computeDEGs: computeDEGsHelpfile
In tRanslatome: Comparison between multiple levels of gene expression

Description Usage Arguments Details Value Author(s) References See Also Examples

This function takes as input an object of the class TranslatomeDataset which contains a normalized data matrix coming from high throughput experiment. It takes as an input a character label specifying the method that we want to employ in order to detect DEGs(t-test, translational efficiency, ANOTA, DESeq, edgeR, RP, limma) and returns an object of the class DEGs, in which each gene is assigned an expression class: up- or down-regulated at the first level, up- or down-regulated at the second level, up-regulated at both levels, down-regulated at both levels, up-regulated at the first level and down-regulated at the second level and vice versa.

1 2	computeDEGs(object, method="limma", significance.threshold= 0.05, FC.threshold= 0, log.transformed = FALSE, mult.cor=TRUE)

`object`	an object of class `TranslatomeDataset`
`method`	a character string that specifies the method that the user wants to employ in the differential expression analysis. It can have one the following values: `limma`, `t-test`, `RP`, `TE`, `ANOTA`, `DESeq` and `none`.By default, this value is set to `limma`,
`significance.threshold`	a numeric value specifying the threshold on the statistical significance below which the genes are considered as differentially expressed, the default is set to `0.05`,
`FC.threshold`	a numeric value specifying the threshold on the absolute log2 fold change, above which the genes are considered as differentially expressed, the default is set to `0`,
`log.transformed`	a boolean variable specifying whether the signals contained in expr.matrix have been previously log2 transformed. By default it is set to `FALSE`,
`mult.cor`	a boolean variable specifying whether the significance threshold is applied on the multiple test corrected or on the original p-values obtained from the DEGs detection method. By default it is set to `TRUE`,

Signals contained in expr.matrix should be previously normalized with standard methods (quantile, percentile shift, ... ) when data is coming from microarrays or in the appropriate cases when it is coming from sequencing experiments.

An object of class DEGs

Toma Tebaldi, Erik Dassi, Galena Kostoska

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Robinson MD, McCarthy DJ, Smyth GK.(2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1):139-40.

TranslatomeDataset DEGs Scatterplot Histogram CVplot MAplot SDplot