ReviewBriefings in bioinformatics2021
A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells.
Review in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
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Who cites it
74 citing papers in PubMed.
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- Integrated bioinformatics and network search identify biomarkers and comorbid mechanisms of atrial fibrillation and ischemic stroke.Medicine · 2026Article
- Comprehensive analysis of COLGALT1 in tumor microenvironment regulation and prognosis of clear cell renal cell carcinoma.Clinical and experimental medicine · 2026Article
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- Decoding Dementia Mechanisms: Identification of Key Oligodendrocyte- Associated Genes through Integrative Bioinformatics and Machine Learning.Current topics in medicinal chemistry · 2026Article
- Identification of Crohn's disease-associated molecular signatures by integrative bioinformatics analysis and experimental validation.Frontiers in immunology · 2026Article
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- Screening and validation of diagnostic markers for keloids via bioinformatics analysis.Biochemistry and biophysics reports · 2025Article
- Bioinformatics analysis identifies BST1 as a potential therapeutic target linked to neutrophil extracellular traps in patients with acute liver failure.Scientific reports · 2025Article
- LncRNAs regulates cell death in osteosarcoma.Scientific reports · 2025Article
- The role of RETN in stomach adenocarcinoma (STAD): insights from pan-cancer analysis of RNA-seq data based on the TCGA database.Discover oncology · 2025Article
- Blood Transcriptome Profiling Highlights the Role of Intestinal Bacterial Translocation in Severe COVID-19.Pathogens (Basel, Switzerland) · 2025Article
- Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock.Scientific reports · 2025Article
- Diabetic kidney disease: m6A modification as a marker of disease progression and subtype classification.Frontiers in medicine · 2025Article
- LST1: a novel biomarker for efferocytosis in the co-occurrence of type 2 diabetes mellitus and clear cell renal cell carcinoma.Frontiers in immunology · 2025Article
- Multiomics Integration and Machine Learning Reveal Colony Stimulating Factor 3 Receptor as a Key Gene and Therapeutic Target in Crohn's Disease.Mediators of inflammation · 2025Article
- Hypoxia upregulate TPM4 expression to strengthen epithelial-mesenchymal transition that promotes lymph node metastasis of papillary thyroid cancer.Journal of Cancer · 2025Article
14 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
Due to the high cost of flow and mass cytometry, there has been a recent surge in the development of computational methods for estimating the relative distributions of cell types from the gene expression profile of a bulk of cells. Here, we review the five common 'digital cytometry' methods: deconvolution of RNA-Seq, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), CIBERSORTx, single sample gene set enrichment analysis and single-sample scoring of molecular phenotypes deconvolution method. The results show that CIBERSORTx B-mode, which uses batch correction to adjust the gene expression profile of the bulk of cells ('mixture data') to eliminate possible cross-platform variations between the mixture data and the gene expression data of single cells ('signature matrix'), outperforms other methods, especially when signature matrix and mixture data come from different platforms. However, in our tests, CIBERSORTx S-mode, which uses batch correction for adjusting the signature matrix instead of mixture data, did not perform better than the original CIBERSORT method, which does not use any batch correction method. This result suggests the need for further investigations into how to utilize batch correction in deconvolution methods.
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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.