Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 67,655 bioRxiv papers from 298,484 authors.

Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders

By Soeren Lukassen, Foo Wei Ten, Roland Eils, Christian Conrad

Posted 20 Aug 2019
bioRxiv DOI: 10.1101/740415

Recent advances in single-cell RNA sequencing (scRNA-Seq) have driven the simultaneous measurement of the expression of 1,000s of genes in 1,000s of single cells. In principle, these growing data sets now allow us to model the gene sets in biological networks at an unprecedented level of detail, in spite of heterogenous cell populations. To this end we propose an unsupervised deep neural network model that is a novel conditional variational autoencoder (CVA), which utilizes weights as matrix factorizations to obtain gene sets, while class-specific inputs to the latent variable space facilitate a plausible identification of cell types. In essence, this artificial neural network model seamlessly leverages the information of functional gene set inference, experimental batch effect correction, and static gene identification, which we conceptually prove here for three single-cell RNA-Seq datasets and suggest for future single-cell-gene analytics.

Download data

  • Downloaded 572 times
  • Download rankings, all-time:
    • Site-wide: 17,999 out of 67,655
    • In bioinformatics: 2,719 out of 6,659
  • Year to date:
    • Site-wide: 5,290 out of 67,655
  • Since beginning of last month:
    • Site-wide: 5,767 out of 67,655

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News