ArticleNature communications2020
Benchmarking of cell type deconvolution pipelines for transcriptomics data.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 290 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
290 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Transcriptomic meta-analysis characterizes molecular commonalities between psoriasis and obesity.Genes and immunity · 2024Pooled it
- A time-resolved meta-analysis of consensus gene expression profiles during human T-cell activation.Genome biology · 2023Pooled it
- Trial
- Inferring Gene Regulatory Networks in Stem Cells: Methods and Applications.Methods in molecular biology (Clifton, N.J.) · 2027Review
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- Deconformer: efficient cell-type composition profiling of cfRNA across diverse cell types.Briefings in bioinformatics · 2026Article
- DeOPUS: cellular deconvolution via optimized power-transformed unmixing with shrinkage.Briefings in bioinformatics · 2026Article
- Advances in Poultry RNA-Omics Research: Technologies, RNA Information Layers, and Applications in Complex Traits.Animals : an open access journal from MDPI · 2026Review
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- Increasing usable reads in RNA-seq protocols.iScience · 2026Article
- Integrative Transcriptomic Analysis Reveals Impaired Oligodendrocyte Maturation and Myelination Signature in the Hippocampus of a Murine Model of Neuropsychiatric Lupus.International journal of molecular sciences · 2026Article
- Integrated inference of cellular compositions and gene expression programs by deconvolution.Cell regeneration (London, England) · 2026Article
- Same-Slide Spatial Multiomics Integration with IN-DEPTH Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment.Cancer discovery · 2026Article
- Signalling Entropy Across Measurement Scales: A Compositional Dilution Lemma and Cross-Modality Invariance for Information-Theoretic Analysis of Cancer Transcriptomes.Entropy (Basel, Switzerland) · 2026Article
- Integrative functional genomics maps synaptic and developmental-regulatory autism risk-gene sets across human cortex.Functional & integrative genomics · 2026Article
- Systematic Evaluation of Competing Brain Transcriptomic Representations Reveals Reciprocal Patterns Across Heterogeneous Contexts.International journal of molecular sciences · 2026Article
- Statescope: an integrative deconvolution framework for discovering cell states in tumors.Nature communications · 2026Article
- An interpretable multi-task whole-slide histopathology AI model for non-small cell lung cancer: Cross-cohort generalisation, spatial attention-transcriptomic integration, and molecular-immune profiling.Clinical and translational medicine · 2026Article
- Cell-Type Deconvolution of Equine BALF RNA-Seq: A Critical Comparison with Matched Single-Cell Data.Genes · 2026Article
230 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance.
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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.