{
  "abstract": "Deep clustering models for single-cell RNA sequencing often assign cells through latent or centroid-based mechanisms that are difficult to inspect. We introduce scDNM-VAE (single-cell Dendritic Neuron Model Variational Autoencoder), a deep clustering framework that combines a variational autoencoder with a dendritic neuron-inspired head. Cluster assignments are governed by learnable signed synaptic weights and thresholds: the weight sign determines the direction of a gate's response to a latent coordinate, its magnitude controls steepness, and the weight-threshold pair determines the transition location. The trained clustering function can therefore be inspected directly without fitting a post-hoc explanation model. We benchmark scDNM-VAE on four datasets spanning immune, cortical, cardiac, and hematopoietic cells against scVI followed by KMeans and an MLP-DEC ablation. scDNM-VAE performs better than scVI on PBMC3k, comparably on the Human Heart Cell Atlas and Paul15, and worse on Zeisel, while producing biologically coherent marker-gene signatures. Ablating each cluster's three highest-magnitude synaptic dimensions causes numerically greater reassignment than random-dimension ablation across all datasets, but the margins are modest and negligible on Zeisel. These results show that signed dendritic gating supports competitive clustering with a parameter-inspectable decision function, while indicating that decision-relevant information is distributed across the latent space.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.17228",
  "author_total": 6,
  "authors": [
    {
      "name": "Melih Agraz"
    },
    {
      "name": "Deniz Karapinar"
    },
    {
      "name": "Aysel Topsir"
    },
    {
      "name": "Qianying Cao"
    },
    {
      "name": "Erol Egrioglu"
    },
    {
      "name": "Gaurav Choudhary"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9z0sgb4em4zbr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.17228",
  "fulltext_url": null,
  "id": "9z0sgb4em4zbr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.17228",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.GN"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7203747371",
  "pdf_url": "https://arxiv.org/pdf/2608.17228",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9z0sgb4em4zbr.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T18:21:35Z",
      "native_id": "2608.17228",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.17228"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    },
    {
      "field": "biology",
      "origin": "arxiv:q-bio.GN",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "scDNM-VAE enables directly inspectable deep clustering of single-cell RNA-seq data through signed dendritic gating",
  "title_text": "scDNM-VAE enables directly inspectable deep clustering of single-cell RNA-seq data through signed dendritic gating",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:50Z",
  "url": "https://research.draconitic.com/paper/9z0sgb4em4zbr",
  "venue": "arXiv",
  "versions": []
}