## Statistics / Two group tests

### Description

Test every gene's expression for a difference between two groups.

### Parameters

- Test type (empiricalBayes, t-test, F-test, Mann-Whitney, LPE) [t-test]
- Method of multiple testing correction (none, Bonferroni, Holm, Hochberg, BH, BY) [BH]
- P-value cut-off (0...1) [0.05]

### Details

Runs the specified test, and returns the genes that have a corrected p-value smaller than the specified p-value cut-off. If you want to get a p-value for every single gene in your data set, set the p-value cut-off to 1. This should return a new gene list of equal length to the original one.

Test empiricalBayes is esssentially a t-test where estimates are shrank using Bayesian methods. This results into better estimates of individual gene's statistical significance. In addition, the empiricalBayes is much faster than the usual t-test. LPE test is especially well suited for small sample sizes. All other test, except F-test, compare differences in two groups mean expression. F-test compares the differences in variance between the two groups.

If interested in performing a paired t, Mann-Whitney or empiricalBayes test, the phenodata column describing the sample pairs must be defined with the pairing parameter. In the phenodata matrix, a unique identifier (like 1,2,3 or pair1,pair2,pair3, or patient1, patient2, patient3, etc) must be given for each pair of samples. Please note that if a sample does not have a pair, it is not used in assessing the significance.

Multiple testing correction options are Bonferroni, Holm, and Hochberg for family-wise error rate (FWER) and Benjamini-Hochberg and Benjamini-Yakutieri for false discovery rate (FDR). Of these Bonferroni is the most conservative, returning the smallest number of significant results, and FDR-based adjustments are less conservative, and return more significant results.

### Output

A text file containing the gene expression values and the p-value for the test.

### References

This tool uses Bioconductor packages limma and LPE. Please cite the following article, if you used empirical Bayes from limma package:

Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Ge- netics and Molecular Biology, Vol. 3, No. 1, Article 3.

Cite the following articles, if you used LPE-test from LPE package:

Jain et. al. Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays, Bioinformatics, 2003, Vol 19, No. 15, pp: 1945-1951.

Jain et. al. Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data, BMC Bioinformatics, 2005, Vol 6, 187.