ReviewComputational and structural biotechnology journal2023
Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome.
Review in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 41 citations in OpenAlex.
- Artificial intelligence in spatial transcriptomics: a review of methods, datasets, and directions.Briefings in bioinformatics · 2026Review
- RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.Nature communications · 2026Article
- Self-collected finger-prick blood for gene expression profiling: Unveiling early immune responses in mild COVID-19.iScience · 2026Article
- Restraint of cancer cell plasticity by spatial homotypic clustering.Cancer cell · 2025Article
- Identification of SAMD9 as an adaptive response gene to environmental changes and its association with overall survival and immunotherapeutic response in glioblastoma.Cancer cell international · 2025Article
- Spatially Resolved Profiling of Compartmentalized Muscle and Brain Inflammation.European journal of immunology · 2025Review
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- A practical guide for choosing an optimal spatial transcriptomics technology from seven major commercially available options.BMC genomics · 2025Review
- SMART: spatial transcriptomics deconvolution using marker-gene-assisted topic model.Genome biology · 2024Article
- Spatial Transcriptomics Unravel the Tissue Complexity of Oral Pathogenesis.Journal of dental research · 2024Review
- Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.Nature neuroscience · 2024Review
- scDOT: optimal transport for mapping senescent cells in spatial transcriptomics.Genome biology · 2024Article
- SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data.Genome biology · 2024Article
- Rediscovering the human thymus through cutting-edge technologies.The Journal of experimental medicine · 2024Review
- Spatial landscapes of cancers: insights and opportunities.Nature reviews. Clinical oncology · 2024Review
- Community assessment of methods to deconvolve cellular composition from bulk gene expression.Nature communications · 2024Article
- siqRNA-seq is a spike-in-independent technique for quantitative mapping of mRNA landscape.BMC genomics · 2024Article
- Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The spatial transcriptome has enabled researchers to resolve transcriptome expression profiles while preserving information about cell location to better understand the complex biological processes that occur in organisms. Due to technical limitations, the current high-throughput spatial transcriptome sequencing methods (known as next-generation sequencing with spatial barcoding methods or spot-based methods) cannot achieve single-cell resolution. A single measurement site, called a spot, in these technologies frequently contains multiple cells of various types. Computational tools for determining the cellular composition of a spot have emerged as a way to break through these limitations. These tools are known as deconvolution tools. Recently, a couple of deconvolution tools based on different strategies have been developed and have shown promise in different aspects. The resulting single-cell resolution expression profiles and/or single-cell composition of spots will significantly affect downstream data mining; thus, it is crucial to choose a suitable deconvolution tool. In this review, we present a list of currently available tools for spatial transcriptome deconvolution, categorize them based on the strategies they employ, and explain their advantages and limitations in detail in order to guide the selection of these tools in future studies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.