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cytometree - Automated Cytometry Gating and Annotation

Given the hypothesis of a bi-modal distribution of cells for each marker, the algorithm constructs a binary tree, the nodes of which are subpopulations of cells. At each node, observed cells and markers are modeled by both a family of normal distributions and a family of bi-modal normal mixture distributions. Splitting is done according to a normalized difference of AIC between the two families. Method is detailed in: Commenges, Alkhassim, Gottardo, Hejblum & Thiebaut (2018) <doi: 10.1002/cyto.a.23601>.

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cpp

6.16 score 11 stars 1 dependents 22 scripts 486 downloads

citcdf - Conditional Independence Testing with Cumulative Distribution Functions

Complex hypothesis testing through conditional cumulative distribution function estimation. Method is detailed in: Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2021). "Distribution-free complex hypothesis testing for single-cell RNA-seq differential expression analysis", bioRxiv <doi:10.1101/2021.05.21.445165>.

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quarto

5.56 score 3 stars 9 scripts 355 downloads

TcGSA - Time-Course Gene Set Analysis

Implementation of Time-course Gene Set Analysis (TcGSA), a method for analyzing longitudinal gene-expression data at the gene set level. Method is detailed in: Hejblum, Skinner & Thiebaut (2015) <doi: 10.1371/journal.pcbi.1004310>.

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5.38 score 6 stars 316 downloads

NPflow - Bayesian Nonparametrics for Automatic Gating of Flow-Cytometry Data

Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions modeling oriented towards flow-cytometry data preprocessing applications. Method is detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.

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openblascppopenmp

4.64 score 3 stars 1 dependents 49 scripts 410 downloads

CytOpT - Optimal Transport for Gating Transfer in Cytometry Data with Domain Adaptation

Supervised learning from a source distribution (with known segmentation into cell sub-populations) to fit a target distribution with unknown segmentation. It relies regularized optimal transport to directly estimate the different cell population proportions from a biological sample characterized with flow cytometry measurements. It is based on the regularized Wasserstein metric to compare cytometry measurements from different samples, thus accounting for possible mis-alignment of a given cell population across sample (due to technical variability from the technology of measurements). Supervised learning technique based on the Wasserstein metric that is used to estimate an optimal re-weighting of class proportions in a mixture model Details are presented in Freulon P, Bigot J and Hejblum BP (2023) <doi:10.1214/22-AOAS1660>.

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3.70 score 1 stars 6 scripts 374 downloads

vici - Vaccine Induced Cellular Immunogenicity with Bivariate Modeling

A shiny app for accurate estimation of vaccine induced immunogenicity with bivariate linear modeling. Method is detailed in: Lhomme, Hejblum, Lacabaratz, Wiedemann, Lelievre, Levy, Thiebaut & Richert (2020). Journal of Immunological Methods, 477:112711. <doi:10.1016/j.jim.2019.112711>.

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3.18 score 1 stars 2 scripts 242 downloads