Define who genes are deferentially expressed by method

Usage

expressionDefinition(
  resultTool,
  groups = c(""),
  lfcMinLimma = -2,
  lfcMaxLimma = 2,
  pValueLimma = 0.05,
  FLimma = 0.8,
  lfcMinSamseq = -2,
  lfcMaxSamseq = 2,
  qValueSamseq = 0.05,
  lfcMinDeseq2 = -2,
  scoreDSamseq = 0.8,
  lfcMaxDeseq2 = 2,
  pValueDeseq2 = 0.05,
  lfcMinEdger = -2,
  lfcMaxEdger = 2,
  pValueEdger = 0.05,
  probNoiseq = 0.8,
  lfcMinKnowseq = -2,
  lfcMaxKnowseq = 2,
  pValueKnowseq = 0.05,
  deClassEbseq = "DE",
  ppThresholdEbseq = 0.8,
  printResults = FALSE,
  pathOutput = "."
)

Arguments

resultTool: table results by consexpressionR function
groups: text, name of samples or treatment
lfcMinLimma: minimum value to consider of log Fold Change (default: -2).
lfcMaxLimma: maximum value to consider of log Fold Change (default: 2)
pValueLimma: minimum P-Value to consider (default: 0.05)
FLimma: The F statistics is used to test the null hypotheses of all groups have the same median of expression
lfcMinSamseq: minimum value to consider of log Fold Change (default: -2).
lfcMaxSamseq: maximum value to consider of log Fold Change (default: 2)
qValueSamseq: q-value is a measure of the statistical significance of the difference in expression between the compared groups, taking the problem of multiple comparisons into account (default: 0.8)
lfcMinDeseq2: minimum value to consider of log Fold Change (default: -2).
scoreDSamseq: statistic used to evaluate the significance of each gene in multi class analysis
lfcMaxDeseq2: maximum value to consider of log Fold Change (default: 2)
pValueDeseq2: minimum P-Value to consider (default: 0.05)
lfcMinEdger: minimum value to consider of log Fold Change (default: -2).
lfcMaxEdger: maximum value to consider of log Fold Change (default: 2)
pValueEdger: minimum P-Value to consider (default: 0.05)
probNoiseq: floating point, minimum probability that a read count comes from a real gene expression peak, rather than background noiseq (default: 0.95)
lfcMinKnowseq: minimum value to consider of log Fold Change (default: -2).
lfcMaxKnowseq: maximum value to consider of log Fold Change (default: 2)
pValueKnowseq: minimum P-Value to consider (default: 0.05)
deClassEbseq: name of class to consider by EBSeq (default: "DE")
ppThresholdEbseq: description
printResults: logical variable: TRUE print report by each tool, FALSE print only consensus result
pathOutput: path to write output, need be a directory (default: ".") #'

Examples

data(gse95077)
treats = c("BM", "JJ")
cons_result <- runExpression(numberReplics = 3,
                              groupName = treats,
                              rDataFrameCount = gse95077,
                              sepCharacter = ",",
                              experimentName = "test_cons",
                              controlDeseq2 = "BM",
                              contrastDeseq2 = "JJ",
                              outDirPath = "." )
#> 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
#> [1] "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
#> perm= 2
#> perm= 3
#> perm= 4
#> perm= 5
#> perm= 6
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#> perm= 100
#> 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)