Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics

Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell...

Full description

Saved in:
Bibliographic Details
Published in:eLife 2024-05, Vol.12
Main Authors: Sang-Aram, Chananchida, Browaeys, Robin, Seurinck, Ruth, Saeys, Yvan
Format: Article
Language:English
Subjects:
Algorithms
benchmark
Benchmarking
Case studies
Computational Biology - methods
Datasets
deconvolution
Gene expression
Gene Expression Profiling - methods
Humans
Liver
Melanoma
Melanoma - genetics
Methods
Reproducibility of Results
Sequence Analysis, RNA - methods
Silver
Simulation
Single-Cell Analysis - methods
single-cell RNA sequencing
Software
spatial transcriptomics
Transcriptome
Transcriptomes
Transcriptomics
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).
ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.88431