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Activity Number: 442 - Methods for Single-Cell and Microbiome Sequencing Data
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
Sponsor: Biometrics Section
Abstract #312621
Title: Learning Directed Acyclic Graphs Integrating Bulk Tumor and Single Cell RNAseq Data
Author(s): Shrabanti Chowdhury* and Jie Peng and Pei Wang
Companies: Icahn school of Medicine at Mount Sinai and University of California Davis and Icahn Medical School at Mount Sinai
Keywords: causal association; regulatory networks; directed acyclic graphs; aggregation; prior

Inferring gene regulatory network (GRN) based on -omics profiles is commonly pursued in biological studies. Among different approaches of constructing (GRN), directed acyclic graph (DAG) models are commonly used to infer causality of the regulatory relationships among interacting genes in a complex biological system. Recently single-cell RNA-sequencing (scRNA-seq) technologies have made it possible to provide insights into transcriptional networks among cell populations based on the sequencing of a large number of cells. In this work we propose joint learning of multiple cell type-specific DAGs through aggregation in scRNAseq data. Aggregation strategy helps to reduce false positives. In DAG structure learning edge directions are not always identifiable without external information. We utilize methylation/protein data from bulk tumor profiling that provide prior information on regulatory directions to enhance the robustness of the network construction.

Authors who are presenting talks have a * after their name.

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