Model ID

Statistical Test Used

Model Assumptions

Outcomes Measured

Notes

M1

Limma (Linear Models for Microarray Data)

Normally distributed residuals, linear relationships

Log-fold changes, adjusted p-values

Widely used for small sample sizes; accounts for multiple testing using an empirical Bayes approach.

M2

DESeq2 (Differential gene expression analysis based on the negative binomial distribution)

Count data follows a negative binomial distribution

Base mean expression, log2 fold changes, p-values

Suitable for RNA-Seq data; uses shrinkage estimation for dispersions and fold changes.

M3

EdgeR (Empirical Analysis of Digital Gene Expression Data in R)

Negative binomially distributed counts, tag wise dispersions

Common dispersion, tagwise dispersion, exact p-values

Optimized for gene expression comparisons with complex experimental designs.

M4

t-test (Independent two-sample t-test)

Normally distributed data, equal variances

Mean expression differences, t-statistics, p-values

Simple comparative analysis; less robust to variance in small sample sizes without equal variances.