Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps
Type
ArticleAuthors
Jansen, CamdenRamirez, Ricardo N.

El-Ali, Nicole C.
Gomez-Cabrero, David
Tegner, Jesper

Merkenschlager, Matthias

Conesa, Ana
Mortazavi, Ali

KAUST Department
Biological and Environmental Sciences and Engineering (BESE) DivisionBioscience Program
Date
2019-11-01Permanent link to this record
http://hdl.handle.net/10754/660332
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Show full item recordAbstract
Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq regions with scRNA-seq genes that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of heterogeneous data.Citation
Jansen, C., Ramirez, R. N., El-Ali, N. C., Gomez-Cabrero, D., Tegner, J., Merkenschlager, M., … Mortazavi, A. (2019). Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps. PLOS Computational Biology, 15(11), e1006555. doi:10.1371/journal.pcbi.1006555Sponsors
This work was supported by were supported by EU FP7 306000 STATegra.Publisher
Public Library of Science (PLoS)Journal
PLoS computational biologyAdditional Links
https://dx.plos.org/10.1371/journal.pcbi.1006555https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006555&type=printable
ae974a485f413a2113503eed53cd6c53
10.1371/journal.pcbi.1006555
Scopus Count
Except where otherwise noted, this item's license is described as Copyright: © 2019 Jansen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.