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Peer-reviewedCC BY 4.0Genome Biology

Mitigating bias in spatial transcriptomic pipelines via human feedback with CSDE

  • Pierre Boyeau
  • Stephen Bates
  • Can Ergen
  • Michael I. Jordan
  • Nir Yosef
Open at sourceDOICite

Abstract

Biological discovery from large-scale assays requires extensive preprocessing, during which raw outputs (e.g., images, sequences) are processed into structured forms amenable to analysis. In spatial transcriptomics, differential expression analysis depends on cell segmentation, gene expression quantification, and cell-type annotation, all prone to systematic errors. We introduce corrected spatial differential expression (CSDE), building on prediction-powered inference to leverage a small set of expert-validated cells to correct for preprocessing bias. Across three case studies, CSDE yields more reliable differential expression estimates than the prevalent approach neglecting preprocessing.

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Cite

BibTeXRIS
BibTeX
@article{boyeau2026mitigating,
  title = {{Mitigating bias in spatial transcriptomic pipelines via human feedback with CSDE}},
  author = {Pierre Boyeau and Stephen Bates and Can Ergen and Michael I. Jordan and Nir Yosef},
  journal = {Genome Biology},
  year = {2026},
  month = oct,
  doi = {10.1186/s13059-026-04302-9},
  url = {https://link.springer.com/article/10.1186/s13059-026-04302-9},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Mitigating bias in spatial transcriptomic pipelines via human feedback with CSDE
AU  - Pierre Boyeau
AU  - Stephen Bates
AU  - Can Ergen
AU  - Michael I. Jordan
AU  - Nir Yosef
PY  - 2026
DA  - 2026/10/27
JO  - Genome Biology
DO  - 10.1186/s13059-026-04302-9
UR  - https://link.springer.com/article/10.1186/s13059-026-04302-9
AB  - Biological discovery from large-scale assays requires extensive preprocessing, during which raw outputs (e.g., images, sequences) are processed into structured forms amenable to analysis. In spatial transcriptomics, differential expression analysis depends on cell segmentation, gene expression quantification, and cell-type annotation, all prone to systematic errors. We introduce corrected spatial differential expression (CSDE), building on prediction-powered inference to leverage a small set of expert-validated cells to correct for preprocessing bias. Across three case studies, CSDE yields more reliable differential expression estimates than the prevalent approach neglecting preprocessing.
SN  - 1474-760X
ER  -