Identifying key multifunctional components shared by critical cancer and normal liver pathways via SparseGMM.
Cell Rep Methods
; 3(1): 100392, 2023 01 23.
Article
en En
| MEDLINE
| ID: mdl-36814838
ABSTRACT
Despite the abundance of multimodal data, suitable statistical models that can improve our understanding of diseases with genetic underpinnings are challenging to develop. Here, we present SparseGMM, a statistical approach for gene regulatory network discovery. SparseGMM uses latent variable modeling with sparsity constraints to learn Gaussian mixtures from multiomic data. By combining coexpression patterns with a Bayesian framework, SparseGMM quantitatively measures confidence in regulators and uncertainty in target gene assignment by computing gene entropy. We apply SparseGMM to liver cancer and normal liver tissue data and evaluate discovered gene modules in an independent single-cell RNA sequencing (scRNA-seq) dataset. SparseGMM identifies PROCR as a regulator of angiogenesis and PDCD1LG2 and HNF4A as regulators of immune response and blood coagulation in cancer. Furthermore, we show that more genes have significantly higher entropy in cancer compared with normal liver. Among high-entropy genes are key multifunctional components shared by critical pathways, including p53 and estrogen signaling.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Perfilación de la Expresión Génica
/
Neoplasias Hepáticas
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Cell Rep Methods
Año:
2023
Tipo del documento:
Article