DeepIMAGER: Deeply Analyzing Gene Regulatory Networks from scRNA-seq Data.

ABSTRACT: Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types poses a challenge yet holds immense value in unraveling the molecular mechanisms governing cellular processes. Current computational methods, which rely solely on expression changes from bulk RNA-seq and/or scRNA-seq data, often result in high rates of false positives and low precision. Here, we introduce an advanced computational tool, DeepIMAGER, for inferring cell-specific GRNs through deep learning and data integration. DeepIMAGER employs a supervised approach that transforms the co-expression patterns of gene pairs into image-like representations and leverages transcription factor (TF) binding information for model training. It is trained using comprehensive datasets that encompass scRNA-seq profil

SUBMITTER: Zhou X 

PROVIDER: S-EPMC11274664 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature