Inference

Gene regulatory network (GRN) underpins cell identity and function. The ability to infer GRNs at scale presents a valuable opportunity to understand gene and cell functions at the systems level and discover potential therapeutic targets. These endeavors were initiated during the microarray era. However, bulk microarray and sequencing technologies have limited cell-type resolution and high cost. Our ability to extract biology with GRN inference [1-3] were inherently constrained by data quality and quantity.

Single-cell technology has revolutionized this landscape by allowing for cost-effective measurements of individual cells. This advance alone has demonstrated tremendous potential in uncovering cell-type specific co-expression networks from single-cell RNA-seq [4]. Moreover, single-cell perturbations and chromatin accessibility measurements have enabled complex GRN inference methods that employ causal inference approaches [4] and distinguish direct effects [5].

Building upon our past work and benefiting from the rapid biotechnological developments within the single-cell community, we aim to illustrate the unique conceptual advantages of single-cell multiomics in GRN inference. We are actively developing computational methods to efficiently harness state-of-the-art data modalities and design comprehensive benchmarks to compare with alternative approaches. We also release open-source computer software to empower individual research labs to perform understand the GRNs in their own datasets.

[1] Wang, L. & Michoel, T. Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data. PLOS Computational Biology 13, e1005703 (2017).

[2] Wang, L. & Michoel, T. Controlling false discoveries in Bayesian gene networks with lasso regression p-values. arXiv:1701.07011 (2017).

[3] Wang, L., Audenaert, P. & Michoel, T. High-Dimensional Bayesian Network Inference From Systems Genetics Data Using Genetic Node Ordering. Front. Genet. 10, (2019).

[4] Wang, L. Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr. Nat Commun 12, 6395 (2021).

[5] Wang, L. et al. Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics. Nat Methods 1–11 (2023).