differentiallyExpressed-method: Identify which genes are differentially expressed
In transcriptogramer: Transcriptional analysis based on transcriptograms

Description Usage Arguments Value Author(s) See Also Examples

This method uses the limma package to identify which genes are differentially expressed, meeting the pValue requirement, for the contrast "case-control". The levels lenght must be equal to the number of samples present in the transcriptogramS2 slot of the object, and its contents is related to the order that the samples appear. FALSE must be used to indicate case samples, and TRUE to indicate control samples. If species is NULL, no translation will be done, if species is a character, the biomaRt package will be used to translate the ENSEMBL Peptide ID to Symbol (Gene Name), and if species is a data.frame, it will be used instead. If the translation fail for some protein, its ENSEMBL Peptide ID will be present into the Symbol column. This method also groups the differentially expressed proteins detected in clusters, and plots a graphical representation of this clustering.

differentiallyExpressed(object, levels, pValue = 0.05,
  species = object@Protein2Symbol, adjustMethod = "BH",
  trend = FALSE, title = "Differential expression",
  boundaryConditions = TRUE, colors = NULL)

## S4 method for signature 'Transcriptogram'
differentiallyExpressed(object, levels,
  pValue = 0.05, species = object@Protein2Symbol,
  adjustMethod = "BH", trend = FALSE,
  title = "Differential expression", boundaryConditions = TRUE,
  colors = NULL)

`object`	An object of class Transcriptogram.
`levels`	A logical vector that classify the columns, referring to samples, of the transcriptogramS2 slot of the `object`. FALSE must be used to indicate case samples, and TRUE to indicate control samples.
`pValue`	A numeric value between 0 and 1 giving the required family-wise error rate or false discovery rate. The default value of this argument is 0.05.
`species`	A character string that will be used, ignoring case sensitivity, to translate the ENSEMBL Peptide ID to Symbol (Gene Name); or a data.frame containing two columns, the first one with ENSEMBL Peptide IDs (character), which may, or not, to contain the taxonomy ID of the species as prefix, and the second containing its respective Symbol (character). The default value of this argument is the content of the object Protein2Symbol slot.
`adjustMethod`	Character string specifying p-value adjustment method, the possible values are 'none', 'BH', 'fdr' (equivalent to 'BH'), 'BY' and 'holm'. The default value for this argument is 'BH'.
`trend`	Logical value, set as TRUE to use the limma-trend approach for RNA-Seq. The default value of this argument is FALSE.
`title`	An overall title for the plot. The default value of this argument is "Differential expression"
`boundaryConditions`	Logical value, defines whether the clusters limits will be extended using the current value of the radius slot. If TRUE, nearby clusters will be merged if its limits overlap. The default value of this argument is TRUE.
`colors`	Color vector used to distinguish the clusters. If NULL, the rainbow palette will be used to generate the colors. The color vector must contain a color for each cluster.

This method creates a data.frame to feed the DE slot of an object of class Transcriptogram. This data.frame of differentially expressed proteins contains log2-fold-change, raw p-values, adjusted p-values, and an integer number that indicates if the protein is downregulated (-1) or upregulated (1).

Diego Morais

transcriptogramPreprocess, GSE9988, GPL570, Hs900, association, DEsymbols, transcriptogramStep1, transcriptogramStep2

transcriptogram <- transcriptogramPreprocess(association, Hs900, 50)
## Not run: 
transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570)
transcriptogram <- transcriptogramStep2(transcriptogram)
levels <- c(rep(FALSE, 3), rep(TRUE, 3))
transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01)

## translating ENSEMBL Peptide IDs to Symbols
transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01,
"Homo sapiens")

## these calls also works
transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01,
"H sapiens")

transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01,
DEsymbols)

## End(Not run)