DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (GigaScience)
Abstract
Background
Single-cell RNA sequencing (scRNA-seq) unfolds complex transcriptomic datasets into detailed cellular maps. Despite recent success, there is a pressing need for specialized methods tailored towards the functional interpretation of these cellular maps.
Findings
Here, we present DrivAER, a machine learning approach for the identification of driving transcriptional programs using autoencoder-based relevance scores. DrivAER scores annotated gene sets on the basis of their relevance to user-specified outcomes such as pseudotemporal ordering or disease status. DrivAER iteratively evaluates the information content of each gene set with respect to the outcome variable using autoencoders. We benchmark our method using extensive simulation analysis as well as comparison to existing methods for functional interpretation of scRNA-seq data. Furthermore, we demonstrate that DrivAER extracts key pathways and transcription factors that regulate complex biological processes from scRNA-seq data.
Conclusions
By quantifying the relevance of annotated gene sets with respect to specified outcome variables, DrivAER greatly enhances our ability to understand the underlying molecular mechanisms.
Article activity feed
-
Now published in GigaScience doi: 10.1093/gigascience/giaa122
Lukas M. Simon 1Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Lukas M. SimonFor correspondence: lukas.simon@uth.tmc.edu zhongming.zhao@uth.tmc.eduFangfang Yan 1Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteZhongming Zhao 1Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA2Human Genetics …
Now published in GigaScience doi: 10.1093/gigascience/giaa122
Lukas M. Simon 1Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Lukas M. SimonFor correspondence: lukas.simon@uth.tmc.edu zhongming.zhao@uth.tmc.eduFangfang Yan 1Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteZhongming Zhao 1Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA2Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA3MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA4Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteFor correspondence: lukas.simon@uth.tmc.edu zhongming.zhao@uth.tmc.edu
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giaa122 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
These peer reviews were as follows:
Reviewer 1: http://dx.doi.org/10.5524/REVIEW.102528 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.102529 Reviewer 3: http://dx.doi.org/10.5524/REVIEW.102530 Reviewer 4: http://dx.doi.org/10.5524/REVIEW.102531
-
-
