Package 'enrichViewNet' reference manual

Title:	From functional enrichment results to biological networks
Description:	This package enables the visualization of functional enrichment results as network graphs. First the package enables the visualization of enrichment results, in a format corresponding to the one generated by gprofiler2, as a customizable Cytoscape network. In those networks, both gene datasets (GO terms/pathways/protein complexes) and genes associated to the datasets are represented as nodes. While the edges connect each gene to its dataset(s). The package also provides the option to create enrichment maps from functional enrichment results. Enrichment maps enable the visualization of enriched terms into a network with edges connecting overlapping genes.
Authors:	Astrid Deschênes [aut, cre] (ORCID: <https://orcid.org/0000-0001-7846-6749>), Pascal Belleau [aut] (ORCID: <https://orcid.org/0000-0002-0802-1071>), Robert L. Faure [aut] (ORCID: <https://orcid.org/0000-0003-1798-4723>), Maria J. Fernandes [aut] (ORCID: <https://orcid.org/0000-0002-3973-025X>), Alexander Krasnitz [aut], David A. Tuveson [aut] (ORCID: <https://orcid.org/0000-0002-8017-2712>)
Maintainer:	Astrid Deschênes <[email protected]>
License:	Artistic-2.0
Version:	1.11.0
Built:	2026-06-04 07:09:18 UTC
Source:	https://github.com/bioc/enrichViewNet

From functional enrichment results to biological networks

Description

The enrichViewNet package enables the visualization of enrichment results, in a format corresponding to the one generated by gprofiler2 (https://cran.r-project.org/web/packages/gprofiler2/index.html) under the form of Cytoscape network (https://cytoscape.org/).

Details

In those networks, both gene datasets (GO terms/pathways/protein complexes) and genes are represented as nodes. A edge connect a gene to its datasets. In the current version, only genes present in at least one gene dataset are retained.

Author(s)

Astrid Deschênes, Pascal Belleau, Robert L Faure, Maria J Fernandes, David A Tuveson

Maintainer: Astrid Deschênes <[email protected]>

Using functional enrichment results in gprofiler2 format to create an enrichment map

Description

User selected enrichment terms are used to create an enrichment map. The selection of the term can by specifying by the source of the terms (GO:MF, REAC, TF, etc.) or by listing the selected term IDs. The map is only generated when there is at least on significant term to graph.

Usage

createEnrichMap(
  gostObject,
  query,
  source = c("TERM_ID", "GO:MF", "GO:CC", "GO:BP", "KEGG", "REAC", "TF", "MIRNA", "HPA",
    "CORUM", "HP", "WP"),
  termIDs = NULL,
  removeRoot = TRUE,
  showCategory = 30L,
  categoryLabel = 1,
  categoryNode = 1,
  line = 1,
  ...
)
createEnrichMap(
  gostObject,
  query,
  source = c("TERM_ID", "GO:MF", "GO:CC", "GO:BP", "KEGG", "REAC", "TF", "MIRNA", "HPA",
    "CORUM", "HP", "WP"),
  termIDs = NULL,
  removeRoot = TRUE,
  showCategory = 30L,
  categoryLabel = 1,
  categoryNode = 1,
  line = 1,
  ...
)

Arguments

gostObject

a list corresponding to gprofiler2 enrichment output that contains and that contains the results from an enrichment analysis.

query

a character string representing the name of the query that is going to be used to generate the graph. The query must exist in the gostObject object.

source

a character string representing the selected source that will be used to generate the network. To hand-pick the terms to be used, "TERM_ID" should be used and the list of selected term IDs should be passed through the termIDs parameter. The possible sources are "GO:BP" for Gene Ontology Biological Process, "GO:CC" for Gene Ontology Cellular Component, "GO:MF" for Gene Ontology Molecular Function, "KEGG" for Kegg, "REAC" for Reactome, "TF" for TRANSFAC, "MIRNA" for miRTarBase, "CORUM" for CORUM database, "HP" for Human phenotype ontology and "WP" for WikiPathways. Default: "TERM_ID".

termIDs

a vector of character strings that contains the term IDS retained for the creation of the network. Default: NULL.

removeRoot

a logical that specified if the root terms of the selected source should be removed (when present). Default: TRUE.

showCategory

a positive integer representing the maximum number of terms to display. If a integer, the first n terms will be displayed. If NULL, all terms will be displayed. Default: 30L.

categoryLabel

a positive numeric representing the amount by which plotting category nodes label size should be scaled relative to the default (1). Default: 1.s

categoryNode

a positive numeric representing the amount by which plotting category nodes should be scaled relative to the default (1). Default: 1.

line

a non-negative numeric representing the scale of line width. Default: 1.

...

additional arguments that will be pass to the emapplot function.

Value

a ggplot object which is the enrichment map for enrichment results.

Author(s)

Astrid Deschênes

Examples


## Loading dataset containing result from an enrichment analysis done with
## gprofiler2
data(parentalNapaVsDMSOEnrichment)

## Extract query information (only one in this dataset)
query <- unique(parentalNapaVsDMSOEnrichment$result$query)

## Create graph for Gene Ontology - Cellular Component related results
createEnrichMap(gostObject=parentalNapaVsDMSOEnrichment, 
    query=query, source="GO:CC", removeRoot=TRUE)


## Loading dataset containing result from an enrichment analysis done with
## gprofiler2
data(parentalNapaVsDMSOEnrichment)

## Extract query information (only one in this dataset)
query <- unique(parentalNapaVsDMSOEnrichment$result$query)

## Create graph for Gene Ontology - Cellular Component related results
createEnrichMap(gostObject=parentalNapaVsDMSOEnrichment, 
    query=query, source="GO:CC", removeRoot=TRUE)

Using functional enrichment results in gprofiler2 format to create an enrichment map in an igraph format

Description

Usage

createEnrichMapAsIgraph(
  gostObject,
  query,
  source = c("TERM_ID", "GO:MF", "GO:CC", "GO:BP", "KEGG", "REAC", "TF", "MIRNA", "HPA",
    "CORUM", "HP", "WP"),
  termIDs = NULL,
  removeRoot = TRUE,
  showCategory = 30L,
  similarityCutOff = 0.2
)
createEnrichMapAsIgraph(
  gostObject,
  query,
  source = c("TERM_ID", "GO:MF", "GO:CC", "GO:BP", "KEGG", "REAC", "TF", "MIRNA", "HPA",
    "CORUM", "HP", "WP"),
  termIDs = NULL,
  removeRoot = TRUE,
  showCategory = 30L,
  similarityCutOff = 0.2
)

Arguments

gostObject

a list corresponding to gprofiler2 enrichment output that contains and that contains the results from an enrichment analysis.

query

a character string representing the name of the query that is going to be used to generate the graph. The query must exist in the gostObject object.

source

termIDs

a vector of character strings that contains the term IDS retained for the creation of the network. Default: NULL.

removeRoot

a logical that specified if the root terms of the selected source should be removed (when present). Default: TRUE.

showCategory

a positive integer representing the maximum number of terms to display. If a integer, the first n terms will be displayed. If NULL, all terms will be displayed. Default: 30L.

similarityCutOff

a positive numeric between 0 and 1 indicating the minimum level of similarity between two terms to have an edge linking the terms. Default: 0.20.

Value

a igraph object which is the enrichment map for enrichment results. The node have 2 attributes: "name" and "size". The "name" corresponds to the term description. While the "size" corresponds to the number of genes found in the specific gene set. The edges have 3 attributes: "similarity", "width", and "weight". All those 3 attributes correspond to the Jaccard coefficient.

Author(s)

Astrid Deschênes

Examples


## Loading dataset containing result from an enrichment analysis done with
## gprofiler2
data(parentalNapaVsDMSOEnrichment)

## Extract query information (only one in this dataset)
query <- unique(parentalNapaVsDMSOEnrichment$result$query)

## Create graph for Gene Ontology - Cellular Component related results
mapG <- createEnrichMapAsIgraph(gostObject=parentalNapaVsDMSOEnrichment, 
    query=query, source="GO:CC", removeRoot=TRUE, 
    showCategory=30L, similarityCutOff=0.20)

## Required library igraph to show the graph
if(requireNamespace("igraph", quietly=TRUE)) {
    ## Using library igraph to show the graph
    library(igraph)
    plot(mapG)
}

if (requireNamespace("ggplot2", quietly=TRUE) &&
        requireNamespace("igraph", quietly=TRUE) &&
        requireNamespace("ggtangle", quietly=TRUE) &&
        requireNamespace("ggnetwork", quietly=TRUE)) {
    ## Using more complex set of libraries to display personalized graph
    library(ggplot2)
    library(igraph)
    library(ggnetwork)
    library(ggtangle)
    emapG <- ggplot(mapG, layout=layout_with_fr) +
                geom_edge(color="gray", linewidth=1) + 
                geom_nodes(aes(size=size)) + 
                geom_nodetext(aes(label=name), color="black", size=3) +
                theme_void()
    emapG
}

## Loading dataset containing result from an enrichment analysis done with
## gprofiler2
data(parentalNapaVsDMSOEnrichment)

## Extract query information (only one in this dataset)
query <- unique(parentalNapaVsDMSOEnrichment$result$query)

## Create graph for Gene Ontology - Cellular Component related results
mapG <- createEnrichMapAsIgraph(gostObject=parentalNapaVsDMSOEnrichment, 
    query=query, source="GO:CC", removeRoot=TRUE, 
    showCategory=30L, similarityCutOff=0.20)

## Required library igraph to show the graph
if(requireNamespace("igraph", quietly=TRUE)) {
    ## Using library igraph to show the graph
    library(igraph)
    plot(mapG)
}

if (requireNamespace("ggplot2", quietly=TRUE) &&
        requireNamespace("igraph", quietly=TRUE) &&
        requireNamespace("ggtangle", quietly=TRUE) &&
        requireNamespace("ggnetwork", quietly=TRUE)) {
    ## Using more complex set of libraries to display personalized graph
    library(ggplot2)
    library(igraph)
    library(ggnetwork)
    library(ggtangle)
    emapG <- ggplot(mapG, layout=layout_with_fr) +
                geom_edge(color="gray", linewidth=1) + 
                geom_nodes(aes(size=size)) + 
                geom_nodetext(aes(label=name), color="black", size=3) +
                theme_void()
    emapG
}

Using functional enrichment results in gprofiler2 format to create an enrichment map with multiple groups from different enrichment analyses

Description

Usage

createEnrichMapMultiBasic(
  gostObjectList,
  queryList,
  source = c("TERM_ID", "GO:MF", "GO:CC", "GO:BP", "KEGG", "REAC", "TF", "MIRNA", "HPA",
    "CORUM", "HP", "WP"),
  termIDs = NULL,
  removeRoot = TRUE,
  showCategory = 30L,
  categoryLabel = 1,
  categoryNode = 1,
  line = 1,
  ...
)
createEnrichMapMultiBasic(
  gostObjectList,
  queryList,
  source = c("TERM_ID", "GO:MF", "GO:CC", "GO:BP", "KEGG", "REAC", "TF", "MIRNA", "HPA",
    "CORUM", "HP", "WP"),
  termIDs = NULL,
  removeRoot = TRUE,
  showCategory = 30L,
  categoryLabel = 1,
  categoryNode = 1,
  line = 1,
  ...
)

Arguments

gostObjectList

a list of gprofiler2 objects that contain the results from an enrichment analysis. The list must contain at least 2 entries. The number of entries must correspond to the number of entries for the queryList parameter.

queryList

a list of character strings representing the names of the queries that are going to be used to generate the graph. The query names must exist in the associated gostObjectList objects and follow the same order. The number of entries must correspond to the number of entries for the gostObjectList parameter.

source

termIDs

a vector of character strings that contains the term IDS retained for the creation of the network. Default: NULL.

removeRoot

a logical that specified if the root terms of the selected source should be removed (when present). Default: TRUE.

showCategory

a positive integer or a vector of characters representing terms. If a integer, the first n terms will be displayed. If vector of terms, the selected terms will be displayed. Default: 30L.

categoryLabel

a positive numeric representing the amount by which plotting category nodes label size should be scaled relative to the default (1). Default: 1.

categoryNode

a positive numeric representing the amount by which plotting category nodes should be scaled relative to the default (1). Default: 1.

line

a non-negative numeric representing the scale of line width. Default: 1.

...

additional arguments that will be pass to the emapplot function.

Value

a ggplot object which is the enrichment map for enrichment results.

Author(s)

Astrid Deschênes

Examples


## Loading dataset containing results from 2 enrichment analyses done with
## gprofiler2
data(parentalNapaVsDMSOEnrichment)
data(rosaNapaVsDMSOEnrichment)

## Extract query information (only one in each dataset)
query1 <- unique(parentalNapaVsDMSOEnrichment$result$query)[1]
query2 <- unique(rosaNapaVsDMSOEnrichment$result$query)[1]

## Create graph for KEGG related results from 
## 2 enrichment analyses
createEnrichMapMultiBasic(gostObjectList=list(parentalNapaVsDMSOEnrichment, 
    rosaNapaVsDMSOEnrichment), 
    queryList=list(query1, query2), source="KEGG", removeRoot=TRUE)


## Loading dataset containing results from 2 enrichment analyses done with
## gprofiler2
data(parentalNapaVsDMSOEnrichment)
data(rosaNapaVsDMSOEnrichment)

## Extract query information (only one in each dataset)
query1 <- unique(parentalNapaVsDMSOEnrichment$result$query)[1]
query2 <- unique(rosaNapaVsDMSOEnrichment$result$query)[1]

## Create graph for KEGG related results from 
## 2 enrichment analyses
createEnrichMapMultiBasic(gostObjectList=list(parentalNapaVsDMSOEnrichment, 
    rosaNapaVsDMSOEnrichment), 
    queryList=list(query1, query2), source="KEGG", removeRoot=TRUE)

Using functional enrichment results in gprofiler2 format to create an enrichment map with multiple groups from different enrichment analyses as an igraph

Description

Usage

createEnrichMapMultiBasicAsIgraph(
  gostObjectList,
  queryList,
  source = c("TERM_ID", "GO:MF", "GO:CC", "GO:BP", "KEGG", "REAC", "TF", "MIRNA", "HPA",
    "CORUM", "HP", "WP"),
  termIDs = NULL,
  removeRoot = TRUE,
  showCategory = 30L,
  similarityCutOff = 0.2
)
createEnrichMapMultiBasicAsIgraph(
  gostObjectList,
  queryList,
  source = c("TERM_ID", "GO:MF", "GO:CC", "GO:BP", "KEGG", "REAC", "TF", "MIRNA", "HPA",
    "CORUM", "HP", "WP"),
  termIDs = NULL,
  removeRoot = TRUE,
  showCategory = 30L,
  similarityCutOff = 0.2
)

Arguments

gostObjectList

queryList

source

termIDs

a vector of character strings that contains the term IDS retained for the creation of the network. Default: NULL.

removeRoot

a logical that specified if the root terms of the selected source should be removed (when present). Default: TRUE.

showCategory

a positive integer representing the maximum number of terms to display. If a integer, the first n terms will be displayed. If NULL, all terms will be displayed. Default: 30L.

similarityCutOff

a positive numeric between 0 and 1 indicating the minimum level of similarity between two terms, calculated using the Jaccard coefficient, to have an edge linking the terms. Default: 0.20.

Value

a igraph object which is the enrichment map for enrichment results. The node have 5 attributes: "name", "size", "pie", "cluster", and "pieName". The "name" corresponds to the term description. While the "size" corresponds to the number of unique genes found in the specific gene set when looking at all the experiments. The edges have 3 attributes: "similarity", "width", and "weight". All those 3 attributes correspond to the Jaccard coefficient.

Author(s)

Astrid Deschênes

Examples


## Loading dataset containing results from 2 enrichment analyses done with
## gprofiler2
data(parentalNapaVsDMSOEnrichment)
data(rosaNapaVsDMSOEnrichment)

## Extract query information (only one in each dataset)
query1 <- unique(parentalNapaVsDMSOEnrichment$result$query)[1]
query2 <- unique(rosaNapaVsDMSOEnrichment$result$query)[1]

## Create graph for KEGG related results from 
## 2 enrichment analyses
emapG <- createEnrichMapMultiBasicAsIgraph(gostObjectList=
    list(parentalNapaVsDMSOEnrichment, rosaNapaVsDMSOEnrichment), 
    queryList=list(query1, query2), source="KEGG", removeRoot=TRUE, 
    similarityCutOff=0.3)

if (requireNamespace("ggplot2", quietly=TRUE) && 
        requireNamespace("igraph", quietly=TRUE) && 
        requireNamespace("scatterpie", quietly=TRUE) && 
        requireNamespace("ggtangle", quietly=TRUE) && 
        requireNamespace("ggrepel", quietly=TRUE)) {
    ## Create a visual representation of the enrichment map
    ## by default
    library(igraph)
    plot(emapG)
    
    ## Add see to reproduce the same graph
    set.seed(12)
    
    library(ggplot2)
    library(ggtangle)
    library(scatterpie)
    library(ggrepel)
    
    emapGraph <- ggplot(emapG, layout=layout_with_fr) + 
                geom_edge(color="gray", linewidth=1)
    
    pieInfo <- as.data.frame(do.call(rbind, V(emapG)$pie))
    colnames(pieInfo) <- V(emapG)$pieName[[1]]
    
    ## Add information about the groups associated with each node in the 
    ## ggplot object so that the node can be colored accordingly
    for (i in seq_len(ncol(pieInfo))) {
        emapGraph$data[colnames(pieInfo)[i]] <- pieInfo[, i]
    }
    
    ## Using scatterpie, ggtangle and ggrepel to generate the graph
    ## geom_scatterpie() allows to have scatter pie plot
    ## geom_text_repel() allows to have minimum overlying terms
    ## coord_fixed() forces the plot to have a 1:1 aspect ratio
    emapGraph + 
        geom_scatterpie(aes(x=x, y=y, r=size/50), 
            cols=c(colnames(pieInfo)), legend_name = "Group", color=NA) +
        geom_scatterpie_legend(radius=emapGraph$data$size/50, n=4, 
            x=max(emapGraph$data$x), y=min(emapGraph$data$y),
            labeller=function(x) {round(x*50)}, label_position="right") +
        geom_text_repel(aes(x=x, y=y, label=label), max.overlaps=20) +
        coord_fixed()
}

## Loading dataset containing results from 2 enrichment analyses done with
## gprofiler2
data(parentalNapaVsDMSOEnrichment)
data(rosaNapaVsDMSOEnrichment)

## Extract query information (only one in each dataset)
query1 <- unique(parentalNapaVsDMSOEnrichment$result$query)[1]
query2 <- unique(rosaNapaVsDMSOEnrichment$result$query)[1]

## Create graph for KEGG related results from 
## 2 enrichment analyses
emapG <- createEnrichMapMultiBasicAsIgraph(gostObjectList=
    list(parentalNapaVsDMSOEnrichment, rosaNapaVsDMSOEnrichment), 
    queryList=list(query1, query2), source="KEGG", removeRoot=TRUE, 
    similarityCutOff=0.3)

if (requireNamespace("ggplot2", quietly=TRUE) && 
        requireNamespace("igraph", quietly=TRUE) && 
        requireNamespace("scatterpie", quietly=TRUE) && 
        requireNamespace("ggtangle", quietly=TRUE) && 
        requireNamespace("ggrepel", quietly=TRUE)) {
    ## Create a visual representation of the enrichment map
    ## by default
    library(igraph)
    plot(emapG)
    
    ## Add see to reproduce the same graph
    set.seed(12)
    
    library(ggplot2)
    library(ggtangle)
    library(scatterpie)
    library(ggrepel)
    
    emapGraph <- ggplot(emapG, layout=layout_with_fr) + 
                geom_edge(color="gray", linewidth=1)
    
    pieInfo <- as.data.frame(do.call(rbind, V(emapG)$pie))
    colnames(pieInfo) <- V(emapG)$pieName[[1]]
    
    ## Add information about the groups associated with each node in the 
    ## ggplot object so that the node can be colored accordingly
    for (i in seq_len(ncol(pieInfo))) {
        emapGraph$data[colnames(pieInfo)[i]] <- pieInfo[, i]
    }
    
    ## Using scatterpie, ggtangle and ggrepel to generate the graph
    ## geom_scatterpie() allows to have scatter pie plot
    ## geom_text_repel() allows to have minimum overlying terms
    ## coord_fixed() forces the plot to have a 1:1 aspect ratio
    emapGraph + 
        geom_scatterpie(aes(x=x, y=y, r=size/50), 
            cols=c(colnames(pieInfo)), legend_name = "Group", color=NA) +
        geom_scatterpie_legend(radius=emapGraph$data$size/50, n=4, 
            x=max(emapGraph$data$x), y=min(emapGraph$data$y),
            labeller=function(x) {round(x*50)}, label_position="right") +
        geom_text_repel(aes(x=x, y=y, label=label), max.overlaps=20) +
        coord_fixed()
}

Using functional enrichment results in gprofiler2 format to create an enrichment map with multiple groups from same or different enrichment analyses

Description

Usage

createEnrichMapMultiComplex(
  gostObjectList,
  queryInfo,
  showCategory = 30L,
  categoryLabel = 1,
  categoryNode = 1,
  line = 1,
  ...
)
createEnrichMapMultiComplex(
  gostObjectList,
  queryInfo,
  showCategory = 30L,
  categoryLabel = 1,
  categoryNode = 1,
  line = 1,
  ...
)

Arguments

gostObjectList

queryInfo

a data.frame contains one row per group being displayed. The number of rows must correspond to the number of entries for the gostObjectList parameter. The mandatory columns are:

queryName: a character string representing the name of the query retained for this group). The query names must exist in the associated gostObjectList objects and follow the same order.
source: a character string representing the selected source that will be used to generate the network. To hand-pick the terms to be used, "TERM_ID" should be used and the list of selected term IDs should be passed through the termIDs parameter. The possible sources are "GO:BP" for Gene Ontology Biological Process, "GO:CC" for Gene Ontology Cellular Component, "GO:MF" for Gene Ontology Molecular Function, "KEGG" for Kegg, "REAC" for Reactome, "TF" for TRANSFAC, "MIRNA" for miRTarBase, "CORUM" for CORUM database, "HP" for Human phenotype ontology and "WP" for WikiPathways. Default: "TERM_ID".
removeRoot: a logical that specified if the root terms of the selected source should be removed (when present).
termIDs: a character strings that contains the term IDS retained for the creation of the network separated by a comma ',' when the "TERM_ID" source is selected. Otherwise, it should be a empty string ("").
groupName: a character strings that contains the name of the group to be shown in the legend. Each group has to have a unique name.

showCategory

categoryLabel

a positive numeric representing the amount by which plotting category nodes label size should be scaled relative to the default (1). Default: 1.

categoryNode

a positive numeric representing the amount by which plotting category nodes should be scaled relative to the default (1). Default: 1.

line

a non-negative numeric representing the scale of line width. Default: 1.

...

additional arguments that will be pass to the emapplot function.

Value

a ggplot object which is the enrichment map for enrichment results.

Author(s)

Astrid Deschênes

Examples


## Loading dataset containing results from 2 enrichment analyses done with
## gprofiler2
data(parentalNapaVsDMSOEnrichment)
data(rosaNapaVsDMSOEnrichment)

## Create list of enrichment objects required to generate the enrichment map
gostObjectList=list(parentalNapaVsDMSOEnrichment, 
    parentalNapaVsDMSOEnrichment, rosaNapaVsDMSOEnrichment, 
    rosaNapaVsDMSOEnrichment)
    
## Create data frame containing required information enabling the 
## selection of the retained enriched terms for each enrichment analysis.
## One line per enrichment analyses present in the gostObjectList parameter
## With this data frame, the enrichment results will be split in 4 groups:
## 1) KEGG significant terms from parental napa vs DMSO (no root term)
## 2) REACTOME significant terms from parental napa vs DMSO (no root term)
## 3) KEGG significant terms from rosa napa vs DMSO (no root term)
## 4) REACTOME significant terms from rosa napa vs DMSO (no root term)
queryDataFrame <- data.frame(queryName=c("parental_napa_vs_DMSO", 
        "parental_napa_vs_DMSO", "rosa_napa_vs_DMSO", "rosa_napa_vs_DMSO"), 
    source=c("KEGG", "REAC", "KEGG", "REAC"), 
    removeRoot=c(TRUE, TRUE, TRUE, TRUE), termIDs=c("", "", "", ""), 
    groupName=c("parental - KEGG", "parental - Reactome", 
        "rosa - KEGG", "rosa - Reactome"), stringsAsFactors=FALSE)
    
## Create graph for KEGG and REACTOME significant results from 
## 2 enrichment analyses
createEnrichMapMultiComplex(gostObjectList=gostObjectList, 
    queryInfo=queryDataFrame, line=1.5)

## Loading dataset containing results from 2 enrichment analyses done with
## gprofiler2
data(parentalNapaVsDMSOEnrichment)
data(rosaNapaVsDMSOEnrichment)

## Create list of enrichment objects required to generate the enrichment map
gostObjectList=list(parentalNapaVsDMSOEnrichment, 
    parentalNapaVsDMSOEnrichment, rosaNapaVsDMSOEnrichment, 
    rosaNapaVsDMSOEnrichment)
    
## Create data frame containing required information enabling the 
## selection of the retained enriched terms for each enrichment analysis.
## One line per enrichment analyses present in the gostObjectList parameter
## With this data frame, the enrichment results will be split in 4 groups:
## 1) KEGG significant terms from parental napa vs DMSO (no root term)
## 2) REACTOME significant terms from parental napa vs DMSO (no root term)
## 3) KEGG significant terms from rosa napa vs DMSO (no root term)
## 4) REACTOME significant terms from rosa napa vs DMSO (no root term)
queryDataFrame <- data.frame(queryName=c("parental_napa_vs_DMSO", 
        "parental_napa_vs_DMSO", "rosa_napa_vs_DMSO", "rosa_napa_vs_DMSO"), 
    source=c("KEGG", "REAC", "KEGG", "REAC"), 
    removeRoot=c(TRUE, TRUE, TRUE, TRUE), termIDs=c("", "", "", ""), 
    groupName=c("parental - KEGG", "parental - Reactome", 
        "rosa - KEGG", "rosa - Reactome"), stringsAsFactors=FALSE)
    
## Create graph for KEGG and REACTOME significant results from 
## 2 enrichment analyses
createEnrichMapMultiComplex(gostObjectList=gostObjectList, 
    queryInfo=queryDataFrame, line=1.5)

Using functional enrichment results in gprofiler2 format to create an enrichment map with multiple groups from same or different enrichment analyses in an igraph format

Description

Usage

createEnrichMapMultiComplexAsIgraph(
  gostObjectList,
  queryInfo,
  showCategory = 30L,
  similarityCutOff = 0.2
)
createEnrichMapMultiComplexAsIgraph(
  gostObjectList,
  queryInfo,
  showCategory = 30L,
  similarityCutOff = 0.2
)

Arguments

gostObjectList

queryInfo

a data.frame contains one row per group being displayed. The number of rows must correspond to the number of entries for the gostObjectList parameter. The mandatory columns are:

queryName: a character string representing the name of the query retained for this group). The query names must exist in the associated gostObjectList objects and follow the same order.
source: a character string representing the selected source that will be used to generate the network. To hand-pick the terms to be used, "TERM_ID" should be used and the list of selected term IDs should be passed through the termIDs parameter. The possible sources are "GO:BP" for Gene Ontology Biological Process, "GO:CC" for Gene Ontology Cellular Component, "GO:MF" for Gene Ontology Molecular Function, "KEGG" for Kegg, "REAC" for Reactome, "TF" for TRANSFAC, "MIRNA" for miRTarBase, "CORUM" for CORUM database, "HP" for Human phenotype ontology and "WP" for WikiPathways. Default: "TERM_ID".
removeRoot: a logical that specified if the root terms of the selected source should be removed (when present).
termIDs: a character strings that contains the term IDS retained for the creation of the network separated by a comma ',' when the "TERM_ID" source is selected. Otherwise, it should be a empty string ("").
groupName: a character strings that contains the name of the group to be shown in the legend. Each group has to have a unique name.

showCategory

a positive integer or NULL. If a integer, the first n terms will be displayed. If NULL, all terms will be displayed. Default: 30L.

similarityCutOff

a positive numeric, larger than zero and small than 1 that represent the minimum similarity level between two nodes (terms) to be linked by an edge. Default: 0.2.

Value

Author(s)

Astrid Deschênes

Examples


## Loading dataset containing results from 2 enrichment analyses done with
## gprofiler2 queries
data(parentalNapaVsDMSOEnrichment)
data(rosaNapaVsDMSOEnrichment)

## The graph will be split in 4 groups
## Groups 1 and 2 are using the parental Napa vs DMSO dataset
## Group 3 and 4 are using the rosa Napa vs DMSO dataset
gostObjectList=list(parentalNapaVsDMSOEnrichment, 
    parentalNapaVsDMSOEnrichment, rosaNapaVsDMSOEnrichment, 
    rosaNapaVsDMSOEnrichment)
    
## Create data frame containing required information enabling the 
## selection of the retained enriched terms for each enrichment analysis.
## One line per enrichment analyses present in the gostObjectList parameter
## With this data frame, the enrichment results will be split in 4 groups:
## 1) KEGG significant terms from parental napa vs DMSO (no root term)
## 2) REACTOME significant terms from parental napa vs DMSO (no root term)
## 3) KEGG significant terms from rosa napa vs DMSO (no root term)
## 4) REACTOME significant terms from rosa napa vs DMSO (no root term)
queryDataFrame <- data.frame(queryName=c("parental_napa_vs_DMSO", 
        "parental_napa_vs_DMSO", "rosa_napa_vs_DMSO", "rosa_napa_vs_DMSO"), 
    source=c("KEGG", "REAC", "KEGG", "REAC"), 
    removeRoot=c(TRUE, TRUE, TRUE, TRUE), termIDs=c("", "", "", ""), 
    groupName=c("parental - KEGG", "parental - Reactome", 
        "rosa - KEGG", "rosa - Reactome"), stringsAsFactors=FALSE)
    
## Create graph for KEGG and REACTOME significant results from 
## 2 enrichment analyses in an igraph format
emap <- createEnrichMapMultiComplexAsIgraph(gostObjectList=gostObjectList, 
    queryInfo=queryDataFrame, showCategory=5)

if (requireNamespace("ggplot2", quietly=TRUE) && 
        requireNamespace("igraph", quietly=TRUE) && 
        requireNamespace("scatterpie", quietly=TRUE) && 
        requireNamespace("ggtangle", quietly=TRUE) && 
        requireNamespace("ggrepel", quietly=TRUE)) {
    ## Create a visual representation of the enrichment map
    ## by default
    library(igraph)
    plot(emap)
    
    ## Add see to reproduce the same graph
    set.seed(12)
    
    library(ggplot2)
    library(ggtangle)
    library(scatterpie)
    library(ggrepel)
    
    emapG <- ggplot(emap, layout=layout_with_fr) + 
                geom_edge(color="gray", linewidth=1)
    
    pieInfo <- as.data.frame(do.call(rbind, V(emap)$pie))
    colnames(pieInfo) <- V(emap)$pieName[[1]]
    
    ## Add information about the groups associated with each node in the 
    ## ggplot object so that the node can be colored accordingly
    for (i in seq_len(ncol(pieInfo))) {
        emapG$data[colnames(pieInfo)[i]] <- pieInfo[, i]
    }
    
    ## Using scatterpie, ggtangle and ggrepel to generate the graph
    ## geom_scatterpie() allows to have scatter pie plot
    ## geom_text_repel() allows to have minimum overlying terms
    ## coord_fixed() forces the plot to have a 1:1 aspect ratio
    emapG + 
        geom_scatterpie(aes(x=x, y=y, r=size/50), 
            cols=c(colnames(pieInfo)), legend_name = "Group", color=NA) +
        geom_scatterpie_legend(radius=emapG$data$size/50, n=4, 
            x=max(emapG$data$x), y=max(emapG$data$y),
            labeller=function(x) {round(x*50)}, label_position="right") +
        geom_text_repel(aes(x=x, y=y, label=label), max.overlaps=20) +
        coord_fixed()
}

## Loading dataset containing results from 2 enrichment analyses done with
## gprofiler2 queries
data(parentalNapaVsDMSOEnrichment)
data(rosaNapaVsDMSOEnrichment)

## The graph will be split in 4 groups
## Groups 1 and 2 are using the parental Napa vs DMSO dataset
## Group 3 and 4 are using the rosa Napa vs DMSO dataset
gostObjectList=list(parentalNapaVsDMSOEnrichment, 
    parentalNapaVsDMSOEnrichment, rosaNapaVsDMSOEnrichment, 
    rosaNapaVsDMSOEnrichment)
    
## Create data frame containing required information enabling the 
## selection of the retained enriched terms for each enrichment analysis.
## One line per enrichment analyses present in the gostObjectList parameter
## With this data frame, the enrichment results will be split in 4 groups:
## 1) KEGG significant terms from parental napa vs DMSO (no root term)
## 2) REACTOME significant terms from parental napa vs DMSO (no root term)
## 3) KEGG significant terms from rosa napa vs DMSO (no root term)
## 4) REACTOME significant terms from rosa napa vs DMSO (no root term)
queryDataFrame <- data.frame(queryName=c("parental_napa_vs_DMSO", 
        "parental_napa_vs_DMSO", "rosa_napa_vs_DMSO", "rosa_napa_vs_DMSO"), 
    source=c("KEGG", "REAC", "KEGG", "REAC"), 
    removeRoot=c(TRUE, TRUE, TRUE, TRUE), termIDs=c("", "", "", ""), 
    groupName=c("parental - KEGG", "parental - Reactome", 
        "rosa - KEGG", "rosa - Reactome"), stringsAsFactors=FALSE)
    
## Create graph for KEGG and REACTOME significant results from 
## 2 enrichment analyses in an igraph format
emap <- createEnrichMapMultiComplexAsIgraph(gostObjectList=gostObjectList, 
    queryInfo=queryDataFrame, showCategory=5)

if (requireNamespace("ggplot2", quietly=TRUE) && 
        requireNamespace("igraph", quietly=TRUE) && 
        requireNamespace("scatterpie", quietly=TRUE) && 
        requireNamespace("ggtangle", quietly=TRUE) && 
        requireNamespace("ggrepel", quietly=TRUE)) {
    ## Create a visual representation of the enrichment map
    ## by default
    library(igraph)
    plot(emap)
    
    ## Add see to reproduce the same graph
    set.seed(12)
    
    library(ggplot2)
    library(ggtangle)
    library(scatterpie)
    library(ggrepel)
    
    emapG <- ggplot(emap, layout=layout_with_fr) + 
                geom_edge(color="gray", linewidth=1)
    
    pieInfo <- as.data.frame(do.call(rbind, V(emap)$pie))
    colnames(pieInfo) <- V(emap)$pieName[[1]]
    
    ## Add information about the groups associated with each node in the 
    ## ggplot object so that the node can be colored accordingly
    for (i in seq_len(ncol(pieInfo))) {
        emapG$data[colnames(pieInfo)[i]] <- pieInfo[, i]
    }
    
    ## Using scatterpie, ggtangle and ggrepel to generate the graph
    ## geom_scatterpie() allows to have scatter pie plot
    ## geom_text_repel() allows to have minimum overlying terms
    ## coord_fixed() forces the plot to have a 1:1 aspect ratio
    emapG + 
        geom_scatterpie(aes(x=x, y=y, r=size/50), 
            cols=c(colnames(pieInfo)), legend_name = "Group", color=NA) +
        geom_scatterpie_legend(radius=emapG$data$size/50, n=4, 
            x=max(emapG$data$x), y=max(emapG$data$y),
            labeller=function(x) {round(x*50)}, label_position="right") +
        geom_text_repel(aes(x=x, y=y, label=label), max.overlaps=20) +
        coord_fixed()
}

Using functional enrichment results from gprofiler2 to create a Cytoscape network

Description

User selected enrichment terms are used to create a Cytoscape network where the selected terms and the genes that where part of the enrichment analysis are all represented as nodes. Edges are linking the genes to their terms. The selection of the term can by specifying the source of the terms (GO:MF, REAC, TF, etc.) or by listing the selected term IDs. The network is only generated when there is at least on significant term to graph. When the enrichment analysis contains more than one query, only one query can be selected to generate the network.

Usage

createNetwork(
  gostObject,
  source = c("TERM_ID", "GO:MF", "GO:CC", "GO:BP", "KEGG", "REAC", "TF", "MIRNA", "HPA",
    "CORUM", "HP", "WP"),
  termIDs = NULL,
  removeRoot = TRUE,
  query = NULL,
  title = "gprofiler network",
  collection = "enrichment results",
  fileName = "gprofilerNetwork.cx"
)
createNetwork(
  gostObject,
  source = c("TERM_ID", "GO:MF", "GO:CC", "GO:BP", "KEGG", "REAC", "TF", "MIRNA", "HPA",
    "CORUM", "HP", "WP"),
  termIDs = NULL,
  removeRoot = TRUE,
  query = NULL,
  title = "gprofiler network",
  collection = "enrichment results",
  fileName = "gprofilerNetwork.cx"
)

Arguments

gostObject

a list created by gprofiler2 that contains the results from an enrichment analysis.

source

termIDs

a vector of character strings that contains the term IDS retained for the creation of the network. Default: NULL.

removeRoot

a logical that specified if the root terms of the selected source should be removed (when present). Default: TRUE.

query

a character string that specified the retained query to generate the network. When NULL, the query present in the result is retained; NULL cannot be used when more than one query is present. Default: NULL.

title

a character string representing the name assigned to the network. Default: "gprofiler network".

collection

a character string representing the collection name assigned to the network. Default: "enrichment results".

fileName

a character string representing the name of the CX JSON file that is created when Cytoscape is not running. The name must have a '.cx' extension. Default: "gprofilerNetwork_01.cx".

Value

TRUE

Author(s)

Astrid Deschênes

Examples


## Loading dataset containing result from an enrichment analysis done with
## gprofiler2
data(parentalNapaVsDMSOEnrichment)

## Some of the enrichment results present in the dataset
head(parentalNapaVsDMSOEnrichment$result)

## Create network for Gene Ontology - Molecular Function related results
## in Cytoscape (when the application is opened)
## Otherwise, create a CX file in the temporary directory
## The file can be opened in Cytoscape
createNetwork(gostObject=parentalNapaVsDMSOEnrichment, source="KEGG", 
    removeRoot=FALSE, title="KEGG Graph", 
    fileName=file.path(tempdir(), "KEGG_demo.cx"))


## Loading dataset containing result from an enrichment analysis done with
## gprofiler2
data(parentalNapaVsDMSOEnrichment)

## Some of the enrichment results present in the dataset
head(parentalNapaVsDMSOEnrichment$result)

## Create network for Gene Ontology - Molecular Function related results
## in Cytoscape (when the application is opened)
## Otherwise, create a CX file in the temporary directory
## The file can be opened in Cytoscape
createNetwork(gostObject=parentalNapaVsDMSOEnrichment, source="KEGG", 
    removeRoot=FALSE, title="KEGG Graph", 
    fileName=file.path(tempdir(), "KEGG_demo.cx"))

The result of a functional enrichment analysis done with `gprofiler2` (https://cran.r-project.org/web/packages/gprofiler2/vignettes/gprofiler2.html).

Description

The object is a list with 2 entries. It contains the results of the enrichment analysis as well as the metadata related to the analysis.

Usage

data(demoGOST)
data(demoGOST)

Format

The list contains two entries. The result entry contains a data.frame with the significant results obtained by an enrichment analysis done with gprofiler2. The meta entry contains a named list with all the metadata for the query.

Details

This dataset can be used to test the createNetwork function.

Value

A list containing two entries. The result entry contains a data.frame with the significant results obtained by an enrichment analysis done with gprofiler2. The meta entry contains a named list with all the metadata for the query.

Examples


## Loading dataset containing result from an enrichment analysis done with
## gprofiler2
data(demoGOST)

## Create network for WikiPathways results 
## in Cytoscape (if the application is open)
## Otherwise, create a CX file in the temporary directory
## The file can be opened in Cytoscape
createNetwork(gostObject=demoGOST, source="WP", title="Wikipathways",
    fileName=file.path(tempdir(), "Wikipathways_Demo.cx"))


## Loading dataset containing result from an enrichment analysis done with
## gprofiler2
data(demoGOST)

## Create network for WikiPathways results 
## in Cytoscape (if the application is open)
## Otherwise, create a CX file in the temporary directory
## The file can be opened in Cytoscape
createNetwork(gostObject=demoGOST, source="WP", title="Wikipathways",
    fileName=file.path(tempdir(), "Wikipathways_Demo.cx"))

The result of a differential expression analysis done between napabucasin treated and DMSO control parental MiaPaCa2 cells. The cells were treated for 2 hours with 0.5 uM napabucasin. The protocol to generate the RNA-seq is described in Froeling F.E.M. et al 2019.

Description

The object is a data.frame with 24184 rows and 4 columns. Each row correspond to a tested gene.

Usage

data(parentalNapaVsDMSODEG)
data(parentalNapaVsDMSODEG)

Format

a data.frame containing the results of a differential expression analysis between napabucasin treated and DMSO control parental MiaPaCa2 cells for all 24184 genes tested. The 4 columns are:

"EnsemblID": a character string representing the unique Ensembl identifier for the tested gene
"log2FoldChange": a numeric representing the expression difference (in log2FoldChange) between the napabucasin treatment and the DMSO control for the tested gene
"padj": a numeric representing the adjusted p-value associated to the difference in expression for the tested gene
"GeneName": a character string representing the name of the tested gene

Details

The differentially expressed genes between napabucasin-treated cells (0.5 uM) and DMSO as vehicle control are reprinted from Clinical Cancer Research, 2019, 25 (23), 7162–7174, Fieke E.M. Froeling, Manojit Mosur Swamynathan, Astrid Deschênes, Iok In Christine Chio, Erin Brosnan, Melissa A. Yao, Priya Alagesan, Matthew Lucito, Juying Li, An-Yun Chang, Lloyd C. Trotman, Pascal Belleau, Youngkyu Park, Harry A. Rogoff, James D. Watson, David A. Tuveson, Bioactivation of napabucasin triggers reactive oxygen species–mediated cancer cell death, with permission from AACR.

Value

a data.frame containing the results of a differential expression analysis between napabucasin treated and DMSO control parental MiaPaCa2 cells for all 24184 genes tested. The 4 columns are:

"EnsemblID": a character string representing the unique Ensembl identifier for the tested gene
"log2FoldChange": a numeric representing the expression difference (in log2FoldChange) between the napabucasin treatment and the DMSO control for the tested gene
"padj": a numeric representing the adjusted p-value associated to the difference in expression for the tested gene
"GeneName": a character string representing the name of the tested gene

Source

The original RNA-sequencing data is available at the Gene Expression Omnibus (GEO) under the accession number GSE135352.

Examples


## Required library
library(gprofiler2)

## Loading data set containing the results of a differentially expressed 
## analysis between 2-hour treatment with 0.5 uM napabucasin and 
## DMSO vehicle control parental MiaPaCa2 cells
data(parentalNapaVsDMSODEG)

allGenes <- unique(parentalNapaVsDMSODEG$EnsemblID)

## Select the significantly differentially expressed genes
selection <- which(abs(parentalNapaVsDMSODEG$log2FoldChange) > 1 & 
                            parentalNapaVsDMSODEG$padj < 0.05)
                            
selectedGenes <- unique(parentalNapaVsDMSODEG$EnsemblID[selection])

## Run an enrichment analysis using WikiPathways dataset
gostres <- gost(query = list(parental_napa_vs_DMSO=selectedGenes),
    organism="hsapiens",
    correction_method = "g_SCS",
    sources=c("WP"), significant=TRUE, evcodes=TRUE,
    custom_bg=allGenes, exclude_iea=TRUE)



## Required library
library(gprofiler2)

## Loading data set containing the results of a differentially expressed 
## analysis between 2-hour treatment with 0.5 uM napabucasin and 
## DMSO vehicle control parental MiaPaCa2 cells
data(parentalNapaVsDMSODEG)

allGenes <- unique(parentalNapaVsDMSODEG$EnsemblID)

## Select the significantly differentially expressed genes
selection <- which(abs(parentalNapaVsDMSODEG$log2FoldChange) > 1 & 
                            parentalNapaVsDMSODEG$padj < 0.05)
                            
selectedGenes <- unique(parentalNapaVsDMSODEG$EnsemblID[selection])

## Run an enrichment analysis using WikiPathways dataset
gostres <- gost(query = list(parental_napa_vs_DMSO=selectedGenes),
    organism="hsapiens",
    correction_method = "g_SCS",
    sources=c("WP"), significant=TRUE, evcodes=TRUE,
    custom_bg=allGenes, exclude_iea=TRUE)

The result of an enrichment analysis has been done using the significantly differentially expressed genes between napabucasin treated and DMSO control parental MiaPaCa2 cells. The cells were treated for 2 hour with 0.5 uM napabucasin. The protocol to generate the RNA-seq is described in Froeling F.E.M. et al 2019.

Description

The enrichment analysis was done with gprofile2 package (Kolberg L et al 2020) with database version 'e109_eg56_p17_1d3191d' and g:SCS multiple testing correction method applying significance threshold of 0.05 (Raudvere U et al 2019). All tested genes were used as background.

Usage

data(parentalNapaVsDMSOEnrichment)
data(parentalNapaVsDMSOEnrichment)

Format

a list created by gprofiler2 that contains the results from the enrichment analysis:

"result": a data.frame with the significantly enriched terms
"meta": a list with the meta-data information

Details

The object is a named list with 2 entries. The 'result' entry contains a data.frame with the enrichment analysis results and the 'meta' entry contains metadata information.

The dataset used for the enrichment analysis is associated to this publication:

Froeling F.E.M. et al.Bioactivation of Napabucasin Triggers Reactive Oxygen Species–Mediated Cancer Cell Death. Clin Cancer Res 1 December 2019; 25 (23): 7162–7174

The enrichment analysis has been done with gprofile2 package (Kolberg L et al 2020) with database version 'e109_eg56_p17_1d3191d' and g:SCS multiple testing correction method applying significance threshold of 0.05 (Raudvere U et al 2019). All tested genes were used as background.

Value

a list containing 2 entries:

"result": a data.frame with the significantly enriched terms
"meta": a list with the meta-data information

Source

The original RNA-sequencing data is available at the Gene Expression Omnibus (GEO) under the accession number GSE135352.

Examples


## Loading dataset containing the results of the enrichment analysis 
## done on a differentially expressed 
## analysis between 2-hour treatment with 0.5 uM napabucasin and 
## DMSO vehicle control parental MiaPaCa2 cells
data(parentalNapaVsDMSOEnrichment)

## Create an enrichment map for the GO:CC terms
createEnrichMap(gostObject=parentalNapaVsDMSOEnrichment, 
    query="parental_napa_vs_DMSO", source="GO:CC")

## Loading dataset containing the results of the enrichment analysis 
## done on a differentially expressed 
## analysis between 2-hour treatment with 0.5 uM napabucasin and 
## DMSO vehicle control parental MiaPaCa2 cells
data(parentalNapaVsDMSOEnrichment)

## Create an enrichment map for the GO:CC terms
createEnrichMap(gostObject=parentalNapaVsDMSOEnrichment, 
    query="parental_napa_vs_DMSO", source="GO:CC")

The result of a differential expression analysis done between napabucasin treated and DMSO control MiaPaCa2 cells stably expressing the Rosa26 control vector. The cells were treated for 2 hours with 0.5 uM napabucasin. The protocol to generate the RNA-seq is described in Froeling F.E.M. et al 2019.

Description

The object is a data.frame with 23542 rows and 4 columns. Each row correspond to a tested gene.

Usage

data(rosaNapaVsDMSODEG)
data(rosaNapaVsDMSODEG)

Format

a data.frame containing the results of a differential expression analysis between napabucasin treated and DMSO control MiaPaCa2 cells stably expressing the Rosa26 control vector for all 23542 genes tested. The 4 columns are:

"EnsemblID": a character string representing the unique Ensembl identifier for the tested gene
"log2FoldChange": a numeric representing the expression difference (in log2FoldChange) between the napabucasin treatment and the DMSO control for the tested gene
"padj": a numeric representing the adjusted p-value associated to the difference in expression for the tested gene
"GeneName": a character string representing the name of the tested gene

Details

Value

"EnsemblID": a character string representing the unique Ensembl identifier for the tested gene
"log2FoldChange": a numeric representing the expression difference (in log2FoldChange) between the napabucasin treatment and the DMSO control for the tested gene
"padj": a numeric representing the adjusted p-value associated to the difference in expression for the tested gene
"GeneName": a character string representing the name of the tested gene

Source

The original RNA-sequencing data is available at the Gene Expression Omnibus (GEO) under the accession number GSE135352.

Examples


## Required library
library(gprofiler2)

## Loading dataset containing the results of a differentially expressed 
## analysis between 2-hour treatment with 0.5 uM napabucasin and 
## DMSO vehicle control MiaPaCa2 cells stably expressing the 
## Rosa26 control vector
data(rosaNapaVsDMSODEG)

allGenes <- unique(rosaNapaVsDMSODEG$EnsemblID)

## Select the significantly differentially expressed genes
selection <- which(abs(rosaNapaVsDMSODEG$log2FoldChange) > 1 & 
                            rosaNapaVsDMSODEG$padj < 0.05)
                            
selectedGenes <- unique(rosaNapaVsDMSODEG$EnsemblID[selection])

## Run an enrichment analysis using Transfac dataset (transcription factor)
gostres <- gost(query = list(rosa_napa_vs_DMSO=selectedGenes),
    organism="hsapiens",
    correction_method = "g_SCS",
    sources=c("TF"), significant=TRUE, evcodes=TRUE,
    custom_bg=allGenes, exclude_iea=TRUE)



## Required library
library(gprofiler2)

## Loading dataset containing the results of a differentially expressed 
## analysis between 2-hour treatment with 0.5 uM napabucasin and 
## DMSO vehicle control MiaPaCa2 cells stably expressing the 
## Rosa26 control vector
data(rosaNapaVsDMSODEG)

allGenes <- unique(rosaNapaVsDMSODEG$EnsemblID)

## Select the significantly differentially expressed genes
selection <- which(abs(rosaNapaVsDMSODEG$log2FoldChange) > 1 & 
                            rosaNapaVsDMSODEG$padj < 0.05)
                            
selectedGenes <- unique(rosaNapaVsDMSODEG$EnsemblID[selection])

## Run an enrichment analysis using Transfac dataset (transcription factor)
gostres <- gost(query = list(rosa_napa_vs_DMSO=selectedGenes),
    organism="hsapiens",
    correction_method = "g_SCS",
    sources=c("TF"), significant=TRUE, evcodes=TRUE,
    custom_bg=allGenes, exclude_iea=TRUE)

The result of an enrichment analysis that has been done using the significantly differentially expressed genes between napabucasin treated and DMSO control MiaPaCa2 cells stably expressing the Rosa26 control vector. The cells were treated for 2 hour with 0.5 uM napabucasin. The protocol to generate the RNA-seq is described in Froeling F.E.M. et al 2019.

Description

Usage

data(rosaNapaVsDMSOEnrichment)
data(rosaNapaVsDMSOEnrichment)

Format

a list created by gprofiler2 that contains the results from the enrichment analysis:

"result": a data.frame with the significantly enriched terms
"meta": a list with the meta-data information

Details

The object is a named list with 2 entries. The 'result' entry contains a data.frame with the enrichment analysis results and the 'meta' entry contains metadata information.

The dataset used for the enrichment analysis is associated to this publication:

Froeling F.E.M. et al.Bioactivation of Napabucasin Triggers Reactive Oxygen Species–Mediated Cancer Cell Death. Clin Cancer Res 1 December 2019; 25 (23): 7162–7174

Value

a list created by gprofiler2 that contains the results from the enrichment analysis:

"result": a data.frame with the significantly enriched terms
"meta": a list with the meta-data information

Source

The original RNA-sequencing data is available at the Gene Expression Omnibus (GEO) under the accession number GSE135352.

Examples


## Loading dataset containing the enrichment analysis done on the 
## differentially expressed analysis between 2-hour treatment with 0.5 uM 
## napabucasin and DMSO control MiaPaCa2 cells stably expressing 
## the Rosa26 control vector
data(rosaNapaVsDMSOEnrichment)

## Create an enrichment map for the KEGG terms
createEnrichMap(gostObject=rosaNapaVsDMSOEnrichment, 
    query="rosa_napa_vs_DMSO", source="KEGG")

## Loading dataset containing the enrichment analysis done on the 
## differentially expressed analysis between 2-hour treatment with 0.5 uM 
## napabucasin and DMSO control MiaPaCa2 cells stably expressing 
## the Rosa26 control vector
data(rosaNapaVsDMSOEnrichment)

## Create an enrichment map for the KEGG terms
createEnrichMap(gostObject=rosaNapaVsDMSOEnrichment, 
    query="rosa_napa_vs_DMSO", source="KEGG")

Validate arguments passed to validateCreateEnrichMapMultiComplex() function

Description

Validate the arguments passed to validateCreateEnrichMapMultiComplex() function. First, the object containing the enrichment results must correspond to a object created by gprofiler2 software. Second, the selected source must at least have one enriched term in the results. Then, if the source is 'TERM_ID', the listed terms must be present in the enrichment results.

Usage

validateCreateEnrichMapMultiComplexAsIgraphArg(
  gostObjectList,
  queryInfo,
  showCategory,
  similarityCutOff = similarityCutOff
)
validateCreateEnrichMapMultiComplexAsIgraphArg(
  gostObjectList,
  queryInfo,
  showCategory,
  similarityCutOff = similarityCutOff
)

Arguments

gostObjectList

queryInfo

a data.frame contains one row per group being displayed. The number of rows must correspond to the number of entries for the gostObjectList parameter. The mandatory columns are:

queryName: a character string representing the name of the query retained for this group). The query names must exist in the associated gostObjectList objects and follow the same order.
source: a character string representing the selected source that will be used to generate the network. To hand-pick the terms to be used, "TERM_ID" should be used and the list of selected term IDs should be passed through the termIDs parameter. The possible sources are "GO:BP" for Gene Ontology Biological Process, "GO:CC" for Gene Ontology Cellular Component, "GO:MF" for Gene Ontology Molecular Function, "KEGG" for Kegg, "REAC" for Reactome, "TF" for TRANSFAC, "MIRNA" for miRTarBase, "CORUM" for CORUM database, "HP" for Human phenotype ontology and "WP" for WikiPathways. Default: "TERM_ID".
removeRoot: a logical that specified if the root terms of the selected source should be removed (when present).
termIDs: a character strings that contains the term IDS retained for the creation of the network separated by a comma ',' when the "TERM_ID" source is selected. Otherwise, it should be a empty string ("").
groupName: a character strings that contains the name of the group to be shown in the legend. Each group has to have a unique name.

showCategory

a positive integer or NULL. If a integer, the first n terms will be displayed. If NULL, all terms will be displayed.

similarityCutOff

a positive numeric, larger than zero and small than 1 that represent the minimum similarity level between two nodes (terms) to be linked by an edge.

Value

TRUE when all arguments are valid

Author(s)

Astrid Deschênes

Examples


## Load the result of an enrichment analysis done with gprofiler2
data(parentalNapaVsDMSOEnrichment)
data(rosaNapaVsDMSOEnrichment)

queryDataFrame <- data.frame(queryName=c("parental_napa_vs_DMSO", 
    "rosa_napa_vs_DMSO"), source=c("KEGG", "WP"), removeRoot=c(TRUE, TRUE),
    termIDs=c("", ""), groupName=c("parental - KEGG", "rosa - WP"), 
    stringsAsFactors=FALSE)

## Check that all arguments are valid
enrichViewNet:::validateCreateEnrichMapMultiComplexAsIgraphArg(
    gostObjectList=list(parentalNapaVsDMSOEnrichment, 
                            rosaNapaVsDMSOEnrichment),
    queryInfo=queryDataFrame, showCategory=20, similarityCutOff=0.2)

## Load the result of an enrichment analysis done with gprofiler2
data(parentalNapaVsDMSOEnrichment)
data(rosaNapaVsDMSOEnrichment)

queryDataFrame <- data.frame(queryName=c("parental_napa_vs_DMSO", 
    "rosa_napa_vs_DMSO"), source=c("KEGG", "WP"), removeRoot=c(TRUE, TRUE),
    termIDs=c("", ""), groupName=c("parental - KEGG", "rosa - WP"), 
    stringsAsFactors=FALSE)

## Check that all arguments are valid
enrichViewNet:::validateCreateEnrichMapMultiComplexAsIgraphArg(
    gostObjectList=list(parentalNapaVsDMSOEnrichment, 
                            rosaNapaVsDMSOEnrichment),
    queryInfo=queryDataFrame, showCategory=20, similarityCutOff=0.2)

Package 'enrichViewNet'

Help Index

From functional enrichment results to biological networks

Description

Details

Author(s)

See Also

Using functional enrichment results in gprofiler2 format to create an enrichment map

Description

Usage

Arguments

Value

Author(s)

Examples

Using functional enrichment results in gprofiler2 format to create an enrichment map in an igraph format

Description

Usage

Arguments

Value

Author(s)

Examples

Using functional enrichment results in gprofiler2 format to create an enrichment map with multiple groups from different enrichment analyses

Description

Usage

Arguments

Value

Author(s)

Examples

Using functional enrichment results in gprofiler2 format to create an enrichment map with multiple groups from different enrichment analyses as an igraph

Description

Usage

Arguments

Value

Author(s)

Examples

Using functional enrichment results in gprofiler2 format to create an enrichment map with multiple groups from same or different enrichment analyses

Description

Usage

Arguments

Value

Author(s)

Examples

Using functional enrichment results in gprofiler2 format to create an enrichment map with multiple groups from same or different enrichment analyses in an igraph format

Description

Usage

Arguments

Value

Author(s)

Examples

Using functional enrichment results from gprofiler2 to create a Cytoscape network

Description

Usage

Arguments

Value

Author(s)

Examples

The result of a functional enrichment analysis done with gprofiler2 (https://cran.r-project.org/web/packages/gprofiler2/vignettes/gprofiler2.html).

Description

Usage

Format

Details

Value

See Also

Examples

The result of a differential expression analysis done between napabucasin treated and DMSO control parental MiaPaCa2 cells. The cells were treated for 2 hours with 0.5 uM napabucasin. The protocol to generate the RNA-seq is described in Froeling F.E.M. et al 2019.

Description

Usage

Format

Details

Value

Source

See Also

Examples

Description

Usage

Format

Details

Value

Source

See Also

The result of a functional enrichment analysis done with `gprofiler2` (https://cran.r-project.org/web/packages/gprofiler2/vignettes/gprofiler2.html).