Artikel ; Online: Evaluation of critical data processing steps for reliable prediction of gene co-expression from large collections of RNA-seq data.
2022 Band 17, Heft 1, Seite(n) e0263344
Abstract: Motivation: Gene co-expression analysis is an attractive tool for leveraging enormous amounts of public RNA-seq datasets for the prediction of gene functions and regulatory mechanisms. However, the optimal data processing steps for the accurate ... ...
| Abstract | Motivation: Gene co-expression analysis is an attractive tool for leveraging enormous amounts of public RNA-seq datasets for the prediction of gene functions and regulatory mechanisms. However, the optimal data processing steps for the accurate prediction of gene co-expression from such large datasets remain unclear. Especially the importance of batch effect correction is understudied. Results: We processed RNA-seq data of 68 human and 76 mouse cell types and tissues using 50 different workflows into 7,200 genome-wide gene co-expression networks. We then conducted a systematic analysis of the factors that result in high-quality co-expression predictions, focusing on normalization, batch effect correction, and measure of correlation. We confirmed the key importance of high sample counts for high-quality predictions. However, choosing a suitable normalization approach and applying batch effect correction can further improve the quality of co-expression estimates, equivalent to a >80% and >40% increase in samples. In larger datasets, batch effect removal was equivalent to a more than doubling of the sample size. Finally, Pearson correlation appears more suitable than Spearman correlation, except for smaller datasets. Conclusion: A key point for accurate prediction of gene co-expression is the collection of many samples. However, paying attention to data normalization, batch effects, and the measure of correlation can significantly improve the quality of co-expression estimates. |
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| Mesh-Begriff(e) | Animals ; Databases, Genetic ; Gene Expression Regulation ; Gene Ontology ; Gene Regulatory Networks ; Genome, Human ; Humans ; Linear Models ; Mice ; Models, Genetic ; RNA-Seq ; Reproducibility of Results ; Statistics, Nonparametric |
| Sprache | Englisch |
| Erscheinungsdatum | 2022-01-28 |
| Erscheinungsland | United States |
| Dokumenttyp | Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2267670-3 |
| ISSN | 1932-6203 ; 1932-6203 |
| ISSN (online) | 1932-6203 |
| ISSN | 1932-6203 |
| DOI | 10.1371/journal.pone.0263344 |
| Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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