Protocol for inferring cell type relationships and linking genomic annotations to cell type hierarchies using CellWalker2

Single-cell multiomic data reveal cell type-specific regulatory mechanisms, but comparing hierarchies and regulatory programs across related cell types is challenging. Here, we present a protocol for integrating single-cell data to infer cell type relationships and label genomic annotations within cell type hierarchies using CellWalker2, a graph diffusion-based framework. We describe steps for cell labeling; comparing cell type hierarchies; mapping regulatory elements or transcription factors onto cell type hierarchies; and assessing statistical significance, using human blood and brain datasets. For complete details on the use and execution of this protocol, please refer to Hu et al.<sup>1</sup>.

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Publication Details

Journal
STAR Protocols
Published
2025-12-01
DOI
https://doi.org/10.1016/j.xpro.2025.104235
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
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article

Protocol for inferring cell type relationships and linking genomic annotations to cell type hierarchies using CellWalker2

Katherine S. Pollard, Pawel F. Przytycki, Zhirui Hu
STAR Protocols
Single-cell and spatial transcriptomics
article

Protocol for inferring cell type relationships and linking genomic annotations to cell type hierarchies using CellWalker2

Katherine S. Pollard, Pawel F. Przytycki, Zhirui Hu
article en

Abstract

Single-cell multiomic data reveal cell type-specific regulatory mechanisms, but comparing hierarchies and regulatory programs across related cell types is challenging. Here, we present a protocol for integrating single-cell data to infer cell type relationships and label genomic annotations within cell type hierarchies using CellWalker2, a graph diffusion-based framework. We describe steps for cell labeling; comparing cell type hierarchies; mapping regulatory elements or transcription factors onto cell type hierarchies; and assessing statistical significance, using human blood and brain datasets. For complete details on the use and execution of this protocol, please refer to Hu et al.<sup>1</sup>.

STAR ProtocolsVol. 6(4)
Boston University (US), Gladstone Institutes (US)
Openalex Percentile: Top 65%
Single-cell and spatial transcriptomics
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Protocol for inferring cell type relationships and linking genomic annotations to cell type hierarchies using CellWalker2 — Katherine S. Pollard, Pawel F. Przytycki, et al. · STAR Protocols (2025) | TGRS Research Map | TGRS