Make expression analysis of multiple tools and return results by tool.

Usage

runExpression(
  numberReplics,
  groupName,
  tableCountPath = NULL,
  sepCharacter = ",",
  rDataFrameCount = NULL,
  experimentName = "genericExperiment",
  outDirPath = tempdir(),
  printResults = FALSE,
  fitTypeDeseq2 = "local",
  controlDeseq2 = "",
  contrastDeseq2 = "",
  methodNormLimma = "TMM",
  methodAdjPvalueLimma = "BH",
  numberTopTableLimma = 1e+06,
  filterIdKnowseq = "ensembl_gene_id",
  notSapiensKnowseq = FALSE,
  methodNormEdgeR = "TMM",
  normNoiseq = "rpkm",
  kNoiseq = 0.5,
  factorNoiseq = "Tissue",
  lcNoiseq = 0,
  replicatesNoiseq = "technical",
  condExpNoiseq = c(""),
  respTypeSamseq = "Two class unpaired",
  npermSamseq = 100,
  fdrEbseq = 0.05,
  maxRoundEbseq = 50,
  methodDeResultsEbseq = "robust",
  deNovoAanalysis = FALSE,
  progressShiny = NULL
)

Arguments

numberReplics: number of replicates (technical or biological) per group
groupName: character vector, group/treatment names (e.g., c("BM","JJ"))
tableCountPath: path to csv file that contains count/abundance data (local)
sepCharacter: separator used to split csv data (comma or tab)
rDataFrameCount: data.frame/matrix with table count; rownames are genes and colnames are samples
experimentName: experiment name
outDirPath: output directory (created if needed)
printResults: if TRUE, writes per-tool outputs to outDirPath
fitTypeDeseq2: DESeq2 fitType
controlDeseq2: control group label for DESeq2
contrastDeseq2: contrast group label for DESeq2
methodNormLimma: normalization method used by limma/edgeR
methodAdjPvalueLimma: p-value adjustment method for limma
numberTopTableLimma: max number of genes for limma topTable
filterIdKnowseq: KnowSeq parameter
notSapiensKnowseq: KnowSeq parameter
methodNormEdgeR: normalization method for edgeR
normNoiseq: NOISeq parameter
kNoiseq: NOISeq parameter
factorNoiseq: NOISeq parameter
lcNoiseq: NOISeq parameter
replicatesNoiseq: NOISeq parameter
condExpNoiseq: NOISeq parameter
respTypeSamseq: SAMSeq parameter
npermSamseq: SAMSeq parameter
fdrEbseq: EBSeq parameter
maxRoundEbseq: EBSeq parameter
methodDeResultsEbseq: EBSeq parameter
deNovoAanalysis: if TRUE, skips KnowSeq (requires annotation)
progressShiny: optional shiny progress callback

Value

An ExpressionResultSet object containing results per method.

Examples

set.seed(42)
counts <- matrix(
  as.integer(c(
    rnbinom(200, mu = 10, size = 1),
    rnbinom(200, mu = 100, size = 1)
  )),
  nrow = 100,
  dimnames = list(
    paste0("gene", seq_len(100)),
    paste0("sample", seq_len(4))
  )
)
groups_info <- c("control", "control", "treated", "treated")
treats <- c("control", "treated")
res <- runExpression(
  numberReplics = 2,
  groupName = treats,
  rDataFrameCount = counts,
  controlDeseq2 = "control",
  contrastDeseq2 = "treated",
  printResults = FALSE,
  outDirPath = tempdir()
)
#> calcNormFactors has been renamed to normLibSizes
#> Using classic mode.
#> baySeq libsizes
#> baySeq getPriors
#> Finding priors...
#> Warning: The '@replicates' slot is not a factor; converting now.
#> done.
#> Finding posterior likelihoods...
#> Length of priorReps:0
#> Length of priorSubset:100
#> Length of subset:100
#> Length of postRows:100
#> Analysing part 1 of 1
#> Preparing data...
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#> done.
#> Estimating likelihoods...
#> ...done!
#> .
#> done.
#> Available columns in results_raw:
#> Warning: ‘>=’ not meaningful for factors
#> calcNormFactors has been renamed to normLibSizes
#> Removing intercept from test coefficients
#> [1] "Computing (M,D) values..."
#> [1] "Computing probability of differential expression..."
#> 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
#> converting counts to integer mode
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
#> Estimating sequencing depths...
#> Resampling to get new data matrices...
#> perm= 1
#> perm= 2
#> perm= 3
#> perm= 4
#> perm= 5
#> perm= 6
#> perm= 7
#> perm= 8
#> perm= 9
#> perm= 10
#> perm= 11
#> perm= 12
#> perm= 13
#> perm= 14
#> perm= 15
#> perm= 16
#> perm= 17
#> perm= 18
#> perm= 19
#> perm= 20
#> perm= 21
#> perm= 22
#> perm= 23
#> perm= 24
#> Number of thresholds chosen (all possible thresholds) = 11
#> Getting all the cutoffs for the thresholds...
#> Getting number of false positives in the permutation...
#> Differential expression analysis completed.
summary(res)
#> Summary of ExpressionResultSet
#> ---------------------------------
#> Methods used: edger, limma, noiseq, ebseq, deseq2, samseq
#> Number of result tables: 6
#> Average number of DEGs per method: 86.83
#> Consensus list is empty or not defined.