Effective Biological Representation Learning by Masking Gene Expression

2026-05-29

AI Summary

Existing transcriptomic foundation models often underperform on RNA-seq data due to technical noise and batch effects, sometimes even falling short of simple linear baselines. To address this limitation, this work proposes TxFM, a masked autoencoding self-supervised model specifically designed for RNA-seq count data. By integrating inductive representation learning with a high-quality, diverse training corpus—DiverseRNA-1.4M, comprising only 1.4 million samples—TxFM substantially enhances gene representation capabilities. Systematic ablation studies validate the importance of key architectural choices, and extensive evaluations demonstrate that TxFM outperforms current foundation models trained on datasets over 100 times larger across multiple downstream tasks. These results underscore the efficacy and superiority of carefully crafted self-supervised learning strategies in transcriptomics.

Abstract

RNA sequencing produces rich and diverse datasets of gene expression, offering compelling insights into cellular state and function that have many applications in drug discovery. Modeling such data is challenging due to inherent technical noise and experimental batch effects, as evidenced by many existing transcriptomic foundation models (FMs) underperforming relative to linear baselines. Such results raise the question of whether deep representation learning provides a distinct advantage over the direct use of raw transcript counts. Our work explores this by developing a new self-supervised model, TxFM, with a focus on inductive representation learning evaluations. TxFM employs a masked autoencoding approach tailored to diverse RNA-seq count data, and our ablation study empirically identifies crucial architecture configurations required for strong transfer performance. Additionally, we curate a public training corpus, DiverseRNA-1.4M, and find that TxFM trained on this curated dataset yields high-fidelity gene representations that outperform FMs trained on atlas-scale corpora over 100x larger. Overall, our results indicate that inductive self-supervised learning is a viable modeling approach for transcriptomics representation, provided a careful synthesis of model architecture and training data curation.

Problem

• gene expression
• representation learning
• RNA-seq
• transcriptomics
• self-supervised learning

Innovation

• masked autoencoding
• self-supervised learning
• transcriptomic representation
• inductive representation learning
• RNA-seq