dearseq - 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, Thiébaut 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 25 days ago

biomedicalinformaticscellbiologydifferentialexpressiondnaseqgeneexpressiongeneticsgenesetenrichmentimmunooncologykeggregressionrnaseqsequencingsystemsbiologytimecoursetranscriptiontranscriptomics

6.14 score 7 stars 1 packages 11 scripts 585 downloads

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>.

Last updated 2 years ago

5.91 score 9 stars 1 packages 15 scripts 272 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>.

Last updated 3 years ago

5.20 score 4 stars 255 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>.

Last updated 11 months ago

4.45 score 4 stars 1 packages 47 scripts 296 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 (2021) <arXiv:2006.09003>.

Last updated 3 years ago

3.70 score 1 stars 5 scripts 205 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 (2019). Submitted.

Last updated 5 months ago

3.48 score 3 scripts 130 downloads

ludic - 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

2.81 score 13 scripts 737 downloads

kernscr - Kernel Machine Score Test for Semi-Competing Risks

Kernel Machine Score Test for Pathway Analysis in the Presence of Semi-Competing Risks. Method is detailed in: Neykov, Hejblum & Sinnott (2018) <doi: 10.1177/0962280216653427>.

Last updated 2 years ago

2.70 score 4 scripts 241 downloads