Title: CellxPert: An Efficient Reasoning Language Model for Single-Cell and Spatial Multi-Omics
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: scRNA-seq, LLM, Generative modeling, Multiomics, CRISPR

Abstract: In this work, we introduce CellxPert, a scalable multimodal foundation model that unifies single-cell and spatial omics within a common representation space. CellxPert jointly encodes transcriptomic (scRNA-seq), chromatin-accessibility (ATAC-seq), and surface-proteomic (CITE-seq) measurements, while directly incorporating MERFISH and imaging mass-cytometry data as 2D or 3D spatial–visual layers. CellxPert facilitates four key downstream tasks out of the box: (i) cell‑type annotation across a broad ontology of 154 largely overlapping identities—the largest label space addressed to date and a stringent test of fine‑grained discrimination, (ii) efficient fine-tuning using Low Rank Adaptation (LoRA), (iii) genome-wide transcriptomic response prediction to in silico perturbations (ISP), and (iv) seamless multi-omic integration across various assays and platforms. Unlike current single-cell foundation models, which approximate gene perturbations by deleting or reordering tokenized gene expression ranks, CellxPert employs a Metropolis–Hastings sampler whose proposal kernel uses the model’s masked conditional distributions to transition to new transcriptomic states conditioned on the perturbed genes. This Markov‑chain procedure mitigates out‑of‑distribution artifacts introduced by abrupt token manipulation and produces trajectories that are biologically interpretable. Evaluations on Perturb-seq and BMMC benchmarks show CellxPert outperforming state-of-the-art baselines in multi-omic integration, with marked gains in cell-type annotation, cross-assay alignment, spatial transcriptomics, and perturbation-aware reasoning.