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Improving gene function predictions using independent transcriptional components

Author

Listed:
  • Carlos G. Urzúa-Traslaviña

    (University of Groningen)

  • Vincent C. Leeuwenburgh

    (University of Groningen
    University of Groningen)

  • Arkajyoti Bhattacharya

    (University of Groningen)

  • Stefan Loipfinger

    (University of Groningen)

  • Marcel A. T. M. Vugt

    (University of Groningen)

  • Elisabeth G. E. Vries

    (University of Groningen)

  • Rudolf S. N. Fehrmann

    (University of Groningen)

Abstract

The interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated using human or mouse transcriptomic data are made available for exploration in a publicly available web portal.

Suggested Citation

  • Carlos G. Urzúa-Traslaviña & Vincent C. Leeuwenburgh & Arkajyoti Bhattacharya & Stefan Loipfinger & Marcel A. T. M. Vugt & Elisabeth G. E. Vries & Rudolf S. N. Fehrmann, 2021. "Improving gene function predictions using independent transcriptional components," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21671-w
    DOI: 10.1038/s41467-021-21671-w
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