Location: Animal Parasitic Diseases LaboratoryTitle: Temporal dynamics in meta longitudinal RNA-Seq data Author
|Oh, Sunghee - Jeju National University|
|Baldwin, Ransom - Randy|
|Song, Seongho - University Of Cincinnati|
|Liu, Fang - Ocean University Of China|
Submitted to: Scientific Reports
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/27/2018
Publication Date: 1/24/2019
Citation: Oh, S., Li, C., Baldwin, R.L., Song, S., Liu, F., Li, R.W. 2019. Temporal dynamics in meta longitudinal RNA-Seq data. Scientific Reports. 9(1):763. https://doi.org/10.1038/s41598-018-37397-7
DOI: https://doi.org/10.1038/s41598-018-37397-7 Interpretive Summary: Mining transcriptome data to identify biomarkers and disease-related genes is one of the most frequently applied approaches in agricultural and biomedical research. However, tools for the analysis of longitudinally repeated measurements at different time points are still lacking. In this study, we developed novel strategies to remove systematic bias and provided a generalized guideline for temporally measured experimental designs. Our protocols should facilitate the better understanding of disease progression and precise estimation and prediction of molecular mechanisms for unobserved time points.
Technical Abstract: Identification of differentially expressed genes has been a high priority task of down-stream analyses to make advances in biomedical research. Due to the advances in technology and reduced costs in transcriptome assessment, compared to small sample size designs of the past, well-balanced experimental designs containing more time points and biological replicates are increasingly becoming available. To date, there are currently no standard approaches to precisely and efficiently analyze these moderate or large scale experimental designs. Investigators have been faced with an array of issues in dealing with more complicated experiments and meta approaches such as: batch effects, normalization, temporal dynamics (differential expression during a series of time points), isoform diversity (isoform level quantification and corresponding differential splicing events). In this report, we propose comprehensive analytical protocols to precisely characterize temporal dynamics in isoform quantification and differential expression by controlling batch effects, other nuisance factors that could have significant confounding effects against main effects of interest in comparative models and could result in misleading interpretations.