Top2All: Benchmarking sparse-to-transcriptome reconstruction from high-expression profiles across sex- and sex-steroid-defined conditions
- 1Department of Molecular Biology, Graduate School of Medical Sciences, Kyushu University
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.
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BibTeX
@misc{nishio2026top2all,
title = {{Top2All: Benchmarking sparse-to-transcriptome reconstruction from high-expression profiles across sex- and sex-steroid-defined conditions}},
author = {Y. Nishio and T. Yamashita and T. Baba},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.31.763361},
url = {https://www.biorxiv.org/content/10.64898/2026.10.31.763361v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Top2All: Benchmarking sparse-to-transcriptome reconstruction from high-expression profiles across sex- and sex-steroid-defined conditions
AU - Y. Nishio
AU - T. Yamashita
AU - T. Baba
PY - 2026
DA - 2026/10/31
PB - bioRxiv
DO - 10.64898/2026.10.31.763361
UR - https://www.biorxiv.org/content/10.64898/2026.10.31.763361v1
AB - 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.
ER -