CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment

Clemens Kohl and Martin Vingron

PLOS Computational Biology, 2026, vol. 22, issue 6, 1-31

Abstract: Clustering for single-cell RNA-seq aims at finding similar cells and grouping them into biologically meaningful clusters. Many available clustering algorithms however do not not provide the cluster defining marker genes or are unable to infer the number of clusters in an unsupervised manner as well as lack tools to easily determine the quality of the label assignments. Therefore, clustering quality is commonly evaluated by visually inspecting low-dimensional embeddings as produced by, e.g., UMAP or t-SNE. These embeddings can, however, distort the true cluster structure and are known to produce radically different embeddings depending on the chosen hyperparameters. In order to improve the interpretability of clustering results, we developed CAdir, a clustering algorithm that can infer the number of clusters in the data, determine cluster specific genes and provides easy to interpret diagnostic plots. CAdir exploits the geometry induced by correspondence analysis (CA) to cluster cells as well as cluster associated genes based on their direction in CA space. Using the angle between the cluster directions, it is able to automatically infer the number of clusters in the data by merging and splitting clusters. A comprehensive set of diagnostic and explanatory plots provides users with valuable feedback about the clustering decisions and the quality of the final as well as intermediary clusters. CAdir is scalable to even the largest data set and provides similar clustering performance to other state-of-the-art cell clustering algorithms in our benchmarking. CAdir can be downloaded from GitHub: https://github.com/VingronLab/CAdir.Author summary: Clustering single-cell RNA-seq data can be highly subjective due to the lack of means to evaluate the quality of the clustering and the lack of information on why the clustering algorithm decided to group certain cells together. Ultimately, a clustering’s quality is determined by how well it is able to distinguish biologically meaningful sub-groups. In practice, this is often assessed by the expression of marker genes, which have to be determined afterwards through statistical methods. CAdir offers an all-in-one solution that co-clusters cells and genes simultaneously and provides visualizations that not only allow for the determination of the cluster quality but also help identify genes of interest for each cluster. These so-called Association Plots of the clustering results help identify malformed clusters and outliers in the data and also highlight the most highly associated marker genes for each cluster. Because for many single-cell RNA-seq experiments the number of cell types is not known in advance, CAdir can determine the number of clusters without user input and annotates clusters with the best matching cell type based on their co-clustered marker genes. In combination with its fast runtime, CAdir can help to speed up routine tasks and make them more efficient.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1014418

DOI: 10.1371/journal.pcbi.1014418

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