{
  "abstract": "Transcriptome-wide profiling at single-cell resolution is limited by sparse detection of gene expression, raising the question of how much transcriptomic information can be recovered from a small set of highly expressed genes. We developed Top2All, a Mouse-Geneformer-based framework for reconstructing transcriptome-wide expression from sample-specific sparse inputs. Using 24 cell-number-normalized RNA-seq profiles of mouse adrenal zona fasciculata cells across eight experimental conditions defined by sex and sex-steroid milieu, we evaluated condition-aware and no-condition models under replicate-wise out-of-fold and leave-one-condition-out validation. Top2All achieved near-unity sample-wise correlation with measured expression, but similarly high correlations were obtained using simple reference profiles, whereas gene-wise evaluation revealed substantially greater differences among methods. Explicit condition information did not consistently improve reconstruction, indicating that sex- and sex-steroid-associated condition information was already strongly encoded in the sparse expression profiles. Nearest-neighbor retrieval provided a stronger baseline than learned reconstruction, and input-depth analysis showed that increasing the number of input genes did not monotonically improve performance. These findings show that a relatively small set of highly expressed genes contains substantial information about transcriptomic state, while accurate reconstruction of gene-specific biological variation remains challenging. Sparse-to-transcriptome models should therefore be evaluated using gene-wise metrics, strong reference baselines, and tests of generalization beyond conditions represented during training.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Molecular Biology, Graduate School of Medical Sciences, Kyushu University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Y. Nishio"
    },
    {
      "name": "T. Yamashita"
    },
    {
      "affs": [
        0
      ],
      "name": "T. Baba"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4q6a1wtv9vq56.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.31.763361",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.31.763361v1.full",
  "id": "4q6a1wtv9vq56",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.31.763361v1",
  "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",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.31.763361v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4q6a1wtv9vq56.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:05Z",
      "native_id": "10.64898/2026.10.31.763361",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.31.763361v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Top2All: Benchmarking sparse-to-transcriptome reconstruction from high-expression profiles across sex- and sex-steroid-defined conditions",
  "title_text": "Top2All: Benchmarking sparse-to-transcriptome reconstruction from high-expression profiles across sex- and sex-steroid-defined conditions",
  "type": "preprint",
  "updated_at": "2026-11-06T00:21:32Z",
  "url": "https://research.draconitic.com/paper/4q6a1wtv9vq56",
  "venue": "bioRxiv",
  "versions": []
}