# Unveiling gene regulatory networks during cellular state transitions without linkage across time points

## TL;DR

This paper introduces COSLIR, a new method for reconstructing gene regulatory networks from single-cell data without needing to track cells over time.

## Contribution

COSLIR directly infers gene regulatory networks using covariance and sparsity without requiring temporal ordering of cells.

## Key findings

- COSLIR achieves perfect accuracy in simulations and performs robustly in real-world single-cell datasets.
- COSLIR outperforms existing methods in developmental biology datasets while maintaining efficient computation time.
- The method bypasses the need for temporal ordering, enhancing accuracy and efficiency in GRN reconstruction.

## Abstract

Time-stamped cross-sectional data, which lack linkage across time points, are commonly generated in single-cell transcriptional profiling. Many previous methods for inferring gene regulatory networks (GRNs) driving cell-state transitions relied on constructing single-cell temporal ordering. Introducing COSLIR (COvariance restricted Sparse LInear Regression), we presented a direct approach to reconstructing GRNs that govern cell-state transitions, utilizing only the first and second moments of samples between two consecutive time points. Simulations validated COSLIR’s perfect accuracy in the oracle case and demonstrated its robust performance in real-world scenarios. When applied to single-cell RT-PCR and RNAseq datasets in developmental biology, COSLIR competed favorably with existing methods. Notably, its running time remained nearly independent of the number of cells. Therefore, COSLIR emerges as a promising addition to GRN reconstruction methods under cell-state transitions, bypassing the single-cell temporal ordering to enhance accuracy and efficiency in single-cell transcriptional profiling.