archive/buildSummaryManifest.R
In Sage-Bionetworks/AMPAD:
#####script to build module summary table
synapseClient::synapseLogin()
#source('moduleAnalysis/summaryManifestFunctions.R')
#####igap gwas enrichments
adGeneticsSummary <- AMPAD::getAdGenetics()
#####deg enrichments
degSummary <- AMPAD::getDeg()
adGeneticsSummarySig <- dplyr::filter(adGeneticsSummary,
GeneSetAdjustedAssociationStatistic <= 0.05)
admodcheat <- dplyr::select(adGeneticsSummarySig,
ModuleNameFull,
GeneSetName,
GeneSetADLinked)
admodcheat <- tidyr::spread(admodcheat,
ModuleNameFull,
GeneSetADLinked)
rownames(admodcheat) <- admodcheat$GeneSetName
admodcheat <- admodcheat[,-1]
admodcheat <- t(admodcheat)
admodcheat[is.na(admodcheat)] <- 0
admodcheat <- data.frame(admodcheat,stringsAsFactors=F)
admodcheat$adGeneticScore <- rowMeans(admodcheat)
admodcheat$ModuleNameFull <- rownames(admodcheat)
admodcheat <- dplyr::arrange(admodcheat,desc(adGeneticScore))
moduleSummarySig <- dplyr::filter(degSummary,
GeneSetAdjustedAssociationStatistic <=0.05)
library(dplyr)
getModuleCheatSheet <- dplyr::select(moduleSummarySig,
ModuleNameFull,
GeneSetName,
GeneSetDirectionAD,
GeneSetBrainRegion,
GeneSetCategoryName,
GeneSetADLinked)
getModuleCheatSheet$genesetdir <- paste0(getModuleCheatSheet$GeneSetName,
getModuleCheatSheet$GeneSetDirectionAD,
getModuleCheatSheet$GeneSetBrainRegion,
getModuleCheatSheet$GeneSetCategoryName)
getModuleCheatSheet <- dplyr::select(getModuleCheatSheet,
ModuleNameFull,
genesetdir,
GeneSetADLinked)
moduleCheatSheet <- tidyr::spread(getModuleCheatSheet,
ModuleNameFull,
GeneSetADLinked)
rownames(moduleCheatSheet) <- moduleCheatSheet$genesetdir
moduleCheatSheet <- moduleCheatSheet[,-1]
moduleCheatSheet <- t(moduleCheatSheet)
moduleSet <- synapseClient::synTableQuery("SELECT DISTINCT ModuleNameFull, Module, method, brainRegion from syn10338156")@values
colnames(moduleSet)[c(3:4)] <- c('ModuleMethod','ModuleBrainRegion')
#dropCols <- which(apply(moduleCheatSheet,2,sum,na.rm=T)==0)
#moduleCheatSheet <- moduleCheatSheet[,-dropCols]
moduleCheatSheet[is.na(moduleCheatSheet)] <- 0
moduleCheatSheet <- data.frame(moduleCheatSheet,stringsAsFactors=F)
moduleCheatSheet$degScore <- rowMeans(moduleCheatSheet)
moduleCheatSheet$ModuleNameFull <- rownames(moduleCheatSheet)
moduleCheatSheet <- dplyr::arrange(moduleCheatSheet,desc(degScore))
degTopScores <- dplyr::select(moduleCheatSheet,degScore,ModuleNameFull)
degTopScores <- dplyr::left_join(degTopScores,moduleSet)
rSynapseUtilities::makeTable(degTopScores,tableName = "deg mods february 27 2018",projectId = 'syn5569099')
# combinedScores <- dplyr::select(moduleCheatSheet,ModuleNameFull,degScore)
# combinedScores$degScore <- as.numeric(scale(combinedScores$degScore,center = FALSE))
# combinedScores <- dplyr::full_join(combinedScores,dplyr::select(admodcheat,ModuleNameFull,adGeneticScore))
# combinedScores$adGeneticScore <- as.numeric(scale(combinedScores$adGeneticScore,center=FALSE))
# combinedScores$aggregate <- combinedScores$degScore + combinedScores$adGeneticScore
# combinedScores <- dplyr::arrange(combinedScores,desc(aggregate))
# combinedScoresReducted <- combinedScores[1:263,]
# combinedScoresReducted <- dplyr::left_join(combinedScoresReducted,moduleSet)
#
# rSynapseUtilities::makeTable(combinedScoresReducted,tableName = "top amp-ad mods september 21 2017",projectId = 'syn5569099')
#
# degTopScores <- dplyr::select(moduleCheatSheet,degScore,ModuleNameFull)
# degTopScores <- dplyr::left_join(degTopScores,moduleSet)
# rSynapseUtilities::makeTable(degTopScores,tableName = "deg mods september 26 2017",projectId = 'syn5569099')
# ########compile annotations of modules
# ####download mods
# combinedScoresReducted <- synapseClient::synTableQuery("SELECT * FROM syn10516371")@values
#
#
# cell_type <- getCellTypes()
# cellSummarySig <- dplyr::filter(cell_type,
# GeneSetAdjustedAssociationStatistic <= 0.05)
#
#
#
# ####get cell type enrichments
# #cellTypeEnrichments <- synapseClient::synTableQuery("SELECT * FROM syn10498382")@values
#
# #colnames(cellTypeEnrichments)[c(2,3,7)] <- c("GeneSetCategoryName",
# # "GeneSetAssociationStatistic",
# # "ModuleBrainRegion")
# #cellTypeEnrichments <- splitByBrainRegionAdjustPvalue(cellTypeEnrichments)
# #cellTypeEnrichments <- dplyr::filter(cellTypeEnrichments,GeneSetAdjustedAssociationStatistic<=0.05)
# combinedScoresReducted2 <- dplyr::left_join(combinedScoresReducted,dplyr::select(cellSummarySig,ModuleNameFull,GeneSetName,GeneSetEffect))
#
#
#
# module_ad_score <- apply(moduleCheatSheet,1,sum)
# sort(module_ad_score,decreasing=T)[1:30]
#
#
# #####pathway annotations
# pathwaySummary <- getPathways()
#
# #####TO DO
# #####eigengene associations
# eigengeneSummary <- getEigengene()
#
# #####mod pres
# modulePreservationSummary <- getModulePreservation()
#
#
#
#
#
# #####compile enrichments
# enrichments <- synapseClient::synTableQuery("SELECT * FROM syn10492048")@values
# enrichments2 <- dplyr::select(enrichments,ModuleNameFull,category,geneSet,fisherPval,fisherOR)
# colnames(enrichments2) <- c('ModuleNameFull',
# 'GeneSetName',
# 'GeneSetCategoryName',
# 'GeneSetAssociationStatistic',
# 'GeneSetEffect')
#
# enrichments2$GeneSetBrainRegion <- rep(NA,nrow(enrichments2))
# enrichments2$GeneSetDirectionAD <- rep(NA,nrow(enrichments2))
# ad_lists <- grep('alzheimer',(enrichments2$GeneSetName))
# #ad_lists2 <- grep('load',unique(enrichments2$GeneSetName))
# #ad_lists3 <- grep("AD",unique(enrichments2$GeneSetName))
# #ad_lists<-grep('',enrichments2$GeneSetName)
# enrichments2$GeneSetADLinked <- rep(FALSE,nrow(enrichments2))
# enrichments2$GeneSetADLinked[ad_lists] <- TRUE
#
# #bonferroni womp womp
#
# bonferroni_fun <- function(x,ntests=1e8){
# return(min(1,x*ntests))
# }
#
# enrichments2$GeneSetAdjustedAssociationStatistic <- sapply(enrichments2$GeneSetAssociationStatistic,bonferroni_fun)
#
# enrichments2 <- dplyr::left_join(moduleSet,enrichments2)
# enrichments3 <- dplyr::filter(enrichments2,GeneSetAdjustedAssociationStatistic <= 0.05)
#
# combinedScoresReducted3 <- dplyr::left_join(combinedScoresReducted2,dplyr::select(enrichments3,ModuleNameFull,GeneSetName,GeneSetCategoryName,GeneSetEffect),by='ModuleNameFull')
#
#
#
#
# ad_lists2 <- which(enrichments2$GeneSetADLinked)
#
# moduleSummary <- rbind(moduleSummary,enrichments2)
#
# moduleSummarySig <- dplyr::filter(moduleSummary,GeneSetAssociationStatistic <=0.05)
#
# library(dplyr)
# getModuleCheatSheet <- dplyr::select(moduleSummarySig,
# ModuleNameFull,
# GeneSetName,
# GeneSetDirectionAD,
# GeneSetBrainRegion,
# GeneSetCategoryName,
# GeneSetADLinked)
# getModuleCheatSheet$genesetdir <- paste0(getModuleCheatSheet$GeneSetName,
# getModuleCheatSheet$GeneSetDirectionAD,
# getModuleCheatSheet$GeneSetBrainRegion,
# getModuleCheatSheet$GeneSetCategoryName)
#
# getModuleCheatSheet <- dplyr::select(getModuleCheatSheet,
# ModuleNameFull,
# genesetdir,
# GeneSetADLinked)
#
# moduleCheatSheet <- tidyr::spread(getModuleCheatSheet,
# ModuleNameFull,
# GeneSetADLinked)
#
# rownames(moduleCheatSheet) <- moduleCheatSheet$genesetdir
# moduleCheatSheet <- moduleCheatSheet[,-1]
# moduleCheatSheet <- t(moduleCheatSheet)
#
# dropCols <- which(apply(moduleCheatSheet,2,sum,na.rm=T)==0)
# moduleCheatSheet <- moduleCheatSheet[,-dropCols]
# module_ad_score <- apply(moduleCheatSheet,1,sum,na.rm=T)
# sort(module_ad_score,decreasing=T)[1:30]
#
#
# enrichmentManifest <- synapseClient::synTableQuery("SELECT * FROM syn10468216")@values
#
# foobar <- readRDS(synapseClient::synGet(enrichmentManifest$id[1])@filePath)
# View(enrichmentManifest)
#
#
#
# # summaryDegManifest <- dplyr::group_by(degResults2,
# # ModuleNameFull,
# # Direction,
# # reducedCategory) %>%
# # dplyr::summarise(medianZ = median(Z),
# # medianOR = median(fisherOR),
# # medianPval=median(fisherPval))
# #
# #
# # summaryDegManifest <- dplyr::mutate(summaryDegManifest,
# # adj = p.adjust(medianPval,
# # method='fdr'))
# #
# # summaryDegManifest2 <- dplyr::filter(summaryDegManifest,
# # adj<=0.05)
# #
# # View(summaryDegManifest2)
# #
# # g <- ggplot2::ggplot(summaryDegManifest,
# # ggplot2::aes(x=Direction,
# # y=medianZ,
# # fill=reducedCategory))
# # g <- g + ggplot2::geom_boxplot(position='dodge')
# # #g <- g + ggplot2::scale_y_log10()
# # g <- g + ggplot2::theme_grey(base_size = 20)
# # g
#
# #extract
#
#
# #dplyr::summarise(numberOfGenes=length(ModuleName)
#
# #categoryKey <- categoryKey[!duplicated(categoryKey),]
#
#
# #####cell type results
# genesets1 <- synapseClient::synGet('syn5923958')
# load(synapseClient::getFileLocation(genesets1))
# cellMarkers <- GeneSets$Cell_Markers
# cellTypeResults <- run_amp_ad_enrichment(cellMarkers,
# "celltypes",
# hgnc=TRUE)
# #####combined manifest
# fullManifest <- rbind(degResults,
# cellTypeResults)
#
# magmaReformat <- dplyr::select(magmaResults,SET,P)
#
# colnames(magmaReformat) <- c('ModuleNameFull','magmaPval')
# magmaReformat <- dplyr::mutate(magmaReformat,magmaZ=qnorm(magmaPval,lower.tail=F))
#
# summaryDegManifest2 <- dplyr::left_join(summaryDegManifest,magmaReformat)
# summaryDegManifest2 <- dplyr::mutate(summaryDegManifest2,combZ=magmaZ/2+medianZ/2)
# summaryDegManifest2 <- dplyr::arrange(summaryDegManifest2,desc(medianZ))
# #split by each category
# fxn1 <- function(x,y){
# foobar <- dplyr::filter(y,reducedCategory==x)
# return(foobar)
# }
# splitSummaries <- lapply(unique(summaryDegManifest2$reducedCategory),fxn1,summaryDegManifest2)
# names(splitSummaries) <- unique(summaryDegManifest2$reducedCategory)
# View(splitSummaries[[1]])
# splitSummaries2 <- lapply(splitSummaries,function(x){
# x <- dplyr::arrange(x,desc(medianZ))
# xup <- dplyr::filter(x,Direction=='UP')
# xdown <- dplyr::filter(x,Direction=='DOWN')
# return(rbind(xup[1:5,],xdown[1:5,]))
# })
#
# View(splitSummaries2[[9]])
#
#
# ##just take top from each up/down
# fxn2 <- function(x){
# foobar1 <- dplyr::filter(x,Direction=='DOWN')
# foobar2 <- dplyr::filter(x,Direction=='UP')
# return(c('down_mod'=foobar1$ModuleNameFull[1],
# 'up_mod'=foobar2$ModuleNameFull[1]))
# }
# getMods <- sapply(splitSummaries,
# fxn2)
# topMods <- t(getMods)
#
# #magmaReformat$category <- rep('MAGMA',nrow(magmaReformat))
# #magmaReformat$fisherOR <- rep(NA,nrow(magmaReformat))
# fullManifest <- dplyr::select(fullManifest,ModuleNameFull,category,fisherPval,fisherOR)
# fullManifest <- rbind(fullManifest,magmaReformat)
# fullManifest <- dplyr::mutate(fullManifest,Z = qnorm(fisherPval,lower.tail=F))
# fullManifestSquare <- dplyr::select(fullManifest,ModuleNameFull,category,Z)
# fullManifestSquare <- tidyr::spread(fullManifestSquare,ModuleNameFull,Z)
# rownames(fullManifestSquare) <- fullManifestSquare$category
# fullManifestSquare <- dplyr::select(fullManifestSquare, -category)
# fullManifestSquare <- data.matrix(fullManifestSquare)
# fullManifestSquare[!is.finite(fullManifestSquare)] <- NA
# fullManifestSquare <- t(fullManifestSquare)
# #fullManifestSquare[is.na(fullManifestSquare)] <- 0
# foobar <- apply(fullManifestSquare,1,median,na.rm=T)
#####combined score
#####top modules
Sage-Bionetworks/AMPAD documentation built on Jan. 13, 2020, 9:18 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#####script to build module summary table
synapseClient::synapseLogin()
#source('moduleAnalysis/summaryManifestFunctions.R')
#####igap gwas enrichments
adGeneticsSummary <- AMPAD::getAdGenetics()
#####deg enrichments
degSummary <- AMPAD::getDeg()
adGeneticsSummarySig <- dplyr::filter(adGeneticsSummary,
GeneSetAdjustedAssociationStatistic <= 0.05)
admodcheat <- dplyr::select(adGeneticsSummarySig,
ModuleNameFull,
GeneSetName,
GeneSetADLinked)
admodcheat <- tidyr::spread(admodcheat,
ModuleNameFull,
GeneSetADLinked)
rownames(admodcheat) <- admodcheat$GeneSetName
admodcheat <- admodcheat[,-1]
admodcheat <- t(admodcheat)
admodcheat[is.na(admodcheat)] <- 0
admodcheat <- data.frame(admodcheat,stringsAsFactors=F)
admodcheat$adGeneticScore <- rowMeans(admodcheat)
admodcheat$ModuleNameFull <- rownames(admodcheat)
admodcheat <- dplyr::arrange(admodcheat,desc(adGeneticScore))
moduleSummarySig <- dplyr::filter(degSummary,
GeneSetAdjustedAssociationStatistic <=0.05)
library(dplyr)
getModuleCheatSheet <- dplyr::select(moduleSummarySig,
ModuleNameFull,
GeneSetName,
GeneSetDirectionAD,
GeneSetBrainRegion,
GeneSetCategoryName,
GeneSetADLinked)
getModuleCheatSheet$genesetdir <- paste0(getModuleCheatSheet$GeneSetName,
getModuleCheatSheet$GeneSetDirectionAD,
getModuleCheatSheet$GeneSetBrainRegion,
getModuleCheatSheet$GeneSetCategoryName)
getModuleCheatSheet <- dplyr::select(getModuleCheatSheet,
ModuleNameFull,
genesetdir,
GeneSetADLinked)
moduleCheatSheet <- tidyr::spread(getModuleCheatSheet,
ModuleNameFull,
GeneSetADLinked)
rownames(moduleCheatSheet) <- moduleCheatSheet$genesetdir
moduleCheatSheet <- moduleCheatSheet[,-1]
moduleCheatSheet <- t(moduleCheatSheet)
moduleSet <- synapseClient::synTableQuery("SELECT DISTINCT ModuleNameFull, Module, method, brainRegion from syn10338156")@values
colnames(moduleSet)[c(3:4)] <- c('ModuleMethod','ModuleBrainRegion')
#dropCols <- which(apply(moduleCheatSheet,2,sum,na.rm=T)==0)
#moduleCheatSheet <- moduleCheatSheet[,-dropCols]
moduleCheatSheet[is.na(moduleCheatSheet)] <- 0
moduleCheatSheet <- data.frame(moduleCheatSheet,stringsAsFactors=F)
moduleCheatSheet$degScore <- rowMeans(moduleCheatSheet)
moduleCheatSheet$ModuleNameFull <- rownames(moduleCheatSheet)
moduleCheatSheet <- dplyr::arrange(moduleCheatSheet,desc(degScore))
degTopScores <- dplyr::select(moduleCheatSheet,degScore,ModuleNameFull)
degTopScores <- dplyr::left_join(degTopScores,moduleSet)
rSynapseUtilities::makeTable(degTopScores,tableName = "deg mods february 27 2018",projectId = 'syn5569099')
# combinedScores <- dplyr::select(moduleCheatSheet,ModuleNameFull,degScore)
# combinedScores$degScore <- as.numeric(scale(combinedScores$degScore,center = FALSE))
# combinedScores <- dplyr::full_join(combinedScores,dplyr::select(admodcheat,ModuleNameFull,adGeneticScore))
# combinedScores$adGeneticScore <- as.numeric(scale(combinedScores$adGeneticScore,center=FALSE))
# combinedScores$aggregate <- combinedScores$degScore + combinedScores$adGeneticScore
# combinedScores <- dplyr::arrange(combinedScores,desc(aggregate))
# combinedScoresReducted <- combinedScores[1:263,]
# combinedScoresReducted <- dplyr::left_join(combinedScoresReducted,moduleSet)
#
# rSynapseUtilities::makeTable(combinedScoresReducted,tableName = "top amp-ad mods september 21 2017",projectId = 'syn5569099')
#
# degTopScores <- dplyr::select(moduleCheatSheet,degScore,ModuleNameFull)
# degTopScores <- dplyr::left_join(degTopScores,moduleSet)
# rSynapseUtilities::makeTable(degTopScores,tableName = "deg mods september 26 2017",projectId = 'syn5569099')
# ########compile annotations of modules
# ####download mods
# combinedScoresReducted <- synapseClient::synTableQuery("SELECT * FROM syn10516371")@values
#
#
# cell_type <- getCellTypes()
# cellSummarySig <- dplyr::filter(cell_type,
# GeneSetAdjustedAssociationStatistic <= 0.05)
#
#
#
# ####get cell type enrichments
# #cellTypeEnrichments <- synapseClient::synTableQuery("SELECT * FROM syn10498382")@values
#
# #colnames(cellTypeEnrichments)[c(2,3,7)] <- c("GeneSetCategoryName",
# # "GeneSetAssociationStatistic",
# # "ModuleBrainRegion")
# #cellTypeEnrichments <- splitByBrainRegionAdjustPvalue(cellTypeEnrichments)
# #cellTypeEnrichments <- dplyr::filter(cellTypeEnrichments,GeneSetAdjustedAssociationStatistic<=0.05)
# combinedScoresReducted2 <- dplyr::left_join(combinedScoresReducted,dplyr::select(cellSummarySig,ModuleNameFull,GeneSetName,GeneSetEffect))
#
#
#
# module_ad_score <- apply(moduleCheatSheet,1,sum)
# sort(module_ad_score,decreasing=T)[1:30]
#
#
# #####pathway annotations
# pathwaySummary <- getPathways()
#
# #####TO DO
# #####eigengene associations
# eigengeneSummary <- getEigengene()
#
# #####mod pres
# modulePreservationSummary <- getModulePreservation()
#
#
#
#
#
# #####compile enrichments
# enrichments <- synapseClient::synTableQuery("SELECT * FROM syn10492048")@values
# enrichments2 <- dplyr::select(enrichments,ModuleNameFull,category,geneSet,fisherPval,fisherOR)
# colnames(enrichments2) <- c('ModuleNameFull',
# 'GeneSetName',
# 'GeneSetCategoryName',
# 'GeneSetAssociationStatistic',
# 'GeneSetEffect')
#
# enrichments2$GeneSetBrainRegion <- rep(NA,nrow(enrichments2))
# enrichments2$GeneSetDirectionAD <- rep(NA,nrow(enrichments2))
# ad_lists <- grep('alzheimer',(enrichments2$GeneSetName))
# #ad_lists2 <- grep('load',unique(enrichments2$GeneSetName))
# #ad_lists3 <- grep("AD",unique(enrichments2$GeneSetName))
# #ad_lists<-grep('',enrichments2$GeneSetName)
# enrichments2$GeneSetADLinked <- rep(FALSE,nrow(enrichments2))
# enrichments2$GeneSetADLinked[ad_lists] <- TRUE
#
# #bonferroni womp womp
#
# bonferroni_fun <- function(x,ntests=1e8){
# return(min(1,x*ntests))
# }
#
# enrichments2$GeneSetAdjustedAssociationStatistic <- sapply(enrichments2$GeneSetAssociationStatistic,bonferroni_fun)
#
# enrichments2 <- dplyr::left_join(moduleSet,enrichments2)
# enrichments3 <- dplyr::filter(enrichments2,GeneSetAdjustedAssociationStatistic <= 0.05)
#
# combinedScoresReducted3 <- dplyr::left_join(combinedScoresReducted2,dplyr::select(enrichments3,ModuleNameFull,GeneSetName,GeneSetCategoryName,GeneSetEffect),by='ModuleNameFull')
#
#
#
#
# ad_lists2 <- which(enrichments2$GeneSetADLinked)
#
# moduleSummary <- rbind(moduleSummary,enrichments2)
#
# moduleSummarySig <- dplyr::filter(moduleSummary,GeneSetAssociationStatistic <=0.05)
#
# library(dplyr)
# getModuleCheatSheet <- dplyr::select(moduleSummarySig,
# ModuleNameFull,
# GeneSetName,
# GeneSetDirectionAD,
# GeneSetBrainRegion,
# GeneSetCategoryName,
# GeneSetADLinked)
# getModuleCheatSheet$genesetdir <- paste0(getModuleCheatSheet$GeneSetName,
# getModuleCheatSheet$GeneSetDirectionAD,
# getModuleCheatSheet$GeneSetBrainRegion,
# getModuleCheatSheet$GeneSetCategoryName)
#
# getModuleCheatSheet <- dplyr::select(getModuleCheatSheet,
# ModuleNameFull,
# genesetdir,
# GeneSetADLinked)
#
# moduleCheatSheet <- tidyr::spread(getModuleCheatSheet,
# ModuleNameFull,
# GeneSetADLinked)
#
# rownames(moduleCheatSheet) <- moduleCheatSheet$genesetdir
# moduleCheatSheet <- moduleCheatSheet[,-1]
# moduleCheatSheet <- t(moduleCheatSheet)
#
# dropCols <- which(apply(moduleCheatSheet,2,sum,na.rm=T)==0)
# moduleCheatSheet <- moduleCheatSheet[,-dropCols]
# module_ad_score <- apply(moduleCheatSheet,1,sum,na.rm=T)
# sort(module_ad_score,decreasing=T)[1:30]
#
#
# enrichmentManifest <- synapseClient::synTableQuery("SELECT * FROM syn10468216")@values
#
# foobar <- readRDS(synapseClient::synGet(enrichmentManifest$id[1])@filePath)
# View(enrichmentManifest)
#
#
#
# # summaryDegManifest <- dplyr::group_by(degResults2,
# # ModuleNameFull,
# # Direction,
# # reducedCategory) %>%
# # dplyr::summarise(medianZ = median(Z),
# # medianOR = median(fisherOR),
# # medianPval=median(fisherPval))
# #
# #
# # summaryDegManifest <- dplyr::mutate(summaryDegManifest,
# # adj = p.adjust(medianPval,
# # method='fdr'))
# #
# # summaryDegManifest2 <- dplyr::filter(summaryDegManifest,
# # adj<=0.05)
# #
# # View(summaryDegManifest2)
# #
# # g <- ggplot2::ggplot(summaryDegManifest,
# # ggplot2::aes(x=Direction,
# # y=medianZ,
# # fill=reducedCategory))
# # g <- g + ggplot2::geom_boxplot(position='dodge')
# # #g <- g + ggplot2::scale_y_log10()
# # g <- g + ggplot2::theme_grey(base_size = 20)
# # g
#
# #extract
#
#
# #dplyr::summarise(numberOfGenes=length(ModuleName)
#
# #categoryKey <- categoryKey[!duplicated(categoryKey),]
#
#
# #####cell type results
# genesets1 <- synapseClient::synGet('syn5923958')
# load(synapseClient::getFileLocation(genesets1))
# cellMarkers <- GeneSets$Cell_Markers
# cellTypeResults <- run_amp_ad_enrichment(cellMarkers,
# "celltypes",
# hgnc=TRUE)
# #####combined manifest
# fullManifest <- rbind(degResults,
# cellTypeResults)
#
# magmaReformat <- dplyr::select(magmaResults,SET,P)
#
# colnames(magmaReformat) <- c('ModuleNameFull','magmaPval')
# magmaReformat <- dplyr::mutate(magmaReformat,magmaZ=qnorm(magmaPval,lower.tail=F))
#
# summaryDegManifest2 <- dplyr::left_join(summaryDegManifest,magmaReformat)
# summaryDegManifest2 <- dplyr::mutate(summaryDegManifest2,combZ=magmaZ/2+medianZ/2)
# summaryDegManifest2 <- dplyr::arrange(summaryDegManifest2,desc(medianZ))
# #split by each category
# fxn1 <- function(x,y){
# foobar <- dplyr::filter(y,reducedCategory==x)
# return(foobar)
# }
# splitSummaries <- lapply(unique(summaryDegManifest2$reducedCategory),fxn1,summaryDegManifest2)
# names(splitSummaries) <- unique(summaryDegManifest2$reducedCategory)
# View(splitSummaries[[1]])
# splitSummaries2 <- lapply(splitSummaries,function(x){
# x <- dplyr::arrange(x,desc(medianZ))
# xup <- dplyr::filter(x,Direction=='UP')
# xdown <- dplyr::filter(x,Direction=='DOWN')
# return(rbind(xup[1:5,],xdown[1:5,]))
# })
#
# View(splitSummaries2[[9]])
#
#
# ##just take top from each up/down
# fxn2 <- function(x){
# foobar1 <- dplyr::filter(x,Direction=='DOWN')
# foobar2 <- dplyr::filter(x,Direction=='UP')
# return(c('down_mod'=foobar1$ModuleNameFull[1],
# 'up_mod'=foobar2$ModuleNameFull[1]))
# }
# getMods <- sapply(splitSummaries,
# fxn2)
# topMods <- t(getMods)
#
# #magmaReformat$category <- rep('MAGMA',nrow(magmaReformat))
# #magmaReformat$fisherOR <- rep(NA,nrow(magmaReformat))
# fullManifest <- dplyr::select(fullManifest,ModuleNameFull,category,fisherPval,fisherOR)
# fullManifest <- rbind(fullManifest,magmaReformat)
# fullManifest <- dplyr::mutate(fullManifest,Z = qnorm(fisherPval,lower.tail=F))
# fullManifestSquare <- dplyr::select(fullManifest,ModuleNameFull,category,Z)
# fullManifestSquare <- tidyr::spread(fullManifestSquare,ModuleNameFull,Z)
# rownames(fullManifestSquare) <- fullManifestSquare$category
# fullManifestSquare <- dplyr::select(fullManifestSquare, -category)
# fullManifestSquare <- data.matrix(fullManifestSquare)
# fullManifestSquare[!is.finite(fullManifestSquare)] <- NA
# fullManifestSquare <- t(fullManifestSquare)
# #fullManifestSquare[is.na(fullManifestSquare)] <- 0
# foobar <- apply(fullManifestSquare,1,median,na.rm=T)
#####combined score
#####top modules
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.