{
  "abstract": "Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations generally outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.",
  "abstract_shown": true,
  "affiliations": [
    "Allen Institute"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "H. Sharma"
    },
    {
      "name": "X.-P. Liu"
    },
    {
      "name": "T. Chartrand"
    },
    {
      "name": "B. Kalmbach"
    },
    {
      "name": "E. Lein"
    },
    {
      "name": "S. Mihalas"
    },
    {
      "affs": [
        0
      ],
      "name": "Z. Lu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5g9tq9dzgc7jk.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.27.744753",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.27.744753v1.full",
  "id": "5g9tq9dzgc7jk",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.27.744753v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7204237426",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.27.744753v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5g9tq9dzgc7jk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:46Z",
      "native_id": "10.64898/2026.08.27.744753",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.27.744753v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features",
  "title_text": "The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:39Z",
  "url": "https://research.draconitic.com/paper/5g9tq9dzgc7jk",
  "venue": "bioRxiv",
  "versions": []
}