Title: Integrative Conditional Autoregressive Horseshoe Model
Authors@R: c(person("Takoua", "Jendoubi", role = c("aut", "cre"), email = "t.jendoubi14@imperial.ac.uk"), person("Timothy M.D.", "Ebbels", role = c("aut"), email = "t.ebbels@imperial.ac.uk") )
Description: Implements the integrative conditional autoregressive horseshoe model
discussed in Jendoubi, T., & Ebbels, T. (2018). "Integrative analysis of
time course metabolic data and biomarker discovery". arXiv preprint <arXiv:1801.07767>.
The model consists in three levels: Metabolic pathways level modeling interdependencies between
variables via a conditional auto-regressive (CAR) component , integrative analysis level to identify
potential associations between heterogeneous omic variables via a Horseshoe prior and experimental
design level to capture experimental design conditions through a mixed-effects model.
The package also provides functions to simulate data from the model, construct pathway matrices,
Imports: RCurl, KEGGgraph, igraph, reshape2, mc2d, abind, Matrix

discussed in Jendoubi, T., Ebbels, T.M. Integrative analysis of time course metabolic data and biomarker discovery.
variables via a conditional auto-regressive (CAR) component, integrative analysis level to identify
Depends: rstan, MASS, stats, ggplot2, glue

Title: Analyzing High-Throughput Single Cell Sequencing Data
Description: A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq and CITE-Seq. Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis.
Imports: Matrix, Rtsne, gridExtra, ggrepel, ggpubr, scatterplot3d,
RColorBrewer, knitr, NbClust, shiny, umap, pheatmap, ape,
ggdendro, plyr, reshape, Hmisc, htmlwidgets, methods