Identify DE genes by method-specific thresholds
Source:R/expressionDefinition.R
expressionDefinition.RdApplies specific filtering rules to each expression method result in the ExpressionResultSet object and returns the DE genes per tool.
Usage
expressionDefinition(
resultTool,
groups = c(""),
lfcMinLimma = -2,
lfcMaxLimma = 2,
pValueLimma = 0.05,
FLimma = 0.8,
lfcMinSamseq = -2,
lfcMaxSamseq = 2,
qValueSamseq = 0.05,
scoreDSamseq = 0.8,
lfcMinDeseq2 = -2,
lfcMaxDeseq2 = 2,
pValueDeseq2 = 0.05,
lfcMinEdger = -2,
lfcMaxEdger = 2,
pValueEdger = 0.05,
probNoiseq = 0.8,
lfcMaxKnowseq = 2,
lfcMinKnowseq = -2,
pValueKnowseq = 0.05,
deClassEbseq = "DE",
ppThresholdEbseq = 0.8,
printResults = FALSE,
pathOutput = "."
)Examples
data(gse95077)
treats <- c("BM", "JJ")
res <- 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...
#> perm= 1
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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 <- expressionDefinition(resultTool = res, groups = treats)