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

Iñigo Prada-LuengoViktoria SchusterYuhu LiangThilde TerkelsenValentina SoraAnders Krogh
Published in: Genome biology (2023)
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.
Keyphrases
  • gene expression
  • rna seq
  • genome wide
  • single cell
  • dna methylation
  • genome wide identification
  • machine learning
  • bioinformatics analysis
  • young adults
  • high intensity
  • genome wide analysis
  • molecular dynamics simulations