computeDEGs {tRanslatome} | R Documentation

## 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, 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`, `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`,

### 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.

### Value

An object of class `DEGs`

### Author(s)

Toma Tebaldi, Erik Dassi, Galena Kostoska

### 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.

### See Also

`TranslatomeDataset` `DEGs` `Scatterplot` `Histogram` `CVplot` `MAplot` `SDplot`

### Examples

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

[Package tRanslatome version 1.18.5 Index]