1 Gonads (GON)

Like the DESeq2 document, the Gonads section will contain the code used throughout the the analysis to achieve the full set of results. First, each dataset is filtered conservatively as per the DESeq2 workflow, and then any batch effects identified in the DEseq workflow are removed here. Then we create a midweight bicorrelation weighted signed network using the recommended options. The Topological Overlap Matrix is also signed.

1.1 Data quality control

tissue<- "GON"
gon_key<- subset(key_behav, Tissue==tissue)
gon_key<- droplevels(gon_key)

gon_data<- data[,colnames(data) %in% rownames(gon_key)]


start<- nrow(gon_data)
#remove genes with less than 5 reads
gon_data$avg_count<- apply(gon_data, 1, mean)
gon_data<- gon_data[gon_data$avg_count>5,]
gon_data$avg_count<-NULL
#gon_data$mad<- apply(gon_data,1,mad)
#gon_data$var<- apply(gon_data, 1, var) # could filter by variance rather than MAD maybe <0.2 but these filtering methods all seem to achieve this. 

#remove genes where >50% of samples have 0 gene expression
gon_data$percent_0<- apply(gon_data, 1, function(x)length(x[x==0]))
thresh<- ncol(gon_data)/2
gon_data<- gon_data[gon_data$percent_0<=thresh,]
gon_data$percent_0<-NULL
nrow(gon_data)

## [1] 16169

dd<- DESeqDataSetFromMatrix(countData=gon_data, colData=gon_key, design= ~ Batch)

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

dd<- DESeq(dd)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 175 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

dd<- dd[which(mcols(dd)$betaConv),]
vsd_data<- getVarianceStabilizedData(dd)
mat<- limma::removeBatchEffect(vsd_data, gon_key$Batch)
datExpr0<- as.data.frame(t(mat))
gsg = goodSamplesGenes(datExpr0, verbose = 3)

##  Flagging genes and samples with too many missing values...
##   ..step 1

gsg$allOK

## [1] TRUE

#write.csv(datExpr0, file="GON_vsd_batch_rm.csv")

1.2 Soft-threshold selection

powers<- c(seq(1, 11, by = 1), seq(12, 26, by = 2))


sft<- pickSoftThreshold(datExpr0, powerVector=powers, verbose=0, networkType="signed")

##    Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1      1   0.0406 16.20          0.920  8090.0   8090.00   8330
## 2      2   0.0284 -3.75          0.684  4440.0   4410.00   4990
## 3      3   0.3020 -5.40          0.739  2610.0   2570.00   3360
## 4      4   0.6060 -4.84          0.877  1630.0   1580.00   2450
## 5      5   0.7690 -4.13          0.946  1070.0   1020.00   1870
## 6      6   0.8240 -3.56          0.972   726.0    675.00   1490
## 7      7   0.8620 -3.20          0.987   511.0    462.00   1230
## 8      8   0.8840 -2.95          0.994   370.0    324.00   1030
## 9      9   0.8980 -2.75          0.996   275.0    232.00    880
## 10    10   0.9070 -2.60          0.995   209.0    169.00    762
## 11    11   0.9090 -2.49          0.992   162.0    125.00    668
## 12    12   0.9090 -2.40          0.987   128.0     93.40    591
## 13    14   0.9020 -2.26          0.977    82.9     54.60    474
## 14    16   0.8920 -2.16          0.967    56.6     33.10    389
## 15    18   0.8740 -2.07          0.949    40.3     20.80    325
## 16    20   0.8780 -1.97          0.946    29.7     13.50    276
## 17    22   0.8660 -1.90          0.933    22.5      8.97    237
## 18    24   0.8510 -1.84          0.913    17.4      6.12    206
## 19    26   0.8540 -1.76          0.909    13.8      4.28    181

The soft power selected will be the first power that that reaches scale free topology.

1.3 Adjacency Matrix

The adjacency matrix was run on an HPC to speed up run time and prevent overloading my personal computer.

softPower=10

datExpr0<- read.csv("GON_vsd_batch_rm.csv")
rownames(datExpr0)<- datExpr0$X
datExpr0$X<- NULL

adjacency<- adjacency(datExpr0, power = softPower, type="signed")
TOM<- TOMsimilarity(adjacency, TOMtype="signed")
#dissTOM<- 1-TOM

save(adjacency, dissTOM, file="GON_network.RData")

1.4 Identify co-expression modules

load("../WGCNA_results/within_tissues/GON_network.RData")
dissTOM<- 1-TOM
geneTree= flashClust(as.dist(dissTOM), method="average")

#plot(geneTree, xlab="", sub="", main= "Gene Clustering on TOM-based dissimilarity", labels= FALSE, hang=0.04)
minModuleSize<-30 
dynamicMods<-cutreeDynamic(dendro= geneTree, distM= dissTOM, deepSplit=2, pamRespectsDendro= FALSE, minClusterSize= minModuleSize)
#table(dynamicMods)

dynamicColors= labels2colors(dynamicMods)


plotDendroAndColors(geneTree, dynamicColors, "Dynamic Tree Cut", dendroLabels= FALSE, hang=0.03, addGuide= TRUE, guideHang= 0.05, main= "Gene dendrogram and module colors")

#-----Merge modules whose expression profiles are very similar
MEList= moduleEigengenes(datExpr0, colors= dynamicColors)
MEs= MEList$eigengenes
#Calculate dissimilarity of module eigenegenes
MEDiss= 1-cor(MEs)
#Cluster module eigengenes
METree= flashClust(as.dist(MEDiss), method= "average")

##  mergeCloseModules: Merging modules whose distance is less than 0.3
##    multiSetMEs: Calculating module MEs.
##      Working on set 1 ...
##      moduleEigengenes: Calculating 17 module eigengenes in given set.
##    multiSetMEs: Calculating module MEs.
##      Working on set 1 ...
##      moduleEigengenes: Calculating 11 module eigengenes in given set.
##    multiSetMEs: Calculating module MEs.
##      Working on set 1 ...
##      moduleEigengenes: Calculating 10 module eigengenes in given set.
##    Calculating new MEs...
##    multiSetMEs: Calculating module MEs.
##      Working on set 1 ...
##      moduleEigengenes: Calculating 10 module eigengenes in given set.

plotDendroAndColors(geneTree, cbind(dynamicColors, mergedColors), c("Dynamic Tree Cut", "Merged dynamic"), dendroLabels= FALSE, hang=0.03, addGuide= TRUE, guideHang=0.05)

moduleColors= mergedColors
colorOrder= c("grey", standardColors(50))
moduleLabels= match(moduleColors, colorOrder)-1
MEs=mergedMEs

1.5 Module-trait correlation

datTraits<- gon_key
all.equal(rownames(datExpr0), rownames(datTraits))


datTraits$sampleID<- as.numeric(as.factor(datTraits$sampleID))
datTraits$Status2<- as.numeric(ifelse(datTraits$Status=="territorial",1,0))


datTraits<- subset(datTraits, select=c("sampleID",  "Status2", "mean_T", "strength.all_study"))
names(datTraits)[names(datTraits)=="Status2"] <- "Status"
names(datTraits)[names(datTraits)=="sampleID"] <- "Individual"


#-----Define numbers of genes and samples
nGenes = ncol(datExpr0);
nSamples = nrow(datExpr0);
#-----Recalculate MEs with color labels
MEs0 = moduleEigengenes(datExpr0, moduleColors)$eigengenes
MEs = orderMEs(MEs0)

#-----Correlations of genes with eigengenes
moduleGeneCor=cor(MEs,datExpr0)
moduleGenePvalue = corPvalueStudent(moduleGeneCor, nSamples);

moduleTraitCor = cor(MEs, datTraits, use = "p");
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples);


wgcnasum<- as.data.frame(moduleTraitPvalue)

wgcnasum<-  data.frame(Tissue=tissue, 
                                    Individual=nrow(wgcnasum[wgcnasum$Individual<0.05,]),
                                Status=nrow(wgcnasum[wgcnasum$Status<0.05,]),
                                    mean_T=nrow(wgcnasum[wgcnasum$mean_T<0.05,]), strength=nrow(wgcnasum[wgcnasum$strength.all_study<0.05,]), 
                       status_meanT=nrow(wgcnasum[which(wgcnasum$Status<0.05 & wgcnasum$mean_T<0.05),]),
                       meanT_strength=nrow(wgcnasum[which(wgcnasum$mean_T<0.05 & wgcnasum$strength.all_study<0.05),]),
                       status_strength=nrow(wgcnasum[which(wgcnasum$Status<0.05 & wgcnasum$strength.all_study<0.05),]),
                       total_modules=ncol(MEs))

wgcna_summary<- list()
wgcna_summary[[tissue]]<- wgcnasum

#---------------------Module-trait heatmap


textMatrix = paste(signif(moduleTraitCor, 2), "\n(",
                   signif(moduleTraitPvalue, 1), ")", sep = "");
dim(textMatrix) = dim(moduleTraitCor)
par(mar = c(6, 8.5, 3, 3));
# Display the correlation values within a heatmap plot
labeledHeatmap(Matrix = moduleTraitCor,
               xLabels = names(datTraits),
               yLabels = names(MEs),
               ySymbols = names(MEs),
               colorLabels = FALSE,
               colors = blueWhiteRed(50),
               textMatrix = textMatrix,
               setStdMargins = FALSE,
               cex.text = 0.5,
               zlim = c(-1,1),
               main = paste("Module-trait relationships in", tissue))

Genes per module in GON
moduleColors	Freq
black	1361
blue	2584
brown	2686
cyan	140
green	1431
grey60	215
midnightblue	1393
pink	590
salmon	243
turquoise	5526

1.6 Module Membership and Gene Significance

#### module membership

datME<- moduleEigengenes(datExpr0,mergedColors)$eigengenes
datKME<- signedKME(datExpr0, datME, outputColumnName="MM.") #use the "signed eigennode connectivity" or module membership

MMPvalue <- as.data.frame(corPvalueStudent(as.matrix(datKME), nSamples)) # Calculate module membership P-values
datKME$gene<- rownames(datKME)
MMPvalue$gene<- rownames(MMPvalue)
genes=names(datExpr0)
geneInfo0 <- data.frame(gene=genes,moduleColor=moduleColors)
geneInfo0 <-  merge(geneInfo0, genes_key, by="gene", all.x=TRUE)
color<- merge(geneInfo0, datKME, by="gene") #these are from your original WGCNA analysis 
#head(color)
write.csv(as.data.frame(color), file = paste("../WGCNA_results/within_tissues/",tissue,"results_ModuleMembership.csv", sep=""), row.names = FALSE)
MMPvalue<- merge(geneInfo0, MMPvalue, by="gene")
write.csv(MMPvalue, file=paste("../WGCNA_results/within_tissues/",tissue,"_results_ModuleMembership_P-value.csv", sep=""), row.names = FALSE)

#### gene-significance
trait = as.data.frame(datTraits$Status) #change here for traits of interest
names(trait) = "status" #change here for traits of interest 
modNames = substring(names(MEs), 3)
geneTraitSignificance = as.data.frame(cor(datExpr0, trait, use = "p"))
GSPvalue = as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance), nSamples))
names(geneTraitSignificance) = paste("GS.", names(trait), sep="")
names(GSPvalue) = paste("p.GS.", names(trait), sep="")
GS<- cbind(geneTraitSignificance,GSPvalue)

trait = as.data.frame(datTraits$mean_T)
names(trait)= "mean_T"
geneTraitSignificance = as.data.frame(cor(datExpr0, trait, use = "p"))
GSPvalue = as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance), nSamples))
names(geneTraitSignificance) = paste("GS.", names(trait), sep="")
names(GSPvalue) = paste("p.GS.", names(trait), sep="")
GS2<- cbind(geneTraitSignificance,GSPvalue)


trait = as.data.frame(datTraits$strength.all_study)
names(trait)= "strength"
geneTraitSignificance = as.data.frame(cor(datExpr0, trait, use = "p"))
GSPvalue = as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance), nSamples))
names(geneTraitSignificance) = paste("GS.", names(trait), sep="")
names(GSPvalue) = paste("p.GS.", names(trait), sep="")
GS3<- cbind(geneTraitSignificance,GSPvalue)

GS$gene<- rownames(GS)
GS<- cbind(GS,GS2, GS3)
GS<- merge(geneInfo0,GS, by="gene")


write.csv(GS, file=paste("../WGCNA_results/within_tissues/",tissue, "_results_GeneSignificance.csv", sep=""), row.names = FALSE)

1.7 Intermodule correlations

# Specify colors
colz<- gsub("ME","",colnames(MEs))
names(colz)<- colnames(MEs)
ann_colors = list(module=colz)

annotation_col<- data.frame(row.names=colnames(MEs), module=colnames(MEs))


correlation<- cor(MEs)
pheatmap(correlation, annotation_col = annotation_col, annotation_colors = ann_colors, annotation_legend=FALSE, legend_breaks = c(-1,-0.5,0,0.5, 1,1), 
main=paste("module eigengene correlations in", tissue, sep=" "), legend_labels = c("-1", "-0.5", "0", "0.5","1","correlation\n\n"))

1.8 Interesting modules

1.8.1 Pink module - mean testosterone

Here I extract the genes associated with a given module and explore them further. This module is potentially associated with mean testosterone. The WGCNA manual says that a good association will show a relationship between the module membership scores of each gene and the gene-significance for that gene.

whichModule="pink"

modcol=paste("MM.",whichModule,sep="")
module_genes<- color[color$moduleColor==whichModule,]


verboseScatterplot(abs(module_genes[, modcol]),
abs(GS[GS$moduleColor==whichModule, "GS.mean_T"]),
xlab = paste("Module Membership in", whichModule, "module"),
ylab = "Gene significance for mean Testosterone",
main = paste("Module membership vs. gene significance\n"),
cex.main = 1.2, cex.lab = 1.2, cex.axis = 1.2, col = whichModule)

1.8.1.1 Top 30 hub genes

Next, I am going to explore the top 30 hub genes based on highest module membership

nTop<-30 
#extract the expression of the top 30 genes and sort them based on module membership. 
sorted=color$gene[order(color[,modcol],decreasing=T)]
hubs=sorted[1:nTop]
hubs=datExpr0[,match(hubs, colnames(datExpr0))]



### Sort individual data based on the trait value
expDesign<- data.frame(row.names=rownames(gon_key), mean_T=gon_key[,"mean_T"], Status=gon_key[,"Status"])
expDesign<- expDesign[order(expDesign$mean_T),]
ann_cols<- list(Status=c(territorial="#414042",floater="#E54849"))
hubs<- hubs[match(rownames(expDesign), rownames(hubs)), ]

#plot heatmap
pheatmap(hubs,scale="column", cluster_rows = FALSE,cluster_cols = FALSE,show_rownames = TRUE, show_colnames = TRUE,border_color=NA,  main=paste(whichModule,"module in",tissue), annotation_row=expDesign, annotation_colors=ann_cols)

Now, let’s plot the top gene in this module to see its relationship with mean testosterone

gone<- plotCounts(dd, gene="CHRNA3", intgroup=c("mean_T", "Class"), returnData=TRUE)
ggplot(gone, aes(x=mean_T, y=count)) + geom_point(size=2) + labs(title="CHRNA3 in GON",x="mean T", y="Normalised Counts") + peri_theme

## Warning: Removed 4 rows containing missing values
## (`geom_point()`).

##### TOM BASED NETWORK ####
## get names of genes from the module


probes <- names(datExpr0)
inModule <- moduleColors==whichModule #creates logical vector of whether that gene is in the module
modProbes <- probes[inModule]


# Select the corresponding Topological Overlap
modTOM <- TOM[inModule, inModule] #TOM object does not have any gene names so need to use the positional information from the logical vector.

dimnames(modTOM) <- list(modProbes, modProbes)

IMConn<- softConnectivity(datExpr0[, modProbes], power = softPower, type="signed")
names(IMConn)<- modProbes
IMConn<- sort(IMConn, decreasing=T) #rank by highest connectivity
nTop<-30
top<- IMConn[1:nTop] #Take the top genes 
top30TOM<- modTOM[names(top),names(top)] 
#rename the TOM columns and rows to reflect the locno + gene ID. 
top_genes<- module_genes[module_genes$gene %in% names(top),]
top_genes<- top_genes[match(names(top), top_genes$gene), ]
colnames(top30TOM)<- top_genes$display_gene_ID
rownames(top30TOM)<- top_genes$display_gene_ID
vis = exportNetworkToVisANT(top30TOM,
weighted = TRUE,
threshold = 0.00)
#take the top 25% of TOM connections
vis<- vis[vis$weight>summary(vis$weight)[5],]
CoExpGraph <- graph.data.frame(vis, directed=FALSE)
nDegree <- degree(CoExpGraph, v=V(CoExpGraph), mode=c("total"))
V(CoExpGraph)$degree <- nDegree

ggplot(ggnetwork(CoExpGraph, layout=with_fr()), aes(x = x, y = y, xend = xend, yend = yend))+geom_edges(aes(color=weight))+geom_nodes(aes(x,y, size=degree),color="black", alpha=0.7) +scale_color_gradient(low="#ffd71f", high="#e50000")+theme_blank()+ geom_nodetext_repel(aes(label=name))

1.8.1.2 Gene Ontology

BP enriched GO terms in pink module in GON
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0001525	angiogenesis	25/490	205/12555	0.0000004	0.0006380	0.0005905	ACVR1/ANXA2/APOLD1/ARHGAP22/CCN2/EGF/ENG/FGF1/FOXC1/HSPG2/ID1/JUN/LOC113990968/LOC114000824/LOC114001403/LOC114001849/MAPK14/MMP2/NRCAM/PLXND1/PROK2/ROBO4/SCG2/TGFBI/THY1	25
GO:0001975	response to amphetamine	8/490	24/12555	0.0000022	0.0016401	0.0015179	ASIC3/DBH/PPP1R9B/RGS10/RGS2/RGS4/SLC18A2/TH	8
GO:0007268	chemical synaptic transmission	22/490	188/12555	0.0000041	0.0020970	0.0019408	AKAP5/CACNA1E/CHRNA3/CPLX1/CRH/DBH/FGF12/GAD2/HTR1E/HTR4/LOC113983406/LOC113990721/NPY/PLP1/PMP22/SLC18A2/SLC6A2/SNCA/SNCG/SV2B/SYN2/SYT1	22
GO:0044267	cellular protein metabolic process	20/490	165/12555	0.0000066	0.0025184	0.0023308	ADORA2B/ANO8/B2M/CHGB/CTSH/FURIN/HSPG2/LAMB2/LGALS1/LOC113986369/LOC114003910/LYZ/MMP2/SCG2/SCG3/SFTPB/SFTPC/SNCA/SNCAIP/TGFBI	20
GO:0009749	response to glucose	10/490	51/12555	0.0000226	0.0068942	0.0063809	ACVR1C/ADIPOQ/CCN2/FKBP1B/GJA1/LOC113992227/LPL/SARM1/SREBF1/TXNIP	10
GO:0010628	positive regulation of gene expression	27/490	310/12555	0.0000826	0.0189665	0.0175543	CCN2/CITED2/CRH/CTSH/EGF/FGF1/GATA5/GJA1/ID1/IL34/LOC113992691/LOC114001703/LRRC32/MAPK14/MYC/NOS1AP/NR5A1/OSR1/OSR2/PDE2A/PLP1/SLC26A9/SLC6A4/STAR/STAT3/TWIST1/ZNF804A	27
GO:0051412	response to corticosterone	5/490	13/12555	0.0000880	0.0189665	0.0175543	CRH/LOC113992757/NEFL/STAR/TH	5
GO:0009617	response to bacterium	9/490	49/12555	0.0000996	0.0189665	0.0175543	ADIPOQ/CCDC80/CLPS/DGKB/FKBP5/LOC114003559/LPL/RGS1/VIL1	9
GO:0050679	positive regulation of epithelial cell proliferation	9/490	55/12555	0.0002504	0.0399249	0.0369522	ATP7A/DLX5/FGF1/ID1/MYC/NR4A3/OSR1/OSR2/TWIST1	9
GO:0010977	negative regulation of neuron projection development	9/490	56/12555	0.0002882	0.0399249	0.0369522	B2M/INPP5J/LGALS1/LOC114000824/NR2F1/PRAG1/RTN4RL2/STMN2/VIM	9

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

2 Pituitary (PIT)

The code for this tissue is mostly above, but each section the R environment is cleared to only keep the base data.

keep<- c("data", "peri_theme", "key","key_behav","dodge","genes_key", "keep", "go2gene_mf","go2name_mf","go2gene_bp","go2name_bp", "wgcna_summary")
rm(list= ls()[!(ls() %in% keep)])

## [1] 14812

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 101 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

##  Flagging genes and samples with too many missing values...
##   ..step 1

## [1] TRUE

##    Power SFT.R.sq  slope truncated.R.sq mean.k. median.k. max.k.
## 1      1 0.003630 -4.320          0.856  7410.0    7420.0   7640
## 2      2 0.000786  0.449          0.935  4240.0    4230.0   4720
## 3      3 0.002610 -0.375          0.918  2650.0    2640.0   3310
## 4      4 0.027300 -0.758          0.906  1780.0    1750.0   2490
## 5      5 0.091300 -1.030          0.896  1250.0    1220.0   1960
## 6      6 0.181000 -1.180          0.892   911.0     881.0   1600
## 7      7 0.279000 -1.310          0.890   687.0     654.0   1340
## 8      8 0.375000 -1.420          0.892   531.0     498.0   1140
## 9      9 0.455000 -1.490          0.896   419.0     386.0    985
## 10    10 0.517000 -1.580          0.894   337.0     304.0    863
## 11    11 0.578000 -1.620          0.908   275.0     243.0    764
## 12    12 0.619000 -1.670          0.911   228.0     196.0    681
## 13    14 0.675000 -1.750          0.914   161.0     133.0    554
## 14    16 0.707000 -1.810          0.913   118.0      92.7    460
## 15    18 0.712000 -1.890          0.903    89.3      66.3    389
## 16    20 0.732000 -1.920          0.912    69.1      48.5    334
## 17    22 0.751000 -1.930          0.922    54.6      36.4    290
## 18    24 0.757000 -1.970          0.925    43.8      27.8    254
## 19    26 0.775000 -1.970          0.934    35.7      21.5    224

The soft-power selected for PIT is 18.

Genes per module in PIT
moduleColors	Freq
bisque4	1328
blue	1594
blue2	156
brown4	208
coral1	2779
coral2	382
coral3	59
darkgreen	397
darkgrey	883
darkolivegreen2	589
darkolivegreen4	253
darkorange2	126
darkslateblue	936
darkviolet	72
firebrick3	1601
floralwhite	295
honeydew	61
lightcyan1	133
lightgreen	1798
orangered3	84
palevioletred2	66
pink4	265
plum1	140
plum2	176
sienna4	56
violet	375

2.1 Interesting Modules

2.1.1 floralwhite module - strength

This module is associated with social network strength.

##  softConnectivity: FYI: connecitivty of genes with less than 4 valid samples will be returned as NA.
##  ..calculating connectivities..

This module is too influenced by PFT3. This is a sample I removed in the DESeq2 analysis. Disregard.

2.1.2 pink4 module - strength

This module is also associated with social network strength

2.1.2.1 Gene Ontology

BP enriched GO terms in pink4 module in PIT
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0007288	sperm axoneme assembly	4/214	20/12079	0.0003714	0.1318112	0.1312432	ARMC2/CFAP206/SPEF2/TTLL1	4
GO:0031647	regulation of protein stability	7/214	76/12079	0.0003854	0.1318112	0.1312432	APTX/ATF7IP/DPM2/LOC113993664/MOSMO/PINX1/QRSL1	7
GO:0006362	transcription elongation from RNA polymerase I promoter	3/214	23/12079	0.0074766	0.6320762	0.6293525	CCNH/ERCC3/TAF1A	3
GO:0050821	protein stabilization	8/214	162/12079	0.0082095	0.6320762	0.6293525	ATF7IP/CCNH/CCT6A/GAPDH/GTPBP4/MFSD1/NCLN/TBL1XR1	8
GO:0006363	termination of RNA polymerase I transcription	3/214	24/12079	0.0084346	0.6320762	0.6293525	CCNH/ERCC3/TAF1A	3
GO:0006890	retrograde vesicle-mediated transport, Golgi to endoplasmic reticulum	5/214	73/12079	0.0094384	0.6320762	0.6293525	ARFGAP2/COPB1/KIFAP3/PITPNB/RINT1	5
GO:0006361	transcription initiation from RNA polymerase I promoter	3/214	25/12079	0.0094614	0.6320762	0.6293525	CCNH/ERCC3/TAF1A	3
GO:0006310	DNA recombination	4/214	51/12079	0.0125004	0.6320762	0.6293525	ACTR8/KIN/LOC113982971/SUPV3L1	4
GO:0000226	microtubule cytoskeleton organization	6/214	109/12079	0.0128965	0.6320762	0.6293525	GAPDH/KNSTRN/MARK3/PRKCZ/TTLL1/TUBA8	6
GO:0070085	glycosylation	2/214	11/12079	0.0154662	0.6320762	0.6293525	B4GALT4/LOC113992792	2

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

3 Ventromedial Hypothalamus (VMH)

## [1] 15223

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 102 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

##    Power SFT.R.sq  slope truncated.R.sq mean.k. median.k. max.k.
## 1      1 0.008020 12.600          0.986  7610.0   7610.00   7770
## 2      2 0.000212 -0.471          0.940  4210.0   4200.00   4600
## 3      3 0.080100 -3.750          0.890  2500.0   2480.00   3050
## 4      4 0.196000 -3.140          0.903  1580.0   1550.00   2160
## 5      5 0.329000 -2.720          0.928  1040.0   1010.00   1600
## 6      6 0.473000 -2.710          0.949   715.0    686.00   1240
## 7      7 0.567000 -2.620          0.955   506.0    478.00    985
## 8      8 0.632000 -2.530          0.961   368.0    341.00    802
## 9      9 0.685000 -2.440          0.966   274.0    248.00    664
## 10    10 0.716000 -2.400          0.964   208.0    184.00    558
## 11    11 0.747000 -2.330          0.968   160.0    139.00    474
## 12    12 0.772000 -2.290          0.972   126.0    106.00    408
## 13    14 0.800000 -2.300          0.970    80.4     64.00    314
## 14    16 0.821000 -2.300          0.970    53.8     40.30    248
## 15    18 0.831000 -2.290          0.967    37.3     26.20    201
## 16    20 0.840000 -2.270          0.969    26.7     17.50    165
## 17    22 0.851000 -2.250          0.972    19.6     12.00    138
## 18    24 0.855000 -2.240          0.972    14.7      8.42    117
## 19    26 0.859000 -2.230          0.972    11.2      6.03    101

The soft-power selected for VMH is 18.

Genes per module in VMH
moduleColors	Freq
antiquewhite4	223
bisque4	103
black	590
blue	2088
blue2	49
brown	2842
brown2	50
brown4	981
coral1	1033
coral2	125
darkgreen	901
darkolivegreen4	51
darkseagreen4	72
darkslateblue	99
darkturquoise	906
darkviolet	47
floralwhite	530
grey	1
honeydew1	620
indianred4	474
lightcoral	53
lightcyan1	253
magenta	512
mediumorchid	65
midnightblue	1746
paleturquoise	292
royalblue	208
skyblue2	63
steelblue	155
thistle	42
thistle3	46

3.1 Interesting Modules

3.1.1 lightcoral module - Status/mean testosterone

The top hub gene was not significantly associated with either mean_T or Status in the DESeq2 analysis.

3.1.1.1 Gene Ontology

BP enriched GO terms in lightcoral module in VMH
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0032094	response to food	2/45	15/12287	0.0013361	0.1789233	0.1543029	BCL10/OPRM1	2
GO:0008154	actin polymerization or depolymerization	2/45	16/12287	0.0015234	0.1789233	0.1543029	CAPG/WASL	2
GO:0001676	long-chain fatty acid metabolic process	2/45	19/12287	0.0021557	0.1789233	0.1543029	ACAD9/ACSL4	2
GO:2000310	regulation of NMDA receptor activity	2/45	31/12287	0.0057008	0.2080429	0.1794156	LOC113983406/OPRM1	2
GO:0002224	toll-like receptor signaling pathway	2/45	33/12287	0.0064432	0.2080429	0.1794156	BCL10/IRAK4	2
GO:0043280	positive regulation of cysteine-type endopeptidase activity involved in apoptotic process	2/45	36/12287	0.0076346	0.2080429	0.1794156	BCL10/GRAMD4	2
GO:0007254	JNK cascade	2/45	39/12287	0.0089175	0.2080429	0.1794156	IRAK4/MAP4K3	2
GO:0016192	vesicle-mediated transport	3/45	130/12287	0.0118818	0.2080429	0.1794156	ARL5B/LOC113995129/LOC113998023	3
GO:0051056	regulation of small GTPase mediated signal transduction	3/45	140/12287	0.0144937	0.2080429	0.1794156	ARHGEF7/LOC113995129/LOC113998023	3
GO:0001843	neural tube closure	2/45	66/12287	0.0242541	0.2080429	0.1794156	BCL10/SEC24B	2

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

4 Anterior Hypothalamus (AH)

## [1] 14928

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

##    Power SFT.R.sq  slope truncated.R.sq mean.k. median.k. max.k.
## 1      1 0.000105 -2.410          0.986  7460.0    7460.0   7550
## 2      2 0.065400  4.700          0.810  4330.0    4330.0   4730
## 3      3 0.052000  1.330          0.796  2760.0    2760.0   3340
## 4      4 0.003500 -0.203          0.819  1880.0    1880.0   2550
## 5      5 0.077200 -0.705          0.850  1350.0    1340.0   2050
## 6      6 0.196000 -0.959          0.881  1000.0     989.0   1700
## 7      7 0.324000 -1.130          0.906   768.0     749.0   1440
## 8      8 0.429000 -1.250          0.925   603.0     579.0   1240
## 9      9 0.510000 -1.340          0.940   483.0     455.0   1090
## 10    10 0.577000 -1.420          0.945   393.0     364.0    967
## 11    11 0.632000 -1.490          0.953   325.0     295.0    866
## 12    12 0.666000 -1.550          0.954   272.0     241.0    781
## 13    14 0.724000 -1.620          0.965   196.0     166.0    648
## 14    16 0.761000 -1.680          0.970   147.0     118.0    549
## 15    18 0.792000 -1.730          0.976   113.0      85.8    472
## 16    20 0.808000 -1.770          0.977    88.7      63.9    411
## 17    22 0.821000 -1.800          0.978    71.0      48.6    362
## 18    24 0.833000 -1.820          0.982    57.8      37.5    321
## 19    26 0.833000 -1.850          0.979    47.6      29.4    288

The soft-power selected for AH is 18.

Genes per module in AH
moduleColors	Freq
antiquewhite2	63
antiquewhite4	2152
bisque4	383
blue2	1283
blue4	141
blueviolet	239
coral3	63
darkmagenta	131
darkolivegreen2	193
darkolivegreen4	147
darkseagreen3	1754
darkslateblue	107
darkviolet	578
firebrick3	417
firebrick4	72
greenyellow	1308
honeydew1	178
indianred3	120
lavenderblush2	64
lightcyan	310
lightpink2	41
lightslateblue	152
magenta4	123
maroon	96
mediumpurple1	56
mediumpurple2	75
navajowhite	104
orangered4	1148
plum	317
plum4	49
royalblue	2177
salmon1	45
skyblue2	86
tan	469
violet	137
yellow3	62
yellow4	86

4.1 Interesting Modules

4.1.1 mediumpurple1 module - Status

4.1.1.1 Gene Ontology

BP enriched GO terms in mediumpurple1 module in AH
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0016082	synaptic vesicle priming	3/48	20/12138	0.0000631	0.0101610	0.0086999	LOC113987015/LOC113991140/LOC113993758	3
GO:0016079	synaptic vesicle exocytosis	3/48	23/12138	0.0000972	0.0101610	0.0086999	LOC113987015/LOC113991140/LOC113993758	3
GO:0007009	plasma membrane organization	3/48	28/12138	0.0001774	0.0123577	0.0105807	LOC113987015/LOC113991140/LOC113993758	3
GO:0006406	mRNA export from nucleus	4/48	96/12138	0.0005477	0.0249930	0.0213992	DDX25/LOC113993656/LOC114004318/NUP160	4
GO:0061025	membrane fusion	3/48	42/12138	0.0005979	0.0249930	0.0213992	LOC113987015/LOC113991140/LOC113993758	3
GO:0038063	collagen-activated tyrosine kinase receptor signaling pathway	2/48	13/12138	0.0011617	0.0346858	0.0296983	LOC113998275/OTOL1	2
GO:0098789	pre-mRNA cleavage required for polyadenylation	2/48	13/12138	0.0011617	0.0346858	0.0296983	LOC113993656/LOC114004318	2
GO:0006388	tRNA splicing, via endonucleolytic cleavage and ligation	2/48	15/12138	0.0015560	0.0406503	0.0348051	LOC113993656/LOC114004318	2
GO:0016254	preassembly of GPI anchor in ER membrane	2/48	16/12138	0.0017738	0.0411915	0.0352685	PIGV/PIGX	2
GO:0006637	acyl-CoA metabolic process	2/48	22/12138	0.0033633	0.0702937	0.0601860	GPAT4/HMGCL	2

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

4.1.2 tan module - Status

Also the inverse module of the mediumpurple1 module based on the intermodule correlation plot

4.1.2.1 Gene Ontology

BP enriched GO terms in tan module in AH
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0070125	mitochondrial translational elongation	10/351	87/12138	0.0001990	0.2147199	0.2128242	CHCHD1/MRPL14/MRPL24/MRPL35/MRPL36/MRPL51/MRPL54/MRPS18C/MRPS22/TSFM	10
GO:0016075	rRNA catabolic process	4/351	14/12138	0.0005467	0.2949453	0.2923413	EXOSC4/EXOSC6/TRIR/XRN1	4
GO:0070126	mitochondrial translational termination	9/351	87/12138	0.0008986	0.3231911	0.3203377	CHCHD1/MRPL14/MRPL24/MRPL35/MRPL36/MRPL51/MRPL54/MRPS18C/MRPS22	9
GO:0055088	lipid homeostasis	5/351	31/12138	0.0017973	0.3598964	0.3567189	ABHD5/CEBPA/DDX3X/LOC114000584/LOC114001595	5
GO:0090503	RNA phosphodiester bond hydrolysis, exonucleolytic	5/351	33/12138	0.0023947	0.3598964	0.3567189	DCP2/EXOSC4/EXOSC6/TRIR/XRN1	5
GO:0009650	UV protection	3/351	10/12138	0.0024730	0.3598964	0.3567189	ERCC4/LOC113992976/XPA	3
GO:0071028	nuclear mRNA surveillance	3/351	10/12138	0.0024730	0.3598964	0.3567189	EXOSC4/EXOSC6/XRN1	3
GO:0030308	negative regulation of cell growth	9/351	104/12138	0.0031382	0.3598964	0.3567189	ADIPOR1/APBB1/BTG1/DDX3X/LOC113996102/LOC113999325/PPP2CA/PSRC1/VGLL4	9
GO:0045930	negative regulation of mitotic cell cycle	4/351	22/12138	0.0033278	0.3598964	0.3567189	BTG1/CDKN1C/FOXC1/LOC113996102	4
GO:0009411	response to UV	5/351	36/12138	0.0035441	0.3598964	0.3567189	ERCC4/ERCC6/LOC113992976/LOC114000584/LOC114001595	5

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

5 Paraventricular Nucleus (PVN)

## [1] 15153

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 15 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

##    Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1      1   0.0195 19.20          0.743  7580.0    7580.0   7660
## 2      2   0.4010 -9.84          0.711  4300.0    4240.0   4880
## 3      3   0.4960 -3.59          0.645  2660.0    2570.0   3470
## 4      4   0.6420 -2.55          0.757  1760.0    1660.0   2670
## 5      5   0.7320 -2.27          0.844  1220.0    1110.0   2180
## 6      6   0.8040 -2.09          0.901   883.0     776.0   1840
## 7      7   0.8580 -1.99          0.937   658.0     556.0   1580
## 8      8   0.8910 -1.93          0.957   504.0     408.0   1390
## 9      9   0.9080 -1.90          0.964   395.0     306.0   1240
## 10    10   0.9180 -1.87          0.967   315.0     233.0   1110
## 11    11   0.9320 -1.83          0.975   256.0     180.0   1010
## 12    12   0.9330 -1.82          0.974   211.0     141.0    924
## 13    14   0.9330 -1.79          0.969   148.0      89.5    785
## 14    16   0.9400 -1.76          0.974   108.0      59.1    679
## 15    18   0.9440 -1.73          0.976    81.4      40.2    596
## 16    20   0.9390 -1.71          0.972    62.9      28.1    528
## 17    22   0.9390 -1.69          0.974    49.7      20.2    472
## 18    24   0.9340 -1.68          0.971    39.9      14.8    424
## 19    26   0.9240 -1.68          0.964    32.6      11.0    384

The soft-power selected for PVN is 9.

Genes per module in PVN
moduleColors	Freq
black	438
blue	5377
darkgreen	383
darkmagenta	75
darkolivegreen	348
darkorange	394
darkred	664
darkturquoise	181
green	555
greenyellow	886
midnightblue	646
paleturquoise	159
saddlebrown	106
sienna3	73
steelblue	106
turquoise	4631
white	129

It doesn’t appear that our interesting module white is associated with any other module of interest.

5.1 Interesting Modules

5.1.1 white module - status

5.1.1.1 Gene Ontology

BP enriched GO terms in white module in PVN
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0006388	tRNA splicing, via endonucleolytic cleavage and ligation	3/104	15/12249	0.0002512	0.1233371	0.1197807	CLP1/CPSF4/RTCB	3
GO:0006270	DNA replication initiation	3/104	28/12249	0.0016693	0.2736082	0.2657187	MCM3/MCM5/ORC1	3
GO:0032922	circadian regulation of gene expression	4/104	70/12249	0.0029266	0.2736082	0.2657187	DRD3/LOC113996216/LOC113999640/USP2	4
GO:0006094	gluconeogenesis	3/104	35/12249	0.0031947	0.2736082	0.2657187	ENO1/MDH1/PGK1	3
GO:0000727	double-strand break repair via break-induced replication	2/104	11/12249	0.0037356	0.2736082	0.2657187	MCM3/MCM5	2
GO:0045598	regulation of fat cell differentiation	2/104	11/12249	0.0037356	0.2736082	0.2657187	LOC113996216/LOC113999640	2
GO:0098609	cell-cell adhesion	5/104	124/12249	0.0040602	0.2736082	0.2657187	ANXA2/FAT2/FBLIM1/LOC113988363/PDLIM1	5
GO:0006953	acute-phase response	2/104	12/12249	0.0044580	0.2736082	0.2657187	AHSG/IL6R	2
GO:0000956	nuclear-transcribed mRNA catabolic process	2/104	13/12249	0.0052395	0.2858411	0.2775989	LOC113987020/XRN2	2
GO:1903146	regulation of autophagy of mitochondrion	2/104	15/12249	0.0069756	0.3425008	0.3326248	ATP13A2/PINK1	2

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

6 Medial Preoptic Area (POM)

Note that the PFT2 POM sample was removed from this analysis as it was previously found to be an outlier.

## [1] 15315

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 28 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

##    Power SFT.R.sq  slope truncated.R.sq mean.k. median.k. max.k.
## 1      1   0.0199  20.40          0.939 7660.00   7660.00 7760.0
## 2      2   0.4150 -33.70          0.906 4170.00   4160.00 4490.0
## 3      3   0.4780 -16.50          0.936 2420.00   2410.00 2830.0
## 4      4   0.4710  -9.84          0.939 1490.00   1470.00 1900.0
## 5      5   0.4360  -6.51          0.928  951.00    934.00 1340.0
## 6      6   0.4420  -4.83          0.924  632.00    615.00  975.0
## 7      7   0.4810  -4.06          0.930  433.00    418.00  733.0
## 8      8   0.5500  -3.84          0.946  304.00    291.00  573.0
## 9      9   0.6080  -3.71          0.956  219.00    207.00  456.0
## 10    10   0.6650  -3.68          0.966  161.00    151.00  370.0
## 11    11   0.7080  -3.62          0.972  120.00    111.00  304.0
## 12    12   0.7390  -3.53          0.975   91.50     83.60  253.0
## 13    14   0.8040  -3.36          0.986   55.10     48.90  181.0
## 14    16   0.8520  -3.19          0.992   34.80     29.90  134.0
## 15    18   0.8910  -3.01          0.996   22.80     19.00  102.0
## 16    20   0.9160  -2.91          0.997   15.50     12.50   79.4
## 17    22   0.9350  -2.81          0.998   10.80      8.42   63.3
## 18    24   0.9490  -2.73          0.997    7.74      5.83   51.7
## 19    26   0.9530  -2.72          0.997    5.67      4.10   44.3

The soft-power selected for POM is 20.

Genes per module in POM
moduleColors	Freq
antiquewhite1	157
bisque4	199
black	316
blue2	769
blue3	48
blue4	164
blueviolet	60
brown	881
brown1	47
brown2	661
chocolate4	144
coral	2067
coral1	155
coral3	185
darkmagenta	233
darkolivegreen1	49
darkolivegreen2	225
darkorange	674
darkorange2	97
darkred	164
darkseagreen1	41
darkseagreen2	55
darkseagreen3	66
darkseagreen4	145
darkslateblue	380
darkturquoise	158
darkviolet	71
deeppink1	47
firebrick2	106
firebrick3	153
firebrick4	74
floralwhite	391
green4	55
honeydew	66
honeydew1	87
indianred4	283
lavenderblush	43
lavenderblush2	66
lightblue3	51
lightcoral	433
lightgreen	177
lightpink1	111
lightpink2	108
lightpink4	88
lightslateblue	62
lightsteelblue	1161
lightsteelblue1	163
magenta3	57
maroon	152
mediumpurple1	62
mediumpurple4	156
moccasin	44
navajowhite	334
navajowhite1	203
orange	251
orangered1	62
pink3	52
pink4	63
plum1	107
powderblue	47
purple	1021
salmon	216
salmon2	69
sienna3	112
skyblue	134
skyblue1	82
skyblue4	109
tan3	45

6.1 Interesting Modules

6.1.1 floralwhite module - Status/mean T

6.1.1.1 Gene Ontology

BP enriched GO terms in floralwhite module in POM
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0007186	G protein-coupled receptor signaling pathway	30/309	495/12339	0.0000069	0.0074407	0.0071573	CXCR2/FZD9/GAL/GALR1/GHRH/GNAI2/GNRH1/GPR21/GPR63/GRPR/HRH2/HTR5A/IAPP/JAK2/LHCGR/LOC113982386/LOC113993087/LOC113993299/LOC114002880/MCHR1/OPRL1/PDE3B/PRLH/QRFPR/RGS20/RORB/RXFP3/SST/SSTR2/UTS2B	30
GO:0007218	neuropeptide signaling pathway	10/309	88/12339	0.0000672	0.0362788	0.0348968	GAL/GALR1/GRPR/LOC113993087/MCHR1/OPRL1/QRFPR/RXFP3/SCG5/SSTR2	10
GO:0051560	mitochondrial calcium ion homeostasis	4/309	16/12339	0.0005532	0.1989591	0.1913796	IMMT/MAIP1/MCUB/MICU3	4
GO:0019915	lipid storage	4/309	18/12339	0.0008940	0.2384799	0.2293948	CD36/LDAH/NRIP1/PNPLA2	4
GO:0007611	learning or memory	6/309	50/12339	0.0014771	0.2384799	0.2293948	CHRNA7/CRHBP/FZD9/GRPR/LOC113982386/PAFAH1B1	6
GO:0030518	intracellular steroid hormone receptor signaling pathway	3/309	10/12339	0.0016376	0.2384799	0.2293948	AR/NR3C1/PGR	3
GO:0036444	calcium import into the mitochondrion	3/309	10/12339	0.0016376	0.2384799	0.2293948	MAIP1/MCUB/MICU3	3
GO:0007399	nervous system development	16/309	280/12339	0.0018609	0.2384799	0.2293948	ACTL6B/BPNT1/FGF13/FZD9/GDPD5/GFRA3/GPSM1/LOC113991778/LSAMP/NEURL1B/NRN1/NRN1L/OLFM1/PCSK2/PHF10/PLPPR1	16
GO:0007565	female pregnancy	6/309	53/12339	0.0020045	0.2384799	0.2293948	CORIN/CRHBP/GNRH1/IGFBP2/LOC113982386/PAPPA	6
GO:0090280	positive regulation of calcium ion import	3/309	11/12339	0.0022102	0.2384799	0.2293948	LOC113982386/LOC114000001/STC1	3

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

6.1.2 pink3 module - strength

6.1.2.1 Gene Ontology

BP enriched GO terms in pink3 module in POM
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0036444	calcium import into the mitochondrion	2/41	10/12339	0.0004767	0.0533864	0.0446343	OPA1/SMDT1	2
GO:0015693	magnesium ion transport	2/41	12/12339	0.0006962	0.0533864	0.0446343	NIPAL3/TUSC3	2
GO:1903830	magnesium ion transmembrane transport	2/41	13/12339	0.0008210	0.0533864	0.0446343	NIPAL3/TUSC3	2
GO:0048208	COPII vesicle coating	3/41	60/12339	0.0010217	0.0533864	0.0446343	CD59/CTSC/PDCD6	3
GO:0006094	gluconeogenesis	2/41	36/12339	0.0063185	0.1905773	0.1593344	GOT1/PGAM1	2
GO:0006110	regulation of glycolytic process	2/41	40/12339	0.0077576	0.1905773	0.1593344	NUP35/PGAM1	2
GO:0022900	electron transport chain	2/41	46/12339	0.0101654	0.1905773	0.1593344	LOC114003129/NDUFAF2	2
GO:0006487	protein N-linked glycosylation	2/41	48/12339	0.0110326	0.1905773	0.1593344	DOLPP1/TUSC3	2
GO:0032981	mitochondrial respiratory chain complex I assembly	2/41	49/12339	0.0114780	0.1905773	0.1593344	NDUFAF2/NDUFB1	2
GO:0036297	interstrand cross-link repair	2/41	50/12339	0.0119313	0.1905773	0.1593344	FANCE/NEIL3	2

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

6.1.3 mediumpurple1 - mean testosterone

6.1.3.1 Gene Ontology

BP enriched GO terms in mediumpurple1 module in POM
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0007084	mitotic nuclear envelope reassembly	2/41	10/12339	0.0004767	0.0517006	0.0420245	LMNA/PPP2R2A	2
GO:0033622	integrin activation	2/41	10/12339	0.0004767	0.0517006	0.0420245	LOC113991759/TLN1	2
GO:0036498	IRE1-mediated unfolded protein response	3/41	50/12339	0.0005988	0.0517006	0.0420245	LMNA/LOC113991759/TLN1	3
GO:0031398	positive regulation of protein ubiquitination	3/41	66/12339	0.0013476	0.0723435	0.0588039	AXIN1/LOC113992269/TRAF6	3
GO:0032147	activation of protein kinase activity	3/41	67/12339	0.0014075	0.0723435	0.0588039	AXIN1/LOC113985569/TRAF6	3
GO:2000060	positive regulation of ubiquitin-dependent protein catabolic process	2/41	19/12339	0.0017773	0.0723435	0.0588039	AXIN1/LOC113992269	2
GO:0006936	muscle contraction	3/41	81/12339	0.0024275	0.0723435	0.0588039	LOC113991759/PXN/TLN1	3
GO:0007160	cell-matrix adhesion	3/41	82/12339	0.0025139	0.0723435	0.0588039	ADAMTS13/PXN/SNED1	3
GO:0007229	integrin-mediated signaling pathway	3/41	82/12339	0.0025139	0.0723435	0.0588039	ADAMTS13/LOC113991759/TLN1	3
GO:0030866	cortical actin cytoskeleton organization	2/41	31/12339	0.0047128	0.1220611	0.0992165	LOC113991759/TLN1	2

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

6.1.4 sienna3 - strength

6.1.4.1 Gene Ontology

BP enriched GO terms in sienna3 module in POM
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0030433	ubiquitin-dependent ERAD pathway	4/92	58/12339	0.0009028	0.2416790	0.2368751	DNAJB14/LOC113995895/RNF103/UBE2G2	4
GO:0006891	intra-Golgi vesicle-mediated transport	3/92	28/12339	0.0011481	0.2416790	0.2368751	GABARAPL2/GOSR1/NAPG	3
GO:0006885	regulation of pH	2/92	10/12339	0.0023802	0.2852676	0.2795972	EDNRB/SLC26A4	2
GO:0045600	positive regulation of fat cell differentiation	3/92	41/12339	0.0034838	0.2852676	0.2795972	CREBL2/LOC113993849/SYAP1	3
GO:0031573	intra-S DNA damage checkpoint	2/92	13/12339	0.0040661	0.2852676	0.2795972	ATF2/EME1	2
GO:0006112	energy reserve metabolic process	2/92	15/12339	0.0054208	0.2852676	0.2795972	GNAS/KL	2
GO:0031396	regulation of protein ubiquitination	2/92	15/12339	0.0054208	0.2852676	0.2795972	DCUN1D1/PINK1	2
GO:0033146	regulation of intracellular estrogen receptor signaling pathway	2/92	15/12339	0.0054208	0.2852676	0.2795972	LOC113993849/UFSP2	2
GO:0045071	negative regulation of viral genome replication	2/92	17/12339	0.0069536	0.3252720	0.3188064	BTBD17/EIF2AK2	2
GO:0051443	positive regulation of ubiquitin-protein transferase activity	2/92	18/12339	0.0077850	0.3277496	0.3212347	DCUN1D1/PINK1	2

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

7 Dorsomedial Intercollicular Nucleus (ICo)

## [1] 15071

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

##    Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1      1   0.0312 25.70          0.970  7540.0    7540.0   7670
## 2      2   0.0490 -4.86          0.903  4250.0    4230.0   4730
## 3      3   0.1430 -3.42          0.860  2600.0    2570.0   3280
## 4      4   0.2250 -2.48          0.856  1700.0    1660.0   2420
## 5      5   0.3260 -2.05          0.880  1160.0    1120.0   1860
## 6      6   0.4310 -1.98          0.902   830.0     788.0   1490
## 7      7   0.5200 -2.00          0.915   610.0     568.0   1230
## 8      8   0.5820 -2.02          0.918   460.0     420.0   1030
## 9      9   0.6320 -2.04          0.923   355.0     317.0    883
## 10    10   0.6730 -2.04          0.931   279.0     243.0    762
## 11    11   0.6950 -2.05          0.932   223.0     189.0    665
## 12    12   0.7230 -2.04          0.939   180.0     149.0    584
## 13    14   0.7460 -2.06          0.940   123.0      95.7    462
## 14    16   0.7750 -2.04          0.949    86.8      63.6    373
## 15    18   0.7800 -2.05          0.947    63.4      43.6    306
## 16    20   0.7980 -2.02          0.952    47.6      30.7    255
## 17    22   0.8110 -1.99          0.956    36.5      22.1    215
## 18    24   0.8190 -1.97          0.956    28.5      16.3    183
## 19    26   0.8340 -1.92          0.960    22.7      12.2    157

The soft-power selected for ICO is 18.

## [1] TRUE

Genes per module in ICO
moduleColors	Freq
bisque4	1297
blue2	63
brown2	618
coral1	1201
darkgrey	1339
darkmagenta	490
darkolivegreen4	68
darkorange2	513
darkseagreen3	2480
darkturquoise	363
darkviolet	631
firebrick4	265
greenyellow	521
honeydew	45
honeydew1	85
indianred4	69
lavenderblush2	47
lavenderblush3	86
lightpink3	49
maroon	237
mediumpurple3	116
navajowhite1	54
orangered4	2718
palevioletred3	90
plum3	59
salmon4	92
skyblue	169
thistle	57
thistle1	93
thistle3	57
turquoise	1097

There appears to be no correlations between modules of interest.

7.1 Interesting Modules

7.1.1 lavenderblush2 - strength

7.1.1.1 Gene Ontology

BP enriched GO terms in lavenderblush2 module in ICO
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0089718	amino acid import across plasma membrane	2/37	10/12201	0.0003966	0.0662263	0.0563539	LOC114001992/SLC22A2	2
GO:0007265	Ras protein signal transduction	3/37	65/12201	0.0009853	0.0822706	0.0700065	CDKN1A/JUN/NRAS	3
GO:0042908	xenobiotic transport	2/37	22/12201	0.0019896	0.1075022	0.0914768	LOC114001992/SLC22A2	2
GO:0051591	response to cAMP	2/37	28/12201	0.0032186	0.1075022	0.0914768	JUN/LOC113993849	2
GO:0071479	cellular response to ionizing radiation	2/37	28/12201	0.0032186	0.1075022	0.0914768	CDKN1A/SWI5	2
GO:0000079	regulation of cyclin-dependent protein serine/threonine kinase activity	2/37	43/12201	0.0074726	0.1853735	0.1577398	CDKN1A/CNPPD1	2
GO:0006977	DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest	2/37	47/12201	0.0088778	0.1853735	0.1577398	CDKN1A/LOC113993849	2
GO:0090305	nucleic acid phosphodiester bond hydrolysis	2/37	52/12201	0.0107870	0.1853735	0.1577398	LOC113989190/ZRANB3	2
GO:0006606	protein import into nucleus	2/37	65/12201	0.0165091	0.1853735	0.1577398	CDKN1A/SNUPN	2
GO:0006098	pentose-phosphate shunt	1/37	10/12201	0.0299258	0.1853735	0.1577398	RPIA	1

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

7.1.2 darkturquoise - mean testosterone

7.1.2.1 Gene Ontology

BP enriched GO terms in darkturquoise module in ICO
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0043392	negative regulation of DNA binding	4/289	21/12201	0.0013420	0.6394570	0.6393909	CTNNBIP1/HDAC2/LOC113990539/NFKBIA	4
GO:0034765	regulation of ion transmembrane transport	8/289	93/12201	0.0016049	0.6394570	0.6393909	GPR89B/KCNA6/KCNG3/KCNH8/KCNJ6/KCNJ8/LOC113993165/TMEM37	8
GO:0036499	PERK-mediated unfolded protein response	3/289	11/12201	0.0018845	0.6394570	0.6393909	ATF3/LOC113998959/LOC114003207	3
GO:0007263	nitric oxide mediated signal transduction	3/289	14/12201	0.0039439	0.7171947	0.7171206	CD36/NPY2R/RASD1	3
GO:0030517	negative regulation of axon extension	3/289	14/12201	0.0039439	0.7171947	0.7171206	KIAA0319/RNF6/RTN4R	3
GO:0006370	7-methylguanosine mRNA capping	4/289	31/12201	0.0058603	0.7171947	0.7171206	GTF2F2/LOC113983394/POLR2J/RNGTT	4
GO:0042246	tissue regeneration	3/289	17/12201	0.0069911	0.7171947	0.7171206	GAP43/LOC113998959/LOC114003207	3
GO:0043254	regulation of protein-containing complex assembly	3/289	17/12201	0.0069911	0.7171947	0.7171206	CD36/HJURP/INSM1	3
GO:0032355	response to estradiol	6/289	75/12201	0.0086076	0.7171947	0.7171206	CASP8/DHH/EZH2/HPGD/MBD3/TXNIP	6
GO:0032496	response to lipopolysaccharide	7/289	100/12201	0.0095616	0.7171947	0.7171206	CASP8/CSF2RB/DUSP10/HDAC2/HPGD/KCNJ8/LOC113998857	7

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

7.1.3 maroon - mean testosterone

7.1.3.1 Gene Ontology

BP enriched GO terms in maroon module in ICO
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0006508	proteolysis	17/205	250/12201	0.0000011	0.0008534	0.0007831	ACE/ADAM8/CAPN3/CAPN5/LGMN/LOC113983631/LOC114004136/MASP1/METAP1D/MMP2/MMP28/PAPLN/PM20D1/PRSS23/RHBDF1/TINAG/USP25	17
GO:0060078	regulation of postsynaptic membrane potential	6/205	38/12201	0.0000369	0.0149480	0.0137169	GABRA5/GABRA6/GABRB2/GRIK1/GRIN3A/LOC113990089	6
GO:0060384	innervation	4/205	15/12201	0.0000914	0.0194990	0.0178931	GABRA5/GABRB2/LRIG1/SULF2	4
GO:0030514	negative regulation of BMP signaling pathway	6/205	45/12201	0.0000986	0.0194990	0.0178931	BMPER/CTDSPL2/SFRP2/SKOR2/SOSTDC1/VWC2L	6
GO:0051932	synaptic transmission, GABAergic	4/205	16/12201	0.0001202	0.0194990	0.0178931	GABRA5/GABRA6/GABRB2/LOC113990089	4
GO:0007215	glutamate receptor signaling pathway	4/205	18/12201	0.0001969	0.0236565	0.0217083	GRID2/GRIK1/GRIN2A/PLCB1	4
GO:0030198	extracellular matrix organization	12/205	208/12201	0.0002042	0.0236565	0.0217083	ADAMTS12/BCAN/COL6A1/EGFLAM/LAMA3/LOC113998335/MMP2/MMP28/NDNF/PAPLN/PTX3/VWA1	12
GO:0042472	inner ear morphogenesis	5/205	36/12201	0.0003147	0.0318980	0.0292710	GATA2/NTN1/OTX1/PAX2/SPRY2	5
GO:0090102	cochlea development	4/205	23/12201	0.0005334	0.0475213	0.0436078	GABRA5/GABRB2/GATA2/PAX2	4
GO:0007268	chemical synaptic transmission	11/205	201/12201	0.0005860	0.0475213	0.0436078	CACNB4/DTNA/GABRA5/GABRA6/GABRB2/GRIK1/GRIN2A/GRM4/LOC113990089/NPTX1/NTSR1	11

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

8 Midbrain Central Grey (GCt)

## [1] 15099

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 11717 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

##    Power SFT.R.sq  slope truncated.R.sq mean.k. median.k. max.k.
## 1      1  0.00530 -12.10          0.974  7550.0    7550.0   7660
## 2      2  0.00467  -1.64          0.886  4270.0    4270.0   4610
## 3      3  0.11400  -3.30          0.881  2630.0    2630.0   3140
## 4      4  0.15500  -2.43          0.877  1730.0    1720.0   2300
## 5      5  0.21600  -2.10          0.884  1190.0    1180.0   1760
## 6      6  0.29000  -1.97          0.894   852.0     837.0   1400
## 7      7  0.36600  -1.90          0.905   630.0     612.0   1140
## 8      8  0.43600  -1.87          0.916   478.0     458.0    947
## 9      9  0.50200  -1.89          0.922   371.0     350.0    802
## 10    10  0.55800  -1.91          0.929   293.0     272.0    688
## 11    11  0.60100  -1.92          0.934   235.0     214.0    597
## 12    12  0.63700  -1.95          0.938   191.0     171.0    524
## 13    14  0.69900  -1.97          0.951   131.0     112.0    412
## 14    16  0.72900  -2.03          0.954    93.7      76.7    336
## 15    18  0.74400  -2.10          0.952    69.0      54.0    281
## 16    20  0.76500  -2.14          0.960    52.1      39.0    238
## 17    22  0.77000  -2.18          0.960    40.3      28.6    204
## 18    24  0.78400  -2.20          0.966    31.7      21.5    177
## 19    26  0.79200  -2.21          0.970    25.3      16.4    154

The soft-power selected for GCT is 18.

Genes per module in GCT
moduleColors	Freq
antiquewhite1	34
antiquewhite2	132
antiquewhite4	95
bisque4	2333
black	914
brown2	156
chocolate4	79
coral	113
coral1	179
coral3	360
cyan	1252
darkgreen	194
darkgrey	408
darkolivegreen2	1719
darkolivegreen4	1267
darkseagreen2	40
darkseagreen3	174
deeppink	1490
firebrick3	53
floralwhite	582
green4	40
indianred3	53
lightcoral	83
lightpink2	47
lightpink3	70
mediumpurple1	134
mediumpurple2	155
mediumpurple4	63
navajowhite	1335
orangered3	274
pink4	302
plum	89
plum4	51
saddlebrown	161
salmon2	72
sienna4	60
skyblue2	152
skyblue4	62
tan	230
yellow4	92

There appears to be a strong correlation between skyblue2 and green4, which are associated with strength.

8.1 Interesting Modules

8.1.1 skyblue2 - strength

I’m not convinced. Let’s move on to correlations with stronger p-values

8.1.2 lightpink3 - mean testosterone

8.1.2.1 Gene Ontology

BP enriched GO terms in lightpink3 module in GCT
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0070528	protein kinase C signaling	2/58	10/12240	0.0009691	0.2332786	0.2190439	LOC113994699/PLEK	2
GO:0033540	fatty acid beta-oxidation using acyl-CoA oxidase	2/58	13/12240	0.0016646	0.2332786	0.2190439	EHHADH/HSD17B4	2
GO:0032703	negative regulation of interleukin-2 production	2/58	21/12240	0.0043738	0.2332786	0.2190439	CD34/LOC113986241	2
GO:0043392	negative regulation of DNA binding	2/58	21/12240	0.0043738	0.2332786	0.2190439	JUN/NFKBIA	2
GO:0006958	complement activation, classical pathway	2/58	24/12240	0.0056963	0.2332786	0.2190439	LOC113984271/LOC113986241	2
GO:0034080	CENP-A containing nucleosome assembly	2/58	28/12240	0.0077074	0.2332786	0.2190439	CENPT/HJURP	2
GO:0000082	G1/S transition of mitotic cell cycle	3/58	87/12240	0.0080763	0.2332786	0.2190439	EIF4EBP1/MCM2/PLK2	3
GO:0051591	response to cAMP	2/58	29/12240	0.0082533	0.2332786	0.2190439	DUSP1/JUN	2
GO:0046627	negative regulation of insulin receptor signaling pathway	2/58	33/12240	0.0106042	0.2332786	0.2190439	PRKCQ/SOCS1	2
GO:0045746	negative regulation of Notch signaling pathway	2/58	37/12240	0.0132150	0.2332786	0.2190439	NFKBIA/TCIM	2

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

8.1.3 antiquewhite1 - strength

8.1.3.1 Gene Ontology

BP enriched GO terms in antiquewhite1 module in GCT
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0043044	ATP-dependent chromatin remodeling	2/27	36/12240	0.0028188	0.1458146	0.1121232	MBD3/SMARCA5	2
GO:1990830	cellular response to leukemia inhibitory factor	2/27	77/12240	0.0123845	0.1458146	0.1121232	SMARCA5/SPRY2	2
GO:0006613	cotranslational protein targeting to membrane	1/27	10/12240	0.0218491	0.1458146	0.1121232	SRP14	1
GO:0030518	intracellular steroid hormone receptor signaling pathway	1/27	10/12240	0.0218491	0.1458146	0.1121232	PGR	1
GO:0106074	aminoacyl-tRNA metabolism involved in translational fidelity	1/27	10/12240	0.0218491	0.1458146	0.1121232	DTD1	1
GO:0002031	G protein-coupled receptor internalization	1/27	11/12240	0.0240085	0.1458146	0.1121232	SAG	1
GO:0042474	middle ear morphogenesis	1/27	11/12240	0.0240085	0.1458146	0.1121232	TSHZ1	1
GO:0050678	regulation of epithelial cell proliferation	1/27	11/12240	0.0240085	0.1458146	0.1121232	PGR	1
GO:0070914	UV-damage excision repair	1/27	11/12240	0.0240085	0.1458146	0.1121232	DDB2	1
GO:0098974	postsynaptic actin cytoskeleton organization	1/27	11/12240	0.0240085	0.1458146	0.1121232	LOC113998505	1

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

8.1.4 antiquewhite2 - strength

8.1.4.1 Gene Ontology

BP enriched GO terms in antiquewhite2 module in GCT
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0009566	fertilization	3/95	21/12240	0.0005443	0.1091090	0.1091090	AAAS/LOC114000708/MEIOB	3
GO:0046854	phosphatidylinositol phosphorylation	4/95	50/12240	0.0005968	0.1091090	0.1091090	FAM126A/PIK3C3/PIK3CB/PIK3R6	4
GO:0090305	nucleic acid phosphodiester bond hydrolysis	4/95	54/12240	0.0008003	0.1091090	0.1091090	DCLRE1C/EXD2/LOC114001323/MEIOB	4
GO:0010975	regulation of neuron projection development	3/95	29/12240	0.0014298	0.1461951	0.1461951	IL1RAPL1/LOC113992691/MARK1	3
GO:0019229	regulation of vasoconstriction	2/95	12/12240	0.0037400	0.2549409	0.2549409	ECE1/EDN3	2
GO:0036092	phosphatidylinositol-3-phosphate biosynthetic process	2/95	12/12240	0.0037400	0.2549409	0.2549409	PIK3C3/PIK3CB	2
GO:0002009	morphogenesis of an epithelium	2/95	14/12240	0.0051048	0.2982672	0.2982672	FRAS1/RIPK4	2
GO:0000729	DNA double-strand break processing	2/95	15/12240	0.0058605	0.2996201	0.2996201	EXD2/LOC114001323	2
GO:0048873	homeostasis of number of cells within a tissue	2/95	21/12240	0.0113724	0.3877285	0.3877285	ADD1/LOC114000708	2
GO:0090023	positive regulation of neutrophil chemotaxis	2/95	21/12240	0.0113724	0.3877285	0.3877285	MCU/MOSPD2	2

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

8.1.5 coral1 - strength

8.1.5.1 Gene Ontology

BP enriched GO terms in coral1 module in GCT
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0007275	multicellular organism development	11/149	303/12240	0.0011903	0.5366347	0.5366347	CDH8/DGKD/EPHA5/ERBB4/ERMARD/NELFA/PAQR5/STRBP/TDRD9/VIRMA/WIF1	11
GO:0071392	cellular response to estradiol stimulus	3/149	31/12240	0.0061911	0.5366347	0.5366347	ESR2/NCOA3/NRIP1	3
GO:0030518	intracellular steroid hormone receptor signaling pathway	2/149	10/12240	0.0062129	0.5366347	0.5366347	ESR2/NR3C1	2
GO:2000010	positive regulation of protein localization to cell surface	2/149	11/12240	0.0075332	0.5366347	0.5366347	ERBB4/MAP1A	2
GO:0042472	inner ear morphogenesis	3/149	36/12240	0.0094090	0.5366347	0.5366347	ALDH1A3/GBX2/PRRX1	3
GO:0018105	peptidyl-serine phosphorylation	6/149	148/12240	0.0094306	0.5366347	0.5366347	CAB39/CAMK1D/MAPK14/MAPKAPK5/SGK2/WNK1	6
GO:0032793	positive regulation of CREB transcription factor activity	2/149	14/12240	0.0121698	0.5366347	0.5366347	CAMK1D/EPHA5	2
GO:0002028	regulation of sodium ion transport	2/149	15/12240	0.0139309	0.5366347	0.5366347	LOC113983777/WNK1	2
GO:0050773	regulation of dendrite development	2/149	15/12240	0.0139309	0.5366347	0.5366347	CAMK1D/CSMD3	2
GO:0001755	neural crest cell migration	3/149	45/12240	0.0172755	0.5366347	0.5366347	ERBB4/GBX2/SEMA5B	3

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

9 Nucleus Taenia (TnA)

## [1] 14696

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 33 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

##    Power SFT.R.sq    slope truncated.R.sq mean.k. median.k. max.k.
## 1      1 9.27e-03 15.90000          0.870 7350.00   7350.00 7430.0
## 2      2 2.09e-03 -2.39000          0.926 4010.00   4000.00 4300.0
## 3      3 4.48e-03  1.51000          0.968 2330.00   2330.00 2730.0
## 4      4 1.51e-02  1.62000          0.973 1440.00   1430.00 1840.0
## 5      5 1.31e-02  1.02000          0.971  922.00    912.00 1310.0
## 6      6 1.39e-02  0.77800          0.973  614.00    604.00  958.0
## 7      7 3.84e-03  0.31700          0.964  422.00    412.00  724.0
## 8      8 4.59e-06  0.00917          0.955  298.00    288.00  564.0
## 9      9 2.28e-02 -0.57300          0.944  215.00    206.00  452.0
## 10    10 9.46e-02 -1.07000          0.938  159.00    150.00  369.0
## 11    11 2.05e-01 -1.44000          0.940  119.00    111.00  306.0
## 12    12 3.35e-01 -1.76000          0.952   90.60     83.60  257.0
## 13    14 5.72e-01 -2.18000          0.978   54.90     49.10  186.0
## 14    16 7.14e-01 -2.41000          0.991   34.80     30.10  140.0
## 15    18 7.92e-01 -2.54000          0.996   22.90     19.10  108.0
## 16    20 8.29e-01 -2.64000          0.993   15.60     12.50   84.7
## 17    22 8.55e-01 -2.68000          0.994   10.90      8.45   67.8
## 18    24 8.75e-01 -2.68000          0.997    7.83      5.83   55.1
## 19    26 8.92e-01 -2.67000          0.998    5.73      4.11   45.4

The soft-power selected for TNA is 18.

Genes per module in TNA
moduleColors	Freq
antiquewhite2	53
antiquewhite4	140
black	1096
blue	1787
blue2	470
brown4	106
coral1	1579
coral3	2067
darkgrey	254
darkorange	1383
darkorange2	1848
darkseagreen4	372
grey	5
honeydew	55
lavenderblush3	217
lightsteelblue	612
lightyellow	585
magenta4	59
mediumpurple4	49
navajowhite1	60
orangered3	81
orangered4	954
palevioletred3	95
skyblue1	82
skyblue2	84
skyblue4	44
tan	457
thistle2	99

Based on inspection of the module memberships it seems that coral1 is the inverse of blue and/or lightyellow

9.1 Interesting Modules

9.1.1 coral1 - mean testosterone

9.1.1.1 Gene Ontology

BP enriched GO terms in coral1 module in TNA
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0001755	neural crest cell migration	16/1250	45/11978	0.0000062	0.0122582	0.0122582	ACVR1/ALX1/ARTN/EDN3/EFNB1/LOC113982696/LOC113984504/LOC113988059/RADIL/SEMA3B/SEMA4A/SEMA4B/SEMA4D/SEMA4G/SHH/SOX8	16
GO:0007411	axon guidance	41/1250	208/11978	0.0000432	0.0429695	0.0429695	ARTN/B3GNT2/BMP7/DOK5/DRAXIN/EFNA1/EFNB1/EPHA1/GAB2/GFRA3/GPC1/LGI1/LOC113982696/LOC113983904/LOC113984118/LOC113984504/LOC113988059/LOC113990338/LRTM2/MAPK7/NEO1/NEXN/NPTN/NRP2/NTN4/PALLD/RAP1GAP/SEMA3B/SEMA4A/SEMA4B/SEMA4D/SEMA4G/SHC3/SHH/SIAH1/SLIT2/SRC/TUBB3/UNC5A/VLDLR/VSTM2L	41
GO:0098962	regulation of postsynaptic neurotransmitter receptor activity	8/1250	18/11978	0.0002306	0.1476696	0.1476696	BEGAIN/CACNG4/DLGAP3/LOC113982730/LOC114000047/NPTX1/NPTX2/SRC	8
GO:0050919	negative chemotaxis	12/1250	38/11978	0.0003237	0.1476696	0.1476696	LOC113982696/LOC113984504/LOC113988059/LRTM2/NRP2/PDGFA/SEMA3B/SEMA4A/SEMA4B/SEMA4D/SEMA4G/SLIT2	12
GO:0035195	gene silencing by miRNA	6/1250	11/11978	0.0003710	0.1476696	0.1476696	CNOT6/CNOT8/EIF6/LIMD1/MOV10L1/WTIP	6
GO:0007267	cell-cell signaling	26/1250	130/11978	0.0008199	0.2719451	0.2719451	ADORA2A/ADRA1A/BMP2/CCN5/CX3CL1/DHH/ECE2/EDN3/EFNA1/EFNB1/ESR2/FGF18/FGFBP2/GJA5/GPNMB/GUCA1B/INHBA/LOC113988059/LOC113991921/LOC113992470/LOC113993120/PDGFA/POMC/SEMA3B/SHH/SSTR3	26
GO:0071470	cellular response to osmotic stress	6/1250	13/11978	0.0011461	0.3258145	0.3258145	LOC113991483/LOC113991484/LOC113991485/LOC113991486/LOC114001824/PKD2	6
GO:0009880	embryonic pattern specification	7/1250	18/11978	0.0014951	0.3305788	0.3305788	BMP7/EFNB1/RIPPLY2/SATB2/SHH/SMAD3/ZBTB16	7
GO:0055074	calcium ion homeostasis	7/1250	18/11978	0.0014951	0.3305788	0.3305788	CAMK2N1/CIB2/FAM20A/KL/LOC114004119/SNCA/TNFSF11	7
GO:0051764	actin crosslink formation	5/1250	10/11978	0.0019733	0.3926931	0.3926931	BAIAP2/DPYSL3/GAS2/GAS2L1/MARCKS	5

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

9.1.2 blue - mean testosterone

9.1.2.1 Gene Ontology

BP enriched GO terms in blue module in TNA
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0006120	mitochondrial electron transport, NADH to ubiquinone	17/1486	38/11978	0.0000008	0.0016443	0.0016443	NDUFA1/NDUFA11/NDUFA4/NDUFA4L2/NDUFA5/NDUFAF1/NDUFB11/NDUFB3/NDUFB4/NDUFB5/NDUFB7/NDUFS1/NDUFS4/NDUFS5/NDUFS6/NDUFS8/NDUFV3	17
GO:0032981	mitochondrial respiratory chain complex I assembly	18/1486	49/11978	0.0000113	0.0118183	0.0118183	ACAD9/ECSIT/LOC113987270/NDUFA1/NDUFA11/NDUFA5/NDUFAF1/NDUFB11/NDUFB3/NDUFB4/NDUFB5/NDUFB7/NDUFS1/NDUFS4/NDUFS5/NDUFS6/NDUFS8/NDUFV3	18
GO:0048013	ephrin receptor signaling pathway	25/1486	92/11978	0.0000975	0.0679597	0.0679597	ACTB/ACTR2/ACTR3/ARPC1A/ARPC1B/ARPC2/ARPC5/CDC42/EPHA4/EPHA8/EPHB2/EPHB3/GIT1/HCK/KALRN/LOC113993801/LOC113998953/NCK2/NCSTN/NGEF/RAC1/RHOA/SDC2/SS18/WASL	25
GO:0034314	Arp2/3 complex-mediated actin nucleation	8/1486	15/11978	0.0001580	0.0825658	0.0825658	ACTR2/ACTR3/ARPC1A/ARPC1B/ARPC2/ARPC5/ARPC5L/WHAMM	8
GO:1902600	proton transmembrane transport	21/1486	75/11978	0.0002186	0.0913576	0.0913576	ATP12A/ATP2C1/ATP5MF/ATP6AP1L/ATP6V0B/ATP6V0E1/ATP6V1E1/HVCN1/LOC113983953/LOC113984288/LOC113985114/LOC113985195/LOC113985992/LOC114000835/LOC114003365/NDUFA4/NDUFA4L2/SLC17A5/SLC25A27/SLC2A10/SLC9A1	21
GO:0035970	peptidyl-threonine dephosphorylation	9/1486	20/11978	0.0003162	0.1101596	0.1101596	DUSP4/DUSP5/DUSP6/DUSP7/LOC113997232/PPM1A/PPM1B/PPM1E/PPP2CA	9
GO:0043687	post-translational protein modification	55/1486	285/11978	0.0005068	0.1513247	0.1513247	ANKRD9/APP/ASB1/ASB16/ASB7/BMP4/BTBD6/C3/CCN1/COP1/COPS5/COPS7A/CP/DCAF13/DCAF17/DCUN1D1/ELOC/EVA1A/FBXL5/FBXL7/FBXO22/FBXO32/FBXO9/FUCA2/IGFBP7/KLHL13/KLHL20/KLHL42/LAMC1/LOC113984874/LOC113987294/LOC113992168/LOC113999119/LOC113999601/MIA3/NUB1/PROC/PSMA1/PSMA3/PSMA4/PSMA7/PSMD10/PSMD11/PSME4/RAB2A/RAB5A/RBBP7/SCG2/SDC2/SKP1/SKP2/SOCS3/SPSB4/UBE2F/UBE2M	55
GO:0043401	steroid hormone mediated signaling pathway	8/1486	18/11978	0.0007617	0.1665755	0.1665755	BMP4/GPER1/LEF1/NR2E1/NR4A3/PAQR7/PGRMC2/PPARD	8
GO:0008277	regulation of G protein-coupled receptor signaling pathway	14/1486	45/11978	0.0007642	0.1665755	0.1665755	ADCYAP1/GIT1/HOMER2/LOC114003843/PDCL/RGS11/RGS12/RGS14/RGS16/RGS2/RGS4/RGS5/ZDHHC3/ZDHHC7	14
GO:0002281	macrophage activation involved in immune response	6/1486	11/11978	0.0009564	0.1665755	0.1665755	IFI35/LOC113985745/LOC113986453/LOC113994309/LOC113998967/NMI	6

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

9.1.3 lightyellow - mean testosterone

9.1.3.1 Gene Ontology

BP enriched GO terms in lightyellow module in TNA
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0006695	cholesterol biosynthetic process	10/504	34/11978	0.0000008	0.0011542	0.0011542	FDFT1/HMGCR/HMGCS1/HSD17B7/INSIG1/INSIG2/LOC113987727/LOC114002832/MSMO1/SQLE	10
GO:0016126	sterol biosynthetic process	6/504	13/11978	0.0000072	0.0049447	0.0049447	CH25H/HMGCR/INSIG1/INSIG2/MSMO1/SQLE	6
GO:0045540	regulation of cholesterol biosynthetic process	8/504	33/11978	0.0000509	0.0233422	0.0233422	FDFT1/GPAM/HMGCR/HMGCS1/LOC113987727/LOC114002832/SQLE/SREBF2	8
GO:0071404	cellular response to low-density lipoprotein particle stimulus	5/504	16/11978	0.0003839	0.1321546	0.1321546	CD81/CD9/HMGCS1/PPARG/SREBF2	5
GO:0033189	response to vitamin A	4/504	10/11978	0.0005311	0.1462786	0.1462786	CAT/LOC113999589/LRAT/PPARG	4
GO:0006625	protein targeting to peroxisome	9/504	66/11978	0.0016812	0.3190921	0.3190921	ACOX1/AGPS/CAT/DAO/GSTK1/LOC113996048/NUDT19/PEX26/UBE2D3	9
GO:0019432	triglyceride biosynthetic process	4/504	14/11978	0.0022133	0.3190921	0.3190921	ACSL4/GK/GPAM/LPIN2	4
GO:2000009	negative regulation of protein localization to cell surface	4/504	14/11978	0.0022133	0.3190921	0.3190921	LEPROT/LEPROTL1/PICALM/TAX1BP3	4
GO:0006629	lipid metabolic process	11/504	96/11978	0.0022807	0.3190921	0.3190921	ACOX1/ACSL4/CH25H/CRK/CRKL/HMGCS1/LCAT/LPIN2/PPARG/SREBF2/TTPA	11
GO:0035666	TRIF-dependent toll-like receptor signaling pathway	5/504	23/11978	0.0023173	0.3190921	0.3190921	BIRC2/LOC113996048/TLR6/TRAF3/UBE2D3	5

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

10 Arcopallium Intermedium (Ai)

## [1] 14598

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 71 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

##    Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1      1   0.0107 25.90          0.978  7300.0   7300.00   7370
## 2      2   0.1290 -8.30          0.891  4070.0   4060.00   4460
## 3      3   0.2070 -4.32          0.909  2460.0   2440.00   3040
## 4      4   0.2960 -3.16          0.936  1580.0   1550.00   2230
## 5      5   0.3840 -2.66          0.952  1070.0   1040.00   1710
## 6      6   0.4630 -2.39          0.961   745.0    716.00   1360
## 7      7   0.5500 -2.26          0.973   538.0    509.00   1110
## 8      8   0.6310 -2.19          0.983   399.0    370.00    929
## 9      9   0.6880 -2.14          0.986   303.0    275.00    788
## 10    10   0.7350 -2.13          0.987   234.0    208.00    677
## 11    11   0.7710 -2.12          0.987   184.0    160.00    588
## 12    12   0.7890 -2.14          0.980   147.0    124.00    516
## 13    14   0.8370 -2.11          0.984    97.2     77.90    406
## 14    16   0.8630 -2.10          0.983    67.1     50.60    327
## 15    18   0.8790 -2.09          0.983    47.9     34.00    270
## 16    20   0.8890 -2.09          0.982    35.2     23.40    227
## 17    22   0.8720 -2.11          0.964    26.5     16.60    193
## 18    24   0.8630 -2.13          0.957    20.4     11.90    166
## 19    26   0.8750 -2.11          0.962    15.9      8.74    145

The soft-power selected for AI is 18.

Genes per module in AI
moduleColors	Freq
antiquewhite2	1026
antiquewhite4	720
bisque4	182
blue2	68
brown2	68
brown4	104
coral1	94
coral2	90
coral3	53
darkgreen	1538
darkgrey	341
darkmagenta	1289
darkolivegreen	1686
darkolivegreen4	166
darkslateblue	482
darkviolet	65
grey	2
honeydew	57
honeydew1	309
indianred4	1184
lightcoral	274
lightcyan1	496
lightpink3	57
magenta	2702
magenta4	59
mediumpurple2	75
mediumpurple4	53
orange	629
orangered3	76
palevioletred3	98
plum	193
salmon2	63
skyblue2	86
thistle	64
thistle1	99
yellow3	50

Of our interesting modules, it appears that thistle1 is negatively correlated with skyblue2

10.1 Interesting Modules

10.1.1 thistle1 - mean testosterone

10.1.1.1 Gene Ontology

BP enriched GO terms in thistle1 module in AI
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0006783	heme biosynthetic process	3/84	23/11917	0.0005404	0.1945428	0.1939740	ALAS1/CPOX/SLC25A39	3
GO:0007005	mitochondrion organization	4/84	84/11917	0.0028888	0.3805802	0.3794674	ALAS1/DNAJA3/PYROXD2/SIRT3	4
GO:0045453	bone resorption	2/84	14/11917	0.0042287	0.3805802	0.3794674	ACP5/NCDN	2
GO:1900181	negative regulation of protein localization to nucleus	2/84	14/11917	0.0042287	0.3805802	0.3794674	LZTS2/TRIM29	2
GO:0001921	positive regulation of receptor recycling	2/84	16/11917	0.0055255	0.3924384	0.3912909	ANXA2/LOC113996134	2
GO:0072593	reactive oxygen species metabolic process	2/84	23/11917	0.0112839	0.3924384	0.3912909	LOC113988359/NDUFA13	2
GO:0007131	reciprocal meiotic recombination	2/84	26/11917	0.0142987	0.3924384	0.3912909	KLHDC3/RAD51D	2
GO:0016525	negative regulation of angiogenesis	3/84	74/11917	0.0152921	0.3924384	0.3912909	COL4A3/ISM1/LOC114002556	3
GO:2000134	negative regulation of G1/S transition of mitotic cell cycle	2/84	28/11917	0.0164802	0.3924384	0.3912909	CDKN2C/RBL1	2
GO:0009408	response to heat	2/84	30/11917	0.0187941	0.3924384	0.3912909	DNAJA3/RPS6KB1	2

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

10.1.2 skyblue2 - mean testosterone

10.1.2.1 Gene Ontology

BP enriched GO terms in skyblue2 module in AI
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0098696	regulation of neurotransmitter receptor localization to postsynaptic specialization membrane	2/80	10/11917	0.0019340	0.3458354	0.3407388	ADAM10/PRKCZ	2
GO:0006509	membrane protein ectodomain proteolysis	2/80	19/11917	0.0070672	0.3458354	0.3407388	ADAM10/SPPL2A	2
GO:0001954	positive regulation of cell-matrix adhesion	2/80	20/11917	0.0078185	0.3458354	0.3407388	EPHA1/PRKCZ	2
GO:0097194	execution phase of apoptosis	2/80	20/11917	0.0078185	0.3458354	0.3407388	CFLAR/STK24	2
GO:0001817	regulation of cytokine production	2/80	21/11917	0.0086041	0.3458354	0.3407388	IGF2BP3/LRRC19	2
GO:0005977	glycogen metabolic process	2/80	23/11917	0.0102765	0.3458354	0.3407388	IL6ST/LOC113990070	2
GO:0010803	regulation of tumor necrosis factor-mediated signaling pathway	2/80	23/11917	0.0102765	0.3458354	0.3407388	LOC113989730/SPPL2A	2
GO:0032715	negative regulation of interleukin-6 production	2/80	26/11917	0.0130308	0.3458354	0.3407388	FOXJ1/SOCS5	2
GO:0021510	spinal cord development	2/80	28/11917	0.0150254	0.3458354	0.3407388	PTN/SUZ12	2
GO:0051781	positive regulation of cell division	2/80	31/11917	0.0182459	0.3458354	0.3407388	GAREM1/PTN	2

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

10.1.3 palevioletred3 - mean testosterone

Not very convincing relationship

10.1.3.1 Gene Ontology

BP enriched GO terms in palevioletred3 module in AI
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:2000772	regulation of cellular senescence	2/80	11/11917	0.0023535	0.3208474	0.3203173	RSL1D1/ZMPSTE24	2
GO:2001214	positive regulation of vasculogenesis	2/80	12/11917	0.0028120	0.3208474	0.3203173	ADM/CD34	2
GO:0042632	cholesterol homeostasis	3/80	52/11917	0.0050824	0.3208474	0.3203173	LOC113994745/NPC2/NUS1	3
GO:0031398	positive regulation of protein ubiquitination	3/80	66/11917	0.0098415	0.3208474	0.3203173	BCL10/TBC1D7/UBE2L3	3
GO:0010951	negative regulation of endopeptidase activity	3/80	67/11917	0.0102538	0.3208474	0.3203173	LOC113992168/PROS1/SPOCK3	3
GO:0001889	liver development	3/80	68/11917	0.0106759	0.3208474	0.3203173	FPGS/SRD5A1/ZMPSTE24	3
GO:0008360	regulation of cell shape	4/80	135/11917	0.0128347	0.3208474	0.3203173	CDC42SE2/RHOA/TBCCD1/ZMPSTE24	4
GO:0016239	positive regulation of macroautophagy	2/80	27/11917	0.0140126	0.3208474	0.3203173	SCOC/SESN2	2
GO:0000209	protein polyubiquitination	5/80	220/11917	0.0160870	0.3208474	0.3203173	FBXO11/LOC113996737/SKP1/UBE2L3/UBE2V1	5
GO:0030449	regulation of complement activation	2/80	30/11917	0.0171426	0.3208474	0.3203173	LOC113992168/PROS1	2

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

10.1.4 lightcoral - mean testosterone

Not a convincing relationship.

11 Lateral Septum (Ls)

## [1] 14901

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 56 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

##    Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1      1   0.0556 39.00          0.907  7450.0   7450.00   7570
## 2      2   0.0483 -5.20          0.860  4130.0   4110.00   4480
## 3      3   0.1620 -4.09          0.900  2460.0   2450.00   3000
## 4      4   0.2850 -3.28          0.937  1560.0   1540.00   2160
## 5      5   0.4100 -2.85          0.962  1040.0   1010.00   1640
## 6      6   0.5170 -2.61          0.976   716.0    689.00   1290
## 7      7   0.6110 -2.48          0.985   510.0    483.00   1050
## 8      8   0.6900 -2.40          0.992   373.0    347.00    869
## 9      9   0.7470 -2.33          0.994   280.0    254.00    734
## 10    10   0.7940 -2.31          0.996   214.0    190.00    631
## 11    11   0.8250 -2.29          0.998   167.0    144.00    549
## 12    12   0.8450 -2.28          0.996   132.0    111.00    484
## 13    14   0.8770 -2.25          0.995    85.7     67.70    384
## 14    16   0.9000 -2.21          0.997    58.3     43.10    313
## 15    18   0.9140 -2.17          0.997    41.1     28.30    260
## 16    20   0.9240 -2.13          0.998    29.9     19.10    219
## 17    22   0.9300 -2.10          0.997    22.3     13.20    187
## 18    24   0.9340 -2.07          0.998    17.0      9.34    161
## 19    26   0.9330 -2.04          0.996    13.2      6.71    141

The soft-power selected for LS is 16.

Genes per module in LS
moduleColors	Freq
antiquewhite4	78
bisque4	98
black	2039
blue	1386
brown	1569
brown4	1884
coral1	80
coral2	398
darkgrey	256
darkmagenta	352
darkorange	818
darkred	284
floralwhite	466
green	1271
grey	2
lightcoral	50
lightgreen	223
lightsteelblue1	961
maroon	161
navajowhite2	357
orangered4	294
paleturquoise	309
palevioletred3	90
plum	62
skyblue3	200
yellow	1083
yellowgreen	128

There doesn’t appear to be any obviously negative correlations with our modules of interest.

11.1 Interesting Modules

11.1.1 darkmagenta - strength

## Warning: Removed 3 rows containing missing values
## (`geom_point()`).

11.1.1.1 Gene Ontology

BP enriched GO terms in darkmagenta module in LS
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0016032	viral process	21/295	406/12091	0.0009823	0.2529708	0.2436046	AAAS/ANKRD17/ATF7/BRD2/DCAF1/DNAJB12/E4F1/EP300/IL2RB/KAT2A/LOC113985512/LOC113989518/LOC113999689/LOC114003727/MPDZ/NUP214/SETD2/SPEN/SSRP1/TPR/UBR4	21
GO:0043967	histone H4 acetylation	5/295	33/12091	0.0011321	0.2529708	0.2436046	EP300/EP400/LOC113997056/LOC113998036/LOC113998501	5
GO:0046854	phosphatidylinositol phosphorylation	6/295	50/12091	0.0012919	0.2529708	0.2436046	LOC113983741/LOC113988212/LOC113992314/PI4K2A/PIK3R2/SOCS7	6
GO:0045652	regulation of megakaryocyte differentiation	5/295	35/12091	0.0014882	0.2529708	0.2436046	EP300/KMT2A/L3MBTL1/TNRC6B/TNRC6C	5
GO:0035194	post-transcriptional gene silencing by RNA	3/295	10/12091	0.0015190	0.2529708	0.2436046	HELZ/TNRC6B/TNRC6C	3
GO:0051726	regulation of cell cycle	8/295	96/12091	0.0023630	0.2529708	0.2436046	BOP1/CSNK2A2/EP300/HIPK2/L3MBTL1/NUP214/PRDM11/SON	8
GO:0010761	fibroblast migration	3/295	12/12091	0.0026857	0.2529708	0.2436046	ARID5B/LOC113988212/LOC113992314	3
GO:0006110	regulation of glycolytic process	5/295	40/12091	0.0027317	0.2529708	0.2436046	AAAS/EP300/LOC113999689/NUP214/TPR	5
GO:0007595	lactation	5/295	42/12091	0.0033944	0.2529708	0.2436046	CAD/LOC113997056/LOC113998036/MED1/NCOR2	5
GO:0019827	stem cell population maintenance	5/295	42/12091	0.0033944	0.2529708	0.2436046	EIF4ENIF1/LOC113993147/LOC113999502/MED30/RIF1	5

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

11.1.2 black - strength

## Warning: Removed 3 rows containing missing values
## (`geom_point()`).

11.1.2.1 Gene Ontology

BP enriched GO terms in black module in LS
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0007281	germ cell development	10/1654	19/12091	0.0000635	0.1183396	0.1180611	ACVR1/AKT1/ANGPT2/BMP4/CELF4/FBXW11/PAFAH1B1/RPS6KB1/SMAD5/TDRD1	10
GO:0006376	mRNA splice site selection	10/1654	20/12091	0.0001114	0.1183396	0.1180611	CELF2/CELF4/LOC113985802/LOC113985815/LOC114002198/LUC7L/LUC7L3/RBMX/SRSF1/SRSF6	10
GO:0005979	regulation of glycogen biosynthetic process	6/1654	10/12091	0.0008323	0.5895228	0.5881357	AKT1/INPP5K/PPP1CB/PPP1R3C/PPP1R3D/PPP1R3G	6
GO:0035338	long-chain fatty-acyl-CoA biosynthetic process	8/1654	18/12091	0.0014705	0.7811873	0.7793493	ACSL3/ACSL4/ELOVL2/ELOVL4/ELOVL5/ELOVL6/HSD17B12/TECR	8
GO:0032869	cellular response to insulin stimulus	21/1654	84/12091	0.0038217	0.8005247	0.7986411	AKT1/C2CD5/CAPN10/CPEB2/FER/HDAC9/INPP5K/INSIG2/LOC113985802/LOC113985815/LPIN2/PTEN/RAB12/RAB8B/RHOQ/RPS6KB1/SORBS1/STAR/SYAP1/TRARG1/YWHAG	21
GO:0006661	phosphatidylinositol biosynthetic process	19/1654	74/12091	0.0042222	0.8005247	0.7986411	CDIPT/CDS1/DGKE/FIG4/INPP4A/INPP5K/MTMR12/MTMR6/MTMR8/PIK3CA/PIK3R4/PIP4K2A/PIP4K2B/PITPNM3/PLEKHA3/PTEN/RAB4A/RAB5A/SACM1L	19
GO:0016024	CDP-diacylglycerol biosynthetic process	6/1654	13/12091	0.0047103	0.8005247	0.7986411	AGPAT3/AGPAT5/CDS1/LCLAT1/LOC113995899/TAMM41	6
GO:0032482	Rab protein signal transduction	6/1654	13/12091	0.0047103	0.8005247	0.7986411	RAB12/RAB21/RAB33B/RAB39A/RAB4A/RAB9A	6
GO:0045842	positive regulation of mitotic metaphase/anaphase transition	6/1654	13/12091	0.0047103	0.8005247	0.7986411	ANAPC11/ANAPC4/ESPL1/LOC113991003/NSMCE2/RB1	6
GO:0071470	cellular response to osmotic stress	6/1654	13/12091	0.0047103	0.8005247	0.7986411	DDX3X/LOC113991482/LOC113991485/LOC113991486/LRRC8D/ZFAND1	6

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

12 Bed Nucleus of the Stria Terminalis (BSTm)

## [1] 15033

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## -- replacing outliers and refitting for 35 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)

## estimating dispersions

## fitting model and testing

##    Power SFT.R.sq   slope truncated.R.sq mean.k. median.k. max.k.
## 1      1 8.90e-06   0.819          0.932  7520.0   7520.00   7570
## 2      2 4.60e-01 -19.200          0.919  4140.0   4120.00   4520
## 3      3 4.54e-01  -6.940          0.905  2450.0   2420.00   3000
## 4      4 4.95e-01  -4.130          0.915  1540.0   1500.00   2140
## 5      5 5.62e-01  -3.070          0.935  1010.0    973.00   1590
## 6      6 6.37e-01  -2.750          0.952   689.0    652.00   1250
## 7      7 6.93e-01  -2.670          0.960   486.0    450.00   1010
## 8      8 7.32e-01  -2.640          0.961   351.0    318.00    841
## 9      9 7.65e-01  -2.600          0.965   260.0    230.00    713
## 10    10 7.99e-01  -2.540          0.977   197.0    169.00    613
## 11    11 8.14e-01  -2.520          0.977   152.0    126.00    535
## 12    12 8.28e-01  -2.510          0.977   119.0     95.20    471
## 13    14 8.51e-01  -2.460          0.980    75.6     56.50    375
## 14    16 8.78e-01  -2.390          0.987    50.4     34.80    306
## 15    18 8.91e-01  -2.350          0.989    34.9     22.30    255
## 16    20 8.89e-01  -2.330          0.984    24.9     14.60    216
## 17    22 9.04e-01  -2.280          0.991    18.3      9.91    185
## 18    24 9.15e-01  -2.220          0.994    13.7      6.84    160
## 19    26 9.15e-01  -2.190          0.992    10.5      4.82    140

The soft-power selected for BSTm is 22.

Genes per module in BSTm
moduleColors	Freq
antiquewhite4	712
bisque4	111
blue	1123
brown	1067
brown4	1377
coral1	69
coral2	490
darkgrey	720
darkmagenta	317
darkolivegreen	929
darkorange2	1057
darkred	492
green	471
grey	141
grey60	996
ivory	220
lightpink4	82
maroon	317
mediumpurple3	128
navajowhite2	87
paleturquoise	337
royalblue	222
saddlebrown	169
salmon4	2361
skyblue	940
thistle1	96

There appears to be a strong correlation between darkred and salmon4, and inverse between darkmagenta and paleturquoise

12.1 Interesting Modules

12.1.1 darkred - strength

12.1.1.1 Gene Ontology

BP enriched GO terms in darkred module in BSTm
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0038007	netrin-activated signaling pathway	5/420	14/12181	0.0000736	0.0904506	0.0869563	DSCAM/LOC113983017/LOC113988456/LOC113993074/UNC5A	5
GO:1900034	regulation of cellular response to heat	10/420	70/12181	0.0001329	0.0904506	0.0869563	AAAS/ATR/CAMK2B/CREBBP/HSP90AB1/LOC113998422/MLST8/NUP54/NUP62CL/NUP85	10
GO:0090303	positive regulation of wound healing	6/420	26/12181	0.0002075	0.0941361	0.0904994	ACTG1/DMTN/LOC113983017/LOC113983228/LOC113993074/OCLN	6
GO:0043984	histone H4-K16 acetylation	5/420	21/12181	0.0006128	0.2085137	0.2004582	KANSL1/KANSL3/KAT7/KMT2A/MSL1	5
GO:0006909	phagocytosis	7/420	46/12181	0.0009214	0.2508137	0.2411241	LOC113988212/LOC113992314/LOC113993578/LRP1/RUBCN/SYT7/TUSC2	7
GO:0051899	membrane depolarization	5/420	26/12181	0.0017189	0.3342980	0.3213832	CACNA1E/CACNB3/CHRNB2/LOC113993166/LOC114000517	5
GO:0060964	regulation of gene silencing by miRNA	7/420	51/12181	0.0017194	0.3342980	0.3213832	AAAS/LOC113983589/LOC113992597/LOC114003727/NUP54/NUP62CL/NUP85	7
GO:0032786	positive regulation of DNA-templated transcription, elongation	4/420	17/12181	0.0023199	0.3351089	0.3221628	CCNT1/ELL/KAT7/LOC113988658	4
GO:0038096	Fc-gamma receptor signaling pathway involved in phagocytosis	8/420	69/12181	0.0024789	0.3351089	0.3221628	ACTG1/BAIAP2/ELMO2/HSP90AB1/LOC113983017/LOC113983228/LOC113993074/NCKIPSD	8
GO:0034622	cellular protein-containing complex assembly	4/420	18/12181	0.0029024	0.3351089	0.3221628	DNAJB12/LOC113995101/LOC113998422/TRAF2	4

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

12.1.2 darkmagenta - strength

12.1.2.1 Gene Ontology

BP enriched GO terms in darkmagenta module in BSTm
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0055119	relaxation of cardiac muscle	3/256	11/12181	0.0013359	0.5366384	0.5205780	ATP1A1/GSN/RGS2	3
GO:0010887	negative regulation of cholesterol storage	3/256	12/12181	0.0017537	0.5366384	0.5205780	ABCA7/LOC113986559/PPARD	3
GO:0051014	actin filament severing	3/256	12/12181	0.0017537	0.5366384	0.5205780	CAPG/FMNL1/GSN	3
GO:2000377	regulation of reactive oxygen species metabolic process	3/256	14/12181	0.0028127	0.5634260	0.5465639	BCO2/MPV17/PINK1	3
GO:0002028	regulation of sodium ion transport	3/256	15/12181	0.0034618	0.5634260	0.5465639	ADRB2/ATP1A1/LOC113988625	3
GO:0006904	vesicle docking involved in exocytosis	4/256	34/12181	0.0053839	0.5634260	0.5465639	CPLX2/RAB15/RABEPK/VPS33B	4
GO:0043407	negative regulation of MAP kinase activity	4/256	34/12181	0.0053839	0.5634260	0.5465639	CDK5RAP3/LOC113991889/PIK3CB/RGS2	4
GO:0008203	cholesterol metabolic process	5/256	58/12181	0.0072969	0.5634260	0.5465639	ABCA7/LOC113986559/LOC113991750/LRP5/PPARD	5
GO:0030168	platelet activation	6/256	83/12181	0.0079613	0.5634260	0.5465639	ADAMTS13/DGKZ/LOC113984938/LOC113991889/LOC113997278/PIK3CB	6
GO:0051056	regulation of small GTPase mediated signal transduction	8/256	141/12181	0.0098161	0.5634260	0.5465639	ARHGAP17/ARHGAP19/ARHGEF16/INPP5B/LOC113997278/OPHN1/RHOU/SYDE2	8

## preparing gene to GO mapping data...

## preparing IC data...

## Warning in if (class(try(col2rgb(palette), silent = TRUE)) == "try-error")
## stop("color palette is not correct"): the condition has length > 1 and only the
## first element will be used

12.1.3 saddlebrown - strength

12.1.3.1 Gene Ontology

BP enriched GO terms in saddlebrown module in BSTm
ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue	geneID	Count
GO:0015701	bicarbonate transport	5/128	33/12181	0.0000222	0.0113948	0.0110398	LOC113995938/LOC114003675/RHAG/SLC4A1/SLC4A4	5
GO:0042307	positive regulation of protein import into nucleus	5/128	37/12181	0.0000394	0.0113948	0.0110398	DMAP1/LOC113989827/LOC114001403/SMO/ZC3H12A	5
GO:2000379	positive regulation of reactive oxygen species metabolic process	4/128	20/12181	0.0000495	0.0113948	0.0110398	LOC114001403/NFE2L2/PDGFRB/ZC3H12A	4
GO:2000036	regulation of stem cell population maintenance	3/128	13/12181	0.0003001	0.0518481	0.0502329	SAV1/SMO/TAL1	3
GO:0050801	ion homeostasis	3/128	18/12181	0.0008240	0.1100942	0.1066645	SLC4A1/SLC4A4/WNK4	3
GO:0006635	fatty acid beta-oxidation	4/128	42/12181	0.0009560	0.1100942	0.1066645	DECR1/ECH1/LOC114001403/SESN2	4
GO:0001525	angiogenesis	8/128	203/12181	0.0013439	0.1326657	0.1285329	IL18/JUN/LOC113989827/LOC114001403/PLXND1/TAL1/WASF2/ZC3H12A	8
GO:0098869	cellular oxidant detoxification	4/128	55/12181	0.0026226	0.2265230	0.2194663	LOC113986834/LOC114003675/SELENOW/SESN2	4
GO:0043123	positive regulation of I-kappaB kinase/NF-kappaB signaling	6/128	146/12181	0.0043831	0.3224010	0.3123574	CARD11/LOC113989827/NEK6/TMEM101/TRIM32/WLS	6
GO:0043249	erythrocyte maturation	2/128	10/12181	0.0046657	0.3224010	0.3123574	LOC114001063/TAL1	2

13 WGCNA summary

14 Session Info

## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_Australia.1252  LC_CTYPE=English_Australia.1252   
## [3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C                      
## [5] LC_TIME=English_Australia.1252    
## 
## attached base packages:
##  [1] grid      parallel  stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] enrichplot_1.8.1            AnnotationHub_2.20.2       
##  [3] BiocFileCache_1.12.0        dbplyr_2.1.1               
##  [5] network_1.16.1              RColorBrewer_1.1-2         
##  [7] MASS_7.3-51.6               dplyr_1.0.7                
##  [9] ggnetwork_0.5.8             igraph_1.2.6               
## [11] ggfortify_0.4.11            GGally_2.1.0               
## [13] effectsize_0.8.6            car_3.0-8                  
## [15] carData_3.0-4               viridis_0.6.1              
## [17] viridisLite_0.4.0           rrvgo_1.0.2                
## [19] UpSetR_1.4.0                limma_3.44.3               
## [21] flashClust_1.01-2           org.Hs.eg.db_3.11.4        
## [23] AnnotationDbi_1.50.0        clusterProfiler_3.16.1     
## [25] fdrtool_1.2.15              ggpubr_0.4.0               
## [27] gridExtra_2.3               wesanderson_0.3.6          
## [29] VennDiagram_1.6.20          futile.logger_1.4.3        
## [31] kableExtra_1.3.1            WGCNA_1.69                 
## [33] fastcluster_1.1.25          dynamicTreeCut_1.63-1      
## [35] pheatmap_1.0.12             lubridate_1.8.0            
## [37] DESeq2_1.28.1               SummarizedExperiment_1.18.1
## [39] DelayedArray_0.14.0         matrixStats_0.56.0         
## [41] Biobase_2.48.0              GenomicRanges_1.40.0       
## [43] GenomeInfoDb_1.24.0         IRanges_2.22.2             
## [45] S4Vectors_0.26.1            BiocGenerics_0.34.0        
## [47] stringr_1.4.0               PCAtools_2.0.0             
## [49] cowplot_1.0.0               lattice_0.20-41            
## [51] ggrepel_0.8.2               ggplot2_3.4.4              
## [53] reshape2_1.4.4              plyr_1.8.6                 
## 
## loaded via a namespace (and not attached):
##   [1] rappdirs_0.3.1                ragg_1.2.1                   
##   [3] tidyr_1.1.4                   acepack_1.4.1                
##   [5] bit64_0.9-7                   knitr_1.29                   
##   [7] irlba_2.3.3                   data.table_1.13.0            
##   [9] rpart_4.1-15                  RCurl_1.98-1.2               
##  [11] doParallel_1.0.15             generics_0.0.2               
##  [13] snow_0.4-3                    preprocessCore_1.50.0        
##  [15] lambda.r_1.2.4                RSQLite_2.2.0                
##  [17] europepmc_0.4                 bit_4.0.4                    
##  [19] webshot_0.5.2                 xml2_1.3.2                   
##  [21] httpuv_1.5.4                  assertthat_0.2.1             
##  [23] xfun_0.29                     hms_1.1.1                    
##  [25] jquerylib_0.1.4               evaluate_0.14                
##  [27] promises_1.1.1                fansi_0.4.1                  
##  [29] progress_1.2.2                readxl_1.3.1                 
##  [31] DBI_1.1.0                     geneplotter_1.66.0           
##  [33] htmlwidgets_1.5.3             reshape_0.8.8                
##  [35] purrr_0.3.4                   ellipsis_0.3.2               
##  [37] backports_1.1.7               insight_0.19.6               
##  [39] annotate_1.66.0               gridBase_0.4-7               
##  [41] vctrs_0.6.5                   abind_1.4-5                  
##  [43] withr_2.5.2                   ggforce_0.3.2                
##  [45] triebeard_0.3.0               treemap_2.4-2                
##  [47] checkmate_2.0.0               prettyunits_1.1.1            
##  [49] cluster_2.1.0                 DOSE_3.14.0                  
##  [51] crayon_1.4.2                  genefilter_1.70.0            
##  [53] pkgconfig_2.0.3               slam_0.1-47                  
##  [55] labeling_0.3                  tweenr_1.0.1                 
##  [57] nlme_3.1-148                  wordcloud_2.6                
##  [59] nnet_7.3-14                   rlang_1.1.2                  
##  [61] lifecycle_1.0.4               downloader_0.4               
##  [63] rsvd_1.0.3                    cellranger_1.1.0             
##  [65] polyclip_1.10-0               datawizard_0.9.0             
##  [67] Matrix_1.6-4                  urltools_1.7.3               
##  [69] base64enc_0.1-3               ggridges_0.5.3               
##  [71] png_0.1-7                     parameters_0.21.3            
##  [73] bitops_1.0-6                  blob_1.2.1                   
##  [75] DelayedMatrixStats_1.10.0     qvalue_2.20.0                
##  [77] jpeg_0.1-8.1                  rstatix_0.7.0                
##  [79] gridGraphics_0.5-1            ggsignif_0.6.0               
##  [81] scales_1.3.0                  memoise_1.1.0                
##  [83] magrittr_2.0.1                zlibbioc_1.34.0              
##  [85] compiler_4.0.2                scatterpie_0.1.5             
##  [87] dqrng_0.2.1                   cli_3.6.1                    
##  [89] XVector_0.28.0                htmlTable_2.0.1              
##  [91] formatR_1.8                   Formula_1.2-3                
##  [93] mgcv_1.8-31                   tidyselect_1.1.0             
##  [95] stringi_1.4.6                 forcats_0.5.1                
##  [97] textshaping_0.3.6             highr_0.8                    
##  [99] yaml_2.2.1                    GOSemSim_2.14.2              
## [101] BiocSingular_1.4.0            locfit_1.5-9.4               
## [103] latticeExtra_0.6-29           fastmatch_1.1-0              
## [105] tools_4.0.2                   rio_0.5.16                   
## [107] rstudioapi_0.13               foreach_1.5.0                
## [109] foreign_0.8-80                farver_2.0.3                 
## [111] ggraph_2.0.5                  digest_0.6.25                
## [113] rvcheck_0.1.8                 BiocManager_1.30.10          
## [115] shiny_1.5.0                   Rcpp_1.0.7                   
## [117] broom_0.7.10                  BiocVersion_3.11.1           
## [119] later_1.1.0.1                 httr_1.4.2                   
## [121] colorspace_1.4-1              rvest_1.0.2                  
## [123] XML_3.99-0.5                  splines_4.0.2                
## [125] graphlayouts_0.7.1            ggplotify_0.0.5              
## [127] systemfonts_1.0.3             xtable_1.8-4                 
## [129] jsonlite_1.7.2                futile.options_1.0.1         
## [131] tidygraph_1.2.0               NLP_0.2-1                    
## [133] R6_2.4.1                      tm_0.7-8                     
## [135] Hmisc_4.4-0                   pillar_1.6.4                 
## [137] htmltools_0.5.2               mime_0.9                     
## [139] glue_1.5.1                    fastmap_1.1.0                
## [141] BiocParallel_1.22.0           interactiveDisplayBase_1.26.3
## [143] codetools_0.2-16              fgsea_1.14.0                 
## [145] utf8_1.1.4                    tibble_3.1.6                 
## [147] curl_4.3                      zip_2.0.4                    
## [149] GO.db_3.11.4                  openxlsx_4.1.5               
## [151] survival_3.2-3                rmarkdown_2.11               
## [153] munsell_0.5.0                 DO.db_2.9                    
## [155] GenomeInfoDbData_1.2.3        iterators_1.0.12             
## [157] impute_1.62.0                 haven_2.3.1                  
## [159] gtable_0.3.0                  bayestestR_0.13.1

WGCNA Analyses Within-Tissues

For manuscript: Neurogenomic landscape of male cooperative behavior in a wild bird

Last Substantive Change April 2021

Last Knit “2024-01-06”