consensusList: make a list of genes considered DE by at least 'threshold' tools

Usage

consensusList(consexpressionList, deTool, threshold = 5)

Arguments

consexpressionList: An ExpressionResultSet object containing the results from each analysis method.
deTool: data.frame. Output of 'listDeByTool' function
threshold: Integer. Minimum number of tools that must agree on a gene being DE (default: 5)

Value

An ExpressionResultSet object with the consensus slot populated

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")
cons_result <- runExpression(
  numberReplics = 2,
  groupName = treats,
  rDataFrameCount = counts,
  controlDeseq2 = "control",
  contrastDeseq2 = "treated"
)
#> 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.
expDef_result <- expressionDefinition(
  resultTool = cons_result,
  groups = treats
)
deByTool <- listDeByTool(cons_result, row.names(counts), expDef_result)
cons_result <- consensusList(cons_result, deByTool)

summary(cons_result)
#> Summary of ExpressionResultSet
#> ---------------------------------
#> Methods used: edger, limma, noiseq, ebseq, deseq2, samseq
#> Number of result tables: 6
#> Average number of DEGs per method: 86.83
#> Number of genes in consensus: 6