Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data.

Area of Science:

• Toxicogenomics
• Transcriptomics data analysis

Background:

• Transcriptomics data preprocessing is vital for developing toxicogenomics (TGx) tools for chemical toxicity assessment.
• Optimal analytical workflows are not always straightforward, requiring careful tool selection impacting downstream analyses.
• Standard guidelines for TGx data preprocessing are currently lacking.

Purpose of the Study:

• To review preprocessing methods for transcriptomics data from microarray, bulk RNA-Sequencing (RNA-Seq), and single-cell RNA-Sequencing (scRNA-Seq).
• To discuss common methods for identifying differentially expressed genes and performing functional enrichment analysis.
• To address the need for standardized preprocessing procedures in TGx.

Main Methods:

• Review of existing literature on transcriptomics data preprocessing techniques.
• Outline of quality check, filtering, normalization, and batch effect correction methods.
• Discussion of gene expression analysis and functional enrichment approaches.

Main Results:

• Identification of key preprocessing steps: quality check, filtering, normalization, batch effect detection/correction.
• Overview of methods applicable to microarray, bulk RNA-Seq, and scRNA-Seq data.
• Summary of common techniques for differential gene expression and functional enrichment analysis.

Conclusions:

• Standardized transcriptomics data preprocessing is essential for generating unbiased data.
• Reliable, robust, and accurate predictive models for chemical toxicity assessment can be developed with optimal preprocessing.
• This review provides guidance for the TGx field, contributing to the second part of a series on Transcriptomics in Toxicogenomics.