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	機械学習支援RNAシーケンス差次的発現解析	RNA-seq 差次的発現
分野	バイオインフォマティクス	バイオインフォマティクス
系統	Process / pipeline	Process / pipeline
提唱年	2015–2019 (rapid development period)	2008–2010 (RNA-seq DE methodology established)
提唱者	Multiple groups; scVI (Lopez et al., 2018) and DCA (Eraslan et al., 2019) are landmark tools	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
種類	Computational bioinformatics pipeline	Quantitative genomics pipeline
原典	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.	Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.
別名	ML-based DE analysis, deep learning RNA-seq DE, neural network differential expression, ML-augmented transcriptomics	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
関連	5	6
概要	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.	RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values.