsForTest$filtered
degOrderdPValues <- DEGordering(countDataset = filteredCounts, method = "edgeR")
## degPValues<- degPValues[order(degPValues$padj) ,]

elqumsan/RNAseqMVA documentation built on March 10, 2021, 8:10 a.m.