## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(CoNI)
## ----install_dependencies-----------------------------------------------------
# dependencies<-c("igraph","doParallel","cocor","tidyverse","foreach","ggrepel","gplots","gridExtra","plyr","ppcor","tidyr","Hmisc")
#
# `%notin%`<-Negate(`%in%`)
# for(package in dependencies){
# if(package %notin% rownames(installed.packages())){
# install.packages(package,dependencies = TRUE)
# }
# }
## ---- eval=FALSE, echo=TRUE---------------------------------------------------
# # if (!requireNamespace("BiocManager", quietly = TRUE))
# # install.packages("BiocManager")
# # BiocManager::install("genefilter")
## ----Download_data------------------------------------------------------------
#Chow Data
data(Chow_MetaboliteData) #Metabolite data
data(Chow_GeneExpData) #Gene expression data
#HFD data
data(HFD_MetaboliteData) #Metabolite data
data(HFD_GeneExpData) #Gene expression data
## ----matchRows----------------------------------------------------------------
#Match rownames both omics data
rownames(Chow_MetaboliteData)<-rownames(Chow_GeneExpData)
rownames(HFD_MetaboliteData)<-rownames(HFD_GeneExpData)
#Shorten names
Chow_metabo<-Chow_MetaboliteData
Chow_gene<-Chow_GeneExpData
HFD_metabo<-HFD_MetaboliteData
HFD_gene<-HFD_GeneExpData
## ----CoNI_chow,eval=FALSE, echo=TRUE------------------------------------------
# #Run for Chow
# # CoNIResults_Chow<-CoNI(edgeD = Chow_gene,vertexD = Chow_metabo,
# # saveRaw = FALSE,filter_highVarianceEdge = TRUE,correlateDFs=TRUE,
# # padjustvertexD = FALSE, split_number = 200,
# # outputDir = "./Chow/",outputName = "CoNIChow",
# # splitedgeD = TRUE,numCores = 2,onlySgRes = TRUE)
## ----load_CoNIResults_chow----------------------------------------------------
#Load chow results
data(CoNIResults_Chow)
## ----CoNI_hfd,eval=FALSE, echo=TRUE-------------------------------------------
# #Run for HFD
# # CoNIResults_HFD<-CoNI(edgeD = HFD_gene,vertexD = HFD_metabo,
# # saveRaw = FALSE,filter_highVarianceEdge = TRUE,
# # padjustvertexD = FALSE, split_number = 200,correlateDFs=TRUE,
# # outputDir = "./HFD/",outputName = "CoNIHFD",
# # splitedgeD = TRUE,numCores = 2,onlySgRes = TRUE)
## ----load_CoNIResults_hfd-----------------------------------------------------
#Load HFD results
data(CoNIResults_HFD)
## ----read_metabolite_annotation-----------------------------------------------
#Read Annotation table
data(MetaboliteAnnotation)
MetaboliteAnnotation<-assign_colorsAnnotation(MetaboliteAnnotation,col="Class")
## ----network_chow-------------------------------------------------------------
#Generate Network
ChowNetwork<-generate_network(ResultsCoNI = CoNIResults_Chow,
colorVertexTable = MetaboliteAnnotation,
outputDir = "./",
outputFileName = "Chow")
## ----network_chow_class-------------------------------------------------------
#Generate Network Chow
ChowNetworkWithClass<-generate_network(ResultsCoNI = CoNIResults_Chow,
colorVertexTable = MetaboliteAnnotation,
outputDir = "./",
outputFileName = "Chow",
Class = MetaboliteAnnotation)
## ----network_hfd_class--------------------------------------------------------
#Generate Network HFD
HFDNetworkWithClass<-generate_network(ResultsCoNI = CoNIResults_HFD,
colorVertexTable = MetaboliteAnnotation,
outputDir = "./",
outputFileName = "HFD",
Class = MetaboliteAnnotation)
## ----network_stats_chow-------------------------------------------------------
library(knitr)
library(kableExtra)
kable(NetStats(Network = ChowNetworkWithClass),caption="Network statistics Chow") %>% kable_styling(full_width = FALSE)
## ----network_stats_hfd--------------------------------------------------------
kable(NetStats(Network = HFDNetworkWithClass),caption="Network statistics HFD") %>% kable_styling(full_width = FALSE)
## -----------------------------------------------------------------------------
library(igraph)
coordinates = layout_with_fr(ChowNetworkWithClass) #define layout
## ----spectral-----------------------------------------------------------------
Spectral = cluster_leading_eigen(ChowNetworkWithClass)
#See membership for the nodes
Spectral$membership
## ----spectral_image,fig.show='hide', out.width = '100%', out.height = '100%', fig.width = 7, fig.heigt = 7----
#Plot the network
plot(ChowNetworkWithClass, vertex.color=membership(Spectral), layout=coordinates)
## ----greedy-------------------------------------------------------------------
greedy = cluster_fast_greedy(ChowNetworkWithClass)
#See membership for the nodes
greedy$membership
## ----greedy_image,fig.show='hide',out.width = '100%', out.height = '100%', fig.width = 7, fig.heigt = 7----
#Plot the network
plot(ChowNetworkWithClass, vertex.color=membership(greedy), layout=coordinates)
## ----betweenness--------------------------------------------------------------
betweenness = cluster_edge_betweenness(ChowNetworkWithClass,weights=NULL)
#See membership for the nodes
betweenness$membership
## ----betweenness_image,fig.show='hide',out.width = '100%', out.height = '100%', fig.width = 7, fig.heigt = 7----
#Plot the network
plot(ChowNetworkWithClass, vertex.color=membership(betweenness), layout=coordinates)
## ----local_controlling_genes_Chow---------------------------------------------
#Chow
#Get results binomial test
LCGenes_ResultsBinomialTable_Chow<- find_localControllingFeatures(ResultsCoNI = CoNIResults_Chow,network = ChowNetworkWithClass)
#Get list local controlling genes
LCGenes_Chow<-as.character(unique(LCGenes_ResultsBinomialTable_Chow$edgeFeatures))
## ----local_controlling_genes_HFD----------------------------------------------
#HFD
#Get results binomial test
LCGenes_ResultsBinomialTable_HFD<- find_localControllingFeatures(ResultsCoNI = CoNIResults_HFD,network = HFDNetworkWithClass)
#Get list local controlling genes
LCGenes_HFD<-as.character(unique(LCGenes_ResultsBinomialTable_HFD$edgeFeatures))
## ----local_controlling_tables-------------------------------------------------
#Chow
#Generate table local controlling genes
TableLCFsChow<-tableLCFs_VFs(CoNIResults = CoNIResults_Chow,LCFs = LCGenes_Chow)
#Show first two rows
TableLCFChowk<-kable(TableLCFsChow[1:2,],caption="Table Local Controlling Genes") %>% kable_styling(full_width = FALSE)
#HFD
#Generate table local controlling genes
TableLCFsHFD<-tableLCFs_VFs(CoNIResults = CoNIResults_HFD,LCFs = LCGenes_HFD)
#Show first two rows
TableLCFHFDk<-kable(TableLCFsHFD[1:2,],caption="Table Local Controlling Genes") %>% kable_styling(full_width = FALSE)
TableLCFHFDk
## ----gene_Magnitude,warning=FALSE,message=FALSE-------------------------------
Top10Chow<-top_n_LF_byMagnitude(CoNIResults_Chow,topn = 10)
Top10HFD<-top_n_LF_byMagnitude(CoNIResults_HFD,topn = 10)
head(Top10HFD[,c(1:3,ncol(Top10HFD))])
## ----CorvsPcor_oneCombination,fig.show='hold',warning=FALSE,message=FALSE,out.width="50%",fig.align='center'----
plotPcorvsCor(ResultsCoNI = CoNIResults_HFD,edgeFeature = "Lpin2",
vertexFeatures = c("PC.ae.C42.2","PC.ae.C42.0"),
vertexD = HFD_metabo,edgeD = HFD_gene,
label_edgeFeature = "Gene",plot_to_screen = TRUE,
outputDir = "./")
## ----CorvsPcor_AllCombinations,fig.show='hold',warning=FALSE,message=FALSE,out.width="40%",fig.show='hide'----
plotPcorvsCor(ResultsCoNI = CoNIResults_HFD,edgeFeature = "Lpin2",
vertexD = HFD_metabo,edgeD = HFD_gene,
label_edgeFeature = "Gene",plot_to_screen = TRUE,
outputDir = "./")
## ----bipartite_graphs---------------------------------------------------------
#Chow
ChowBipartiteGraph<-createBipartiteGraph("./TableForNetwork_Chow.csv",MetaboliteAnnotation)
#Save bipartite graph
write.graph(ChowBipartiteGraph,file="./Chow_bipartite.graphml",format="graphml")
#HFD
HFDBipartiteGraph<-createBipartiteGraph("./TableForNetwork_HFD.csv",MetaboliteAnnotation)
#Save bipartite graph
write.graph(HFDBipartiteGraph,file="./HFD_bipartite.graphml",format="graphml")
## -----------------------------------------------------------------------------
Chow_HypergraphIncidenceM<-createBipartiteGraph("./TableForNetwork_Chow.csv",MetaboliteAnnotation,incidenceMatrix = TRUE)
## ----triplet_comparison-------------------------------------------------------
Compare_Triplets(Treat1_path = "./TableForNetwork_Chow.csv",
Treat2_path = "./TableForNetwork_HFD.csv",
OutputName = "Shared_triplets_HFDvsChow.csv")
## -----------------------------------------------------------------------------
(LCP_sharedGene_HFDvsChow<-Compare_VertexClasses_sharedEdgeFeatures(
Treat1_path = "./TableForNetwork_HFD.csv",
Treat2_path = "./TableForNetwork_Chow.csv",
OutputName = "Comparison_LipidClassesPerGene_HFDvsChow.csv",
Treat1Name = "HFD",
Treat2Name = "Chow"))
## ----gene_metabolitePairProfile,fig.align='center',fig.align='center',fig.width = 6, fig.height = 5----
create_edgeFBarplot(CompTreatTable = LCP_sharedGene_HFDvsChow,
edgeF = "Gm4553",
treat1 = "HFD",
treat2 = "Chow",
factorOrder = c("HFD","Chow"),
col1="#E76BF3",
col2 = "#F8766D",EdgeFeatureType = "Gene")
## ----global_metabolitePairProfile,fig.align='center',fig.width = 6, fig.height = 5----
(HFDvsChow_GlobalLipidProfile<-create_GlobalBarplot(CompTreatTable = LCP_sharedGene_HFDvsChow,
treat1 = "HFD",
treat2 = "Chow",
factorOrder = c("HFD","Chow"),
col1="#E76BF3",
col2 = "#F8766D",
maxpairs = 1,
szggrepel = 6,
szaxisTxt = 15,
szaxisTitle = 15,
xlb = "Metabolite-pair classes"))
## ----stacked_metabolitePairProfile,fig.align='center',fig.show='hold',fig.width = 6, fig.height = 5----
create_stackedGlobalBarplot_perTreatment(CompTreatTable = LCP_sharedGene_HFDvsChow,
treat = "HFD",
max_pairsLegend = 1,
xlb = "Metabolite-class-pairs",
szTitle = 20,
szggrepel = 6,
szaxisTxt = 15,
szaxisTitle = 15)
create_stackedGlobalBarplot_perTreatment(CompTreatTable = LCP_sharedGene_HFDvsChow,
treat = "Chow",
max_pairsLegend = 1,
ylim = 3,
xlb = "Metabolite-pair classes",
szTitle = 20,
szggrepel = 4,
szaxisTxt = 15,
szaxisTitle = 15)
## ----stacked_metabolitePairProfile_grid,fig.align='center',fig.width = 7, fig.height = 5----
HFDvsChow_StackedLipidProfile<-getstackedGlobalBarplot_and_Grid(
CompTreatTable = LCP_sharedGene_HFDvsChow,
xlb = "Metabolite-pair classes",
Treat1 = "HFD",
Treat2 = "Chow",
szTitle = 20,
szggrepel = 6,
szaxisTxt = 15, szaxisTitle = 15)
plot(HFDvsChow_StackedLipidProfile)
## ----metabolite_classes_per_gene,fig.align='center',fig.width = 7, fig.height = 5----
HFD_vs_Chow_LCP_Gene<-getVertexsPerEdgeFeature_and_Grid(LCP_sharedGene_HFDvsChow,"HFD","Chow",
Annotation=MetaboliteAnnotation,
ggrep=FALSE,
small = TRUE,
szTitle = 20,
szaxisTxt = 15,
szaxisTitle = 15)
plot(HFD_vs_Chow_LCP_Gene)