computeDEGs: computeDEGsHelpfile

Description

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, SAM, 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.

Usage

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

Arguments

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, SAM, 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,

Value

An object of class DEGs

Details

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.

References

Smyth GK. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol., 3:Article3.

Tian L, Greenberg SA, Kong SW, Altschuler J, Kohane IS, Park PJ.(2005) Discovering statistically significant pathways in expression profiing studies. Proc Natl Acad Sci USA, 102(38):13544-9.

Courtes FC et al. (2013) Translatome analysis of CHO cells identify key growth genes. Journal of Biotechnology, 167, 215-24.

Breitling R, Armengaud P, Amtmann A, Herzyk P.(2004) Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett., 573(1-3):83-92.

Tusher VG, Tibshirani R, Chu G.(2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA., 2001, 98(9):5116-21.

Larsson O, Sonenberg N, Nadon R.(2011) anota: Analysis of differential translation in genome-wide studies. Bioinformatics, 27(10):1440-1.

Anders S, Huber W.(2010) Differential expression analysis for sequence count data. Genome Biology, 11(10):R106.

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.

Examples

Run this code

data(tRanslatomeSampleData)
computeDEGs(translatome.analysis, method= "limma", ,mult.cor=TRUE)

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