Title: CellPainTR: Generalizable Representation Learning for Cross-Dataset Cell Painting Analysis
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Representation Learning, Transformers, Foundation Models, Out-of-Distribution Generalization, Batch Correction, Cell Painting, Self-Supervised Learning

Abstract: Large-scale biological discovery requires integrating massive, heterogeneous datasets like those from the JUMP Cell Painting consortium, but technical batch effects and a lack of generalizable models remain critical roadblocks. To address this, we introduce CellPainTR, a Transformer-based architecture designed to learn foundational representations of cellular morphology that are robust to batch effects. We validate CellPainTR on the large-scale JUMP dataset, demonstrating its ability to successfully integrate data from 13 diverse laboratory sources, which serves as our primary evidence for its cross-dataset generalization capabilities. It outperforms established methods in both batch integration and biological signal preservation. Furthermore, we probe the limits of this learned robustness with a challenging out-of-distribution (OOD) "stress test," applying our model to the unseen Bray et al. dataset without any fine-tuning. Even under this severe domain shift, it significantly outperforms established methods that were re-fit directly on the data. Our work represents a significant step towards creating truly foundational models for image-based profiling, enabling more reliable and scalable cross-study biological analysis.

TL;DR: CellPainTR, a Transformer-based model, learns robust representations to integrate massive, multi-source Cell Painting datasets and demonstrates remarkable out-of-distribution generalization without fine-tuning.