ArticleBMC bioinformatics2020
CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed, 84 citations in OpenAlex.
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- Cell Marker Accordion: interpretable single-cell and spatial omics annotation in health and disease.Nature communications · 2025Article
- HiCat: a semi-supervised approach for cell type annotation.Briefings in bioinformatics · 2025Article
- Cell Marker Accordion: interpretable single-cell and spatial omics annotation in health and disease.bioRxiv : the preprint server for biology · 2025Article
- Neonatal fungi promote lifelong metabolic health through macrophage-dependent β cell development.Science (New York, N.Y.) · 2025Article
- Targeting LMO2-induced autocrine FLT3 signaling to overcome chemoresistance in early T-cell precursor acute lymphoblastic leukemia.Leukemia · 2025Article
- A microRNA-regulated transcriptional state defines intratumoral CD8Cell reports · 2025Article
- Synovial macrophage diversity and activation of M-CSF signaling in post-traumatic osteoarthritis.eLife · 2025Article
- Inhibition of Aspartate β-Hydroxylase Enhances Anti-Tumor Immunity.ImmunoTargets and therapy · 2025Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundSingle cell RNA sequencing (scRNAseq) has provided invaluable insights into cellular heterogeneity and functional states in health and disease. During the analysis of scRNAseq data, annotating the biological identity of cell clusters is an important step before downstream analyses and it remains technically challenging. The current solutions for annotating single cell clusters generally lack a graphical user interface, can be computationally intensive or have a limited scope. On the other hand, manually annotating single cell clusters by examining the expression of marker genes can be subjective and labor-intensive. To improve the quality and efficiency of annotating cell clusters in scRNAseq data, we present a web-based R/Shiny app and R package, Cluster Identity PRedictor (CIPR), which provides a graphical user interface to quickly score gene expression profiles of unknown cell clusters against mouse or human references, or a custom dataset provided by the user. CIPR can be easily integrated into the current pipelines to facilitate scRNAseq data analysis.
resultsCIPR employs multiple approaches for calculating the identity score at the cluster level and can accept inputs generated by popular scRNAseq analysis software. CIPR provides 2 mouse and 5 human reference datasets, and its pipeline allows inter-species comparisons and the ability to upload a custom reference dataset for specialized studies. The option to filter out lowly variable genes and to exclude irrelevant reference cell subsets from the analysis can improve the discriminatory power of CIPR suggesting that it can be tailored to different experimental contexts. Benchmarking CIPR against existing functionally similar software revealed that our algorithm is less computationally demanding, it performs significantly faster and provides accurate predictions for multiple cell clusters in a scRNAseq experiment involving tumor-infiltrating immune cells.
conclusionsCIPR facilitates scRNAseq data analysis by annotating unknown cell clusters in an objective and efficient manner. Platform independence owing to Shiny framework and the requirement for a minimal programming experience allows this software to be used by researchers from different backgrounds. CIPR can accurately predict the identity of a variety of cell clusters and can be used in various experimental contexts across a broad spectrum of research areas.
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Registered trials
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.