N-of-one differential gene expression without control samples using a deep generative model

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Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can identify the closest normal representation for a given disease sample. This enables control-free, single-sample differential expression analysis. In breast cancer, we demonstrate how our approach selects marker genes and outperforms a state-of-the-art method. Furthermore, significant genes identified by the model are enriched in driver genes across cancers. Our results show that the in silico closest normal provides a more favorable comparison than control samples.

Original languageEnglish
Article number263
JournalGenome Biology
Issue number1
Number of pages17
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

    Research areas

  • Deep generative models, Deep learning, DEG, DEseq2, Differential expression analysis, Transcriptomics

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