ArticleCommunications biology2021
AdRoit is an accurate and robust method to infer complex transcriptome composition.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- DeOPUS: cellular deconvolution via optimized power-transformed unmixing with shrinkage.Briefings in bioinformatics · 2026Article
- Multitissue, multi-time point transcriptomic atlas of aging in mice and rats.Science advances · 2026Article
- Attention-guided enhanced deconvolution enables reference-free cell type estimation in spatial transcriptomics.Scientific reports · 2026Article
- From pixels to cell types: a comprehensive review of computational methods for spatial transcriptomics deconvolution.Genomics & informatics · 2025Review
- Spatialsmooth: a spatially-aware convolutional autoencoder framework for enhanced deconvolution of spatial transcriptomics data.BMC genomics · 2025Article
- Single-Cell Transcriptomics Uncovers Core Signature for Regulating Mitochondrial Homeostasis During Testicular Ageing.Cell proliferation · 2025Article
- STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks.Genome biology · 2024Article
- Harmonized cross-species cell atlases of trigeminal and dorsal root ganglia.Science advances · 2024Article
- Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.Nucleic acids research · 2024Review
- Statistical Inference of Cell-type Proportions Estimated from Bulk Expression Data.Journal of the American Statistical Association · 2024Article
- Estimating cell-type-specific gene co-expression networks from bulk gene expression data with an application to Alzheimer's disease.Journal of the American Statistical Association · 2024Article
- Defining a Unique Gene Expression Profile in Mature and Developing Keloids.JID innovations : skin science from molecules to population health · 2023Article
- A guidebook of spatial transcriptomic technologies, data resources and analysis approaches.Computational and structural biotechnology journal · 2023Review
- Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome.Computational and structural biotechnology journal · 2023Review
- A comprehensive comparison on cell-type composition inference for spatial transcriptomics data.Briefings in bioinformatics · 2022Review
- Museum of spatial transcriptomics.Nature methods · 2022Review
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Authors and funding
11 authors.
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Abstract
Bulk RNA sequencing provides the opportunity to understand biology at the whole transcriptome level without the prohibitive cost of single cell profiling. Advances in spatial transcriptomics enable to dissect tissue organization and function by genome-wide gene expressions. However, the readout of both technologies is the overall gene expression across potentially many cell types without directly providing the information of cell type constitution. Although several in-silico approaches have been proposed to deconvolute RNA-Seq data composed of multiple cell types, many suffer a deterioration of performance in complex tissues. Here we present AdRoit, an accurate and robust method to infer the cell composition from transcriptome data of mixed cell types. AdRoit uses gene expression profiles obtained from single cell RNA sequencing as a reference. It employs an adaptive learning approach to alleviate the sequencing technique difference between the single cell and the bulk (or spatial) transcriptome data, enhancing cross-platform readout comparability. Our systematic benchmarking and applications, which include deconvoluting complex mixtures that encompass 30 cell types, demonstrate its preferable sensitivity and specificity compared to many existing methods as well as its utilities. In addition, AdRoit is computationally efficient and runs orders of magnitude faster than most methods.
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