| Title: | Gene Set Enrichment Analysis with Networks |
|---|---|
| Description: | Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis. |
| Authors: | Dongmin Jung |
| Maintainer: | Dongmin Jung <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.25.0 |
| Built: | 2024-07-24 05:17:43 UTC |
| Source: | https://github.com/bioc/gsean |
Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis.
The DESCRIPTION file:
| Package: | gsean |
| Type: | Package |
| Title: | Gene Set Enrichment Analysis with Networks |
| Description: | Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis. |
| Version: | 1.25.0 |
| Date: | 2023-05-24 |
| Author: | Dongmin Jung |
| Maintainer: | Dongmin Jung <[email protected]> |
| Depends: | R (>= 3.5), fgsea, PPInfer |
| Suggests: | SummarizedExperiment, pasilla, org.Dm.eg.db, AnnotationDbi, knitr, plotly, WGCNA, rmarkdown |
| License: | Artistic-2.0 |
| biocViews: | Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, GeneExpression, NetworkEnrichment, Pathways, DifferentialExpression |
| NeedsCompilation: | no |
| VignetteBuilder: | knitr |
| Repository: | https://bioc.r-universe.dev |
| RemoteUrl: | https://github.com/bioc/gsean |
| RemoteRef: | HEAD |
| RemoteSha: | aa9b841aa892d43d5f92b5c1fe3474f94619dc11 |
Index of help topics:
GO_dme Gene Ontology terms with gene ID for Drosophila
melanogaster
KEGG_hsa KEGG pathways with gene symbol for human
centrality_gsea Gene Set Enrichment Analysis with centrality
measure
exprs2adj Convert gene expression data to adjacency
matrix by using correlation coefficients
gsean Gene Set Enrichment Analysis with Networks
gsean-package Gene Set Enrichment Analysis with Networks
label_prop_gsea Over-representaion analysis with the label
propagation algorithm
Dongmin Jung
Maintainer: Dongmin Jung <[email protected]>
GSEA is performed with centrality measure
centrality_gsea(geneset, x, adjacency, pseudo = 1, nperm = 1000, centrality = function(x) rowSums(abs(x)), weightParam = 1, minSize = 1, maxSize = Inf, gseaParam = 1, nproc = 0, BPPARAM = NULL)centrality_gsea(geneset, x, adjacency, pseudo = 1, nperm = 1000, centrality = function(x) rowSums(abs(x)), weightParam = 1, minSize = 1, maxSize = Inf, gseaParam = 1, nproc = 0, BPPARAM = NULL)
geneset |
list of gene sets |
x |
Named vector of gene-level statistics. Names should be the same as in gene sets. |
adjacency |
adjacency matrix |
pseudo |
pseudo number for log2 transformation (default: 1) |
nperm |
number of permutations (default: 1000) |
centrality |
centrality measure, degree centrality or node strength is default |
weightParam |
weight parameter value for the centrality measure, equally weight if weightParam = 0 (default: 1) |
minSize |
minimal size of a gene set (default: 1) |
maxSize |
maximal size of a gene set (default: Inf) |
gseaParam |
GSEA parameter value (default: 1) |
nproc |
see fgsea::fgsea |
BPPARAM |
see fgsea::fgsea |
GSEA result
Dongmin Jung
fgsea::fgsea
data(examplePathways) data(exampleRanks) exampleRanks <- exampleRanks[1:100] adjacency <- diag(length(exampleRanks)) rownames(adjacency) <- names(exampleRanks) set.seed(1) result.GSEA <- centrality_gsea(examplePathways, exampleRanks, adjacency)data(examplePathways) data(exampleRanks) exampleRanks <- exampleRanks[1:100] adjacency <- diag(length(exampleRanks)) rownames(adjacency) <- names(exampleRanks) set.seed(1) result.GSEA <- centrality_gsea(examplePathways, exampleRanks, adjacency)
A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis.
exprs2adj(x, pseudo = 1, ...)exprs2adj(x, pseudo = 1, ...)
x |
gene expression data |
pseudo |
pseudo number for log2 transformation (default: 1) |
... |
additional parameters for correlation; see WGCNA::cor |
adjacency matrix
Dongmin Jung
fgsea::fgsea, WGCNA::cor
data(exampleRanks) Names <- names(exampleRanks) exprs <- matrix(rnorm(10*length(exampleRanks)), ncol = 10) adjacency <- exprs2adj(exprs)data(exampleRanks) Names <- names(exampleRanks) exprs <- matrix(rnorm(10*length(exampleRanks)), ncol = 10) adjacency <- exprs2adj(exprs)
The data set contains all Gene Ontology terms for Drosophila melanogaster and genes are identified by gene ID. There are 2823 categories.
GO_dmeGO_dme
a list of gene sets
GO gene sets
Dongmin Jung
http://www.go2msig.org/cgi-bin/prebuilt.cgi?taxid=7227
load(system.file("data", "GO_dme.rda", package = "gsean"))load(system.file("data", "GO_dme.rda", package = "gsean"))
GSEA or ORA is performed with networks from gene expression data
gsean(geneset, x, exprs, pseudo = 1, threshold = 0.99, nperm = 1000, centrality = function(x) rowSums(abs(x)), weightParam = 1, minSize = 1, maxSize = Inf, gseaParam = 1, nproc = 0, BPPARAM = NULL, corParam = list(), tmax = 10, ...)gsean(geneset, x, exprs, pseudo = 1, threshold = 0.99, nperm = 1000, centrality = function(x) rowSums(abs(x)), weightParam = 1, minSize = 1, maxSize = Inf, gseaParam = 1, nproc = 0, BPPARAM = NULL, corParam = list(), tmax = 10, ...)
geneset |
list of gene sets |
x |
Named vector of gene-level statistics for GSEA or set of genes for ORA. Names should be the same as in gene sets. |
exprs |
gene expression data |
pseudo |
pseudo number for log2 transformation (default: 1) |
threshold |
threshold of correlation for nodes to be considered neighbors for ORA (default: 0.99) |
nperm |
number of permutations (default: 1000) |
centrality |
centrality measure, degree centrality or node strength is default |
weightParam |
weight parameter value for the centrality measure, equally weight if weightParam = 0 (default: 1) |
minSize |
minimal size of a gene set (default: 1) |
maxSize |
maximal size of a gene set (default: Inf) |
gseaParam |
GSEA parameter value (default: 1) |
nproc |
see fgsea::fgsea |
BPPARAM |
see fgsea::fgsea |
corParam |
additional parameters for correlation; see WGCNA::cor |
tmax |
maximum number of iterations for label propagtion (default: 10) |
... |
additional parameters for label propagation; see RANKS::label.prop |
GSEA result
Dongmin Jung
exprs2adj, label_prop_gsea, centrality_gsea
data(examplePathways) data(exampleRanks) exampleRanks <- exampleRanks[1:100] Names <- names(exampleRanks) exprs <- matrix(rnorm(10*length(exampleRanks)), ncol = 10) rownames(exprs) <- names(exampleRanks) set.seed(1) result.GSEA <- gsean(examplePathways, exampleRanks, exprs)data(examplePathways) data(exampleRanks) exampleRanks <- exampleRanks[1:100] Names <- names(exampleRanks) exprs <- matrix(rnorm(10*length(exampleRanks)), ncol = 10) rownames(exprs) <- names(exampleRanks) set.seed(1) result.GSEA <- gsean(examplePathways, exampleRanks, exprs)
The data set contains 186 KEGG pathways for Drosophila melanogaster and genes are identified by gene symbol.
KEGG_hsaKEGG_hsa
a list of gene sets
KEGG gene sets
Dongmin Jung
http://software.broadinstitute.org/gsea/msigdb/collections.jsp
load(system.file("data", "KEGG_hsa.rda", package = "gsean"))load(system.file("data", "KEGG_hsa.rda", package = "gsean"))
ORA is performed by GSEA with the label propagation algorithm
label_prop_gsea(geneset, x, adjacency, threshold = 0.99, nperm = 1000, minSize = 1, maxSize = Inf, gseaParam = 1, nproc = 0, BPPARAM = NULL, ...)label_prop_gsea(geneset, x, adjacency, threshold = 0.99, nperm = 1000, minSize = 1, maxSize = Inf, gseaParam = 1, nproc = 0, BPPARAM = NULL, ...)
geneset |
list of gene sets |
x |
set of genes |
adjacency |
adjacency matrix |
threshold |
threshold of correlation for nodes to be considered neighbors (default: 0.99) |
nperm |
number of permutations (default: 1000) |
minSize |
minimal size of a gene set (default: 1) |
maxSize |
maximal size of a gene set (default: Inf) |
gseaParam |
GSEA parameter value (default: 1) |
nproc |
see fgsea::fgsea |
BPPARAM |
see fgsea::fgsea |
... |
additional parameters for label propagation; see RANKS::label.prop |
GSEA result
Dongmin Jung
fgsea::fgsea
data(examplePathways) data(exampleRanks) exampleRanks <- exampleRanks[1:100] geneNames <- names(exampleRanks) set.seed(1) x <- sample(geneNames, 10) adjacency <- diag(length(exampleRanks)) rownames(adjacency) <- geneNames result.GSEA <- label_prop_gsea(examplePathways, x, adjacency)data(examplePathways) data(exampleRanks) exampleRanks <- exampleRanks[1:100] geneNames <- names(exampleRanks) set.seed(1) x <- sample(geneNames, 10) adjacency <- diag(length(exampleRanks)) rownames(adjacency) <- geneNames result.GSEA <- label_prop_gsea(examplePathways, x, adjacency)