Causal analysis of cell-cell communication with single-cell and spatial omics data

Mentors:
Lead: Zixuan Cang (Mathematics, NCSU)
Collaborator: Shu Yang (Statistics, NCSU)

Intellectual merit and significance:
Multicellular organisms ensure robust developmental pathways and intricate functionalities through intercellular communication. Recent single-cell and spatial omics technologies offer an unbiased lens for dissecting cell-cell communication (CCC) [1]. Computational methodologies have been established to infer CCC [1, 2]. Yet the processes surrounding CCC activities remain mostly unknown, with limited computational tools to explore them. DRUMS students will develop causal inference [3] methodologies to uncover intracellular processes that either modulate or are modulated by CCC activities and discern the causal relationships within CCC.

Objectives:
(i) Explore approaches to processing single-cell and spatial data for CCC. (ii) Investigate how to formulate causal inference methodologies identifying upstream genes regulating CCC activity and downstream genes influenced by CCC. (iii) Integrate new methods into a comprehensive software package.

Outcomes:
A causal inference method implemented in a user-friendly software package for identifying intracellular processes causally linked to CCC and for deriving experimentally testable hypotheses.

References:
1. Cang Z, Zhao Y, Almet AA, Stabell A, Ramos R, Plikus M, Nie Q. Screening cell–cell communication in spatial transcriptomics via collective optimal transport. Nature Meth. 2023;20:218-228.
2. Jin S, Guerrero-Juarez C, Zhang L, Chang I, Ramos R, Kuan C, Nie Q. Inference and analysis of cell-cell communication using CellChat. Nature communications. 2021;12:1088.
3. Tan X, Yang S, Ye W, Faries D, Lipkovich I, Kadziola Z. When doubly robust methods meet machine learning for estimating treatment effects from real-world data: A comparative study. arXiv. 2022;arXiv:2204.10969.