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

• Published in: Nanomaterials (Basel, Switzerland) Vol. 10; no. 5; p. 903
• Main Authors: Federico, Antonio, Serra, Angela, Ha, My Kieu, Kohonen, Pekka, Choi, Jang-Sik, Liampa, Irene, Nymark, Penny, Sanabria, Natasha, Cattelani, Luca, Fratello, Michele, Kinaret, Pia Anneli Sofia, Jagiello, Karolina, Puzyn, Tomasz, Melagraki, Georgia, Gulumian, Mary, Afantitis, Antreas, Sarimveis, Haralambos, Yoon, Tae-Hyun, Grafström, Roland, Greco, Dario
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 08.05.2020; MDPI AG
• Subjects: data preprocessing; Medicin och hälsovetenskap; microarray; Review; RNA-Seq; scRNA-Seq; toxicogenomics; transcriptomics; data preprocessing; microarray; quality check; scRNA-Seq; toxicogenomics; RNA-Seq; normalization; batch effect; transcriptomics; differential expression
• ISSN: 2079-4991; 2079-4991
• DOI: 10.3390/nano10050903

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.