AMPAD:

#login to synapse
synapser::synLogin()

#pull mods
foo <- synapser::synTableQuery("select * from syn11932957")$asDataFrame()
foo2 <- dplyr::select(foo,GeneID,Module)
foo2$Presence <- 1
foo3 <- tidyr::pivot_wider(foo2,
                           id_cols = "GeneID",
                           names_from = "Module",
                           values_from = "Presence")
foo3[is.na(foo3)] <- 0
foo3 <- data.frame(foo3,stringsAsFactors=F)
foo4 <- dplyr::select(foo3,GeneID,TCXblue,IFGyellow,PHGyellow)

tiff(filename = 'consensusClusterA.tiff', height = 4, width = 6,units='in',pointsize=14,res=300)
UpSetR::upset(foo4,nintersects = NA,show.numbers=F)
dev.off()

nUniqueGenesA <- data.frame(Module=c('TCXblue','PHGyellow','IFGyellow'),nGenes=c(979,366,127),stringsAsFactors=F)

foo4 <- dplyr::select(foo3,GeneID,DLPFCblue,CBEturquoise,STGblue,PHGturquoise,IFGturquoise,TCXturquoise,FPturquoise)
tiff(filename = 'consensusClusterB.tiff', height = 4, width = 6,units='in',pointsize=14,res=300)
UpSetR::upset(foo4,nsets=7,nintersects = NA,point.size=1,show.numbers = F)
dev.off()

nUniqueGenesB <- data.frame(Module=c('CBEturquoise','DLPFCblue','IFGturquoise','PHGturquoise','STGblue','TCXturquoise','FPturquoise'),nGenes=c(593,349,275,209,163,69,40),stringsAsFactors=F)

foo4 <- dplyr::select(foo3,GeneID,IFGbrown,STGbrown,DLPFCyellow,TCXgreen,FPyellow,CBEyellow,PHGbrown)
tiff(filename = 'consensusClusterC.tiff', height = 4, width = 6,units='in',pointsize=14,res=300)
UpSetR::upset(foo4,nsets=7,nintersects = NA,point.size=1,show.numbers = F)
dev.off()

nUniqueGenesC <- data.frame(Module=c('IFGbrown','FPyellow','STGbrown','DLPFCyellow','TCXgreen','PHGbrown','CBEyellow'),nGenes=c(966,641,233,178,141,139,28),stringsAsFactors=F)

foo4 <- dplyr::select(foo3,GeneID,DLPFCbrown,STGyellow,PHGgreen,CBEbrown,TCXyellow,IFGblue,FPblue)
tiff(filename = 'consensusClusterD.tiff', height = 4, width = 6,units='in',pointsize=14,res=300)
UpSetR::upset(foo4,nsets=7,nintersects = NA,point.size=1,show.numbers = F)
dev.off()

nUniqueGenesD <- data.frame(Module=c('IFGblue','TCXyellow','FPblue','STGyellow','PHGgreen','DLPFCbrown','CBEbrown'),nGenes=c(1148,673,627,344,122,103,56),stringsAsFactors=F)


foo4 <- dplyr::select(foo3,GeneID,FPbrown,CBEblue,DLPFCturquoise,TCXbrown,STGturquoise,PHGblue)
tiff(filename = 'consensusClusterE.tiff', height = 4, width = 6,units='in',pointsize=14,res=300)
UpSetR::upset(foo4,nsets=6,nintersects = NA,point.size=1,show.numbers = F)
dev.off()

nUniqueGenesE <- data.frame(Module=c('CBEblue','PHGblue','DLPFCturquoise','STGturquoise','TCXbrown','FPbrown'),nGenes=c(1862,951,447,423,358,201),stringsAsFactors=F)


nUniqueGenes <- rbind(nUniqueGenesA,
                      nUniqueGenesB,
                      nUniqueGenesC,
                      nUniqueGenesD,
                      nUniqueGenesE)

library(dplyr)
modSize <- dplyr::group_by(foo2,Module) %>%
  dplyr::summarise(mSize=sum(Presence))

sumMat1 <- dplyr::left_join(modSize,nUniqueGenes)
sumMat1$percentUnique <- sumMat1$nGenes/sumMat1$mSize

customDf <- data.frame(moduleName=c('TCXblue',
                                    'IFGyellow',
                                    'PHGyellow',
                                    'DLPFCblue',
                                    'CBEturquoise',
                                    'STGblue',
                                    'PHGturquoise',
                                    'IFGturquoise',
                                    'TCXturquoise',
                                    'FPturquoise',
                                    'IFGbrown',
                                    'STGbrown',
                                    'DLPFCyellow',
                                    'TCXgreen',
                                    'FPyellow',
                                    'CBEyellow',
                                    'PHGbrown',
                                    'DLPFCbrown',
                                    'STGyellow',
                                    'PHGgreen',
                                    'CBEbrown',
                                    'TCXyellow',
                                    'IFGblue',
                                    'FPblue',
                                    'FPbrown',
                                    'CBEblue',
                                    'DLPFCturquoise',
                                    'TCXbrown',
                                    'STGturquoise',
                                    'PHGblue'),
                       Cluster= c(rep('Consensus Cluster A',3),
                                  rep('Consensus Cluster B',7),
                                  rep('Consensus Cluster C',7),
                                  rep('Consensus Cluster D',7),
                                  rep('Consensus Cluster E',6)),
                       stringsAsFactors=F)


sumMat1 <- dplyr::left_join(sumMat1,customDf,by=c('Module'='moduleName'))

summary(lm(percentUnique ~ 1,dplyr::filter(sumMat1,Cluster=="Consensus Cluster D" | Cluster=="Consensus Cluster E")))

summary(lm(percentUnique ~ 1,dplyr::filter(sumMat1,Cluster=="Consensus Cluster A" | Cluster=="Consensus Cluster B" | Cluster=="Consensus Cluster C")))