listDeByTool: creates a data.frame with genes (rows) and DE tools (columns). Each tool that identifies a gene as DE is marked with 1.

Usage

listDeByTool(consexpressionList, geneNames, deList)

Arguments

consexpressionList: An ExpressionResultSet object with DE results.
geneNames: Character vector. Names of all genes in the experiment.
deList: List. Each element contains a character vector with DE gene names (from each tool).

Value

A data.frame with genes as rows and tools as columns; 1 indicates DE by that tool.

Examples

data(gse95077)
treats <- c("BM", "JJ")
cons_result <- runExpression(numberReplics = 3, groupName = treats, rDataFrameCount = gse95077, controlDeseq2 = "BM", contrastDeseq2 = "JJ" )
#> Using classic mode.
#> Getting annotation of the Homo Sapiens...
#> Using reference genome 38.
#> Calculating gene expression values...
#> RQ fit ......
#> SQN 
#> 
#>  
#>  ===== ERROR: KnowSeq execution is failed === 
#> Error in mclustBIC(data = structure(c(-3.52446659876917, -2.42801002792396, : could not find function "mclustBIC"
#> 
#> Removing intercept from test coefficients
#> 
#>  ------------ limma executed!
#> [1] "Computing (M,D) values..."
#> [1] "Computing probability of differential expression..."
#> 
#>  ------------ NOISeq executed! 
#> Warning: `expect_is()` was deprecated in the 3rd edition.
#> ℹ Use `expect_type()`, `expect_s3_class()`, or `expect_s4_class()` instead
#> Warning: `expect_is()` was deprecated in the 3rd edition.
#> ℹ Use `expect_type()`, `expect_s3_class()`, or `expect_s4_class()` instead
#> Warning: `expect_is()` was deprecated in the 3rd edition.
#> ℹ Use `expect_type()`, `expect_s3_class()`, or `expect_s4_class()` instead
#> Initial number of DE patterns = 2
#> Final number of DE patterns = 2
#> 
#>  ------------ EBSeq executed!
#> Warning: some variables in design formula are characters, converting to factors
#> Dataset is COUNT data
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
#> 
#>  ------------ DESeq2 executed!
#> Estimating sequencing depths...
#> Resampling to get new data matrices...
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#> Number of thresholds chosen (all possible thresholds) = 8
#> Getting all the cutoffs for the thresholds...
#> Getting number of false positives in the permutation...
#> 
#>  ------------ SAMSeq executed!
#> 
#>  ------------ DIFERENTIAL EXPRESSION ANALYSIS WAS COMPLETE!
expDef_result <- expressionDefinition(resultTool = cons_result, groups = treats)
deByTool <- listDeByTool(cons_result, geneNames = rownames(gse95077), deList = expDef_result)

#      DESeq2 limma edgeR
# gene1      1     1     1
# gene2      0     0     1
# gene3      1     0     1
# gene4      0     1     0
# gene5      0     0     0
# gene6      1     1     1