{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sequential-dirichlet-process-mixtures-of","title":"Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry Data","arxiv_id":"1702.04407","date":"2017-02-14","proceeding":null,"authors":["Boris P. Hejblum","Chariff Alkhassim","Raphael Gottardo","François Caron","Rodolphe Thiébaut"],"abstract":"Flow cytometry is a high-throughput technology used to quantify multiple\nsurface and intracellular markers at the level of a single cell. This enables\nto identify cell sub-types, and to determine their relative proportions.\nImprovements of this technology allow to describe millions of individual cells\nfrom a blood sample using multiple markers. This results in high-dimensional\ndatasets, whose manual analysis is highly time-consuming and poorly\nreproducible. While several methods have been developed to perform automatic\nrecognition of cell populations, most of them treat and analyze each sample\nindependently. However, in practice, individual samples are rarely independent\n(e.g. longitudinal studies). Here, we propose to use a Bayesian nonparametric\napproach with Dirichlet process mixture (DPM) of multivariate skew\n$t$-distributions to perform model based clustering of flow-cytometry data. DPM\nmodels directly estimate the number of cell populations from the data, avoiding\nmodel selection issues, and skew $t$-distributions provides robustness to\noutliers and non-elliptical shape of cell populations. To accommodate repeated\nmeasurements, we propose a sequential strategy relying on a parametric\napproximation of the posterior. We illustrate the good performance of our\nmethod on simulated data, on an experimental benchmark dataset, and on new\nlongitudinal data from the DALIA-1 trial which evaluates a therapeutic vaccine\nagainst HIV. On the benchmark dataset, the sequential strategy outperforms all\nother methods evaluated, and similarly, leads to improved performance on the\nDALIA-1 data. We have made the method available for the community in the R\npackage NPflow.","url_abs":"http://arxiv.org/abs/1702.04407v4","url_pdf":"http://arxiv.org/pdf/1702.04407v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sequential-dirichlet-process-mixtures-of","repo_url":"https://CRAN.R-project.org/package=NPflow ","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"sequential-dirichlet-process-mixtures-of","repo_url":"https://github.com/borishejblum/NPflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}