Investigation of cell development and tissue structure network based on natural Language processing of scRNA-seq data.

Background: Single-cell multi-omics technologies, particularly single-cell RNA sequencing (scRNA-seq), have revolutionized our understanding of cellular heterogeneity and development by providing insights into gene expression at the single-cell level. Investigating the influence of genes on cellular behavior is crucial for elucidating cell fate determination and differentiation, cell development processes, and disease mechanisms.

Methods: Inspired by NLP, we present a novel scRNA-seq analysis method that treats genes as analogous to words. Using word2vec to embed gene sequences derived from gene networks, we generate vector representations of genes, which are then used to represent cells by summing gene vectors and subsequently tissues by aggregating cell vectors.

Results: Our NLP-based approach analyzes scRNA-seq data by generating vector representations of genes, cells, and tissues. This multi-scale analysis includes mapping cell states in vector space to reveal developmental trajectories, quantifying cell similarity using Euclidean distance, and constructing inter-tissue relationship networks from aggregated cell vectors.

Conclusions: This method offers a computationally efficient approach for analyzing scRNA-seq data by constructing embedding representations similar to those used in large language model pre-training, but without requiring high-performance computing clusters. By generating gene embeddings that capture functional relationships, this method facilitates the study of cell development trajectories, the impact of gene perturbations, cell clustering, and the construction and analysis of tissue networks. This provides a valuable tool for single-cell data analysis.