Differential gene expression analysis is a common approach used to identify genes that are differentially expressed between two or more biological samples. This analysis is typically performed using RNA sequencing (RNA-seq) data, although other techniques such as microarrays can also be used.
The general workflow for differential gene expression analysis involves the following steps:
- Quality control and preprocessing: The quality of the sequence reads is assessed using quality control software, and low-quality reads and adapter sequences are removed. The remaining reads are aligned to a reference genome or transcriptome using alignment software, and the alignment statistics are evaluated.
- Read quantification: The number of reads that map to each gene or transcript is counted, and the expression levels are estimated using quantification software. This step generates a gene expression matrix, which lists the number of reads that map to each gene or transcript in each sample.
- Normalization: The gene expression matrix is normalized to account for differences in sequencing depth and other technical factors that could affect gene expression levels. Popular normalization methods include TMM (trimmed mean of M-values), RPKM (reads per kilobase per million mapped reads), and TPM (transcripts per million).
- Statistical analysis: The normalized gene expression matrix is analyzed using statistical software to identify genes that are differentially expressed between two or more groups of samples. This step involves comparing the expression levels of each gene across different groups, and calculating statistical significance and fold changes.
- Gene annotation and functional analysis: The differentially expressed genes are annotated and analyzed for enrichment in functional categories and pathways. This step can provide insights into the biological processes and molecular mechanisms that are regulated in the sample.
Some common statistical tests used for differential gene expression analysis include t-tests, ANOVA, and regression models. Multiple testing correction methods such as the Benjamini-Hochberg procedure can also be used to control the false discovery rate.
Overall, differential gene expression analysis provides a powerful tool for identifying genes that are differentially expressed between different biological conditions, and can provide insights into the molecular mechanisms underlying physiological processes and disease states. However, this analysis requires careful experimental design, rigorous statistical analysis, and careful consideration of potential confounding factors that could affect gene expression levels.