DEGsExtraction: DEGsExtraction performs the analysis to extract the...
In KnowSeq: KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline

Description Usage Arguments Value Examples

The function performs the analysis to extract the Differentially Expressed Genes (DEGs) among the classes to compare. The number of final DEGs can change depending on the p-value and the LFC indicated by parameters of the function. Furthermore, the function detects if the number of classes are greater than 2 to perform a multiclass DEGs analysis.

DEGsExtraction(
  expressionMatrix,
  labels,
  pvalue = 0.05,
  lfc = 1,
  cov = 1,
  nmax = 1,
  multiDegsMethod = "cov",
  number = Inf,
  CV = FALSE,
  numFolds = 5
)

`expressionMatrix`	The expressionMatrix parameter is an expression matrix or data.frame that contains the genes in the rows and the samples in the columns.
`labels`	A vector or factors that contains the labels for each of the samples in the expressionMatrix parameter.
`pvalue`	The value of the p-value which determines the DEGs. If one or more genes have a p-value lower or equal to the selected p-value, they would be considered as DEGs.
`lfc`	The value of the LFC which determines the DEGs. If one or more genes have a LFC greater or equal to the selected LFC, they would be considered as DEGs.
`cov`	This value only works when there are more than two classes in the labels. This parameter establishes a minimum number of pair of classes combination in which exists differential expression to consider a genes as expressed genes.
`nmax`	This value only works when there are more than two classes in the labels. NMAX indicates the maximum number of DEGs selected for each class pair comparison.
`multiDegsMethod`	Select the multiclass extraction method for the process: cov or nmax
`number`	The maximum number of desired genes as output of limma. As default, the function returns all the extracted DEGs with the selected parameters.
`CV`	A boolean value that has to be setted to TRUE if the user would to run a Cross-Validation DEGs extraction process.
`numFolds`	This parameter indicates the number of folds for the Cross-Validation process.

A list that contains two objects. The table with statistics of the different DEGs and a reduced expression matrix which contains the DEGs and the samples.

dir <- system.file("extdata", package="KnowSeq")
load(paste(dir,"/expressionExample.RData",sep = ""))

expressionMatrix <- calculateGeneExpressionValues(countsMatrix,myAnnotation, genesNames = TRUE)

DEGsInformation <- DEGsExtraction(expressionMatrix, labels, lfc = 2.0,
pvalue = 0.01, number = Inf)

topTable <- DEGsInformation$Table

DEGsMatrix <- DEGsInformation$DEGsMatrix