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DEsubs
In DEsubs: DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq expression experiments
Table of Contents
- Package setup
- User input
- Pathway network construction
- Pathway network processing
- Subpathway extraction
- Subpathway enrichment analysis
- Visualization
1. Package Setup
DEsubs is a network-based systems biology R package that extracts
disease-perturbed subpathways within a pathway network as recorded by
RNA-seq experiments. It contains an extensive and customizable framework
with a broad range of operation modes at all stages of the subpathway
analysis, enabling a case-specific approach. The operation modes refer to
the pathway network construction and processing, the subpathway extraction,
visualization and enrichment analysis with regard to various biological and
pharmacological features. It's capabilities render it a valuable tool for both
the modeler and experimentalist searching for the identification of more robust
systems-level drug targets and biomarkers for complex diseases.
Before loading the package, please specify a user-accessible home directory
using the following commands, which currently reflect the default directories
for each architecture:
if (.Platform[['OS.type']] == 'unix')
{
options('DEsubs_CACHE'=file.path(path.expand("~"), 'DEsubs') )
}
if (.Platform[['OS.type']] == 'windows')
{
options('DEsubs_CACHE'=file.path(
gsub("\\\\", "/", Sys.getenv("USERPROFILE")), "AppData/DEsubs"))
}
Now the package, as well as the toy-data can be loaded as follows:
library('DEsubs')
load(system.file('extdata', 'data.RData', package='DEsubs'))
# set global chunk options:
if (.Platform[['OS.type']] == 'unix')
{
options('DEsubs_CACHE'=file.path(path.expand("~"), 'DEsubs') )
}
if (.Platform[['OS.type']] == 'windows')
{
options('DEsubs_CACHE'=file.path(
gsub("\\\\", "/", Sys.getenv("USERPROFILE")), "AppData/DEsubs"))
}
library(knitr)
library('DEsubs')
load(system.file('extdata', 'data.RData', package='DEsubs'))
\newpage
2. User Input
DEsubs accepts RNA-seq expression paired case-control profile data. The following
example in Table 1 shows the right structure for RNA-seq expression data input.
Gene Case 1 Case 2 Case 3 Case 4 Control 1 Control 2 Control 3 Control 4
Gene 1 1879 2734 2369 2636 2188 9743 9932 10099
Gene 2 97 124 146 114 126 33 19 31
Gene 3 485 485 469 428 475 128 135 103
... ... ... ... ... ... ... ... ...
Gene N-1 84 25 67 62 61 277 246 297
Gene N 120 312 78 514 210 324 95 102
Table: Example of user input format
3. Pathway network construction
KEGG signaling pathway maps have been downloaded and converted to pathway
networks using CHRONOS package. Pathway networks for the seven supported
organisms are included in the package itself (see Table 2).
Supported Organisms R command
-------------- ----------------
Homo sapiens 'hsa'
Mus musculus 'mmu'
Drosophila melanogaster 'dme'
Saccharomyces cerevisiae 'sce'
Arabidopsis thaliana 'ath'
Rattus norvegicus 'rno'
Danio rerio 'dre'
-------------- ----------------
Table: Supported KEGG organisms
DEsubs operates with Entrez ID labels, however twelve other label systems
are supported after converting to Entrez IDs via a lexicon included in
the package itself (see Table 3).
Supported Labels R command
------------------ --------------------------------------------------
Entrez 'entrezgene'
Ensemble 'ensembl_gene_id', 'ensembl_transcript_id'
'ensembl_peptide_id'
HGNC 'hgnc_id', 'hgnc_symbol', 'hgnc_transcript_name'
Refseq 'refseq_mrna', 'refseq_peptide'
------------------ -------------------------------------------------
Table: Supported gene labels
\newpage
4. Pathway network processing
Next, the RNA-seq data are mapped onto the nodes and edges of the pathway
network and two the pruning rules are applied to isolate interactions of
interest among statistically significant differentially expressed genes
(DEGs). DEGs are identified using the differential expression analysis tools
in Table 5 by considering the FDR-adjusted P-value of each gene (Q-value).
Instead of selecting one tools in Table 5, the user can import a custom
ranked list of genes accompanied by their Q-values (argument rankedList).
Based on this information, the NodeRule prunes the nodes of the original
network G=(V,E), where Q-threshold (argument DEpar) defaults to 0.05:
$$ Qvalue(i) < Q.threshold, i \in V $$
Next, the interactions among the selected genes are pruned based on both
prior biological knowledge and the expression profiles of neighbouring genes,
, where C-threshold defaults to 0.6 (argument CORpar):
$$ cor(i,j)*reg(i,j) > C.threshold, , i,j \in V $$
If genes i,j are connected with an edge with an activation type, then reg
is set to 1, while if it the activation type is inhibitory, it is set to -1.
The correlation between the profiles of the two genes i, j is calculated
using the measures in Table 4 (argument CORtool).
DEsubs.run <- DEsubs( org='hsa',
mRNAexpr=mRNAexpr,
mRNAnomenclature='entrezgene',
pathways='All',
DEtool=NULL,
DEpar=0.05,
CORtool='pearson',
CORpar=0.7,
subpathwayType=NULL,
rankedList=rankedList, verbose=FALSE)
Type R command
-------------- ----------------
Pearson product-moment correlation coefficient 'pearson'
Spearman rank correlation coefficient 'spearman'
Kendall rank correlation coefficient 'kedhall'
-------------- ----------------
Table: Edge Rule options
Supported Labels R command
-------------- ----------------
[@robinson2010edger] 'edgeR'
[@anders2010differential] 'DESeq'
[@leng2013ebseq] 'EBSeq'
[@smyth2004linear] 'vst+limma'
[@anders2010differential]; [@smyth2004linear] 'voom+limma'
[@di2011nbp] 'NBPSeq'
[@auer2011two] 'TSPM'
-------------- ----------------
Table: Node Rule options
\newpage
5. Subpathway Extraction
5.1. Main Categories
Subpathway extraction is based on five main categories, (i) components,
(ii) communities, (iii) streams, (iv) neighborhoods, (v) cascades. Each one
sketches different topological aspect within the network. Indicative examples
and a short description of DEsubs five main subpathway categories can be found
in Figure 1.
cap <- paste0(
'Stream, neighborhood and cascade types build each subpathway ',
'(blue nodes) by starting from a gene of interest (red nodes). ',
'Components and communities are densely linked group of genes with the ',
'difference that the genes sharing common properties are maintained ',
'within the graph (green nodes).')
knitr::include_graphics('figures/fig1.png')
The component category extracts strongly connected group of genes
indicating dense local areas within the network. The community category
extracts linked genes sharing a common property within the network.
Thus the user can observe local gene sub-areas with a specific role within
the network. Cascade, stream and neighborhood categories are generated
starting from a gene of interest (GOI) in order to view the local
perturbations within the network from different points of interest.
The generation is performed by traversing either the forward or the
backward propagation that stems from the GOI and is illustrated via three
different topological schemes, gene sequences ('cascade' category),
gene streams ('stream' category) and gene direct neighbors
('neighborhood' category).
5.2. Gene of interest (GOI)
Genes having crucial topological or functional roles within the network are
considered as GOIs (see Table 6). The topological roles are portrayed using
various topological measures from igraph package [@csardi2006igraph]
capturing the local as well as global aspects of the network. Genes with
crucial functional roles are considered as key genes for several
biological and pharmacological features through $fscore$, a measure which
estimates how a gene acts as a bridge among specific functional terms.
In more detail, fscore is the number of condition-based functional terms
in which a gene participates. For a functional condition $fc$ with n terms
and $p_i^j=1$ or 0 if gene(i) participates or not in $term(j)$, the fscore
of $gene(i)$ is as follows: $$ fscore(i)=\sum_{j=1}^{n}p_i^j $$
A high value of $fscore(i)$ for a condition $j$ indicates that gene $i$
participates to several functional terms of condition $j$
(for e.g. terms for diseases), hence it operates as a bridge for the terms of
condition $j$ within the graph. As functional conditions we considered
various biological and pharmacological features related with pathways, gene
ontologies, diseases, drugs, microRNAs and transcription factors.
External references are used to imprint gene associations by each
feature separately. The references based on the approach of
[@barneh2015updates]; [@chen2013enrichr]; [@li2011implications];
[@vrahatis2016chronos]. Details are shown in Table 6.
Summarizing, a user-defined threshold (namely top) is used for the selection
of GOIs. After the calculation of topological measures and functional
measures by each condition (through $fscore$), the genes with the top best
values are considered as GOIs. The parameter top is user-defined with default
value $top=30$. In GOIs with crucial functional role we add and those having
the lowest Q-value by each user-defined experimental dataset. Thus, the user
is allowed to generate subpathways starting from the most statistical
significant DEGs of his own experiment. A short description for all GOI types
along with the corresponding parameters are shown in Table 6.
Type Description R command
Topological
Degree Number of adjacent interactions of the gene 'degree'
Betweenness Number of shortest paths from all vertices to all 'betweenness'
others that pass through that node
Closeness Inverse of farness, which is the sum of distances 'closeness'
to all other nodes
Hub score Kleinbergs hub centrality score 'hub_score'
Eccentricity Shortest path distance from the farthest 'eccentricity'
node in the graph
Page rank Google Page Rank 'page_rank'
Start Nodes Nodes without any incoming links 'start_nodes'
Functional
DEG Genes highly differentially expressed 'deg'
according to the experimental data
Pathways Genes acting as bridges among KEGG pathways 'KEGG'
Biological Process Genes acting as bridges among 'GO_bp'
Gene Ontology Biological Process terms
Cellular Component Genes acting as bridges among 'GO_cc'
Gene Ontology Cellular Component terms
Molecular Function Genes acting as bridges among 'GO_mf'
Gene Ontology Molecular Function terms
Disease Genes acting as bridges for OMIM targets 'Disease_OMIM'
Disease Genes acting as bridges for GAD targets 'Disease_GAD'
Drug Genes acting as bridges for DrugBank targets 'Drug_DrugBank'
microRNA Genes acting as bridges for microRNA targets 'miRNA'
Transcription Factors Genes acting as bridges for TF targets 'TF'
Table: Gene of interest (GOI) types
5.3. All subpathway options
Cascade, stream and neighborhood subpathway types can start from seventeen
(17) different GOI types and their generation is performed either with
forward or backward propagation. Thus, thirty-four (34) different types are
created for each of the three types. Also, the component-based types are
sixteen and the community-based types are six based on igraph package.
DEsubs therefore supports 124 subpathway types as described in Tables 7-11.
Description R parameter
Topological
Forward and backward streams starting from 'fwd.stream.topological.degree'
genes/nodes with crucial topological roles 'fwd.stream.topological.betweenness'
within the network 'fwd.stream.topological.closeness'
'fwd.stream.topological.hub_score'
'fwd.stream.topological.eccentricity'
'fwd.stream.topological.page_rank'
'fwd.stream.topological.start_nodes'
'bwd.stream.topological.degree'
'bwd.stream.topological.betweenness'
'bwd.stream.topological.closeness'
'bwd.stream.topological.hub_score'
'bwd.stream.topological.eccentricity'
'bwd.stream.topological.page_rank'
'bwd.stream.topological.start_nodes'
Functional
Forward and backward streams starting from 'fwd.stream.functional.GO_bp'
genes/nodes with crucial functional roles 'fwd.stream.functional.GO_cc'
within the network 'fwd.stream.functional.GO_mf'
'fwd.stream.functional.Disease_OMIM'
'fwd.stream.functional.Disease_GAD'
'fwd.stream.functional.Drug_DrugBank'
'fwd.stream.functional.miRNA'
'fwd.stream.functional.TF'
'fwd.stream.functional.KEGG'
'fwd.stream.functional.DEG'
'bwd.stream.functional.GO_bp'
'bwd.stream.functional.GO_cc'
'bwd.stream.functional.GO_mf'
'bwd.stream.functional.Disease_OMIM'
'bwd.stream.functional.Disease_GAD'
'bwd.stream.functional.Drug_DrugBank'
'bwd.stream.functional.miRNA'
'bwd.stream.functional.TF'
'bwd.stream.functional.KEGG'
'bwd.stream.functional.DEG'
Table: Subpathway Options - STREAM
Description R parameter
Topological
Forward and backward neighbourhoods starting 'fwd.neighbourhood.topological.degree'
from genes/nodes with crucial topological 'fwd.neighbourhood.topological.betweenness'
roles within the network 'fwd.neighbourhood.topological.closeness'
'fwd.neighbourhood.topological.hub_score'
'fwd.neighbourhood.topological.eccentricity'
'fwd.neighbourhood.topological.page_rank'
'fwd.neighbourhood.topological.start_nodes'
'bwd.neighbourhood.topological.degree'
'bwd.neighbourhood.topological.betweenness'
'bwd.neighbourhood.topological.closeness'
'bwd.neighbourhood.topological.hub_score'
'bwd.neighbourhood.topological.eccentricity'
'bwd.neighbourhood.topological.page_rank'
'bwd.neighbourhood.topological.start_nodes'
Functional
Forward and backward neighbourhoods starting 'fwd.neighbourhood.functional.GO_bp'
from genes/nodes with crucial topological 'fwd.neighbourhood.functional.GO_cc'
roles within the network 'fwd.neighbourhood.functional.GO_mf'
'fwd.neighbourhood.functional.Disease_OMIM'
'fwd.neighbourhood.functional.Disease_GAD'
'fwd.neighbourhood.functional.Drug_DrugBank'
'fwd.neighbourhood.functional.miRNA'
'fwd.neighbourhood.functional.TF'
'fwd.neighbourhood.functional.KEGG'
'fwd.neighbourhood.functional.DEG'
'bwd.neighbourhood.functional.GO_bp'
'bwd.neighbourhood.functional.GO_cc'
'bwd.neighbourhood.functional.GO_mf'
'bwd.neighbourhood.functional.Disease_OMIM'
'bwd.neighbourhood.functional.Disease_GAD'
'bwd.neighbourhood.functional.Drug_DrugBank'
'bwd.neighbourhood.functional.miRNA'
'bwd.neighbourhood.functional.TF'
'bwd.neighbourhood.functional.KEGG'
'bwd.neighbourhood.functional.DEG'
Table: Subpathway Options - NEIGHBOURHOOD
Description R parameter
Topological
Forward and backward cascades starting 'fwd.cascade.topological.degree'
from genes/nodes with crucial topological 'fwd.cascade.topological.betweenness'
roles within the network 'fwd.cascade.topological.closeness'
'fwd.cascade.topological.hub_score'
'fwd.cascade.topological.eccentricity'
'fwd.cascade.topological.page_rank'
'fwd.cascade.topological.start_nodes'
'bwd.cascade.topological.degree'
'bwd.cascade.topological.betweenness'
'bwd.cascade.topological.closeness'
'bwd.cascade.topological.hub_score'
'bwd.cascade.topological.eccentricity'
'bwd.cascade.topological.page_rank'
'bwd.cascade.topological.start_nodes'
Functional
Forward and backward cascades starting 'fwd.cascade.functional.GO_bp'
from genes/nodes with crucial topological 'fwd.cascade.functional.GO_cc'
roles within the network 'fwd.cascade.functional.GO_mf'
'fwd.cascade.functional.Disease_OMIM'
'fwd.cascade.functional.Disease_GAD'
'fwd.cascade.functional.Drug_DrugBank'
'fwd.cascade.functional.miRNA'
'fwd.cascade.functional.TF'
'fwd.cascade.functional.KEGG'
'fwd.cascade.functional.DEG'
'bwd.cascade.functional.GO_bp'
'bwd.cascade.functional.GO_cc'
'bwd.cascade.functional.GO_mf'
'bwd.cascade.functional.Disease_OMIM'
'bwd.cascade.functional.Disease_GAD'
'bwd.cascade.functional.Drug_DrugBank'
'bwd.cascade.functional.miRNA'
'bwd.cascade.functional.TF'
'bwd.cascade.functional.KEGG'
'bwd.cascade.functional.DEG'
Table: Subpathway Options - CASCADE
Type Description R parameter
Random Walk Community structures that minimize 'community.infomap'
the expected description length of
a random walker trajectory
Modular Community structures via a modularity 'community.louvain'
measure and a hierarchical approach
Walktraps Densely connected subgraphs via 'community.walktrap'
random walks
Leading eigen Densely connected subgraphs based 'community.leading_eigen'
on the leading non-negative eigen-
vector of the modularity matrix
Betweeneess Community structures detection 'community.edge_betweenness'
via edge betweenness
Greedy Community structures via greedy 'community.fast_greedy'
optimization of modularity
Table: Subpathway Options - COMMUNITY
Type Description R parameter
Cliques A subgraph where every two distinct 'component.3-cliques'
vertices in the clique are adjacent ...
'component.9-cliques'
K-core A maximal subgraph in which each 'component.3-coreness'
vertex has at least degree k ...
'component.9-coreness'
Max cliques Largest of maximal cliques 'component.max_cliques'
Components All single components 'component.decompose'
Table: Subpathway Options - COMPONENT
An example follows where community.walktrap is selected as the
subpathway type.
DEsubs.run <- DEsubs(
org='hsa',
mRNAexpr=mRNAexpr,
mRNAnomenclature='entrezgene',
pathways='All',
DEtool=NULL, DEpar=0.05,
CORtool='pearson', CORpar=0.7,
subpathwayType='community.walktrap',
rankedList=rankedList,
verbose=FALSE)
\newpage
cap <- paste0(
'Subpathway extraction options consist of five main categories. ',
'The three of them (cascade, neighborhood, stream) are sub-categorized ',
'according to features (topological or functional) and the direction of ',
'propagation (forward or backward) of the gene of interest where each ',
'subpathway is starting. The other two (component, community) are ',
'sub-categorized according to various topological properties.')
knitr::include_graphics('figures/fig2.png')
6. Subpathway enrichment analysis
Eight different datasets with external resources are stored locally for
further enrichment analysis of resulting subpathways. Each dataset is formed
with a list of terms related to biological and pharmacological features and
the respective associated genes for each term. A detailed description is shown
in Table 12. Additionally, the user can supply a custom gene-set in the form
of an .RData file storing a matrix, named targetsPerClass.
The matrix should store the terms as rownames and the targets of
each term at each row. Since some rows are bound to havemore elements that
others, empty cells should be filled with '0' characters.
Once the file is stored in a directory DEsubs/Data, it will be permanently
availiable along with the other eight resources, using the filename (without
the .RData suffix) as the new functional feature type along with the any of
the default eight features.
The enrichment analysis is performed based on the cumulative hypergeometric
distribution, where G is the number of genes in the user input list, l the
number of those genes included in the subpathway, D the number of associated
genes for a term and d the number of genes included in the subpathway
[@li2013subpathway]. Terms with P < 0.05 are regarded as terms
with significant association with the respective subpathway.
$$ P = 1 - \sum_{x=0}^{d}\frac{\binom{D}{x}\binom{G-D}{l-x}}{\binom{G}{l}} $$
Type Description (# of terms) Source
-------------- -------------- --------------
Pathway Term KEGG pathway maps (179) [@chen2013enrichr]
GO Biological Process Genes sharing a common [@chen2013enrichr]
biological process (5.192)
GO Cellular Component Genes sharing a common [@chen2013enrichr]
cellular component (641)
GO Molecular Function Genes sharing a common [@chen2013enrichr]
molecular level (1.136)
OMIM Disease Disease related genes (90) [@chen2013enrichr]
GAD Disease Disease related genes (412) [@li2011implications]
DrugBank Drug Gene targets of drugs (1.488) [@barneh2015updates]
Transcription Factor Gene targetsof transcription [@chen2013enrichr]
factors (290)
-------------- -------------- --------------
Table: List of external databases
Term Target 1 Target 2 Target 3 Target 4 Target 5 Target 6 Target 7
Term 1 ACAD9 ACAD8 SH3GLB1 ESCO2 ESCO1 ADH1C '0'
Term 2 ADH1C ADH1B ADHFE1 ADH1A ADH6 ADH7 ADH4
... ... ... ... ... ... ... ...
Term N PTPN1 RHOA ACTN4 ACTN3 ACTN2 '0' '0'
Table: Example of custom gene set
\newpage
7. Visualization
DEsubs visualizes its results at a gene, subpathway and organism level through
various schemes such as bar plots, heat maps, directed weighted graphs,
circular diagrams [@gu2014circlize] and dot plots. Indicative examples are
illustrated in figures 2-8 based on DEsubs executions using the human pathway
network and a synthetic dataset. Bar plots show the genes with the best Q-value
from the user-selected DE analysis tool (the user defines the desired gene
number). The figures are exported in the directory Output within the user
specified location. Heat maps show the genes with the highest values either
in our topological or functional measures (see Table 6).
cap <- paste0(
'Bar plots show the genes with the best Q-value from the user-selected ',
'DE analysis tool (the user defines the desired gene number). ',
'Heat maps show the genes with the highest values either in our ',
'topological or functional measures (see Table 6). ',
'Each extracted subpathway is illustrated though a directed graph by ',
'imprinting the degree of DE and correlation among the respective gene ',
'members. Subpathway enrichment in association with biological and ',
'pharmacological features (such as pathway terms, gene ontologies, ',
'regulators, diseases and drug targets) is depicted through circular ',
'diagrams. The total picture of the enriched subpathways is performed ',
'with dot plots.')
knitr::include_graphics('figures/fig3.png')
\newpage
7.1 Gene Level Visualization
res <- geneVisualization(
DEsubs.out=DEsubs.out, top=10,
measures.topological=c( 'degree', 'betweenness', 'closeness',
'eccentricity', 'page_rank'),
measures.functional=c( 'KEGG', 'GO_bp','GO_cc', 'GO_mf',
'Disease_OMIM', 'Disease_GAD',
'Drug_DrugBank','miRNA', 'TF'),
size.topological=c(5,4),
size.functional=c(7,4),
size.barplot=c(5,6),
export='plot', verbose=FALSE)
cap <- 'Bars illustrate the genes with the highest Q-values.'
res <- geneVisualization(
DEsubs.out=DEsubs.out,
top=10,
measures.topological=NULL,
measures.functional=NULL,
measures.barplot=TRUE,
size.barplot=c(5,6),
export='plot',
verbose=FALSE)
cap <- 'Heat map represents the twelve genes with the highest values of
functional measures. The values are scaled and the red graduation indicates
the value degree.'
res <- geneVisualization(
DEsubs.out=DEsubs.out,
top=12,
measures.topological=NULL,
measures.functional=c( 'KEGG', 'GO_bp','GO_cc', 'GO_mf',
'Disease_OMIM', 'Disease_GAD',
'Drug_DrugBank','miRNA', 'TF'),
measures.barplot=FALSE,
size.functional=c(7,4),
export='plot',
verbose=FALSE)
cap <- 'Heat map represents the twelve genes with the highest values of
topological measures. The values are scaled and the red graduation indicates
the value degree.'
res <- geneVisualization(
DEsubs.out=DEsubs.out,
top=12,
measures.topological=c( 'degree', 'betweenness', 'closeness',
'eccentricity', 'page_rank'),
measures.functional=NULL,
measures.barplot=FALSE,
size.topological=c(5,4),
export='plot',
verbose=FALSE)
\newpage
7.2. Subpathway Level Visualization
Each extracted subpathway is illustrated though a directed graph by imprinting
the degree of differential expression and correlation among the respective
gene members. Additionally, it can be extracted in a variety of formats so
that it can be used by external software, such as .txt, .json, .gml,
.ncol, .lgl, .graphml and .dot formats.
res <- subpathwayToGraph(
DEsubs.out=DEsubs.out,
submethod='community.walktrap',
subname='sub6', verbose=FALSE,
export='plot' )
library('DEsubs')
load(system.file('extdata', 'data.RData', package='DEsubs'))
cap <- 'Graph illustrates the links of a subpathway. Red graduation
in nodes indicate the Q-value degree, the edge width indicates the correlation
degree between the respective genes. Green or red color in edges indicates
the positive or negative correlation respectively'
res <- subpathwayToGraph(
DEsubs.out=DEsubs.out,
submethod='community.walktrap',
subname='sub6',
verbose=FALSE,
export='plot' )
Subpathway enrichment in association with biological and pharmacological
features (such as pathway terms, gene ontologies, regulators, diseases and
drug targets) is depicted through circular diagrams.
res <- subpathwayVisualization(
DEsubs.out=DEsubs.out,
references=c('GO', 'TF'),
submethod='community.walktrap',
subname='sub1',
scale=c(1, 1),
export='plot',
verbose=FALSE)
cap <- 'Circular Diagram shows the associations among genes including in a
subpathway and Gene Ontology terms where are enriched'
library('DEsubs')
load(system.file('extdata', 'data.RData', package='DEsubs'))
res <- subpathwayVisualization(
DEsubs.out=DEsubs.out,
references='GO',
submethod='community.walktrap',
subname='sub1',
scale=1,
export='plot',
verbose=FALSE)
cap <- 'Circular Diagram shows the associations among genes included in a
subpathway and enriched Transcription Factors.'
res <- subpathwayVisualization(
DEsubs.out=DEsubs.out,
references='TF',
submethod='community.walktrap',
subname='sub1',
scale=1.0,
export='plot',
verbose=FALSE)
\newpage
7.3. Organism Level Visualization
The total picture of the enriched subpathways is performed with dot plots.
The number of features represented are selected using topTerms argument.
res <- organismVisualization(
DEsubs.out=DEsubs.out,
references='KEGG',
topSubs=10,
topTerms=20,
export='plot',
verbose=FALSE)
cap <- 'Dot plot shows the enriched associations among experiment-specific
extracted subpathways and pathwaysfrom KEGG database.
Twenty pathways were selected as the desired number of terms.'
res <- organismVisualization(
DEsubs.out=DEsubs.out,
references='KEGG',
topSubs=10, topTerms=20,
export='plot', verbose=FALSE)
References
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Table of Contents
- Package setup
- User input
- Pathway network construction
- Pathway network processing
- Subpathway extraction
- Subpathway enrichment analysis
- Visualization
1. Package Setup
DEsubs is a network-based systems biology R package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework with a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. It's capabilities render it a valuable tool for both the modeler and experimentalist searching for the identification of more robust systems-level drug targets and biomarkers for complex diseases.
Before loading the package, please specify a user-accessible home directory using the following commands, which currently reflect the default directories for each architecture:
if (.Platform[['OS.type']] == 'unix') { options('DEsubs_CACHE'=file.path(path.expand("~"), 'DEsubs') ) } if (.Platform[['OS.type']] == 'windows') { options('DEsubs_CACHE'=file.path( gsub("\\\\", "/", Sys.getenv("USERPROFILE")), "AppData/DEsubs")) }
Now the package, as well as the toy-data can be loaded as follows:
library('DEsubs') load(system.file('extdata', 'data.RData', package='DEsubs'))
# set global chunk options: if (.Platform[['OS.type']] == 'unix') { options('DEsubs_CACHE'=file.path(path.expand("~"), 'DEsubs') ) } if (.Platform[['OS.type']] == 'windows') { options('DEsubs_CACHE'=file.path( gsub("\\\\", "/", Sys.getenv("USERPROFILE")), "AppData/DEsubs")) } library(knitr) library('DEsubs') load(system.file('extdata', 'data.RData', package='DEsubs'))
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2. User Input
DEsubs accepts RNA-seq expression paired case-control profile data. The following example in Table 1 shows the right structure for RNA-seq expression data input.
Gene Case 1 Case 2 Case 3 Case 4 Control 1 Control 2 Control 3 Control 4
Gene 1 1879 2734 2369 2636 2188 9743 9932 10099 Gene 2 97 124 146 114 126 33 19 31 Gene 3 485 485 469 428 475 128 135 103 ... ... ... ... ... ... ... ... ... Gene N-1 84 25 67 62 61 277 246 297 Gene N 120 312 78 514 210 324 95 102
Table: Example of user input format
3. Pathway network construction
KEGG signaling pathway maps have been downloaded and converted to pathway networks using CHRONOS package. Pathway networks for the seven supported organisms are included in the package itself (see Table 2).
Supported Organisms R command -------------- ---------------- Homo sapiens 'hsa' Mus musculus 'mmu' Drosophila melanogaster 'dme' Saccharomyces cerevisiae 'sce' Arabidopsis thaliana 'ath' Rattus norvegicus 'rno' Danio rerio 'dre' -------------- ---------------- Table: Supported KEGG organisms
DEsubs operates with Entrez ID labels, however twelve other label systems are supported after converting to Entrez IDs via a lexicon included in the package itself (see Table 3).
Supported Labels R command ------------------ -------------------------------------------------- Entrez 'entrezgene' Ensemble 'ensembl_gene_id', 'ensembl_transcript_id' 'ensembl_peptide_id' HGNC 'hgnc_id', 'hgnc_symbol', 'hgnc_transcript_name' Refseq 'refseq_mrna', 'refseq_peptide' ------------------ ------------------------------------------------- Table: Supported gene labels
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4. Pathway network processing
Next, the RNA-seq data are mapped onto the nodes and edges of the pathway network and two the pruning rules are applied to isolate interactions of interest among statistically significant differentially expressed genes (DEGs). DEGs are identified using the differential expression analysis tools in Table 5 by considering the FDR-adjusted P-value of each gene (Q-value). Instead of selecting one tools in Table 5, the user can import a custom ranked list of genes accompanied by their Q-values (argument rankedList).
Based on this information, the NodeRule prunes the nodes of the original network G=(V,E), where Q-threshold (argument DEpar) defaults to 0.05:
$$ Qvalue(i) < Q.threshold, i \in V $$
Next, the interactions among the selected genes are pruned based on both prior biological knowledge and the expression profiles of neighbouring genes, , where C-threshold defaults to 0.6 (argument CORpar):
$$ cor(i,j)*reg(i,j) > C.threshold, , i,j \in V $$
If genes i,j are connected with an edge with an activation type, then reg is set to 1, while if it the activation type is inhibitory, it is set to -1. The correlation between the profiles of the two genes i, j is calculated using the measures in Table 4 (argument CORtool).
DEsubs.run <- DEsubs( org='hsa', mRNAexpr=mRNAexpr, mRNAnomenclature='entrezgene', pathways='All', DEtool=NULL, DEpar=0.05, CORtool='pearson', CORpar=0.7, subpathwayType=NULL, rankedList=rankedList, verbose=FALSE)
Type R command -------------- ---------------- Pearson product-moment correlation coefficient 'pearson' Spearman rank correlation coefficient 'spearman' Kendall rank correlation coefficient 'kedhall' -------------- ---------------- Table: Edge Rule options
Supported Labels R command -------------- ---------------- [@robinson2010edger] 'edgeR' [@anders2010differential] 'DESeq' [@leng2013ebseq] 'EBSeq' [@smyth2004linear] 'vst+limma' [@anders2010differential]; [@smyth2004linear] 'voom+limma' [@di2011nbp] 'NBPSeq' [@auer2011two] 'TSPM' -------------- ---------------- Table: Node Rule options
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5. Subpathway Extraction
5.1. Main Categories
Subpathway extraction is based on five main categories, (i) components, (ii) communities, (iii) streams, (iv) neighborhoods, (v) cascades. Each one sketches different topological aspect within the network. Indicative examples and a short description of DEsubs five main subpathway categories can be found in Figure 1.
cap <- paste0( 'Stream, neighborhood and cascade types build each subpathway ', '(blue nodes) by starting from a gene of interest (red nodes). ', 'Components and communities are densely linked group of genes with the ', 'difference that the genes sharing common properties are maintained ', 'within the graph (green nodes).') knitr::include_graphics('figures/fig1.png')
The component category extracts strongly connected group of genes indicating dense local areas within the network. The community category extracts linked genes sharing a common property within the network. Thus the user can observe local gene sub-areas with a specific role within the network. Cascade, stream and neighborhood categories are generated starting from a gene of interest (GOI) in order to view the local perturbations within the network from different points of interest. The generation is performed by traversing either the forward or the backward propagation that stems from the GOI and is illustrated via three different topological schemes, gene sequences ('cascade' category), gene streams ('stream' category) and gene direct neighbors ('neighborhood' category).
5.2. Gene of interest (GOI)
Genes having crucial topological or functional roles within the network are considered as GOIs (see Table 6). The topological roles are portrayed using various topological measures from igraph package [@csardi2006igraph] capturing the local as well as global aspects of the network. Genes with crucial functional roles are considered as key genes for several biological and pharmacological features through $fscore$, a measure which estimates how a gene acts as a bridge among specific functional terms. In more detail, fscore is the number of condition-based functional terms in which a gene participates. For a functional condition $fc$ with n terms and $p_i^j=1$ or 0 if gene(i) participates or not in $term(j)$, the fscore of $gene(i)$ is as follows: $$ fscore(i)=\sum_{j=1}^{n}p_i^j $$
A high value of $fscore(i)$ for a condition $j$ indicates that gene $i$ participates to several functional terms of condition $j$ (for e.g. terms for diseases), hence it operates as a bridge for the terms of condition $j$ within the graph. As functional conditions we considered various biological and pharmacological features related with pathways, gene ontologies, diseases, drugs, microRNAs and transcription factors. External references are used to imprint gene associations by each feature separately. The references based on the approach of [@barneh2015updates]; [@chen2013enrichr]; [@li2011implications]; [@vrahatis2016chronos]. Details are shown in Table 6.
Summarizing, a user-defined threshold (namely top) is used for the selection of GOIs. After the calculation of topological measures and functional measures by each condition (through $fscore$), the genes with the top best values are considered as GOIs. The parameter top is user-defined with default value $top=30$. In GOIs with crucial functional role we add and those having the lowest Q-value by each user-defined experimental dataset. Thus, the user is allowed to generate subpathways starting from the most statistical significant DEGs of his own experiment. A short description for all GOI types along with the corresponding parameters are shown in Table 6.
Type Description R command
Topological Degree Number of adjacent interactions of the gene 'degree' Betweenness Number of shortest paths from all vertices to all 'betweenness' others that pass through that node Closeness Inverse of farness, which is the sum of distances 'closeness' to all other nodes Hub score Kleinbergs hub centrality score 'hub_score' Eccentricity Shortest path distance from the farthest 'eccentricity' node in the graph Page rank Google Page Rank 'page_rank' Start Nodes Nodes without any incoming links 'start_nodes' Functional DEG Genes highly differentially expressed 'deg' according to the experimental data Pathways Genes acting as bridges among KEGG pathways 'KEGG' Biological Process Genes acting as bridges among 'GO_bp' Gene Ontology Biological Process terms Cellular Component Genes acting as bridges among 'GO_cc' Gene Ontology Cellular Component terms Molecular Function Genes acting as bridges among 'GO_mf' Gene Ontology Molecular Function terms Disease Genes acting as bridges for OMIM targets 'Disease_OMIM' Disease Genes acting as bridges for GAD targets 'Disease_GAD' Drug Genes acting as bridges for DrugBank targets 'Drug_DrugBank' microRNA Genes acting as bridges for microRNA targets 'miRNA' Transcription Factors Genes acting as bridges for TF targets 'TF'
Table: Gene of interest (GOI) types
5.3. All subpathway options
Cascade, stream and neighborhood subpathway types can start from seventeen (17) different GOI types and their generation is performed either with forward or backward propagation. Thus, thirty-four (34) different types are created for each of the three types. Also, the component-based types are sixteen and the community-based types are six based on igraph package. DEsubs therefore supports 124 subpathway types as described in Tables 7-11.
Description R parameter
Topological Forward and backward streams starting from 'fwd.stream.topological.degree' genes/nodes with crucial topological roles 'fwd.stream.topological.betweenness' within the network 'fwd.stream.topological.closeness' 'fwd.stream.topological.hub_score' 'fwd.stream.topological.eccentricity' 'fwd.stream.topological.page_rank' 'fwd.stream.topological.start_nodes' 'bwd.stream.topological.degree' 'bwd.stream.topological.betweenness' 'bwd.stream.topological.closeness' 'bwd.stream.topological.hub_score' 'bwd.stream.topological.eccentricity' 'bwd.stream.topological.page_rank' 'bwd.stream.topological.start_nodes' Functional Forward and backward streams starting from 'fwd.stream.functional.GO_bp' genes/nodes with crucial functional roles 'fwd.stream.functional.GO_cc' within the network 'fwd.stream.functional.GO_mf' 'fwd.stream.functional.Disease_OMIM' 'fwd.stream.functional.Disease_GAD' 'fwd.stream.functional.Drug_DrugBank' 'fwd.stream.functional.miRNA' 'fwd.stream.functional.TF' 'fwd.stream.functional.KEGG' 'fwd.stream.functional.DEG' 'bwd.stream.functional.GO_bp' 'bwd.stream.functional.GO_cc' 'bwd.stream.functional.GO_mf' 'bwd.stream.functional.Disease_OMIM' 'bwd.stream.functional.Disease_GAD' 'bwd.stream.functional.Drug_DrugBank' 'bwd.stream.functional.miRNA' 'bwd.stream.functional.TF' 'bwd.stream.functional.KEGG' 'bwd.stream.functional.DEG'
Table: Subpathway Options - STREAM
Description R parameter
Topological Forward and backward neighbourhoods starting 'fwd.neighbourhood.topological.degree' from genes/nodes with crucial topological 'fwd.neighbourhood.topological.betweenness' roles within the network 'fwd.neighbourhood.topological.closeness' 'fwd.neighbourhood.topological.hub_score' 'fwd.neighbourhood.topological.eccentricity' 'fwd.neighbourhood.topological.page_rank' 'fwd.neighbourhood.topological.start_nodes' 'bwd.neighbourhood.topological.degree' 'bwd.neighbourhood.topological.betweenness' 'bwd.neighbourhood.topological.closeness' 'bwd.neighbourhood.topological.hub_score' 'bwd.neighbourhood.topological.eccentricity' 'bwd.neighbourhood.topological.page_rank' 'bwd.neighbourhood.topological.start_nodes' Functional Forward and backward neighbourhoods starting 'fwd.neighbourhood.functional.GO_bp' from genes/nodes with crucial topological 'fwd.neighbourhood.functional.GO_cc' roles within the network 'fwd.neighbourhood.functional.GO_mf' 'fwd.neighbourhood.functional.Disease_OMIM' 'fwd.neighbourhood.functional.Disease_GAD' 'fwd.neighbourhood.functional.Drug_DrugBank' 'fwd.neighbourhood.functional.miRNA' 'fwd.neighbourhood.functional.TF' 'fwd.neighbourhood.functional.KEGG' 'fwd.neighbourhood.functional.DEG' 'bwd.neighbourhood.functional.GO_bp' 'bwd.neighbourhood.functional.GO_cc' 'bwd.neighbourhood.functional.GO_mf' 'bwd.neighbourhood.functional.Disease_OMIM' 'bwd.neighbourhood.functional.Disease_GAD' 'bwd.neighbourhood.functional.Drug_DrugBank' 'bwd.neighbourhood.functional.miRNA' 'bwd.neighbourhood.functional.TF' 'bwd.neighbourhood.functional.KEGG' 'bwd.neighbourhood.functional.DEG'
Table: Subpathway Options - NEIGHBOURHOOD
Description R parameter
Topological Forward and backward cascades starting 'fwd.cascade.topological.degree' from genes/nodes with crucial topological 'fwd.cascade.topological.betweenness' roles within the network 'fwd.cascade.topological.closeness' 'fwd.cascade.topological.hub_score' 'fwd.cascade.topological.eccentricity' 'fwd.cascade.topological.page_rank' 'fwd.cascade.topological.start_nodes' 'bwd.cascade.topological.degree' 'bwd.cascade.topological.betweenness' 'bwd.cascade.topological.closeness' 'bwd.cascade.topological.hub_score' 'bwd.cascade.topological.eccentricity' 'bwd.cascade.topological.page_rank' 'bwd.cascade.topological.start_nodes' Functional Forward and backward cascades starting 'fwd.cascade.functional.GO_bp' from genes/nodes with crucial topological 'fwd.cascade.functional.GO_cc' roles within the network 'fwd.cascade.functional.GO_mf' 'fwd.cascade.functional.Disease_OMIM' 'fwd.cascade.functional.Disease_GAD' 'fwd.cascade.functional.Drug_DrugBank' 'fwd.cascade.functional.miRNA' 'fwd.cascade.functional.TF' 'fwd.cascade.functional.KEGG' 'fwd.cascade.functional.DEG' 'bwd.cascade.functional.GO_bp' 'bwd.cascade.functional.GO_cc' 'bwd.cascade.functional.GO_mf' 'bwd.cascade.functional.Disease_OMIM' 'bwd.cascade.functional.Disease_GAD' 'bwd.cascade.functional.Drug_DrugBank' 'bwd.cascade.functional.miRNA' 'bwd.cascade.functional.TF' 'bwd.cascade.functional.KEGG' 'bwd.cascade.functional.DEG'
Table: Subpathway Options - CASCADE
Type Description R parameter
Random Walk Community structures that minimize 'community.infomap' the expected description length of a random walker trajectory Modular Community structures via a modularity 'community.louvain' measure and a hierarchical approach Walktraps Densely connected subgraphs via 'community.walktrap' random walks Leading eigen Densely connected subgraphs based 'community.leading_eigen' on the leading non-negative eigen- vector of the modularity matrix Betweeneess Community structures detection 'community.edge_betweenness' via edge betweenness Greedy Community structures via greedy 'community.fast_greedy' optimization of modularity
Table: Subpathway Options - COMMUNITY
Type Description R parameter
Cliques A subgraph where every two distinct 'component.3-cliques' vertices in the clique are adjacent ... 'component.9-cliques' K-core A maximal subgraph in which each 'component.3-coreness' vertex has at least degree k ... 'component.9-coreness' Max cliques Largest of maximal cliques 'component.max_cliques' Components All single components 'component.decompose'
Table: Subpathway Options - COMPONENT
An example follows where community.walktrap is selected as the subpathway type.
DEsubs.run <- DEsubs( org='hsa', mRNAexpr=mRNAexpr, mRNAnomenclature='entrezgene', pathways='All', DEtool=NULL, DEpar=0.05, CORtool='pearson', CORpar=0.7, subpathwayType='community.walktrap', rankedList=rankedList, verbose=FALSE)
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cap <- paste0( 'Subpathway extraction options consist of five main categories. ', 'The three of them (cascade, neighborhood, stream) are sub-categorized ', 'according to features (topological or functional) and the direction of ', 'propagation (forward or backward) of the gene of interest where each ', 'subpathway is starting. The other two (component, community) are ', 'sub-categorized according to various topological properties.') knitr::include_graphics('figures/fig2.png')
6. Subpathway enrichment analysis
Eight different datasets with external resources are stored locally for further enrichment analysis of resulting subpathways. Each dataset is formed with a list of terms related to biological and pharmacological features and the respective associated genes for each term. A detailed description is shown in Table 12. Additionally, the user can supply a custom gene-set in the form of an .RData file storing a matrix, named targetsPerClass. The matrix should store the terms as rownames and the targets of each term at each row. Since some rows are bound to havemore elements that others, empty cells should be filled with '0' characters. Once the file is stored in a directory DEsubs/Data, it will be permanently availiable along with the other eight resources, using the filename (without the .RData suffix) as the new functional feature type along with the any of the default eight features.
The enrichment analysis is performed based on the cumulative hypergeometric distribution, where G is the number of genes in the user input list, l the number of those genes included in the subpathway, D the number of associated genes for a term and d the number of genes included in the subpathway [@li2013subpathway]. Terms with P < 0.05 are regarded as terms with significant association with the respective subpathway.
$$ P = 1 - \sum_{x=0}^{d}\frac{\binom{D}{x}\binom{G-D}{l-x}}{\binom{G}{l}} $$
Type Description (# of terms) Source
-------------- -------------- --------------
Pathway Term KEGG pathway maps (179) [@chen2013enrichr]
GO Biological Process Genes sharing a common [@chen2013enrichr]
biological process (5.192)
GO Cellular Component Genes sharing a common [@chen2013enrichr]
cellular component (641)
GO Molecular Function Genes sharing a common [@chen2013enrichr]
molecular level (1.136)
OMIM Disease Disease related genes (90) [@chen2013enrichr]
GAD Disease Disease related genes (412) [@li2011implications]
DrugBank Drug Gene targets of drugs (1.488) [@barneh2015updates]
Transcription Factor Gene targetsof transcription [@chen2013enrichr]
factors (290)
-------------- -------------- --------------
Table: List of external databases
Term Target 1 Target 2 Target 3 Target 4 Target 5 Target 6 Target 7
Term 1 ACAD9 ACAD8 SH3GLB1 ESCO2 ESCO1 ADH1C '0'
Term 2 ADH1C ADH1B ADHFE1 ADH1A ADH6 ADH7 ADH4
... ... ... ... ... ... ... ...
Term N PTPN1 RHOA ACTN4 ACTN3 ACTN2 '0' '0'
Table: Example of custom gene set
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7. Visualization
DEsubs visualizes its results at a gene, subpathway and organism level through various schemes such as bar plots, heat maps, directed weighted graphs, circular diagrams [@gu2014circlize] and dot plots. Indicative examples are illustrated in figures 2-8 based on DEsubs executions using the human pathway network and a synthetic dataset. Bar plots show the genes with the best Q-value from the user-selected DE analysis tool (the user defines the desired gene number). The figures are exported in the directory Output within the user specified location. Heat maps show the genes with the highest values either in our topological or functional measures (see Table 6).
cap <- paste0( 'Bar plots show the genes with the best Q-value from the user-selected ', 'DE analysis tool (the user defines the desired gene number). ', 'Heat maps show the genes with the highest values either in our ', 'topological or functional measures (see Table 6). ', 'Each extracted subpathway is illustrated though a directed graph by ', 'imprinting the degree of DE and correlation among the respective gene ', 'members. Subpathway enrichment in association with biological and ', 'pharmacological features (such as pathway terms, gene ontologies, ', 'regulators, diseases and drug targets) is depicted through circular ', 'diagrams. The total picture of the enriched subpathways is performed ', 'with dot plots.') knitr::include_graphics('figures/fig3.png')
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7.1 Gene Level Visualization
res <- geneVisualization( DEsubs.out=DEsubs.out, top=10, measures.topological=c( 'degree', 'betweenness', 'closeness', 'eccentricity', 'page_rank'), measures.functional=c( 'KEGG', 'GO_bp','GO_cc', 'GO_mf', 'Disease_OMIM', 'Disease_GAD', 'Drug_DrugBank','miRNA', 'TF'), size.topological=c(5,4), size.functional=c(7,4), size.barplot=c(5,6), export='plot', verbose=FALSE)
cap <- 'Bars illustrate the genes with the highest Q-values.' res <- geneVisualization( DEsubs.out=DEsubs.out, top=10, measures.topological=NULL, measures.functional=NULL, measures.barplot=TRUE, size.barplot=c(5,6), export='plot', verbose=FALSE)
cap <- 'Heat map represents the twelve genes with the highest values of functional measures. The values are scaled and the red graduation indicates the value degree.' res <- geneVisualization( DEsubs.out=DEsubs.out, top=12, measures.topological=NULL, measures.functional=c( 'KEGG', 'GO_bp','GO_cc', 'GO_mf', 'Disease_OMIM', 'Disease_GAD', 'Drug_DrugBank','miRNA', 'TF'), measures.barplot=FALSE, size.functional=c(7,4), export='plot', verbose=FALSE)
cap <- 'Heat map represents the twelve genes with the highest values of topological measures. The values are scaled and the red graduation indicates the value degree.' res <- geneVisualization( DEsubs.out=DEsubs.out, top=12, measures.topological=c( 'degree', 'betweenness', 'closeness', 'eccentricity', 'page_rank'), measures.functional=NULL, measures.barplot=FALSE, size.topological=c(5,4), export='plot', verbose=FALSE)
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7.2. Subpathway Level Visualization
Each extracted subpathway is illustrated though a directed graph by imprinting the degree of differential expression and correlation among the respective gene members. Additionally, it can be extracted in a variety of formats so that it can be used by external software, such as .txt, .json, .gml, .ncol, .lgl, .graphml and .dot formats.
res <- subpathwayToGraph( DEsubs.out=DEsubs.out, submethod='community.walktrap', subname='sub6', verbose=FALSE, export='plot' )
library('DEsubs') load(system.file('extdata', 'data.RData', package='DEsubs')) cap <- 'Graph illustrates the links of a subpathway. Red graduation in nodes indicate the Q-value degree, the edge width indicates the correlation degree between the respective genes. Green or red color in edges indicates the positive or negative correlation respectively' res <- subpathwayToGraph( DEsubs.out=DEsubs.out, submethod='community.walktrap', subname='sub6', verbose=FALSE, export='plot' )
Subpathway enrichment in association with biological and pharmacological features (such as pathway terms, gene ontologies, regulators, diseases and drug targets) is depicted through circular diagrams.
res <- subpathwayVisualization( DEsubs.out=DEsubs.out, references=c('GO', 'TF'), submethod='community.walktrap', subname='sub1', scale=c(1, 1), export='plot', verbose=FALSE)
cap <- 'Circular Diagram shows the associations among genes including in a subpathway and Gene Ontology terms where are enriched' library('DEsubs') load(system.file('extdata', 'data.RData', package='DEsubs')) res <- subpathwayVisualization( DEsubs.out=DEsubs.out, references='GO', submethod='community.walktrap', subname='sub1', scale=1, export='plot', verbose=FALSE)
cap <- 'Circular Diagram shows the associations among genes included in a subpathway and enriched Transcription Factors.' res <- subpathwayVisualization( DEsubs.out=DEsubs.out, references='TF', submethod='community.walktrap', subname='sub1', scale=1.0, export='plot', verbose=FALSE)
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7.3. Organism Level Visualization
The total picture of the enriched subpathways is performed with dot plots. The number of features represented are selected using topTerms argument.
res <- organismVisualization( DEsubs.out=DEsubs.out, references='KEGG', topSubs=10, topTerms=20, export='plot', verbose=FALSE)
cap <- 'Dot plot shows the enriched associations among experiment-specific extracted subpathways and pathwaysfrom KEGG database. Twenty pathways were selected as the desired number of terms.' res <- organismVisualization( DEsubs.out=DEsubs.out, references='KEGG', topSubs=10, topTerms=20, export='plot', verbose=FALSE)
References
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