scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

Genome Medicine

Ting Jin¹  Daifeng Wang²  Panagiotis Roussos³  Jiawei Huang⁴  Mufang Ying⁵  Shuang Liu⁶  Peter Rehani⁷ 

[1] Department of Biostatistics and Medical Informatics, University of Wisconsin – Madison, 53706, Madison, WI, USA;Waisman Center, University of Wisconsin – Madison, 53705, Madison, WI, USA;Department of Biostatistics and Medical Informatics, University of Wisconsin – Madison, 53706, Madison, WI, USA;Waisman Center, University of Wisconsin – Madison, 53705, Madison, WI, USA;Department of Computer Sciences, University of Wisconsin – Madison, 53706, Madison, WI, USA;Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 10029, New York, NY, USA;Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 10029, New York, NY, USA;Department of Statistics, University of Wisconsin – Madison, 53706, Madison, WI, USA;Department of Statistics, University of Wisconsin – Madison, 53706, Madison, WI, USA;Present address: Department of Statistics, Rutgers University, 08854, Piscataway, NJ, USA;Waisman Center, University of Wisconsin – Madison, 53705, Madison, WI, USA;Waisman Center, University of Wisconsin – Madison, 53705, Madison, WI, USA;Department of Integrative Biology, University of Wisconsin – Madison, 53706, Madison, WI, USA;Present address: Morgridge Institute for Research, 53715, Madison, WI, USA;

关键词: Single-cell genomics;    Single-cell multi-omics integration;    Cell-type gene regulatory network;    Cell-type disease risk genes;    Cross-disease functional genomics;    Schizophrenia;    Alzheimer’s disease;   

DOI  :  10.1186/s13073-021-00908-9

来源: Springer

【 摘 要 】

Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom.

【 授权许可】

CC BY