1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
|
\name{Mergeomics-package}
\alias{Mergeomics}
\alias{Mergeomics-package}
\docType{package}
\title{
Integrative network analysis of omics data
}
\description{
The Mergeomics pipeline serves as a flexible framework for integrating
multidimensional omics-disease associations, functional genomics,
canonical pathways and gene-gene interaction networks to generate
mechanistic hypotheses. It includes two main parts,
1) Marker set enrichment analysis (MSEA);
2) Weighted Key Driver Analysis (wKDA).
}
\details{
\tabular{ll}{
Package: \tab Mergeomics\cr
Type: \tab Package\cr
Version: \tab 1.1.10\cr
Date: \tab 2016-01-04\cr
License: \tab GPL (>= 2)\cr
Depends: R (>= 3.0.1)\cr
URL: \tab http://mergeomics.research.idre.ucla.edu/\cr
}
Mergeomics amalgamates disease association information derived from
multidimensional omics data (e.g., genome, epigenome, transcriptome,
metablome) with functional genomics (e.g., eQTLs, ENCODE), canonical
pathways (e.g., KEGG, Reactome), and molecular networks (e.g., gene
regulatory networks, protein-protein interaction networks).
Two main steps of the pipeline are: Marker set enrichment analysis
(MSEA) and weighted key driver analysis (wKDA). MSEA takes the following
data as input: i) disease association data (GWAS, EWAS, TWAS...),
ii) functional genomics (eQTLs and/or ENCODE information), and iii)
functionally related genes information extracted from knowledge-based
biological pathways or data-driven network modules (e.g., coexpressed
genes in a given tissue relevant to a disease of interest). These datasets
are integrated via MSEA to return gene sets that are significantly enriched
for markers showing low p value associations with a given disease. Then,
the disease related gene sets are examined to detect the key drivers by
using the wKDA step of the pipeline, which requires pre-defined directional
networks such as tissue-specific Bayesian networks, protein-protein
interaction networks, etc. wKDA maps the disease related gene sets to the
pre-defined directional networks to identify key driver genes that are more
likely regulators of the disease gene sets based on their central positions
in the gene networks. The key drivers and their local network topology can
be viewed and downloaded after the completion of the analysis via
Visualization step. Our pipeline provides users to perform MSEA and wKDA
together or separately using either their own input data or selecting
preloaded sample datasets.
The details of the functions and parameter settings are described in the
Manual of the package.
}
\author{
Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia Yang
Maintainer: <zeyneb@ucla.edu>
}
\references{
Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
Mergeomics: multidimensional data integration to identify pathogenic
perturbations to biological systems. BMC genomics. 2016;17(1):874.
}
\keyword{
Integrative Genomics; Multidimensional Data Integration; Gene Networks;
Key Drivers
}
|
