PT  - JOURNAL ARTICLE
AU  - Jianhao Peng
AU  - Ullas V. Chembazhi
AU  - Sushant Bangru
AU  - Ian M. Traniello
AU  - Auinash Kalsotra
AU  - Idoia Ochoa
AU  - Mikel Hernaez
TI  - SimiC: A Single Cell Gene Regulatory Network Inference method with Similarity Constraints
AID  - 10.1101/2020.04.03.023002
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.04.03.023002
4099  - http://biorxiv.org/content/early/2020/04/04/2020.04.03.023002.short
4100  - http://biorxiv.org/content/early/2020/04/04/2020.04.03.023002.full
AB  - Motivation With the use of single-cell RNA sequencing (scRNA-Seq) technologies, it is now possible to acquire gene expression data for each individual cell in samples containing up to millions of cells. These cells can be further grouped into different states along an inferred cell differentiation path, which are potentially characterized by similar, but distinct enough, gene regulatory networks (GRNs). Hence, it would be desirable for scRNA-Seq GRN inference methods to capture the GRN dynamics across cell states. However, current GRN inference methods produce a unique GRN per input dataset (or independent GRNs per cell state), failing to capture these regulatory dynamics.Results We propose a novel single-cell GRN inference method, named SimiC, that jointly infers the GRNs corresponding to each state. SimiC models the GRN inference problem as a LASSO optimization problem with an added similarity constraint, on the GRNs associated to contiguous cell states, that captures the inter-cell-state homogeneity. We show on a mouse hepatocyte single-cell data generated after partial hepatectomy that, contrary to previous GRN methods for scRNA-Seq data, SimiC is able to capture the transcription factor (TF) dynamics across liver regeneration, as well as the cell-level behavior for the regulatory program of each TF across cell states. In addition, on a honey bee scRNA-Seq experiment, SimiC is able to capture the increased heterogeneity of cells on whole-brain tissue with respect to a regional analysis tissue, and the TFs associated specifically to each sequenced tissue.Availability SimiC is written in Python and includes an R API. It can be downloaded from https://github.com/jianhao2016/simicLASSO_git.Contact idoia{at}illinois.edu, mhernaez{at}illinois.eduSupplementary information Supplementary data are available at the code repository.