Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps.

ABSTRACT: 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 co

SUBMITTER: Jansen C 

PROVIDER: S-EPMC6855564 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature