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>.
Last updated 3 years ago
4 stars 1.92 score 46 dependenciescytometree - 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>.
Last updated 2 years ago
9 stars 1.71 score 36 dependencies 1 dependentsdearseq - Differential Expression Analysis for RNA-seq data through a robust variance component test
Differential Expression Analysis RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with repeated or longitudinal data. Methods are detailed in: i) Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604 ; and ii) Gauthier M, Agniel D, ThiƩbaut R & Hejblum BP (2020) dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, NAR Genomics and Bioinformatics, 2(4):lqaa093.
Last updated 4 months ago
biomedicalinformaticscellbiologydifferentialexpressiondnaseqgeneexpressiongeneticsgenesetenrichmentimmunooncologykeggregressionrnaseqsequencingsystemsbiologytimecoursetranscriptiontranscriptomics
7 stars 1.64 score 50 dependencies 1 dependentsNPflow - 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>.
Last updated 8 months ago
4 stars 1.41 score 63 dependencies 1 dependentsvici - 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 (2019). Submitted.
Last updated 2 months ago
1.18 score 138 dependenciesludic - Linkage Using Diagnosis Codes
Probabilistic record linkage without direct identifiers using only diagnosis codes. Method is detailed in: Hejblum, Weber, Liao, Palmer, Churchill, Szolovits, Murphy, Kohane & Cai (2019) <doi: 10.1038/sdata.2018.298> ; Zhang, Hejblum, Weber, Palmer, Churchill, Szolovits, Murphy, Liao, Kohane & Cai (2021) <doi: 10.1101/2021.05.02.21256490>.
Last updated 3 years ago
1.10 score 20 dependencies