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  3. Parameter-free Representations Outperform Single-cell Foundation Models On Downstream Benchmarks.
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  3. Parameter-free Representations Outperform Single-cell Foundation Models On Downstream Benchmarks.

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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Parameter-free representations outperform single-cell foundation models on downstream benchmarks.

Huan Souza1, Pankaj Mehta1,2

  • 1Department of Physics, Boston University, Boston, MA, 02215, USA.

Biorxiv : the Preprint Server for Biology
|February 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Simple linear models can match complex foundation models for analyzing single-cell RNA sequencing (scRNA-seq) data. This research shows interpretable methods achieve state-of-the-art results, even on novel cell types and organisms.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) data possesses inherent statistical structure, driving the development of advanced foundation models.
  • Transformer-based models like TranscriptFormer learn gene expression patterns by embedding genes into latent spaces, achieving state-of-the-art (SOTA) results in various biological tasks.

Purpose of the Study:

  • To investigate if SOTA performance in analyzing scRNA-seq data can be achieved using computationally efficient, interpretable methods instead of complex deep learning representations.
  • To evaluate the efficacy of simple normalization and linear modeling pipelines against established foundation models.

Main Methods:

  • Development of interpretable pipelines utilizing careful data normalization techniques.
  • Application of linear methods for gene expression data analysis.
  • Benchmarking against SOTA foundation models on established datasets and out-of-distribution tasks.

    • Main Results:

    • Simple linear pipelines achieved SOTA or near-SOTA performance across multiple benchmarks for scRNA-seq data analysis.
    • These methods outperformed foundation models on out-of-distribution tasks, including novel cell types and organisms not present in training data.
    • Demonstrated that linear representations can effectively capture the biology of cell identity.

    Conclusions:

    • Computationally intensive deep learning representations are not always necessary for achieving high performance in scRNA-seq data analysis.
    • Rigorous benchmarking is crucial for evaluating the true capabilities of computational methods in genomics.
    • Interpretable, linear models offer a powerful and efficient alternative for understanding cell identity from gene expression data.