Execute EBSeq gene Expression analysis

Execute EBSeq gene Expression analysis

Usage

runEbseq(
  countMatrix,
  designExperiment,
  fdr = 0.05,
  ppThreshold = 0.8,
  maxRound = 50,
  methodDeResults = "robust",
  groups = c("")
)

Arguments

countMatrix

either a matrix of raw (read) counts.

designExperiment

replicate and treatment by samples

fdr

parameter used in EBTest function: fdr False Discovery Rate cutt off

ppThreshold

posterior Probability Threshold

maxRound

parameter used in EBTest function: Number of iterations. The default value is 50.

methodDeResults

parameter used in GetDEResults function: "robust" or "classic". Using the "robust" option, EBSeq is more robust to genes with outliers and genes with extremely small variances. Using the "classic" option, the results will be more comparable to those obtained by using the GetPPMat() function from earlier version (<= 1.7.0) of EBSeq

groups

text, name of samples or treatment

Value

EBSeq report in data Frame fromat

Examples

data(gse95077)
treats = c("BM", "JJ")
designExperimentModel <- rep(treats, each = 3)
toolResult <- NULL
toolResult$ebseq <- runEbseq(countMatrix = gse95077,
                              designExperiment = designExperimentModel,
                              groups = treats)
#> 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