Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 70,927 bioRxiv papers from 309,498 authors.

Transcriptome-wide splicing quantification in single cells

By Yuanhua Huang, Guido Sanguinetti

Posted 05 Jan 2017
bioRxiv DOI: 10.1101/098517 (published DOI: 10.1186/s13059-017-1248-5)

Single cell RNA-seq (scRNA-seq) has revolutionised our understanding of transcriptome variability, with profound implications both fundamental and translational. While scRNA-seq provides a comprehensive measurement of stochasticity in transcription, the limitations of the technology have prevented its application to dissect variability in RNA processing events such as splicing. Here we present BRIE (Bayesian Regression for Isoform Estimation), a Bayesian hierarchical model which resolves these problems by learning an informative prior distribution from multiple single cells. BRIE combines the mixture modelling approach for isoform quantification with a regression approach to learn sequence features which are predictive of splicing events. We validate BRIE on several scRNA-seq data sets, showing that BRIE yields reproducible estimates of exon inclusion ratios in single cells and provides an effective tool for differential isoform quantification between scRNA-seq data sets. BRIE therefore expands the scope of scRNA-seq experiments to probe the stochasticity of RNA-processing.

Download data

  • Downloaded 1,213 times
  • Download rankings, all-time:
    • Site-wide: 6,035 out of 70,897
    • In bioinformatics: 1,137 out of 6,940
  • Year to date:
    • Site-wide: 35,934 out of 70,897
  • Since beginning of last month:
    • Site-wide: 34,927 out of 70,897

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide


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