Benchmarking cell-type clustering methods for spatially resolved transcriptomics data

Abstract Spatially resolved transcriptomics technologies enable the measurement of transcriptome information while retaining the spatial context at the regional, cellular or sub-cellular level. While previous computational methods have relied on gene expression information alone for clustering singl...

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Vydáno v:Briefings in bioinformatics Ročník 24; číslo 1
Hlavní autoři: Cheng, Andrew, Hu, Guanyu, Li, Wei Vivian
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 19.01.2023
Oxford Publishing Limited (England)
Témata:
Accuracy
Benchmarking
Cluster Analysis
Clustering
Computer applications
Datasets
Gene expression
Gene Expression Profiling - methods
Histology
Image enhancement
Information retrieval
Parameter identification
Parameter robustness
Population studies
Quorum sensing
Robustness
Software
Spatial data
Synthetic data
Transcriptome
Transcriptomes
Transcriptomics
Clustering
Spatial trasncriptomics
Single-cell genomics
ISSN:1467-5463, 1477-4054, 1477-4054
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Shrnutí:Abstract Spatially resolved transcriptomics technologies enable the measurement of transcriptome information while retaining the spatial context at the regional, cellular or sub-cellular level. While previous computational methods have relied on gene expression information alone for clustering single-cell populations, more recent methods have begun to leverage spatial location and histology information to improve cell clustering and cell-type identification. In this study, using seven semi-synthetic datasets with real spatial locations, simulated gene expression and histology images as well as ground truth cell-type labels, we evaluate 15 clustering methods based on clustering accuracy, robustness to data variation and input parameters, computational efficiency, and software usability. Our analysis demonstrates that even though incorporating the additional spatial and histology information leads to increased accuracy in some datasets, it does not consistently improve clustering compared with using only gene expression data. Our results indicate that for the clustering of spatial transcriptomics data, there are still opportunities to enhance the overall accuracy and robustness by improving information extraction and feature selection from spatial and histology data.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbac475