ArticleGenome biology2025
xCell 2.0: robust algorithm for cell type proportion estimation predicts response to immune checkpoint blockade.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
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- Circulating extracellular microRNAs as tissue-specific biomarkers of human health and disease.Nature communications · 2026Article
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- Prostate Cancer: Dissecting Novel Immunosuppressive Mechanisms Through Context-Specific Transcriptomic Programs and MDSC Cells.International journal of molecular sciences · 2026Article
- Integrative single-cell and machine-learning analysis identifies acBMC cancer · 2026Article
- Immune profiling by bulk RNA-seq and multiplex immunofluorescence imaging across multiple cancer types.Frontiers in oncology · 2026Article
- Decoding astrocytic tryptophan metabolism in the pathogenesis of epilepsy: evidence from artificial intelligence-driven multi-omics and clinical validation.Frontiers in neuroscience · 2026Article
- A Pan-Cancer Atlas of TIPE2 Identifies Its Association With the Tumor Immune Microenvironment, Prognosis, and Immunotherapeutic Potential.Human mutation · 2026Article
- Structure-based virtual screening identifies ethyl rosmarinate as a SLC1A6-targeted therapeutic candidate for bladder cancer.Frontiers in pharmacology · 2026Article
- In Silico Analysis of the Dual Role of Tumor Microenvironment on Colon Cancer Subtypes.Cancer informatics · 2026Article
- Delineating novel diagnostic biomarkers and therapeutic targets for oral submucosal fibrosis: an integrative multi-omics and machine learning approach.Frontiers in bioinformatics · 2026Article
- Integrated transcriptomic and co-expression network analysis identifiesTranslational pediatrics · 2025Article
- Machine learning constructs a ferroptosis related signature for predicting prognosis and drug sensitivity in lung cancer.Cellular oncology (Dordrecht, Netherlands) · 2025Article
- Immune transcriptomic differences in paediatric patients with SARS-CoV-2 compared to other lower respiratory tract infections.bioRxiv : the preprint server for biology · 2025Article
- xCell 2.0: robust algorithm for cell type proportion estimation predicts response to immune checkpoint blockade.Genome biology · 2025Article
- Targeting neutrophil-driven inflammation in adult-onset still's disease: molecular insights from gene expression profiles.Arthritis research & therapy · 2025Article
- Characterization of Parthanatos in Breast Cancer: Implications for Prognosis and PARP Inhibitor Resistance.Bioengineering (Basel, Switzerland) · 2025Article
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Abstract
backgroundAccurate estimation of cell type proportions from bulk gene expression data is essential for understanding the cellular heterogeneity underlying complex tissues and diseases. Here, we introduce xCell 2.0, an advanced version of the xCell algorithm, featuring a training function that permits the utilization of any reference dataset. xCell 2.0 generates cell type gene signatures using an improved methodology, including automated handling of cell type dependencies and more robust signature generation.
resultsWe benchmark xCell 2.0 against eleven popular deconvolution methods using nine human and mouse reference sets and 26 validation datasets, encompassing 1711 samples and 67 cell types. Additionally, we validate xCell 2.0 using the independent Deconvolution DREAM Challenge dataset. xCell 2.0 outperforms all other tested methods across distinct reference datasets, demonstrating superior accuracy and consistency across diverse biological contexts. xCell 2.0 also shows the best performance in minimizing spillover effects between related cell types. In a test example of pan-cancer immune cell checkpoint blockage response prediction, xCell 2.0-derived TME features significantly improve prediction accuracy compared to models using only cancer type and treatment information, and outperformed other deconvolution methods and established prediction scores.
conclusionsxCell 2.0 is a versatile and robust tool for cell type deconvolution that maintains high performance across various reference types and biological contexts. It is available both via a locally hosted web application and as a Bioconductor-compatible package, equipped with a large collection of pre-trained cell type signatures for human and mouse research.
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