Abstract: Developments in single-cell RNA sequencing (scRNA-seq) advanced our understanding of transcriptional programs of different cell types and cellular stages at the individual cell level. Single-cell RNA-seq datasets across multiple individuals and time points are now routinely generated for different conditions. Analysis of personalized dynamic gene networks constructed from these datasets could unravel subject-specific network-level variation critical for phenotypic differences. While there have been developments in the gene module discovery methods on networks estimated from scRNA-seq data, these have mostly focused on static gene networks. In this work, we develop MuDCoD to cluster genes in personalized dynamic gene networks and identify gene modules that vary not only across time but also among subjects. To this end, MuDCoD extends the global spectral clustering framework of the previously developed method, PisCES, to promote information sharing among the subject as well as the time domain. Our computational experiments across a wide variety of settings indicate that, when present, MuDCoD leverages shared signals among networks of the subjects, and performs robustly when subjects do not share any apparent information. An application to human-induced pluripotent stem cell scRNA-seq data during dopaminergic neuron differentiation indicates that MuDCoD enables robust inference for identifying time-varying personalized gene modules. Our results illustrate how personalized dynamic community detection can aid the exploration of subject-specific biological processes that vary across time.
Journal Link: 10.1101/2021.11.30.470619 Journal Link: Publisher Website Journal Link: Downaload PDF Journal Link: Google Scholar