A unified analysis of atlas single-cell data

Recent efforts to generate atlas-scale single-cell data provide opportunities for joint analysis across tissues and modalities. Existing methods use cells as the reference unit, hindering downstream gene-based analysis and removing genuine biological variation. Here we present GIANT, an integration method designed for atlas-scale gene analysis across cell types and tissues. GIANT converts data sets into gene graphs and recursively embeds genes without additional alignment. Applying GIANT to two recent atlas data sets yields unified gene-embedding spaces across human tissues and data modalities. Further evaluations demonstrate GIANT's usefulness in discovering diverse gene functions and underlying gene regulation in cells from different tissues.

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Publication Details

Journal
Genome Research
Published
2025-02-18
DOI
https://doi.org/10.1101/gr.279631.124
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

A unified analysis of atlas single-cell data

Ziv Bar‐Joseph, Matt Ruffalo, Nam D. Nguyen, Hao Chen
Genome Research
Single-cell and spatial transcriptomics
article

A unified analysis of atlas single-cell data

Ziv Bar‐Joseph, Matt Ruffalo, Nam D. Nguyen, Hao Chen
article en

Abstract

Recent efforts to generate atlas-scale single-cell data provide opportunities for joint analysis across tissues and modalities. Existing methods use cells as the reference unit, hindering downstream gene-based analysis and removing genuine biological variation. Here we present GIANT, an integration method designed for atlas-scale gene analysis across cell types and tissues. GIANT converts data sets into gene graphs and recursively embeds genes without additional alignment. Applying GIANT to two recent atlas data sets yields unified gene-embedding spaces across human tissues and data modalities. Further evaluations demonstrate GIANT's usefulness in discovering diverse gene functions and underlying gene regulation in cells from different tissues.

Genome ResearchVol. 35(5)
University of Illinois Urbana-Champaign (US), University of Illinois Chicago (US), University of Chicago (US), Carnegie Mellon University (US)
Single-cell and spatial transcriptomics
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A unified analysis of atlas single-cell data — Ziv Bar‐Joseph, Matt Ruffalo, et al. · Genome Research (2025) | TGRS Research Map | TGRS