| title | author | date | output | geometry | header-includes | mainfont | fontsize | always_allow_html | |||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Adipor1 in HF - Knockout |
Mark E. Pepin |
10/1/2018 |
|
margin=1in |
|
Times |
10pt |
true |
##Heatmap
library(dplyr)
library(tidyr)
library(pheatmap)
library(openxlsx)
##Pull data for analysis
Heart_raw<-read.xlsx("../1_Input/2_Protein/Heart - PN-0064 Heart - mep.xlsx", colNames = T, rowNames = F, sheet = "Cleaned_NoMVs")
Heart_raw.cleaned<-Heart_raw %>% mutate(Gene.Symbol = strsplit(as.character(Gene.Symbol), split = ";")) %>% unnest(Gene.Symbol)
Heart_raw$Gene.Symbol<-make.unique(Heart_raw$Gene.Symbol, sep = ".")
Heart_raw.complete.cleaned<-Heart_raw.cleaned[!is.na(Heart_raw.cleaned$Gene.Symbol),]
LFQs.Heart_raw<-dplyr::select(Heart_raw.complete.cleaned, contains("LFQ"))
rownames(LFQs.Heart_raw)<-make.unique(Heart_raw.complete.cleaned$Gene.Symbol, sep = ".")Once we established that the populations under consideration truly display divergene expression patterns, we sought to determine whether unbiased global gene expression patterns recapitulate the described phenotypes within each heart failure group. To accomplish this, an unsupervised Principal Components Analysis (PCA) was initially used with normalized counts.
Before running the principal components analysis, it was necessary to first determine the number of PC's required to account for 80% of the variance, a machine-learning algorithmm benchmark that provides sufficient confidence in the analysis.
#Plot Features of the PCA
library(dplyr)
library(plotly)
#transpose the dataset (required for PCA)
data.pca<-t(LFQs.Heart_raw[rowSums(LFQs.Heart_raw)>1,])
data.pca<-as.data.frame(data.pca)
##Import the data to be used for annotation
Index<-c(0,0,0,0,1,1,1,1)
Index<-as.data.frame(Index)
##merge the file
data.pca_Final<-cbind(Index, data.pca)
rownames(data.pca_Final)<-data.pca_Final$Row.names
pca.comp<-prcomp(data.pca_Final[,(ncol(Index)+2):ncol(data.pca_Final)])
pcaCharts=function(x) {
x.var <- x$sdev ^ 2
x.pvar <- x.var/sum(x.var)
par(mfrow=c(2,2))
plot(x.pvar,xlab="Principal component",
ylab="Proportion of variance", ylim=c(0,1), type='b')
plot(cumsum(x.pvar),xlab="Principal component",
ylab="Cumulative Proportion of variance",
ylim=c(0,1),
type='b')
screeplot(x)
screeplot(x,type="l")
par(mfrow=c(1,1))
}
pcaCharts(pca.comp)
png(file=paste0("../2_Output/2_Protein/Proteomics_PCA.Charts.png"))
pcaCharts(pca.comp)
dev.off()## quartz_off_screen
## 2
From the previous calculations, it is appears that 3 principal components are necessary (accounting for >80% cumulative variance).
##Create a 3D-PCA for Inspection
library(plotly)
##Index
PCs<-cbind(pca.comp$x, Index)
rownames(PCs)<-rownames(data.pca)
ax_text<-list(
family = "times",
size = 12,
color = "black")
t <- list(
family = "times",
size = 14,
color = "black")
p <- plot_ly(PCs, x = ~PC1, y = ~PC2, z = ~PC3,
marker = list(color = ~Index,
colorscale = c('#FFE1A1', '#683531'),
showscale = TRUE),
text=rownames(PCs)) %>%
add_markers() %>%
layout(scene = list(
xaxis = list(title = 'PC1', zerolinewidth = 4,
zerolinecolor="darkgrey", linecolor="darkgrey",
linewidth=4, titlefont=t, tickfont=ax_text),
yaxis = list(title = 'PC2', zerolinewidth = 4,
zerolinecolor="darkgrey", linecolor="darkgrey",
linewidth=4, titlefont=t, tickfont=ax_text),
zaxis = list(title = 'PC3', zerolinewidth = 4,
zerolinecolor="darkgrey", linecolor="darkgrey",
linewidth=4, titlefont=t, tickfont=ax_text)),
annotations = list(
x = 1.13,
y = 1.03,
text = 'Diabetes',
xref = '1',
yref = '0',
showarrow = FALSE))
p #must comment out for PDF generation via knitr (Pandoc)library(pheatmap)
library(dplyr)
##Import Data Matrix
Results_HM<-dplyr::filter(Heart_raw.complete.cleaned, minuslog_pval>2)
HM_data.p05<-data.matrix(dplyr::select(Results_HM, contains("LFQ")))
rownames(HM_data.p05)<-Results_HM$Gene.Symbol
#Import the Index File
paletteLength <- 100
myColor <- colorRampPalette(c("dodgerblue4", "white", "brown4"))(paletteLength)
pheatmap(HM_data.p05, color = myColor, scale = "row")
pheatmap(HM_data.p05, color = myColor, scale = "row", filename = "../2_Output/2_Protein/Heatmap.Heart_LFQs.p01.pdf")##Volcano Plot
# Load packages
library(dplyr)
library(ggplot2)
library(ggrepel)
library(openxlsx)
# Read data from the web
results<-read.xlsx("../1_Input/2_Protein/Heart - PN-0064 Heart - mep.xlsx", sheet = "IPA_Import")
results = mutate(results, sig=ifelse(results$minuslogpvalue>1.3 & abs(results$log2FC)>0.585, "p < 0.05 and |FC| > 1.5", "Not Sig"))
#plot the ggplot
p = ggplot(results, aes(log2FC, minuslogpvalue)) + theme(panel.background = element_rect("white", colour = "black", size=2), panel.grid.major = element_line(colour = "gray50", size=.75), panel.grid.minor = element_line(colour = "gray50", size=0.4)) +
geom_point(aes(fill=sig), colour="black", shape=21) + labs(x=expression(Log[2](Fold-Change)), y=expression(-Log[10](P-value))) + xlim(-5,5)+ geom_hline(yintercept = 0, size = 1) + geom_vline(xintercept=0, size=1)+
scale_fill_manual(values=c("black", "tomato"))
#add a repelling effect to the text labels.
p+geom_text_repel(data=filter(results, minuslogpvalue>2 & abs(log2FC)>2), aes(label=Gene))
pdf(file = "../2_Output/2_Protein/Volcano.Plot_Heart.pdf")
p+geom_text_repel(data=filter(results, minuslogpvalue>2 & abs(log2FC)>1), aes(label=Gene))
dev.off()## quartz_off_screen
## 2
#Network Analysis
##Load the network data
# Load the packages used for the network analysis
library("igraph")
library("network")
library("sna")
library("ndtv")
library("openxlsx")
library(tidyr)
library(dplyr)
# Load the data used for the network analysis
links <- read.xlsx("../1_Input/Pathways/Adiponectin_Network_AA.ONLY_ICM.v.NICM.xlsx")
links<- links %>% mutate(From = strsplit(as.character(From), split = ";")) %>% unnest(From)
links<- links %>% mutate(To = strsplit(as.character(To), split = ";")) %>% unnest(To)
links<-unique(links[,2:3])
nodes <- read.xlsx("../1_Input/1_RNA/LRT-Ischemia-Race Interaction_DESeq2.xlsx", sheet = "Unfiltered")
nodes<-dplyr::mutate(nodes, Logpvalue=-log(pvalue, 10))
nodes<-dplyr::select(nodes, -pvalue)
nodes<-nodes %>% mutate(Gene = strsplit(as.character(external_gene_name), split = ";")) %>% unnest(Gene)
nodes$Gene<-make.unique(nodes$Gene, sep = ".")
nodes<-nodes[!is.na(nodes$Gene),]
nodes<-dplyr::select(nodes, id=Gene, log2FoldChange, Logpvalue)
nodes_To<-merge(nodes, links, by.x = "id", by.y = "To")
nodes_To<-dplyr::select(nodes_To, -"From")
nodes_From<-merge(nodes, links, by.x = "id", by.y = "From")
nodes_From<-dplyr::select(nodes_From, -To)
nodes_both<-rbind(nodes_To, nodes_From)
nodes_filtered<-distinct(nodes_both)
#create colors for nodes based on fold-change
nodes_filtered = mutate(nodes_filtered, colrs=ifelse(nodes_filtered$log2FoldChange>0, "darkgoldenrod1", "dodgerblue1"))
nodes_filtered[nodes_filtered$id=="ADIPOQ",4]<-"tomato"
# Examine the data
head(nodes)## id log2FoldChange Logpvalue
## 1 TSPAN6 -0.174198936 0.89967284
## 2 TNMD 1.367731837 4.09888460
## 3 DPM1 -0.072854878 0.03328028
## 4 SCYL3 0.148501007 0.48531617
## 5 C1orf112 0.008380419 0.35216380
## 6 FGR 1.141066575 1.43322194
head(links)## From To
## 1 ACACB ACACB
## 2 ACACB LEP
## 3 ACACB MLXIPL
## 4 ACACB PPARG
## 5 ACACB SCD
## 6 ADIPOQ CASP3
# since there are a few repeated links, we can consolidate them
links<-links[order(links$From, links$To),]
rownames(links)<-NULLlibrary("igraph")
net<-graph.data.frame(links, nodes_filtered, directed=T)
net## IGRAPH 5d103c8 DN-- 24 134 --
## + attr: name (v/c), log2FoldChange (v/n), Logpvalue (v/n), colrs
## | (v/c)
## + edges from 5d103c8 (vertex names):
## [1] ACACB ->ACACB ACACB ->LEP ACACB ->MLXIPL ACACB ->PPARG
## [5] ACACB ->SCD ADIPOQ->ACACB ADIPOQ->ADIPOQ ADIPOQ->CASP3
## [9] ADIPOQ->CEBPA ADIPOQ->CTGF ADIPOQ->GPD1 ADIPOQ->HMGCS2
## [13] ADIPOQ->LEP ADIPOQ->LHCGR ADIPOQ->MLXIPL ADIPOQ->MMP9
## [17] ADIPOQ->PCK1 ADIPOQ->PFKFB1 ADIPOQ->PPARG ADIPOQ->SCD
## [21] ADIPOQ->UCP1 ADIPOQ->UCP2 AGT ->ADIPOQ AGT ->AGT
## [25] AGT ->CASP3 AGT ->CTGF AGT ->IGF1 AGT ->LEP
## + ... omitted several edges
net<-simplify(net, remove.multiple = F, remove.loops = T)
V(net)$color<-nodes_filtered$colrs #change the node colors based on the fold-change (red = up)
V(net)$size<-abs(V(net)$log2FoldChange)*5
#Create a layout format
l <- layout_with_kk(net)
l <- norm_coords(l, ymin=-.9, ymax=.9, xmin=-.9, xmax=0.9)
#Highlight the ADIPOQ Targets
inc.edges <- incident(net, V(net)[[2]], mode="all")
ecol <- rep("gray60", ecount(net))
ecol[inc.edges] <- "tomato"
#colors
nodeColors=c("darkgoldenrod1", "dodgerblue1")
#plot the network
par(bg="white")
plot(net, edge.arrow.size=0.4, label.cex=2, vertex.label.color="black", vertex.label.cex=.7, vertex.frame.color=NA, layout = l, rescale=F, edge.color=ecol, edge.width=2)
legend(x=-1.5, y=-1.1, c("Upregulated", "Downregulated"), pch=21, col=nodeColors, pt.bg = nodeColors, pt.cex=2, cex = 0.8, bty = "n", ncol = 1, title = "Gene Expression", title.col = "black", text.col = "black")
dev.off()## pdf
## 3
##Create a pdf file of the network
pdf(file="../2_Output/1_RNA/ADIPOQ_Gene.Network.pdf")
par(bg="white")
plot(net, edge.arrow.size=0.4, label.cex=2, vertex.label.color="black", vertex.label.cex=.7, vertex.frame.color=NA, layout = l, rescale=F, , edge.color = ecol)
legend(x=-1.5, y=-1.1, c("Upregulated", "Downregulated"), pch=21, col=nodeColors, pt.bg = nodeColors, pt.cex=2, cex = 0.8, bty = "n", ncol = 1, text.col = "black", title = "Gene Expression", title.col = "black")
dev.off()## pdf
## 3
##Interactive Plot
tkid <- tkplot(net) #tkid is the id of the tkplot that will open
l <- tkplot.getcoords(tkid) # grab the coordinates from tkplot
par(bg="black")
plot(net, layout=l)
dev.off## function (which = dev.cur())
## {
## if (which == 1)
## stop("cannot shut down device 1 (the null device)")
## .External(C_devoff, as.integer(which))
## dev.cur()
## }
## <bytecode: 0x7fe17f2a3670>
## <environment: namespace:grDevices>
##3-D Network Plot
library(rgl)
library(igraph)
bg3d("black")
coords<-layout_with_fr(net, dim=3)
rgplot <- rglplot(net, layout=coords, edge.arrow.size=0.4, label.cex=2, vertex.label.color="white", vertex.label.cex=0.5, vertex.frame.color=NA, vertex.label.dist=2, rescale=T, edge.color=ecol, edge.width=4)
# par(bg="black")
# plot(net, layout=l)
# dev.off
for (i in 1:360){
rgl.viewpoint(theta=i, phi=0)
rgl.snapshot(filename = paste0("animated/", paste("3D.Network", formatC(i, width=3, flag="0"), sep="_"), ".png"), fmt = "png", top = TRUE )
}
system("convert -delay 5 animated/*.png 3D.Network_plot.gif")
file.remove(list.files(path="/animated/", pattern=".png"))## logical(0)
##Correlation Analysis the CHAAMPS RNA-sequencing Data
# Format for Correlation
library(Hmisc)
library(corrplot)
library(pheatmap)
cor.m<-rcorr(data.matrix(data.pca))
cor.r<-data.matrix(cor(data.matrix(data.pca)))
paletteLength <- 100
myColor <- rev(colorRampPalette(c("dodgerblue4", "white", "red"))(paletteLength))
##Create a network based on this correlation.
cor.r[cor.r<0.9]=NA
cor.r[cor.r>0.9]=1
cor.r[is.na(cor.r)]=0
# remove nodes lacking any connections
cor.r_filtered<-cor.r
cor.r_filtered<-cor.r_filtered[rowSums(cor.r_filtered)>2,]
vector<-rownames(cor.r_filtered)
cor.r_filtered<-cor.r_filtered[,vector]
#create the adjacency matrix
corr_net<-graph_from_adjacency_matrix(cor.r_filtered, diag = F, mode = "undirected")
corr_net<-simplify(corr_net)
#Identify protein communities
ceb <- cluster_edge_betweenness(corr_net)
dendPlot(ceb, mode = "hclust", ann = F)
length(ceb)## [1] 38
membership(ceb)## Mgst3 Tefm Ndufb5 Atp5a1 Pcca
## 1 2 1 3 4
## Tcaim TCAIM Hars2 Dars2 Tmem261
## 5 5 5 4 6
## Foxred1 Atad1 Ldhd Ndufab1 Mp68
## 6 4 6 4 4
## Timm9 Aldh2 Mpc2 Mcee Mdh2
## 5 4 7 8 5
## Bphl Idh3a Rdh14 mt-Atp8 Mtatp8
## 9 3 7 6 6
## Pgam2 Pgam1 Atpif1 Ctsb Aars2
## 2 2 6 6 6
## Nfu1 Trap1 Mpc1 Mlycd Surf1
## 5 6 10 4 11
## Hint2 Actr1b Echdc1 Ckmt2 Adck1
## 8 4 6 12 3
## Uqcr10 Acad8 Dbt Tomm7 Adck3
## 4 3 4 4 4
## Hadhb Fh Spg7 Mrpl4 Bckdk
## 11 6 4 1 10
## Cox7a1 Hk1 Carkd Lace1 Cox7c
## 4 3 3 4 4
## Usmg5 Suclg1 Uqcrh Supv3l1 Ndufa3
## 6 4 6 13 6
## Msrb2 Mcat Hibch Cpt1a Nudt8
## 8 8 8 14 5
## Mrpl23 Hspe1 Sdhc mt-Nd4 Mtnd4
## 8 6 15 16 16
## Acss1 Ndufa7 Them4 Ndufa12 Pdp1
## 4 8 17 8 15
## Mrpl14 Nars2 Chchd6 Cars2 Hspd1
## 8 4 6 6 18
## Mipep Samm50 Atp5g2 Atp5g1 Atp5g3
## 6 4 4 4 4
## Idh3g Prdx5 Atpaf1 Lactb Tha1
## 8 8 6 19 4
## Mthfsl Mthfs Timm8a1 Timm8a2 Dap3
## 4 4 19 19 8
## Ndufv3 Rexo2 Nudt6 Tnxb Isca2
## 4 9 1 1 6
## Ucp3 Ndufb7 Trmu Rab10 Timmdc1
## 1 8 11 17 8
## Atp5c1 Ppox Mrpl17 Cox5b Dnaja3
## 15 4 4 6 8
## Higd2a Cox6c Ndufa9 AK157302 Isca1
## 5 6 8 8 8
## Glrx5 Flad1 Ndufb4 Ndufaf2 Ndufv3.1
## 10 6 8 4 5
## Dnajc4 Mtx2 Cox7c.1 Myh7 Mrpl2
## 8 8 4 4 1
## Fam162a Mrpl19 Atp5j Ndufs6 Romo1
## 7 12 4 6 4
## Ndufa11 Myh4 Myh1 Timm22 Hsd17b10
## 14 8 8 4 8
## Mut 9030617O03Rik Myh6 Iqcg Vdac3
## 8 11 8 17 20
## Eci2 Rb1cc1 Ppa2 Ndufa1 Cox7a2
## 8 9 21 6 4
## Fam173a Abhd11 Gpx1 Iars2 Mrpl53
## 6 11 6 8 10
## 37316 Hbb-bs Hbbt1 Hbb-y Hbb-b2
## 22 11 11 11 11
## Hbb-b1 Nubpl Timm21 Serhl Slc25a29
## 11 11 11 8 17
## Abhd10 Ndufa6 Glud1 Gfm2 Nqo1
## 10 7 23 8 6
## Tsfm Ptges2 Mccc1 Etfdh Immt
## 8 8 4 1 3
## Ddx28 Mrps18c Polg Wasl Tomm5
## 6 8 11 24 4
## Tmem126a Cisd3 Macrod1 Polrmt Bola1
## 11 8 8 11 6
## Cox5a Oat Eno1 Eno2 Cpt1b
## 6 4 18 18 25
## Coq3 Ndufa4 Pdpr Myh14 Dnajc30
## 26 10 6 8 4
## Atp5j2 Mrps24 Acot13 1700021F05Rik Tomm22
## 10 8 8 11 6
## Ubn1 Olfr173 Fis1 Dnajc28 Ndufaf3
## 8 4 8 27 8
## Dhx30 Ndufb9 9330101J02Rik Bcl11b Etfb
## 11 8 4 22 17
## Mrpl46 Pcyox1l Rmnd1 Cox4i1 Lamp1
## 8 8 8 8 17
## Gstk1 Timm17b Ndufb6 Ankra2 Rdx
## 4 4 8 17 15
## Msn Ezr Nit1 Mterf2 Mrpl16
## 15 15 10 8 26
## Krt17 Oprm1 Sf1 Gm10553 U2af2
## 8 19 8 17 8
## Cryz Sfxn3 Cltc Tomm6 Agpat5
## 8 8 17 11 8
## Tomm20 Aldh5a1 Cox15 Vdac1 Hdhd2
## 22 4 11 8 12
## Fam213b Tomm40 Dnajc19 Fxn Cbr2
## 11 6 10 8 15
## Coq10a Cox7a2l Nmnat3 Cyb5r3 Pet112
## 4 10 8 12 28
## Cecr5 Spryd4 Fam210a Apoh Gapdh
## 8 11 11 15 22
## Gm7293 Gm5069 Gm3839 Ndufb8 Mrpl12
## 22 22 22 4 4
## Clpb Cd36 Atp5l Actg2 Acta1
## 19 17 10 25 25
## Actc1 Acta2 Ndufaf4 Pcx Pc
## 25 25 11 11 11
## Atp5b Adhfe1 Cox8b Uqcrb Acaa2
## 4 17 4 6 17
## Ptpmt1 Gpd1l Slc25a20 Mrps15 Bckdha
## 11 8 8 21 26
## Dut Uqcrq Hspa8 Hspa2 Mtpap
## 4 10 4 4 15
## Pccb Ndufs1 Opa3 Rhot1 Fundc2
## 14 14 21 28 6
## Aifm1 Tbrg4 Park7 Nit2 Acad10
## 26 24 8 11 8
## Ndufs7 Tpi1 Ttn Slc25a11 Acad9
## 8 8 8 11 24
## mt-Nd5 Mtnd5 Mrps36 Golga3 Ndufb10
## 16 16 6 8 17
## Mto1 Mfn1 Pdf Ociad1 Acox1
## 6 10 17 19 17
## Mrpl9 Gars Timm8b Maob Hibadh
## 8 10 19 4 8
## Echdc3 Ppm1k mt-Nd3 Mtnd3 Pygm
## 8 29 20 20 17
## Timm13 Atp5i Timm44 Pgam5 Pam16
## 19 10 14 19 10
## Higd1a D10Jhu81e Nnt Mecr Lonp1
## 19 8 6 8 17
## Ubox5 Fastkd5 Irgm1 Chchd3 Uba52
## 10 10 30 8 29
## Gm7808 Kxd1 Ubc Ubb Rps27a
## 29 29 29 29 29
## Col6a3 Dlat Mrpl48 Slc25a5 Gm10108
## 15 31 4 27 19
## Cycs Slc25a40 Ears2 Plec Rdh13
## 19 15 28 19 22
## Slc25a4 Idh2 Ndufa2 Nt5dc3 Gpc1
## 27 26 10 32 22
## Acaa1a Acaa1b 4930544G11Rik Rhoa Serpinh1
## 22 22 17 17 22
## Mtch1 Cars2.1 Akr7a2 Cisd1 Srl
## 26 32 8 6 17
## Uqcrc2 Lyrm4 Pptc7 Clpx Mrps36.1
## 17 3 8 4 33
## Lap3 Timm17a Trim72 Pdha1 Ndufa5
## 32 14 17 10 10
## Itgb1 Mrpl11 C1qbp Oxnad1 Mccc2
## 17 11 8 30 4
## Coq9 Ndufaf5 Mrc1 Ndufb2 Bcat2
## 17 29 15 10 8
## Cdh7 Sqrdl Ndufc2 Fcho2 Qdpr
## 8 8 10 15 4
## Scp2 Hspa9 Decr1 mt-Nd4l Mtnd4l
## 17 4 8 8 8
## Hspd1.1 Acot9 Acot10 Ndufa13 Atp1a1
## 4 33 33 8 17
## Aldh4a1 Fahd1 Ogdh.1 Ak4 Acad11
## 4 3 11 30 17
## Mars2 Coq5 Casq2 Nadk2 Gk
## 4 11 17 15 34
## Gyk Cyc1 Bcs1l Bak1 Hk2
## 34 26 10 10 8
## Mrpl47 Slc25a3.1 Glod4 Dld Txnrd2
## 10 14 10 28 32
## Uba1 Hsdl2 Qil1 Ccsmst1 Oxa1l
## 35 4 10 31 21
## Acadl Suclg2 Popdc2 Prdx3 Slc25a51
## 28 10 17 10 30
## Smim20 Pmpca Gadd45gip1 Slc25a13 Lyrm7
## 26 28 26 29 6
## Gm9755 Tufm Slc25a10 38412 Thnsl1
## 28 28 30 33 8
## Kct2 Ndufb3 Mrpl39 Oxct1 Erc2
## 26 6 29 23 11
## Mrps6 Me3 Mgll Sdhd Ndufb11
## 29 4 28 10 4
## Gpam Pisd Gm20671 Acsf3 Etfa
## 33 36 36 4 8
## Acot2 Pdhx Mrrf Mrpl41 Gm6434
## 17 34 26 6 6
## Ict1 mt-Co2 Mtco2 Coq7 Fahd2
## 4 28 28 4 26
## Nln Tmem38a Sardh Lnpep Slc25a32
## 4 17 10 17 37
## Nfu1.1 Pkm Ssbp1 Clpp Mrpl24
## 8 26 17 26 10
## Shmt2 Uqcc1 Tmem177 Lars2 Hhatl
## 10 26 11 10 17
## Ccbl2 Gstz1 Mrpl28 mt-Atp6 Mtatp6
## 4 4 28 17 17
## Aldh6a1 Gpd2 Cox6b1 Mrps34 Msra
## 32 10 10 8 8
## Acsl1 Mrps9 Slirp Hsdl1 Mtfp1
## 4 11 10 8 4
## Auh Gtpbp3 Trnt1 Mb Hsp90ab1
## 11 33 35 17 38
## Hsp90aa1 Gpx4 Mtch2 Fdx1 Vwa8
## 38 38 6 37 35
## Mrps14 Isoc2a Lyrm5 Lamc1 Ndufs3
## 8 8 10 33 30
## Phb Hsd17b4 Pyurf Ptcd3 Ghitm
## 31 28 37 10 6
## Ryr2 Lias Letm1 Kars Fam169b
## 17 31 26 11 19
## Hagh Slc30a9 Atp5s Tst Pld1
## 26 33 26 17 26
## Bckdhb Cbr1 Mrps27 Atp5e Hspa5
## 8 26 10 10 17
## Ptcd2 Acad12 Pmpcb Alb Atp2a2
## 17 26 10 28 17
## Atp2a1 Cox6a1 Anxa6 Kpna6 Pet100
## 17 10 17 26 17
## Elac2 Vwa8.1 Aldh7a1 Atp1a2 Atp1a3
## 10 26 28 17 17
## Cacna2d1 Hsp90b1 Atp5f1 Mmab Mcur1
## 17 17 22 26 17
## Abcb8 Mrps35 Acads Mrps22 Mrps2
## 11 29 38 11 36
## Idh3b Mmaa mt-Cytb Mt-Cyb Hsd17b8
## 33 8 32 32 26
## H2-Ke6 Nfs1 Pdk4 Eno3 Phb2
## 26 11 33 10 8
## Olfr569 Olfr582 Hscb Pdhb Actb
## 8 8 4 4 28
## Actg1
## 28
modularity(ceb)## [1] 0.7791704
# #Define communities based on network connectivity
# clp<-cluster_label_prop(corr_net)
# plot(clp, corr_net, edge.arrow.size=0.0, vertex.label.cex=0.01, vertex.size = 3, edge.width = 1.2, edge.color = "black")
cfg <- cluster_fast_greedy(as.undirected(corr_net))
plot(cfg, as.undirected(corr_net))
V(corr_net)$community <- cfg$membership
colrs <- adjustcolor( c("tomato", "dodgerblue3", "goldenrod2", "darkgreen", "black"), alpha=.8)
plot(corr_net, vertex.color=colrs[V(corr_net)$community], edge.arrow.size=0.0, vertex.label.cex=0.01, vertex.size = 3, edge.width = 1.2, edge.color = "black")
l<-layout_on_sphere(corr_net) #can change the layout design
plot(corr_net, edge.arrow.size=0.0, vertex.label.cex=0.01, vertex.size = 3, edge.width = 1.2, edge.color = "black")
#create a 3-dimensional network
library(rgl)
library(igraph)
bg3d("white")
coords<-layout_with_fr(corr_net, dim=3)
rgplot <- rglplot(corr_net, layout=coords, edge.arrow.size=0.4, label.cex=0.01, vertex.label.color="black", vertex.label.cex=0.001, vertex.frame.color=NA, edge.color="tomato" , ertex.label.dist=2, rescale=T, edge.width=0.2)
dev.off()## pdf
## 3