☃️RNA-Seq Bonus Part: Mitigating Unwanted Variation☃️
When conducting RNA-Seq differential expression analysis, accounting for variation beyond the variable of interest (treatment or phenotype) is crucial. Variables like batch effects, library selection, or other technical factors can introduce systematic shifts in data.
⚖️DESeq2 for Covariates:
In cases where known sources of variation (like batch effects) exist, DESeq2 helps include covariates in the design formula, thereby, controlling for unwanted variation and ensuring analysis focuses on the biological factors that matter.
🦏In R:
dds <- DESeq(dds)
DEresults <- results(dds, contrast = c('group', 'CASE', 'CTRL'))
This method ensures that differential expression results are not confounded by technical variations.
🧰Estimating Unwanted Variations with RUVSeq:
When the sources of variation are unknown, RUVSeq (or sva) comes to the rescue! By using reference genes (or even empirical data), RUVSeq helps clean up the counts table by estimating and correcting for hidden technical variations.
🦏In R:
set_g <- RUVg(x = set, cIdx = house_keeping_genes, k = 1)
This allows for better separation between biological conditions in PCA plots and improves the accuracy of downstream analyses
🥗Removing Unwanted Variations:
RUVg: Removes unwanted variations using a set of reference genes.
RUVs: Better suited for replicates and confounding designs, improving sample clustering and biological separation. Both methods ensure the biological signal is not overwhelmed by technical noise.
👀MUST UNDERSTAND
RNA-Seq data analysis is about identifying gene expression differences, refining results by accounting for external variations. With tools like DESeq2 and RUVSeq, improvements on the reliability of findings can be made for confident biological inferences.