# CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions

## TL;DR

CellPolaris is a new tool that uses transfer learning to build gene regulatory networks and identify key transcription factors that guide cell fate transitions.

## Contribution

CellPolaris introduces a transfer learning framework for GRN construction and TF perturbation simulation, enabling cell-type-specific regulatory insights.

## Key findings

- CellPolaris accurately constructs gene regulatory networks using transcriptomic data and transfer learning.
- The framework identifies master transcription factors critical for cell fate transitions with high overlap to experimentally validated regulators.
- CellPolaris simulates developmental consequences of TF perturbations, such as Rfx2 knockout during spermatid differentiation.

## Abstract

Cell fate decisions are orchestrated by intricate gene regulatory networks (GRNs), which govern gene expression with precise spatiotemporal control. However, accurately capturing context‐specific nature of gene regulation remains challenging, particularly when integrating multi‐omics data at bulk and single‐cell level across diverse cellular contexts.

Here, we present CellPolaris, a unified computational framework designed to decode the roles of transcription factors (TFs) in developmental processes. CellPolaris performs TF‐centered GRN construction, master TF identification, and TF perturbation simulation. By leveraging transfer learning, the framework generates tissue‐specific or cell‐type‐specific GRNs using pre‐constructed high‐confidence GRNs of diverse contexts and requires only transcriptomic data as input. Using these learned GRNs, CellPolaris identifies underlying master TFs critical for cell fate transitions and simulates the effects of TF perturbations on developmental processes. Benchmarking tests demonstrate the robust performance of CellPolaris in GRN construction. The efficacy of CellPolaris is supported by the significant overlap between predicted top‐ranked master regulators and known TF combinations experimentally validated in cell fate conversion experiments. Furthermore, CellPolaris accurately simulates the developmental consequences of Rfx2 knockout during round spermatid differentiation. In summary, we present CellPolaris, a comprehensive framework that enables GRN construction through transfer learning, identification of key TFs driving cell fate transitions, and simulation of TF perturbations. This tool allows us to further elucidate the regulatory mechanisms underlying developmental processes and cell state transitions.

CellPolaris decodes how transcription factors guide cell fate by building gene regulatory networks from transcriptomic data using transfer learning. It generates tissue‐ and cell‐type‐specific networks, identifies master regulators in cell state transitions, and simulates TF perturbations in developmental processes. The authors benchmark CellPolaris for GRN construction and demonstrate its applications in cell reprogramming and differentiation.