Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Аналіз диференційної експресії РНК-сек з використанням машинного навчання× | Аналіз збагачення шляхів× | |
|---|---|---|
| Галузь | Біоінформатика | Біоінформатика |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2015–2019 (rapid development period) | 2003–2005 |
| Автор методу≠ | Multiple groups; scVI (Lopez et al., 2018) and DCA (Eraslan et al., 2019) are landmark tools | Mootha et al. (2003); systematised by Subramanian et al. (2005) |
| Тип≠ | Computational bioinformatics pipeline | Statistical functional annotation method |
| Основоположне джерело≠ | Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), 1053–1058. link ↗ | Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550. DOI ↗ |
| Інші назви | ML-based DE analysis, deep learning RNA-seq DE, neural network differential expression, ML-augmented transcriptomics | PEA, overrepresentation analysis, ORA, functional enrichment analysis |
| Пов'язані≠ | 5 | 6 |
| Підсумок≠ | Machine learning-assisted RNA-seq differential expression analysis augments classical statistical DE testing (DESeq2, edgeR, limma-voom) with ML models — including neural networks, random forests, and variational autoencoders — to better handle the high dimensionality, zero-inflation, and batch effects inherent in RNA-seq count data. The approach improves feature selection, noise reduction, and detection power, especially in large or complex experimental designs. | Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments. |
| ScholarGateНабір даних ↗ |
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