Sequential Dirichlet process mixture of skew $t$-distributions for model-based clustering of flow cytometry data

Autor: Hejblum, Boris P., Alkhassim, Chariff, Gottardo, Raphael, Caron, François, Thiébaut, Rodolphe

Publikováno v: Annals of Applied Statistics
Annals of Applied Statistics, Institute of Mathematical Statistics, 2019, 13 (1), pp.638-660
Annals of Applied Statistics, 2019, 13 (1), pp.638-660

International audience; Flow cytometry is a high-throughput technology used to quantify multiple surface and intracellular markers at the level of a single cell. This enables us to identify cell sub-types, and to determine their relative proportions.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::8c40ad98600a3e9c798866365aa8ac09
https://hal.archives-ouvertes.fr/hal-03161057

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