A point cloud segmentation framework for image-based spatial transcriptomics.
Commun Biol
; 7(1): 823, 2024 Jul 06.
Article
em En
| MEDLINE
| ID: mdl-38971915
ABSTRACT
Recent progress in image-based spatial RNA profiling enables to spatially resolve tens to hundreds of distinct RNA species with high spatial resolution. It presents new avenues for comprehending tissue organization. In this context, the ability to assign detected RNA transcripts to individual cells is crucial for downstream analyses, such as in-situ cell type calling. Yet, accurate cell segmentation can be challenging in tissue data, in particular in the absence of a high-quality membrane marker. To address this issue, we introduce ComSeg, a segmentation algorithm that operates directly on single RNA positions and that does not come with implicit or explicit priors on cell shape. ComSeg is applicable in complex tissues with arbitrary cell shapes. Through comprehensive evaluations on simulated and experimental datasets, we show that ComSeg outperforms existing state-of-the-art methods for in-situ single-cell RNA profiling and in-situ cell type calling. ComSeg is available as a documented and open source pip package at https//github.com/fish-quant/ComSeg .
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Perfilação da Expressão Gênica
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Análise de Célula Única
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Transcriptoma
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article