igraph, ggplot2, GGally, network, sna, scales, stats,
grDevices

The package can identify the dysregulated KEGG pathways based on
global influence from the internal effect of pathways and
crosstalk between pathways.
Junwei Han,Yanjun Xu,Haixiu Yang,Chunquan Li and Xia Li
The package can identify the dysregulated KEGG pathways
based on global influence from the internal effect of pathways
and crosstalk between pathways. (1) The PAGI package can
prioritize the pathways associated with two biological states
by statistical significance or FDR. (2) The PAGI package can
evaluated the global influence factor (GIF) score in the global
gene-gene network constructed based on the relationships of
genes extracted from each pathway in KEGG database and the
overlapped genes between pathways.
Statistics, crosstalk, Pathways, Graphs, Networks

Pathway analysis methods for genomewide association data
The two Bayesian hierarchical pathway analysis methods Sparse normal/Adaptive lasso and Normal/Bayes factors proposed by Evangelou et al (2013) are implemented. Further, the well-known Fisher's method is implemented

Pathway Analysis Methods for Genomewide Association Data
Bayesian hierarchical methods for pathway analysis of genomewide association data: Normal/Bayes factors and Sparse Normal/Adaptive lasso. The Frequentist Fisher's product method is included as well.

Identify Intra and Inter-Donor Variations in Bulk or Single Cell
Longitudinal Dataset
c(person(given = "Suhas",
family = "Vasaikar",
role = c("aut", "cre"),
email = "suhas.vasaikar@alleninstitute.org"),
person(given = "Aarthi",
family = "Talla",
role = c("aut", "ctb"),
email = "aarthi.talla@alleninstitute.org"),
person(given = "Xiaojun",
family = "Li",
role = c("aut", "ctb"),
email = "xiaojun.li@alleninstitute.org")
)
It is a platform for analyzing longitudinal data from bulk as well as single cell datasets. It allows to identify variations in molecular features within and across donors over longitudinal time points. The analysis can be done on bulk expression dataset without known cell type information or single cell with cell type/user-defined groups. It allows to infer stable and variable features in given donor and each cell type (or user defined group). The outlier analysis can be performed to identify technical/biological perturbed samples in donor/participant. Further, differential analysis can be performed to decipher time-wise changes in gene expression in a cell type.
R (>= 4.0), methods, grid, graphics, stats, grDevices
Seurat (>= 3.9), ggrepel (>= 0.9), pbapply (>= 1.4), lme4 (>=
1.1), ggforce (>= 0.3), MAST (>= 1.14), factoextra (>= 1.0),
Rtsne (>= 0.15), knitr(>= 1.30), dplyr, ggplot2, reshape2,
ComplexHeatmap, circlize, cowplot, pheatmap, tidyverse, utils
Suhas Vasaikar [aut, cre],
Aarthi Talla [aut, ctb],
Xiaojun Li [aut, ctb]