In Sage-Bionetworks/AMPAD:
make pairwise enrichment matrix
AMPAD::pairwiseMatrixOfEnrichments('syn11932957')
Pull targeted genesets
targetedGeneSets <- AMPAD::collateEnrichmentSets()
#str(targetedGeneSets)
make simple summary table of differential expressed genes from random effect analysis
degMetaDf <- data.frame(gene=targetedGeneSets$degMeta$ad.control.random.DOWN,direction='Down')
For each of the three build the data frame of the respective Analyses
targetedEnrichment <- list()
targetedEnrichment$ad <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$ad,
'AD',
hgnc = TRUE,
manifestId = 'syn11932957')
targetedEnrichment$cell <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$cell,
'Cell',
hgnc= TRUE,
manifestId = 'syn11932957')
targetedEnrichment$cell2 <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$cell2,
'Mathys',
hgnc= TRUE,
manifestId = 'syn11932957')
targetedEnrichment$scz <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$scz,
'scz',
hgnc= FALSE,
manifestId = 'syn11932957')
targetedEnrichment$control <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$control,
'control',
hgnc= FALSE,
manifestId = 'syn11932957')
targetedEnrichment$mssm2 <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$MSSM2,
'zhang',
hgnc= TRUE,
manifestId = 'syn11932957')
targetedEnrichment$mito <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$mito,
'Mito',
hgnc= TRUE,
manifestId = 'syn11932957')
targetedEnrichment$deg <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$deg,
'DEG',
hgnc=FALSE,
manifestId = 'syn11932957')
targetedEnrichment$degMeta <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$degMeta,
'DEGmeta',
hgnc=FALSE,
manifestId = 'syn11932957')
targetedEnrichment$targetPathways <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$targetedPathways,
'TargetedPathways',
hgnc=FALSE,
manifestId = 'syn11932957')
targetedEnrichment$ad_deg <- AMPAD::run_amp_ad_enrichment2_lists(targetedGeneSets$deg,
'ADDEG',
targetedGeneSets$ad,
hgnc = T,
testhgnc = F)
targetedEnrichment$ad_degmeta <- AMPAD::run_amp_ad_enrichment2_lists(targetedGeneSets$degMeta,
'ADDEGMeta',
targetedGeneSets$ad,
hgnc = T,
testhgnc = F)
targetedEnrichment$degFull <- rbind(targetedEnrichment$deg,targetedEnrichment$ad_deg)
targetedEnrichment$degMetaFull <- rbind(targetedEnrichment$degMeta, targetedEnrichment$ad_degmeta)
Add adjusted p-values
targetedEnrichment$ad$adj.pval <- p.adjust(targetedEnrichment$ad$fisherPval,method='fdr')
targetedEnrichment$deg$adj.pval <- p.adjust(targetedEnrichment$deg$fisherPval,method='fdr')
targetedEnrichment$cell$adj.pval <- p.adjust(targetedEnrichment$cell$fisherPval,method='fdr')
targetedEnrichment$cell2$adj.pval <- p.adjust(targetedEnrichment$cell2$fisherPval,method='fdr')
targetedEnrichment$scz$adj.pval <- p.adjust(targetedEnrichment$scz$fisherPval,method='fdr')
targetedEnrichment$control$adj.pval <- p.adjust(targetedEnrichment$control$fisherPval,method='fdr')
targetedEnrichment$mito$adj.pval <- p.adjust(targetedEnrichment$mito$fisherPval,method='fdr')
targetedEnrichment$mssm2$adj.pval <- p.adjust(targetedEnrichment$mssm2$fisherPval,method='fdr')
targetedEnrichment$degMeta$adj.pval <- p.adjust(targetedEnrichment$degMeta$fisherPval,method='fdr')
targetedEnrichment$targetPathways$adj.pval <- p.adjust(targetedEnrichment$targetPathways$fisherPval,method='fdr')
targetedEnrichment$degFull$adj.pval <- p.adjust(targetedEnrichment$degFull$fisherPval,method='fdr')
targetedEnrichment$degMetaFull$adj.pval <- p.adjust(targetedEnrichment$degMetaFull$fisherPval,method='fdr')
Make cell type summary plots
x- axis : module
y- axis fisher-'s odds ratio
modMeta <- AMPAD::getModuleMetainfo('syn11932957')
dummyDf <- targetedEnrichment$cell
dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA
#dummyDf <- dplyr::filter(targetedEnrichment$cell,adj.pval<=0.05)
dummyDf <- dplyr::left_join(dummyDf,modMeta)
dummyDf$category <- gsub('Zhang\\.','',dummyDf$category)
dummyDf$category <- factor(dummyDf$category,levels = rev(c('Astrocyte',
'Endothelial',
'Microglia',
'Neuron',
'MyelinOligos',
'NewOligos',
'OPC')))
#dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion))
customDf <- data.frame(moduleName=c('TCXblue',
'IFGyellow',
'PHGyellow',
'DLPFCblue',
'CBEturquoise',
'STGblue',
'PHGturquoise',
'IFGturquoise',
'TCXturquoise',
'FPturquoise',
'IFGbrown',
'STGbrown',
'DLPFCyellow',
'TCXgreen',
'FPyellow',
'CBEyellow',
'PHGbrown',
'DLPFCbrown',
'STGyellow',
'PHGgreen',
'CBEbrown',
'TCXyellow',
'IFGblue',
'FPblue',
'FPbrown',
'CBEblue',
'DLPFCturquoise',
'TCXbrown',
'STGturquoise',
'PHGblue'),
Cluster= c(rep('Consensus Cluster A',3),
rep('Consensus Cluster B',7),
rep('Consensus Cluster C',7),
rep('Consensus Cluster D',7),
rep('Consensus Cluster E',6)),
stringsAsFactors=F)
dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName'))
dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A',
'Consensus Cluster B',
'Consensus Cluster C',
'Consensus Cluster D',
'Consensus Cluster E')))
dummyDf$Module <- factor(dummyDf$Module,levels = (c('TCXblue',
'IFGyellow',
'PHGyellow',
'DLPFCblue',
'CBEturquoise',
'STGblue',
'PHGturquoise',
'IFGturquoise',
'TCXturquoise',
'FPturquoise',
'IFGbrown',
'STGbrown',
'DLPFCyellow',
'TCXgreen',
'FPyellow',
'CBEyellow',
'PHGbrown',
'DLPFCbrown',
'STGyellow',
'PHGgreen',
'CBEbrown',
'TCXyellow',
'IFGblue',
'FPblue',
'FPbrown',
'CBEblue',
'DLPFCturquoise',
'TCXbrown',
'STGturquoise',
'PHGblue')))
g <- ggplot2::ggplot(dummyDf,
ggplot2::aes(x = Module,
y = category,
size = fisherOR,
color = Cluster))
g <- g + ggplot2::geom_count()
#g <- g + ggplot2::scale_y_log10()
g <- g + ggplot2::labs(y = 'Cell Type Signature',
x = 'AD Coexpression Module')
#g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
#g <- g + cowplot::theme_cowplot(12)
#g <- g + ggplot2::theme_update(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
g <- g + AMPAD::cowplot_rotated(11)
#g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(),
# axis.ticks.x=ggplot2::element_blank())
#g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110),trans='log')
#g <- g + ggplot2::coord_flip()
#g <- g + ggplot2::ggtitle('Enrichment for Cell Type Specific Signatures')
g
ggplot2::ggsave('figure2B.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8)
#ggplot2::ggsave('cell_enrichments.png')
modMeta <- AMPAD::getModuleMetainfo('syn11932957')
dummyDf <- targetedEnrichment$cell2
dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA
#dummyDf <- dplyr::filter(targetedEnrichment$cell,adj.pval<=0.05)
dummyDf <- dplyr::left_join(dummyDf,modMeta)
# dummyDf$category <- gsub('Zhang\\.','',dummyDf$category)
# dummyDf$category <- factor(dummyDf$category,levels = rev(c('Astrocyte',
# 'Endothelial',
# 'Microglia',
# 'Neuron',
# 'MyelinOligos',
# 'NewOligos',
# 'OPC')))
#dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion))
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)
dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName'))
dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A',
'Consensus Cluster B',
'Consensus Cluster C',
'Consensus Cluster D',
'Consensus Cluster E')))
dummyDf$Module <- factor(dummyDf$Module,levels = (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')))
g <- ggplot2::ggplot(dummyDf,
ggplot2::aes(x = Module,
y = category,
size = fisherOR,
color = Cluster))
g <- g + ggplot2::geom_count()
#g <- g + ggplot2::scale_y_log10()
#g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
#g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(),
# axis.ticks.x=ggplot2::element_blank())
#g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110),trans='log')
#g <- g + ggplot2::coord_flip()
#g <- g + ggplot2::ggtitle('Enrichment for Cell Type Specific Signatures')
g <- g + ggplot2::labs(y = 'Lake et al. Cell Type Signature',
x = 'AD Coexpression Module')
g <- g + AMPAD::cowplot_rotated(11)
g
ggplot2::ggsave('lake.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8)
#ggplot2::ggsave('cell_enrichments.png')
modMeta <- AMPAD::getModuleMetainfo('syn11932957')
dummyDf <- targetedEnrichment$scz
dummyDf <- dplyr::filter(dummyDf,category == 'yellow' | category == 'red' | category == 'blue' | category == 'lightyellow' | category == 'greenyellow' | category == 'cyan' | category == 'grey60')
dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA
#dummyDf <- dplyr::filter(targetedEnrichment$cell,adj.pval<=0.05)
dummyDf <- dplyr::left_join(dummyDf,modMeta)
# dummyDf$category <- gsub('Zhang\\.','',dummyDf$category)
# dummyDf$category <- factor(dummyDf$category,levels = rev(c('Astrocyte',
# 'Endothelial',
# 'Microglia',
# 'Neuron',
# 'MyelinOligos',
# 'NewOligos',
# 'OPC')))
#dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion))
customDf <- data.frame(moduleName=c('TCXblue',
'IFGyellow',
'PHGyellow',
'DLPFCblue',
'CBEturquoise',
'STGblue',
'PHGturquoise',
'IFGturquoise',
'TCXturquoise',
'FPturquoise',
'IFGbrown',
'STGbrown',
'DLPFCyellow',
'TCXgreen',
'FPyellow',
'CBEyellow',
'PHGbrown',
'DLPFCbrown',
'STGyellow',
'PHGgreen',
'CBEbrown',
'TCXyellow',
'IFGblue',
'FPblue',
'FPbrown',
'CBEblue',
'DLPFCturquoise',
'TCXbrown',
'STGturquoise',
'PHGblue'),
Cluster= c(rep('Consensus Cluster A',3),
rep('Consensus Cluster B',7),
rep('Consensus Cluster C',7),
rep('Consensus Cluster D',7),
rep('Consensus Cluster E',6)),
stringsAsFactors=F)
dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName'))
dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A',
'Consensus Cluster B',
'Consensus Cluster C',
'Consensus Cluster D',
'Consensus Cluster E')))
dummyDf$Module <- factor(dummyDf$Module,levels = (c('TCXblue',
'IFGyellow',
'PHGyellow',
'DLPFCblue',
'CBEturquoise',
'STGblue',
'PHGturquoise',
'IFGturquoise',
'TCXturquoise',
'FPturquoise',
'IFGbrown',
'STGbrown',
'DLPFCyellow',
'TCXgreen',
'FPyellow',
'CBEyellow',
'PHGbrown',
'DLPFCbrown',
'STGyellow',
'PHGgreen',
'CBEbrown',
'TCXyellow',
'IFGblue',
'FPblue',
'FPbrown',
'CBEblue',
'DLPFCturquoise',
'TCXbrown',
'STGturquoise',
'PHGblue')))
g <- ggplot2::ggplot(dummyDf,
ggplot2::aes(x = Module,
y = category,
size = fisherOR,
color = Cluster))
g <- g + ggplot2::geom_count()
#g <- g + ggplot2::scale_y_log10()
#g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
#g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(),
# axis.ticks.x=ggplot2::element_blank())
#g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110),trans='log')
#g <- g + ggplot2::coord_flip()
#g <- g + ggplot2::ggtitle('Enrichment for Cell Type Specific Signatures')
g <- g + ggplot2::labs(y = 'CommonMind Differentially Expressed Module',
x = 'AD Coexpression Module')
g <- g + AMPAD::cowplot_rotated(11)
g
ggplot2::ggsave('cmc.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8)
#ggplot2::ggsave('cell_enrichments.png')
modMeta <- AMPAD::getModuleMetainfo('syn11932957')
dummyDf <- targetedEnrichment$mssm2
dummyDf <- dplyr::filter(dummyDf,category == 'Yellow' | category == 'Pink' | category == 'Gray.1' | category == 'Seashell' | category == 'Red.3' | category == 'Green.yellow' | category == 'Red' | category == 'Gold.2' | category == 'Tan' | category == 'Gold.3' | category == 'Light.yellow' | category == 'Brown.2' | category == 'Dark.cyan' | category == 'Khaki' | category == 'Grey.60' | category == 'Purple' | category == 'Green.4' | category == 'Honey.dew' | category == 'Red.2' | category == 'Beige')
dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA
#dummyDf <- dplyr::filter(targetedEnrichment$cell,adj.pval<=0.05)
dummyDf <- dplyr::left_join(dummyDf,modMeta)
# dummyDf$category <- gsub('Zhang\\.','',dummyDf$category)
# dummyDf$category <- factor(dummyDf$category,levels = rev(c('Astrocyte',
# 'Endothelial',
# 'Microglia',
# 'Neuron',
# 'MyelinOligos',
# 'NewOligos',
# 'OPC')))
#dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion))
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)
dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName'))
dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A',
'Consensus Cluster B',
'Consensus Cluster C',
'Consensus Cluster D',
'Consensus Cluster E')))
dummyDf$Module <- factor(dummyDf$Module,levels = (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')))
g <- ggplot2::ggplot(dummyDf,
ggplot2::aes(x = Module,
y = category,
size = fisherOR,
color = Cluster))
g <- g + ggplot2::geom_count()
#g <- g + ggplot2::scale_y_log10()
#g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
#g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(),
# axis.ticks.x=ggplot2::element_blank())
#g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110),trans='log')
#g <- g + ggplot2::coord_flip()
#g <- g + ggplot2::ggtitle('Enrichment for Cell Type Specific Signatures')
g <- g + ggplot2::labs(y = 'Zhang et al. Module',
x = 'AD Coexpression Module')
g <- g + AMPAD::cowplot_rotated(11)
g
ggplot2::ggsave('zhanggaiteri.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8)
#ggplot2::ggsave('cell_enrichments.png')
modMeta <- AMPAD::getModuleMetainfo('syn11932957')
dummyDf <- targetedEnrichment$mssm2
dummyDf <- dplyr::filter(dummyDf,!(category == 'Yellow' | category == 'Pink' | category == 'Gray.1' | category == 'Seashell' | category == 'Red.3' | category == 'Green.yellow' | category == 'Red' | category == 'Gold.2' | category == 'Tan' | category == 'Gold.3' | category == 'Light.yellow' | category == 'Brown.2' | category == 'Dark.cyan' | category == 'Khaki' | category == 'Grey.60' | category == 'Purple' | category == 'Green.4' | category == 'Honey.dew' | category == 'Red.2' | category == 'Beige'))
dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA
#dummyDf <- dplyr::filter(targetedEnrichment$cell,adj.pval<=0.05)
dummyDf <- dplyr::left_join(dummyDf,modMeta)
# dummyDf$category <- gsub('Zhang\\.','',dummyDf$category)
# dummyDf$category <- factor(dummyDf$category,levels = rev(c('Astrocyte',
# 'Endothelial',
# 'Microglia',
# 'Neuron',
# 'MyelinOligos',
# 'NewOligos',
# 'OPC')))
#dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion))
customDf <- data.frame(moduleName=c('TCXblue',
'IFGyellow',
'PHGyellow',
'DLPFCblue',
'CBEturquoise',
'STGblue',
'PHGturquoise',
'IFGturquoise',
'TCXturquoise',
'FPturquoise',
'IFGbrown',
'STGbrown',
'DLPFCyellow',
'TCXgreen',
'FPyellow',
'CBEyellow',
'PHGbrown',
'DLPFCbrown',
'STGyellow',
'PHGgreen',
'CBEbrown',
'TCXyellow',
'IFGblue',
'FPblue',
'FPbrown',
'CBEblue',
'DLPFCturquoise',
'TCXbrown',
'STGturquoise',
'PHGblue'),
Cluster= c(rep('Consensus Cluster A',3),
rep('Consensus Cluster B',7),
rep('Consensus Cluster C',7),
rep('Consensus Cluster D',7),
rep('Consensus Cluster E',6)),
stringsAsFactors=F)
dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName'))
dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A',
'Consensus Cluster B',
'Consensus Cluster C',
'Consensus Cluster D',
'Consensus Cluster E')))
dummyDf$Module <- factor(dummyDf$Module,levels = (c('TCXblue',
'IFGyellow',
'PHGyellow',
'DLPFCblue',
'CBEturquoise',
'STGblue',
'PHGturquoise',
'IFGturquoise',
'TCXturquoise',
'FPturquoise',
'IFGbrown',
'STGbrown',
'DLPFCyellow',
'TCXgreen',
'FPyellow',
'CBEyellow',
'PHGbrown',
'DLPFCbrown',
'STGyellow',
'PHGgreen',
'CBEbrown',
'TCXyellow',
'IFGblue',
'FPblue',
'FPbrown',
'CBEblue',
'DLPFCturquoise',
'TCXbrown',
'STGturquoise',
'PHGblue')))
g <- ggplot2::ggplot(dummyDf,
ggplot2::aes(x = Module,
y = category,
size = fisherOR,
color = Cluster))
g <- g + ggplot2::geom_count()
#g <- g + ggplot2::scale_y_log10()
g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
#g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(),
# axis.ticks.x=ggplot2::element_blank())
#g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110),trans='log')
#g <- g + ggplot2::coord_flip()
#g <- g + ggplot2::ggtitle('Enrichment for Cell Type Specific Signatures')
g <- g + ggplot2::labs(y = 'Zhang et al. Module',
x = 'AD Coexpression Module')
g
ggplot2::ggsave('mathys.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8)
#ggplot2::ggsave('cell_enrichments.png')
mitochondria
modMeta <- AMPAD::getModuleMetainfo('syn11932957')
dummyDf <- targetedEnrichment$mito
dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA
#dummyDf <- dplyr::filter(targetedEnrichment$cell,adj.pval<=0.05)
dummyDf <- dplyr::left_join(dummyDf,modMeta)
strFxn <- function(x){
nc <- nchar(x)
if(nc<50){
ind1<-floor(nc/2)
str1 <- substr(x,1,ind1)
str2 <- substr(x,ind1+1,nc)
return(paste0(str1,'\n',str2))
}else{
ind1<-floor(nc/3)
ind2 <- 2*ind1
str1 <- substr(x,1,ind1)
str2 <- substr(x,ind1+1,ind2)
str3 <- substr(x,ind2+1,nc)
return(paste0(str1,'\n',str2,'\n',str3))
}
}
dummyDf$category <- sapply(dummyDf$category,strFxn)
#dummyDf$category <- gsub('Zhang\\.','',dummyDf$category)
#dummyDf$category <- factor(dummyDf$category,levels = rev(c('Astrocyte',
# 'Endothelial',
# 'Microglia',
# 'Neuron',
# 'MyelinOligos',
# 'NewOligos',
# 'OPC')))
#dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion))
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)
dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName'))
dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A',
'Consensus Cluster B',
'Consensus Cluster C',
'Consensus Cluster D',
'Consensus Cluster E')))
dummyDf$Module <- factor(dummyDf$Module,levels = rev(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')))
g <- ggplot2::ggplot(dummyDf,
ggplot2::aes(x = Module,
y = category,
size = fisherOR,
color = Cluster))
g <- g + ggplot2::geom_count()
#g <- g + ggplot2::scale_y_log10()
g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
#g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(),
# axis.ticks.x=ggplot2::element_blank())
#g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110),trans='log')
g <- g + ggplot2::coord_flip()
#g <- g + ggplot2::ggtitle('Enrichment for Cell Type Specific Signatures')
g <- g + ggplot2::labs(y = 'Mitochondrial signatures',
x = 'AD Coexpression Module')
g
overlap table with amp-ad related gene sets
dummyDf <- targetedEnrichment$ad
dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA
dummyDf <- dplyr::left_join(dummyDf,modMeta)
dummyDf <- dplyr::filter(dummyDf,adj.pval<=0.05)
dummyDf <- dplyr::filter(dummyDf,category == 'Nominated_targets' |
category == 'MSSM' |
category == 'Mayo_simple' |
category == 'Mayo_comprehensive' |
category == 'Emory' |
category == 'Columbia_Broad_Rush_m109')
dummyDf <- dplyr::select(dummyDf,
Module,
category,
mod_size,
category_size,
nInter,
adj.pval,
fisherOR)
colnames(dummyDf) <- c('Module Name',
'AMP-AD Gene Set',
'Module Size',
'AMP-AD Gene Set Size',
'Size of Intersection',
'Adjusted P-value',
'FET Odds Ratio')
write.csv(dummyDf,file='amp-ad_overlap_stats.csv',quote=F,row.names=F)
differential expression meta analysis summary tables
dummyDf <- targetedEnrichment$degMetaFull
dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA
dummyDf <- dplyr::left_join(dummyDf,modMeta)
dummyDf <- dplyr::filter(dummyDf,adj.pval<=0.05)
dummyDf$Module[is.na(dummyDf$Module)] <- dummyDf$ModuleNameFull[is.na(dummyDf$Module)]
dummyDf <- dplyr::select(dummyDf,
Module,
category,
mod_size,
category_size,
nInter,
adj.pval,
fisherOR)
colnames(dummyDf) <- c('Module or Gene Set Name',
'DEG Gene Set',
'Module or Gene Set Size',
'DEG Gene Set Size',
'Size of Intersection',
'Adjusted P-value',
'FET Odds Ratio')
dummyDf <- dplyr::arrange(dummyDf,desc(`FET Odds Ratio`))
adUp <- dplyr::filter(dummyDf,`DEG Gene Set` == 'ad.control.random.UP')
adDown <- dplyr::filter(dummyDf,`DEG Gene Set` == 'ad.control.random.DOWN')
adUpMale <- dplyr::filter(dummyDf,`DEG Gene Set` == 'ad.control.MALE.random.UP')
adDownMale <- dplyr::filter(dummyDf,`DEG Gene Set` == 'ad.control.MALE.random.DOWN')
adUpFemale <- dplyr::filter(dummyDf,`DEG Gene Set` == 'ad.control.FEMALE.random.UP')
adDownFemale <- dplyr::filter(dummyDf,`DEG Gene Set` == 'ad.control.FEMALE.random.DOWN')
write.csv(adUp,file='adup.csv',quote=F,row.names=F)
write.csv(adDown,file='adDown.csv',quote=F,row.names=F)
write.csv(adUpMale,file='adUpMale.csv',quote=F,row.names=F)
write.csv(adDownMale,file='adDownMale.csv',quote=F,row.names=F)
write.csv(adUpFemale,file='adUpFemale.csv',quote=F,row.names=F)
write.csv(adDownFemale,file='adDownFemale.csv',quote=F,row.names=F)
#View(dplyr::filter(dummyDf,category == 'ad.control.FEMALE.random.UP'))
AD genetics summary
dummyDf <- targetedEnrichment$ad
dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA
dummyDf <- dplyr::left_join(dummyDf,modMeta)
#dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion))
dummyDf$category <- factor(dummyDf$category,
levels = rev(c('genecards',
'pantherPresenilin',
'dbgap',
'igap',
'jensenDisease',
'omimExpanded',
'biocarta',
'wikipathwaysMouse',
'wikipathwaysHuman',
'pantherAmyloid',
'kegg',
'omim',
'Nominated_targets',
'MSSM',
'Mayo_simple',
'Mayo_comprehensive',
'Emory',
'Columbia_Broad_Rush_m109')))
dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName'))
dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A',
'Consensus Cluster B',
'Consensus Cluster C',
'Consensus Cluster D',
'Consensus Cluster E')))
dummyDf$Module <- factor(dummyDf$Module,levels = (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')))
# g <- ggplot2::ggplot(dummyDf,
# ggplot2::aes(x = Module,
# y = category,
# size = fisherOR,
# color = -log10(adj.pval)))
# g <- g+ggplot2::geom_count()
# #g <- g + ggplot2::scale_y_log10()
# #g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
# g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(),
# axis.ticks.x=ggplot2::element_blank())
# g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.4,6,12,18,25))
# #g <- g + ggplot2::coord_flip()
# g <- g + ggplot2::ggtitle('Enrichment for AD Signatures')
g <- ggplot2::ggplot(dummyDf,
ggplot2::aes(x = Module,
y = category,
size = fisherOR,
color = Cluster))
g <- g + ggplot2::geom_count()
#g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
g <- g + ggplot2::labs(y = 'AD Signature',
x = 'AD Coexpression Module')
g <- g + AMPAD::cowplot_rotated(9)
g
ggplot2::ggsave('figure2C.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8)
#ggplot2::ggsave('ad_signature_enrichments.png')
dummyDf <- targetedEnrichment$ad
dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA
dummyDf <- dplyr::left_join(dummyDf,modMeta)
dummyDf<- dplyr::filter(dummyDf,category == 'AMP.AD.Nominated.Targets' |
category == 'AMP.AD.McKenzie.et.al..2017.Red' |
category == 'AMP.AD.McKenzie.et.al..2017.Light.green' |
category == 'AMP.AD.McKenzie.et.al..2017.Green' |
category == 'AMP.AD.Allen.et.al..2018.AD.PSP.TCX40.CS' |
category == 'AMP.AD.Allen.et.al..2018.AD.PSP.TCX10.CS' |
category == 'AMP.AD.Johnson.et.al..2018.RNA.Binding' |
category == 'AMP.AD.Mostafavi.et.al..2018.m109')
dummyDf$category[dummyDf$category=='AMP.AD.Nominated.Targets'] = 'Nominated\nTargets'
dummyDf$category[dummyDf$category=='AMP.AD.McKenzie.et.al..2017.Red'] = 'MSSM Oligo\nRed'
dummyDf$category[dummyDf$category=='AMP.AD.McKenzie.et.al..2017.Light.green'] = 'MSSM Oligo\nLight green'
dummyDf$category[dummyDf$category=='AMP.AD.McKenzie.et.al..2017.Green'] = 'MSSM Oligo\nGreen'
dummyDf$category[dummyDf$category=='AMP.AD.Allen.et.al..2018.AD.PSP.TCX40.CS'] = 'Mayo Oligo\nAD.PSP.TCX40.CS'
dummyDf$category[dummyDf$category=='AMP.AD.Allen.et.al..2018.AD.PSP.TCX10.CS'] = 'Mayo Oligo\nAD.PSP.TCX10.CS'
dummyDf$category[dummyDf$category=='AMP.AD.Johnson.et.al..2018.RNA.Binding'] = 'Emory\nRNA binding'
dummyDf$category[dummyDf$category=='AMP.AD.Mostafavi.et.al..2018.m109'] = 'Rush m109'
#dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion))
dummyDf$category <- factor(dummyDf$category,
levels = (c('Nominated\nTargets',
'MSSM Oligo\nRed',
'MSSM Oligo\nLight green',
'MSSM Oligo\nGreen',
'Mayo Oligo\nAD.PSP.TCX40.CS',
'Mayo Oligo\nAD.PSP.TCX10.CS',
'Emory\nRNA binding',
'Rush m109')))
dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName'))
dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Astrocytic',
'Microglial',
'Neuronal',
'Oligodendroglial',
'Proteostasis')))
dummyDf$Module <- factor(dummyDf$Module,levels = rev(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')))
# g <- ggplot2::ggplot(dummyDf,
# ggplot2::aes(x = Module,
# y = category,
# size = fisherOR,
# color = -log10(adj.pval)))
# g <- g+ggplot2::geom_count()
# #g <- g + ggplot2::scale_y_log10()
# #g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
# g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(),
# axis.ticks.x=ggplot2::element_blank())
# g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.4,6,12,18,25))
# #g <- g + ggplot2::coord_flip()
# g <- g + ggplot2::ggtitle('Enrichment for AD Signatures')
g <- ggplot2::ggplot(dummyDf,
ggplot2::aes(x = Module,
y = category,
size = fisherOR,
color = Cluster))
g <- g + ggplot2::geom_count()
g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
g <- g + ggplot2::coord_flip()
g <- g + ggplot2::labs(y = 'AD Signature',
x = 'AD Coexpression Module')
g
#ggplot2::ggsave('figure2C.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8)
ggplot2::ggsave('amp_ad_new_modules.png',units='in',width=4.91,height=4.8,scale=1.4)
dummyDf <- dplyr::filter(targetedEnrichment$degMeta,adj.pval<=0.05)
dummyDf <- dplyr::filter(dummyDf,category == 'ad.control.FEMALE.random.DOWN' |
category == 'ad.control.FEMALE.random.UP' |
category == 'ad.control.MALE.random.DOWN' |
category == 'ad.control.MALE.random.UP')
dummyDf$adj.pval[dummyDf$adj.pval==0] = 10^-300
dummyDf$category <- gsub('ad\\.control\\.FEMALE\\.random\\.DOWN','Female, Down',dummyDf$category)
dummyDf$category <- gsub('ad\\.control\\.MALE\\.random\\.DOWN','Male, Down',dummyDf$category)
dummyDf$category <- gsub('ad\\.control\\.FEMALE\\.random\\.UP','Female, Up',dummyDf$category)
dummyDf$category <- gsub('ad\\.control\\.MALE\\.random\\.UP','Male, Up',dummyDf$category)
dummyDf <- dplyr::left_join(dummyDf,modMeta)
#dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion))
dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName'))
dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A',
'Consensus Cluster B',
'Consensus Cluster C',
'Consensus Cluster D',
'Consensus Cluster E')))
dummyDf$category <- factor(dummyDf$category,rev(c('Female, Up',
'Male, Up',
'Female, Down',
'Male, Down')))
dummyDf$Module <- factor(dummyDf$Module,levels = (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')))
# g <- ggplot2::ggplot(dummyDf,
# ggplot2::aes(x = Module,
# y = category,
# size = fisherOR,
# color = -log10(adj.pval)))
# g <- g+ggplot2::geom_count()
# #g <- g + ggplot2::scale_y_log10()
# #g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
# g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(),
# axis.ticks.x=ggplot2::element_blank())
# g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110,300),trans='log')
# #g <- g + ggplot2::coord_flip()
# g <- g + ggplot2::ggtitle('Enrichment for AD DEG Signatures')
g <- ggplot2::ggplot(dummyDf,
ggplot2::aes(x = Module,
y = category,
size = fisherOR,
color = Cluster))
g <- g + ggplot2::geom_count()
#g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
g <- g + ggplot2::labs(y = 'DEG Sex Specific Meta Analysis',
x = 'AD Coexpression Module')
g <- g + AMPAD::cowplot_rotated(11)
g
ggplot2::ggsave('figure2D.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8)
#ggplot2::ggsave('deg_signature_enrichments.png')
#modMeta <- AMPAD::getModuleMetainfo('syn11932957')
dummyDf <- dplyr::filter(targetedEnrichment$targetPathways,adj.pval<=0.05)
dummyDf <- dplyr::left_join(dummyDf,modMeta)
dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion))
g <- ggplot2::ggplot(dummyDf,
ggplot2::aes(x = Module,
y = category,
size = fisherOR,
color = -log10(adj.pval)))
g <- g + ggplot2::geom_count()
#g <- g + ggplot2::scale_y_log10()
g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1))
g <- g + ggplot2::coord_flip()
g
Reformate DEG results for visualization
tcx_keep <- intersect(grep('TCX',targetedEnrichment$deg$ModuleNameFull),
grep('TCX',targetedEnrichment$deg$category))
phg_keep <- intersect(grep('PHG',targetedEnrichment$deg$ModuleNameFull),
grep('PHG',targetedEnrichment$deg$category))
cbe_keep <- intersect(grep('CBE',targetedEnrichment$deg$ModuleNameFull),
grep('CBE',targetedEnrichment$deg$category))
dlpfc_keep <- intersect(grep('DLPFC',targetedEnrichment$deg$ModuleNameFull),
grep('DLPFC',targetedEnrichment$deg$category))
stg_keep <- intersect(grep('STG',targetedEnrichment$deg$ModuleNameFull),
grep('STG',targetedEnrichment$deg$category))
ifg_keep <- intersect(grep('IFG',targetedEnrichment$deg$ModuleNameFull),
grep('IFG',targetedEnrichment$deg$category))
fp_keep <- intersect(grep('FP',targetedEnrichment$deg$ModuleNameFull),
grep('FP',targetedEnrichment$deg$category))
deg <- targetedEnrichment$deg[c(tcx_keep,phg_keep,cbe_keep,dlpfc_keep,stg_keep,ifg_keep,fp_keep),]
deg$adj.pval <- p.adjust(deg$fisherPval,method='fdr')
deg <- dplyr::filter(deg,adj.pval<=0.05)
dumfun <- function(x){
y<-grep('UP',x)
y2 <- grep('DOWN',x)
z <- x
z[y] <- 'Up'
z[y2] <- 'Down'
return(z)
}
sexfun <- function(x){
y<-grep('\\.FEMALE',x)
y2 <- grep('\\.MALE',x)
z <- rep('All',length(x))
z[y] <- 'Female'
z[y2] <- 'Male'
return(z)
}
deg <- dplyr::mutate(deg,direction = dumfun(category))
deg <- dplyr::mutate(deg,sex = sexfun(category))
deg <- dplyr::mutate(deg,dirbysex = paste0(direction,'.',sex))
deg <- dplyr::left_join(deg,modMeta)
Plot DEG results
g <- ggplot2::ggplot(deg,ggplot2::aes(x = Module,
y = fisherOR,
fill = direction))
g <- g + ggplot2::geom_col(position = 'dodge')
g <- g + ggplot2::facet_grid(. ~ sex)
g <- g + ggplot2::scale_y_log10()
g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
g <- g + ggplot2::coord_flip()
g
degMeta <- targetedEnrichment$degMeta
degMeta$adj.pval <- p.adjust(degMeta$fisherPval,method='fdr')
degMeta <- dplyr::filter(degMeta,adj.pval<=0.05)
dumfun <- function(x){
y<-grep('UP',x)
y2 <- grep('DOWN',x)
z <- x
z[y] <- 'Up'
z[y2] <- 'Down'
return(z)
}
sexfun <- function(x){
y<-grep('\\.FEMALE',x)
y2 <- grep('\\.MALE',x)
z <- rep('All',length(x))
z[y] <- 'Female'
z[y2] <- 'Male'
return(z)
}
degMeta <- dplyr::mutate(degMeta,direction = dumfun(category))
degMeta <- dplyr::mutate(degMeta,sex = sexfun(category))
degMeta <- dplyr::mutate(degMeta,dirbysex = paste0(direction,'.',sex))
degMeta <- dplyr::left_join(degMeta,modMeta)
ind <- grep('fixed',degMeta$category)
g <- ggplot2::ggplot(degMeta[ind,],ggplot2::aes(x = Module,
y = fisherOR,
fill = category))
g <- g + ggplot2::geom_col(position = 'dodge')
g <- g + ggplot2::facet_grid(. ~ sex)
g <- g + ggplot2::scale_y_log10()
g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1))
g <- g + ggplot2::coord_flip()
g
Overlap plot
AMPAD::pairwiseMatrixOfEnrichments('syn11932957')
Make PC statistics
AMPAD::makePcStatistics('March 2 2018')
Run pathway enrichments and push to synapse
pathwayGeneSets <- AMPAD::collatePathways()
fullPathwayEnrichmentsList <- mapply(AMPAD::run_amp_ad_enrichment,
pathwayGeneSets,
names(pathwayGeneSets),
MoreArgs = list(hgnc = TRUE,
manifestId = 'syn11932957'),
SIMPLIFY = FALSE)
fullPathwayEnrichments <- do.call(rbind,fullPathwayEnrichmentsList)
rSynapseUtilities::makeTable(fullPathwayEnrichments,
"Aggregate Module Pathway Enrichments March 7 2018",
'syn2370594')
Diagnose DEG differences
load(synapseClient::synGet('syn10496554',version = 10)@filePath)
amp.ad.de.geneSetsNew <- amp.ad.de.geneSets
load(synapseClient::synGet('syn10496554',version = 9)@filePath)
setdiff(amp.ad.de.geneSetsNew$`Diagnosis.Sex.DLPFC.AD-CONTROL.MALE.DOWN`,amp.ad.de.geneSets$`Diagnosis.Sex.DLPFC.AD-CONTROL.MALE.DOWN`)
look at glymphatic flow in tcx vs cbe
foobar <- AMPAD::pullExpressionAndPhenoWinsorized()
cbe_diag <- foobar$mayoCER$logitDiagnosis
redCBEMatrix <- foobar$mayoCER[,colnames(foobar$mayoCER)%in%targetedGeneSets$targetedPathways$WP1877]
ab <- svd(scale(redCBEMatrix))
plot(as.factor(cbe_diag),ab$u[,2])
summary(lm(ab$u[,3] ~ cbe_diag))
pheatmap::pheatmap(cbind(cbe_diag,redCBEMatrix))
tcx_diag <- foobar$mayoTCX$logitDiagnosis
redTCXMatrix <- foobar$mayoTCX[,colnames(foobar$mayoTCX)%in%targetedGeneSets$targetedPathways$WP1877]
ab2 <- svd(scale(redTCXMatrix))
plot(as.factor(tcx_diag),ab2$u[,1])
summary(lm(ab2$u[,3] ~ tcx_diag))
pheatmap::pheatmap(cbind(tcx_diag,redTCXMatrix))
summarize pathway enrichments for neuronal modules down in women
neuron <- list()
neuron$cbeyellow <- AMPAD::summarizeModulesNew('aggregateCBEyellowCBE')
neuron$dlpfcyellow <- AMPAD::summarizeModulesNew('aggregateDLPFCyellowDLPFC')
neuron$ifgbrown <- AMPAD::summarizeModulesNew('aggregateIFGbrownIFG')
neuron$phgbrown <- AMPAD::summarizeModulesNew('aggregatePHGbrownPHG')
neuron$stgbrown <- AMPAD::summarizeModulesNew('aggregateSTGbrownSTG')
neuron$tcxgreen <- AMPAD::summarizeModulesNew('aggregateTCXgreenTCX')
neuron$fpyellow <- AMPAD::summarizeModulesNew('aggregateFPyellowFP')
neuron2 <- do.call(rbind,neuron)
group by and summarize fisher odd's ratios across the 7 tissue types
neuron3 <- neuron2
neuron3$fisherOR[neuron3$fisherOR==Inf] <- 55
foobar2 <- dplyr::group_by(neuron3,category)
foobar3 <- dplyr::summarise(foobar2,nObserved = length(ModuleNameFull),meanOR = mean(fisherOR))
summarize neuroinfllmatory modules
infl <- list()
infl$tcxblue <- AMPAD::summarizeModulesNew('aggregateTCXblueTCX')
infl$ifgyellow <- AMPAD::summarizeModulesNew('aggregateIFGyellowIFG')
infl$phgyellow <- AMPAD::summarizeModulesNew('aggregatePHGyellowPHG')
infl$dlpfcblue<- AMPAD::summarizeModulesNew('aggregateDLPFCblueDLPFC')
infl$cbeturquoise <- AMPAD::summarizeModulesNew('aggregateCBEturquoiseCBE')
infl$stgblue<- AMPAD::summarizeModulesNew('aggregateSTGblueSTG')
infl$phgturquoise <- AMPAD::summarizeModulesNew('aggregatePHGturquoisePHG')
infl$ifgturquoise <- AMPAD::summarizeModulesNew('aggregateIFGturquoiseIFG')
infl$tcxturquoise <- AMPAD::summarizeModulesNew('aggregateTCXturquoiseTCX')
infl2 <- do.call(rbind,infl)
infl3 <- infl2
infl3$fisherOR[infl3$fisherOR==Inf] <- 55
foobar2 <- dplyr::group_by(infl3,category)
foobar3 <- dplyr::summarise(foobar2,nObserved = length(ModuleNameFull),meanOR = mean(fisherOR))
summary oligodendrocyte pathways
oligo <- list()
oligo$dlpfcbrown <- AMPAD::summarizeModulesNew('aggregateDLPFCbrownDLPFC')
oligo$stgyellow <- AMPAD::summarizeModulesNew('aggregateSTGyellowSTG')
oligo$phggreen <- AMPAD::summarizeModulesNew('aggregatePHGgreenPHG')
oligo$cbebrown<- AMPAD::summarizeModulesNew('aggregateCBEbrownCBE')
oligo$tcxyellow <- AMPAD::summarizeModulesNew('aggregateTCXyellowTCX')
oligo$ifgblue <- AMPAD::summarizeModulesNew('aggregateIFGblueIFG')
oligo$fpblue <- AMPAD::summarizeModulesNew('aggregateFPblueFP')
oligo2 <- do.call(rbind,oligo)
oligo3 <- oligo2
oligo3$fisherOR[oligo3$fisherOR==Inf] <- 55
foobar2 <- dplyr::group_by(oligo3,category)
foobar3 <- dplyr::summarise(foobar2,nObserved = length(ModuleNameFull),meanOR = mean(fisherOR))
Male specific modules
proteo <- list()
proteo$cbeblue <- AMPAD::summarizeModulesNew('aggregateCBEblueCBE')
proteo$dlpfcturquoise <- AMPAD::summarizeModulesNew('aggregateDLPFCturquoiseDLPFC')
proteo$tcxbrown <- AMPAD::summarizeModulesNew('aggregateTCXbrownTCX')
proteo$stgturquoise<- AMPAD::summarizeModulesNew('aggregateSTGturquoiseSTG')
proteo$phgblue <- AMPAD::summarizeModulesNew('aggregatePHGbluePHG')
proteo2 <- do.call(rbind,proteo)
proteo3 <- proteo2
proteo3$fisherOR[proteo3$fisherOR==Inf] <- 55
foobar2 <- dplyr::group_by(proteo3,category)
foobar3 <- dplyr::summarise(foobar2,nObserved = length(ModuleNameFull),meanOR = mean(fisherOR))
Sage-Bionetworks/AMPAD documentation built on Jan. 13, 2020, 9:18 p.m.
R Package Documentation
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make pairwise enrichment matrix
AMPAD::pairwiseMatrixOfEnrichments('syn11932957')
Pull targeted genesets
targetedGeneSets <- AMPAD::collateEnrichmentSets() #str(targetedGeneSets)
make simple summary table of differential expressed genes from random effect analysis
degMetaDf <- data.frame(gene=targetedGeneSets$degMeta$ad.control.random.DOWN,direction='Down')
For each of the three build the data frame of the respective Analyses
targetedEnrichment <- list() targetedEnrichment$ad <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$ad, 'AD', hgnc = TRUE, manifestId = 'syn11932957') targetedEnrichment$cell <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$cell, 'Cell', hgnc= TRUE, manifestId = 'syn11932957') targetedEnrichment$cell2 <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$cell2, 'Mathys', hgnc= TRUE, manifestId = 'syn11932957') targetedEnrichment$scz <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$scz, 'scz', hgnc= FALSE, manifestId = 'syn11932957') targetedEnrichment$control <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$control, 'control', hgnc= FALSE, manifestId = 'syn11932957') targetedEnrichment$mssm2 <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$MSSM2, 'zhang', hgnc= TRUE, manifestId = 'syn11932957') targetedEnrichment$mito <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$mito, 'Mito', hgnc= TRUE, manifestId = 'syn11932957') targetedEnrichment$deg <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$deg, 'DEG', hgnc=FALSE, manifestId = 'syn11932957') targetedEnrichment$degMeta <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$degMeta, 'DEGmeta', hgnc=FALSE, manifestId = 'syn11932957') targetedEnrichment$targetPathways <- AMPAD::run_amp_ad_enrichment2(targetedGeneSets$targetedPathways, 'TargetedPathways', hgnc=FALSE, manifestId = 'syn11932957') targetedEnrichment$ad_deg <- AMPAD::run_amp_ad_enrichment2_lists(targetedGeneSets$deg, 'ADDEG', targetedGeneSets$ad, hgnc = T, testhgnc = F) targetedEnrichment$ad_degmeta <- AMPAD::run_amp_ad_enrichment2_lists(targetedGeneSets$degMeta, 'ADDEGMeta', targetedGeneSets$ad, hgnc = T, testhgnc = F) targetedEnrichment$degFull <- rbind(targetedEnrichment$deg,targetedEnrichment$ad_deg) targetedEnrichment$degMetaFull <- rbind(targetedEnrichment$degMeta, targetedEnrichment$ad_degmeta)
Add adjusted p-values
targetedEnrichment$ad$adj.pval <- p.adjust(targetedEnrichment$ad$fisherPval,method='fdr') targetedEnrichment$deg$adj.pval <- p.adjust(targetedEnrichment$deg$fisherPval,method='fdr') targetedEnrichment$cell$adj.pval <- p.adjust(targetedEnrichment$cell$fisherPval,method='fdr') targetedEnrichment$cell2$adj.pval <- p.adjust(targetedEnrichment$cell2$fisherPval,method='fdr') targetedEnrichment$scz$adj.pval <- p.adjust(targetedEnrichment$scz$fisherPval,method='fdr') targetedEnrichment$control$adj.pval <- p.adjust(targetedEnrichment$control$fisherPval,method='fdr') targetedEnrichment$mito$adj.pval <- p.adjust(targetedEnrichment$mito$fisherPval,method='fdr') targetedEnrichment$mssm2$adj.pval <- p.adjust(targetedEnrichment$mssm2$fisherPval,method='fdr') targetedEnrichment$degMeta$adj.pval <- p.adjust(targetedEnrichment$degMeta$fisherPval,method='fdr') targetedEnrichment$targetPathways$adj.pval <- p.adjust(targetedEnrichment$targetPathways$fisherPval,method='fdr') targetedEnrichment$degFull$adj.pval <- p.adjust(targetedEnrichment$degFull$fisherPval,method='fdr') targetedEnrichment$degMetaFull$adj.pval <- p.adjust(targetedEnrichment$degMetaFull$fisherPval,method='fdr')
Make cell type summary plots
x- axis : module y- axis fisher-'s odds ratio
modMeta <- AMPAD::getModuleMetainfo('syn11932957') dummyDf <- targetedEnrichment$cell dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA #dummyDf <- dplyr::filter(targetedEnrichment$cell,adj.pval<=0.05) dummyDf <- dplyr::left_join(dummyDf,modMeta) dummyDf$category <- gsub('Zhang\\.','',dummyDf$category) dummyDf$category <- factor(dummyDf$category,levels = rev(c('Astrocyte', 'Endothelial', 'Microglia', 'Neuron', 'MyelinOligos', 'NewOligos', 'OPC'))) #dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion)) 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) dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName')) dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A', 'Consensus Cluster B', 'Consensus Cluster C', 'Consensus Cluster D', 'Consensus Cluster E'))) dummyDf$Module <- factor(dummyDf$Module,levels = (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'))) g <- ggplot2::ggplot(dummyDf, ggplot2::aes(x = Module, y = category, size = fisherOR, color = Cluster)) g <- g + ggplot2::geom_count() #g <- g + ggplot2::scale_y_log10() g <- g + ggplot2::labs(y = 'Cell Type Signature', x = 'AD Coexpression Module') #g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) #g <- g + cowplot::theme_cowplot(12) #g <- g + ggplot2::theme_update(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) g <- g + AMPAD::cowplot_rotated(11) #g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(), # axis.ticks.x=ggplot2::element_blank()) #g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110),trans='log') #g <- g + ggplot2::coord_flip() #g <- g + ggplot2::ggtitle('Enrichment for Cell Type Specific Signatures') g ggplot2::ggsave('figure2B.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8) #ggplot2::ggsave('cell_enrichments.png')
modMeta <- AMPAD::getModuleMetainfo('syn11932957') dummyDf <- targetedEnrichment$cell2 dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA #dummyDf <- dplyr::filter(targetedEnrichment$cell,adj.pval<=0.05) dummyDf <- dplyr::left_join(dummyDf,modMeta) # dummyDf$category <- gsub('Zhang\\.','',dummyDf$category) # dummyDf$category <- factor(dummyDf$category,levels = rev(c('Astrocyte', # 'Endothelial', # 'Microglia', # 'Neuron', # 'MyelinOligos', # 'NewOligos', # 'OPC'))) #dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion)) customDf <- data.frame(moduleName=c('TCXblue', 'IFGyellow', 'PHGyellow', 'DLPFCblue', 'CBEturquoise', 'STGblue', 'PHGturquoise', 'IFGturquoise', 'TCXturquoise', 'FPturquoise', 'IFGbrown', 'STGbrown', 'DLPFCyellow', 'TCXgreen', 'FPyellow', 'CBEyellow', 'PHGbrown', 'DLPFCbrown', 'STGyellow', 'PHGgreen', 'CBEbrown', 'TCXyellow', 'IFGblue', 'FPblue', 'FPbrown', 'CBEblue', 'DLPFCturquoise', 'TCXbrown', 'STGturquoise', 'PHGblue'), Cluster= c(rep('Consensus Cluster A',3), rep('Consensus Cluster B',7), rep('Consensus Cluster C',7), rep('Consensus Cluster D',7), rep('Consensus Cluster E',6)), stringsAsFactors=F) dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName')) dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A', 'Consensus Cluster B', 'Consensus Cluster C', 'Consensus Cluster D', 'Consensus Cluster E'))) dummyDf$Module <- factor(dummyDf$Module,levels = (c('TCXblue', 'IFGyellow', 'PHGyellow', 'DLPFCblue', 'CBEturquoise', 'STGblue', 'PHGturquoise', 'IFGturquoise', 'TCXturquoise', 'FPturquoise', 'IFGbrown', 'STGbrown', 'DLPFCyellow', 'TCXgreen', 'FPyellow', 'CBEyellow', 'PHGbrown', 'DLPFCbrown', 'STGyellow', 'PHGgreen', 'CBEbrown', 'TCXyellow', 'IFGblue', 'FPblue', 'FPbrown', 'CBEblue', 'DLPFCturquoise', 'TCXbrown', 'STGturquoise', 'PHGblue'))) g <- ggplot2::ggplot(dummyDf, ggplot2::aes(x = Module, y = category, size = fisherOR, color = Cluster)) g <- g + ggplot2::geom_count() #g <- g + ggplot2::scale_y_log10() #g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) #g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(), # axis.ticks.x=ggplot2::element_blank()) #g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110),trans='log') #g <- g + ggplot2::coord_flip() #g <- g + ggplot2::ggtitle('Enrichment for Cell Type Specific Signatures') g <- g + ggplot2::labs(y = 'Lake et al. Cell Type Signature', x = 'AD Coexpression Module') g <- g + AMPAD::cowplot_rotated(11) g ggplot2::ggsave('lake.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8) #ggplot2::ggsave('cell_enrichments.png')
modMeta <- AMPAD::getModuleMetainfo('syn11932957') dummyDf <- targetedEnrichment$scz dummyDf <- dplyr::filter(dummyDf,category == 'yellow' | category == 'red' | category == 'blue' | category == 'lightyellow' | category == 'greenyellow' | category == 'cyan' | category == 'grey60') dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA #dummyDf <- dplyr::filter(targetedEnrichment$cell,adj.pval<=0.05) dummyDf <- dplyr::left_join(dummyDf,modMeta) # dummyDf$category <- gsub('Zhang\\.','',dummyDf$category) # dummyDf$category <- factor(dummyDf$category,levels = rev(c('Astrocyte', # 'Endothelial', # 'Microglia', # 'Neuron', # 'MyelinOligos', # 'NewOligos', # 'OPC'))) #dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion)) 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) dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName')) dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A', 'Consensus Cluster B', 'Consensus Cluster C', 'Consensus Cluster D', 'Consensus Cluster E'))) dummyDf$Module <- factor(dummyDf$Module,levels = (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'))) g <- ggplot2::ggplot(dummyDf, ggplot2::aes(x = Module, y = category, size = fisherOR, color = Cluster)) g <- g + ggplot2::geom_count() #g <- g + ggplot2::scale_y_log10() #g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) #g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(), # axis.ticks.x=ggplot2::element_blank()) #g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110),trans='log') #g <- g + ggplot2::coord_flip() #g <- g + ggplot2::ggtitle('Enrichment for Cell Type Specific Signatures') g <- g + ggplot2::labs(y = 'CommonMind Differentially Expressed Module', x = 'AD Coexpression Module') g <- g + AMPAD::cowplot_rotated(11) g ggplot2::ggsave('cmc.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8) #ggplot2::ggsave('cell_enrichments.png')
modMeta <- AMPAD::getModuleMetainfo('syn11932957') dummyDf <- targetedEnrichment$mssm2 dummyDf <- dplyr::filter(dummyDf,category == 'Yellow' | category == 'Pink' | category == 'Gray.1' | category == 'Seashell' | category == 'Red.3' | category == 'Green.yellow' | category == 'Red' | category == 'Gold.2' | category == 'Tan' | category == 'Gold.3' | category == 'Light.yellow' | category == 'Brown.2' | category == 'Dark.cyan' | category == 'Khaki' | category == 'Grey.60' | category == 'Purple' | category == 'Green.4' | category == 'Honey.dew' | category == 'Red.2' | category == 'Beige') dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA #dummyDf <- dplyr::filter(targetedEnrichment$cell,adj.pval<=0.05) dummyDf <- dplyr::left_join(dummyDf,modMeta) # dummyDf$category <- gsub('Zhang\\.','',dummyDf$category) # dummyDf$category <- factor(dummyDf$category,levels = rev(c('Astrocyte', # 'Endothelial', # 'Microglia', # 'Neuron', # 'MyelinOligos', # 'NewOligos', # 'OPC'))) #dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion)) customDf <- data.frame(moduleName=c('TCXblue', 'IFGyellow', 'PHGyellow', 'DLPFCblue', 'CBEturquoise', 'STGblue', 'PHGturquoise', 'IFGturquoise', 'TCXturquoise', 'FPturquoise', 'IFGbrown', 'STGbrown', 'DLPFCyellow', 'TCXgreen', 'FPyellow', 'CBEyellow', 'PHGbrown', 'DLPFCbrown', 'STGyellow', 'PHGgreen', 'CBEbrown', 'TCXyellow', 'IFGblue', 'FPblue', 'FPbrown', 'CBEblue', 'DLPFCturquoise', 'TCXbrown', 'STGturquoise', 'PHGblue'), Cluster= c(rep('Consensus Cluster A',3), rep('Consensus Cluster B',7), rep('Consensus Cluster C',7), rep('Consensus Cluster D',7), rep('Consensus Cluster E',6)), stringsAsFactors=F) dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName')) dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A', 'Consensus Cluster B', 'Consensus Cluster C', 'Consensus Cluster D', 'Consensus Cluster E'))) dummyDf$Module <- factor(dummyDf$Module,levels = (c('TCXblue', 'IFGyellow', 'PHGyellow', 'DLPFCblue', 'CBEturquoise', 'STGblue', 'PHGturquoise', 'IFGturquoise', 'TCXturquoise', 'FPturquoise', 'IFGbrown', 'STGbrown', 'DLPFCyellow', 'TCXgreen', 'FPyellow', 'CBEyellow', 'PHGbrown', 'DLPFCbrown', 'STGyellow', 'PHGgreen', 'CBEbrown', 'TCXyellow', 'IFGblue', 'FPblue', 'FPbrown', 'CBEblue', 'DLPFCturquoise', 'TCXbrown', 'STGturquoise', 'PHGblue'))) g <- ggplot2::ggplot(dummyDf, ggplot2::aes(x = Module, y = category, size = fisherOR, color = Cluster)) g <- g + ggplot2::geom_count() #g <- g + ggplot2::scale_y_log10() #g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) #g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(), # axis.ticks.x=ggplot2::element_blank()) #g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110),trans='log') #g <- g + ggplot2::coord_flip() #g <- g + ggplot2::ggtitle('Enrichment for Cell Type Specific Signatures') g <- g + ggplot2::labs(y = 'Zhang et al. Module', x = 'AD Coexpression Module') g <- g + AMPAD::cowplot_rotated(11) g ggplot2::ggsave('zhanggaiteri.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8) #ggplot2::ggsave('cell_enrichments.png')
modMeta <- AMPAD::getModuleMetainfo('syn11932957') dummyDf <- targetedEnrichment$mssm2 dummyDf <- dplyr::filter(dummyDf,!(category == 'Yellow' | category == 'Pink' | category == 'Gray.1' | category == 'Seashell' | category == 'Red.3' | category == 'Green.yellow' | category == 'Red' | category == 'Gold.2' | category == 'Tan' | category == 'Gold.3' | category == 'Light.yellow' | category == 'Brown.2' | category == 'Dark.cyan' | category == 'Khaki' | category == 'Grey.60' | category == 'Purple' | category == 'Green.4' | category == 'Honey.dew' | category == 'Red.2' | category == 'Beige')) dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA #dummyDf <- dplyr::filter(targetedEnrichment$cell,adj.pval<=0.05) dummyDf <- dplyr::left_join(dummyDf,modMeta) # dummyDf$category <- gsub('Zhang\\.','',dummyDf$category) # dummyDf$category <- factor(dummyDf$category,levels = rev(c('Astrocyte', # 'Endothelial', # 'Microglia', # 'Neuron', # 'MyelinOligos', # 'NewOligos', # 'OPC'))) #dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion)) 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) dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName')) dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A', 'Consensus Cluster B', 'Consensus Cluster C', 'Consensus Cluster D', 'Consensus Cluster E'))) dummyDf$Module <- factor(dummyDf$Module,levels = (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'))) g <- ggplot2::ggplot(dummyDf, ggplot2::aes(x = Module, y = category, size = fisherOR, color = Cluster)) g <- g + ggplot2::geom_count() #g <- g + ggplot2::scale_y_log10() g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) #g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(), # axis.ticks.x=ggplot2::element_blank()) #g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110),trans='log') #g <- g + ggplot2::coord_flip() #g <- g + ggplot2::ggtitle('Enrichment for Cell Type Specific Signatures') g <- g + ggplot2::labs(y = 'Zhang et al. Module', x = 'AD Coexpression Module') g ggplot2::ggsave('mathys.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8) #ggplot2::ggsave('cell_enrichments.png')
mitochondria
modMeta <- AMPAD::getModuleMetainfo('syn11932957') dummyDf <- targetedEnrichment$mito dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA #dummyDf <- dplyr::filter(targetedEnrichment$cell,adj.pval<=0.05) dummyDf <- dplyr::left_join(dummyDf,modMeta) strFxn <- function(x){ nc <- nchar(x) if(nc<50){ ind1<-floor(nc/2) str1 <- substr(x,1,ind1) str2 <- substr(x,ind1+1,nc) return(paste0(str1,'\n',str2)) }else{ ind1<-floor(nc/3) ind2 <- 2*ind1 str1 <- substr(x,1,ind1) str2 <- substr(x,ind1+1,ind2) str3 <- substr(x,ind2+1,nc) return(paste0(str1,'\n',str2,'\n',str3)) } } dummyDf$category <- sapply(dummyDf$category,strFxn) #dummyDf$category <- gsub('Zhang\\.','',dummyDf$category) #dummyDf$category <- factor(dummyDf$category,levels = rev(c('Astrocyte', # 'Endothelial', # 'Microglia', # 'Neuron', # 'MyelinOligos', # 'NewOligos', # 'OPC'))) #dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion)) customDf <- data.frame(moduleName=c('TCXblue', 'IFGyellow', 'PHGyellow', 'DLPFCblue', 'CBEturquoise', 'STGblue', 'PHGturquoise', 'IFGturquoise', 'TCXturquoise', 'FPturquoise', 'IFGbrown', 'STGbrown', 'DLPFCyellow', 'TCXgreen', 'FPyellow', 'CBEyellow', 'PHGbrown', 'DLPFCbrown', 'STGyellow', 'PHGgreen', 'CBEbrown', 'TCXyellow', 'IFGblue', 'FPblue', 'FPbrown', 'CBEblue', 'DLPFCturquoise', 'TCXbrown', 'STGturquoise', 'PHGblue'), Cluster= c(rep('Consensus Cluster A',3), rep('Consensus Cluster B',7), rep('Consensus Cluster C',7), rep('Consensus Cluster D',7), rep('Consensus Cluster E',6)), stringsAsFactors=F) dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName')) dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A', 'Consensus Cluster B', 'Consensus Cluster C', 'Consensus Cluster D', 'Consensus Cluster E'))) dummyDf$Module <- factor(dummyDf$Module,levels = rev(c('TCXblue', 'IFGyellow', 'PHGyellow', 'DLPFCblue', 'CBEturquoise', 'STGblue', 'PHGturquoise', 'IFGturquoise', 'TCXturquoise', 'FPturquoise', 'IFGbrown', 'STGbrown', 'DLPFCyellow', 'TCXgreen', 'FPyellow', 'CBEyellow', 'PHGbrown', 'DLPFCbrown', 'STGyellow', 'PHGgreen', 'CBEbrown', 'TCXyellow', 'IFGblue', 'FPblue', 'FPbrown', 'CBEblue', 'DLPFCturquoise', 'TCXbrown', 'STGturquoise', 'PHGblue'))) g <- ggplot2::ggplot(dummyDf, ggplot2::aes(x = Module, y = category, size = fisherOR, color = Cluster)) g <- g + ggplot2::geom_count() #g <- g + ggplot2::scale_y_log10() g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) #g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(), # axis.ticks.x=ggplot2::element_blank()) #g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110),trans='log') g <- g + ggplot2::coord_flip() #g <- g + ggplot2::ggtitle('Enrichment for Cell Type Specific Signatures') g <- g + ggplot2::labs(y = 'Mitochondrial signatures', x = 'AD Coexpression Module') g
overlap table with amp-ad related gene sets
dummyDf <- targetedEnrichment$ad dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA dummyDf <- dplyr::left_join(dummyDf,modMeta) dummyDf <- dplyr::filter(dummyDf,adj.pval<=0.05) dummyDf <- dplyr::filter(dummyDf,category == 'Nominated_targets' | category == 'MSSM' | category == 'Mayo_simple' | category == 'Mayo_comprehensive' | category == 'Emory' | category == 'Columbia_Broad_Rush_m109') dummyDf <- dplyr::select(dummyDf, Module, category, mod_size, category_size, nInter, adj.pval, fisherOR) colnames(dummyDf) <- c('Module Name', 'AMP-AD Gene Set', 'Module Size', 'AMP-AD Gene Set Size', 'Size of Intersection', 'Adjusted P-value', 'FET Odds Ratio') write.csv(dummyDf,file='amp-ad_overlap_stats.csv',quote=F,row.names=F)
differential expression meta analysis summary tables
dummyDf <- targetedEnrichment$degMetaFull dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA dummyDf <- dplyr::left_join(dummyDf,modMeta) dummyDf <- dplyr::filter(dummyDf,adj.pval<=0.05) dummyDf$Module[is.na(dummyDf$Module)] <- dummyDf$ModuleNameFull[is.na(dummyDf$Module)] dummyDf <- dplyr::select(dummyDf, Module, category, mod_size, category_size, nInter, adj.pval, fisherOR) colnames(dummyDf) <- c('Module or Gene Set Name', 'DEG Gene Set', 'Module or Gene Set Size', 'DEG Gene Set Size', 'Size of Intersection', 'Adjusted P-value', 'FET Odds Ratio') dummyDf <- dplyr::arrange(dummyDf,desc(`FET Odds Ratio`)) adUp <- dplyr::filter(dummyDf,`DEG Gene Set` == 'ad.control.random.UP') adDown <- dplyr::filter(dummyDf,`DEG Gene Set` == 'ad.control.random.DOWN') adUpMale <- dplyr::filter(dummyDf,`DEG Gene Set` == 'ad.control.MALE.random.UP') adDownMale <- dplyr::filter(dummyDf,`DEG Gene Set` == 'ad.control.MALE.random.DOWN') adUpFemale <- dplyr::filter(dummyDf,`DEG Gene Set` == 'ad.control.FEMALE.random.UP') adDownFemale <- dplyr::filter(dummyDf,`DEG Gene Set` == 'ad.control.FEMALE.random.DOWN') write.csv(adUp,file='adup.csv',quote=F,row.names=F) write.csv(adDown,file='adDown.csv',quote=F,row.names=F) write.csv(adUpMale,file='adUpMale.csv',quote=F,row.names=F) write.csv(adDownMale,file='adDownMale.csv',quote=F,row.names=F) write.csv(adUpFemale,file='adUpFemale.csv',quote=F,row.names=F) write.csv(adDownFemale,file='adDownFemale.csv',quote=F,row.names=F) #View(dplyr::filter(dummyDf,category == 'ad.control.FEMALE.random.UP'))
AD genetics summary
dummyDf <- targetedEnrichment$ad dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA dummyDf <- dplyr::left_join(dummyDf,modMeta) #dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion)) dummyDf$category <- factor(dummyDf$category, levels = rev(c('genecards', 'pantherPresenilin', 'dbgap', 'igap', 'jensenDisease', 'omimExpanded', 'biocarta', 'wikipathwaysMouse', 'wikipathwaysHuman', 'pantherAmyloid', 'kegg', 'omim', 'Nominated_targets', 'MSSM', 'Mayo_simple', 'Mayo_comprehensive', 'Emory', 'Columbia_Broad_Rush_m109'))) dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName')) dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A', 'Consensus Cluster B', 'Consensus Cluster C', 'Consensus Cluster D', 'Consensus Cluster E'))) dummyDf$Module <- factor(dummyDf$Module,levels = (c('TCXblue', 'IFGyellow', 'PHGyellow', 'DLPFCblue', 'CBEturquoise', 'STGblue', 'PHGturquoise', 'IFGturquoise', 'TCXturquoise', 'FPturquoise', 'IFGbrown', 'STGbrown', 'DLPFCyellow', 'TCXgreen', 'FPyellow', 'CBEyellow', 'PHGbrown', 'DLPFCbrown', 'STGyellow', 'PHGgreen', 'CBEbrown', 'TCXyellow', 'IFGblue', 'FPblue', 'FPbrown', 'CBEblue', 'DLPFCturquoise', 'TCXbrown', 'STGturquoise', 'PHGblue'))) # g <- ggplot2::ggplot(dummyDf, # ggplot2::aes(x = Module, # y = category, # size = fisherOR, # color = -log10(adj.pval))) # g <- g+ggplot2::geom_count() # #g <- g + ggplot2::scale_y_log10() # #g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) # g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(), # axis.ticks.x=ggplot2::element_blank()) # g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.4,6,12,18,25)) # #g <- g + ggplot2::coord_flip() # g <- g + ggplot2::ggtitle('Enrichment for AD Signatures') g <- ggplot2::ggplot(dummyDf, ggplot2::aes(x = Module, y = category, size = fisherOR, color = Cluster)) g <- g + ggplot2::geom_count() #g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) g <- g + ggplot2::labs(y = 'AD Signature', x = 'AD Coexpression Module') g <- g + AMPAD::cowplot_rotated(9) g ggplot2::ggsave('figure2C.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8) #ggplot2::ggsave('ad_signature_enrichments.png')
dummyDf <- targetedEnrichment$ad dummyDf$fisherOR[dummyDf$adj.pval>=0.05] <- NA dummyDf <- dplyr::left_join(dummyDf,modMeta) dummyDf<- dplyr::filter(dummyDf,category == 'AMP.AD.Nominated.Targets' | category == 'AMP.AD.McKenzie.et.al..2017.Red' | category == 'AMP.AD.McKenzie.et.al..2017.Light.green' | category == 'AMP.AD.McKenzie.et.al..2017.Green' | category == 'AMP.AD.Allen.et.al..2018.AD.PSP.TCX40.CS' | category == 'AMP.AD.Allen.et.al..2018.AD.PSP.TCX10.CS' | category == 'AMP.AD.Johnson.et.al..2018.RNA.Binding' | category == 'AMP.AD.Mostafavi.et.al..2018.m109') dummyDf$category[dummyDf$category=='AMP.AD.Nominated.Targets'] = 'Nominated\nTargets' dummyDf$category[dummyDf$category=='AMP.AD.McKenzie.et.al..2017.Red'] = 'MSSM Oligo\nRed' dummyDf$category[dummyDf$category=='AMP.AD.McKenzie.et.al..2017.Light.green'] = 'MSSM Oligo\nLight green' dummyDf$category[dummyDf$category=='AMP.AD.McKenzie.et.al..2017.Green'] = 'MSSM Oligo\nGreen' dummyDf$category[dummyDf$category=='AMP.AD.Allen.et.al..2018.AD.PSP.TCX40.CS'] = 'Mayo Oligo\nAD.PSP.TCX40.CS' dummyDf$category[dummyDf$category=='AMP.AD.Allen.et.al..2018.AD.PSP.TCX10.CS'] = 'Mayo Oligo\nAD.PSP.TCX10.CS' dummyDf$category[dummyDf$category=='AMP.AD.Johnson.et.al..2018.RNA.Binding'] = 'Emory\nRNA binding' dummyDf$category[dummyDf$category=='AMP.AD.Mostafavi.et.al..2018.m109'] = 'Rush m109' #dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion)) dummyDf$category <- factor(dummyDf$category, levels = (c('Nominated\nTargets', 'MSSM Oligo\nRed', 'MSSM Oligo\nLight green', 'MSSM Oligo\nGreen', 'Mayo Oligo\nAD.PSP.TCX40.CS', 'Mayo Oligo\nAD.PSP.TCX10.CS', 'Emory\nRNA binding', 'Rush m109'))) dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName')) dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Astrocytic', 'Microglial', 'Neuronal', 'Oligodendroglial', 'Proteostasis'))) dummyDf$Module <- factor(dummyDf$Module,levels = rev(c('TCXblue', 'IFGyellow', 'PHGyellow', 'DLPFCblue', 'CBEturquoise', 'STGblue', 'PHGturquoise', 'IFGturquoise', 'TCXturquoise', 'FPturquoise', 'IFGbrown', 'STGbrown', 'DLPFCyellow', 'TCXgreen', 'FPyellow', 'CBEyellow', 'PHGbrown', 'DLPFCbrown', 'STGyellow', 'PHGgreen', 'CBEbrown', 'TCXyellow', 'IFGblue', 'FPblue', 'FPbrown', 'CBEblue', 'DLPFCturquoise', 'TCXbrown', 'STGturquoise', 'PHGblue'))) # g <- ggplot2::ggplot(dummyDf, # ggplot2::aes(x = Module, # y = category, # size = fisherOR, # color = -log10(adj.pval))) # g <- g+ggplot2::geom_count() # #g <- g + ggplot2::scale_y_log10() # #g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) # g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(), # axis.ticks.x=ggplot2::element_blank()) # g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.4,6,12,18,25)) # #g <- g + ggplot2::coord_flip() # g <- g + ggplot2::ggtitle('Enrichment for AD Signatures') g <- ggplot2::ggplot(dummyDf, ggplot2::aes(x = Module, y = category, size = fisherOR, color = Cluster)) g <- g + ggplot2::geom_count() g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) g <- g + ggplot2::coord_flip() g <- g + ggplot2::labs(y = 'AD Signature', x = 'AD Coexpression Module') g #ggplot2::ggsave('figure2C.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8) ggplot2::ggsave('amp_ad_new_modules.png',units='in',width=4.91,height=4.8,scale=1.4)
dummyDf <- dplyr::filter(targetedEnrichment$degMeta,adj.pval<=0.05) dummyDf <- dplyr::filter(dummyDf,category == 'ad.control.FEMALE.random.DOWN' | category == 'ad.control.FEMALE.random.UP' | category == 'ad.control.MALE.random.DOWN' | category == 'ad.control.MALE.random.UP') dummyDf$adj.pval[dummyDf$adj.pval==0] = 10^-300 dummyDf$category <- gsub('ad\\.control\\.FEMALE\\.random\\.DOWN','Female, Down',dummyDf$category) dummyDf$category <- gsub('ad\\.control\\.MALE\\.random\\.DOWN','Male, Down',dummyDf$category) dummyDf$category <- gsub('ad\\.control\\.FEMALE\\.random\\.UP','Female, Up',dummyDf$category) dummyDf$category <- gsub('ad\\.control\\.MALE\\.random\\.UP','Male, Up',dummyDf$category) dummyDf <- dplyr::left_join(dummyDf,modMeta) #dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion)) dummyDf <- dplyr::left_join(dummyDf,customDf,by=c('Module'='moduleName')) dummyDf$Cluster <- factor(dummyDf$Cluster,levels = (c('Consensus Cluster A', 'Consensus Cluster B', 'Consensus Cluster C', 'Consensus Cluster D', 'Consensus Cluster E'))) dummyDf$category <- factor(dummyDf$category,rev(c('Female, Up', 'Male, Up', 'Female, Down', 'Male, Down'))) dummyDf$Module <- factor(dummyDf$Module,levels = (c('TCXblue', 'IFGyellow', 'PHGyellow', 'DLPFCblue', 'CBEturquoise', 'STGblue', 'PHGturquoise', 'IFGturquoise', 'TCXturquoise', 'FPturquoise', 'IFGbrown', 'STGbrown', 'DLPFCyellow', 'TCXgreen', 'FPyellow', 'CBEyellow', 'PHGbrown', 'DLPFCbrown', 'STGyellow', 'PHGgreen', 'CBEbrown', 'TCXyellow', 'IFGblue', 'FPblue', 'FPbrown', 'CBEblue', 'DLPFCturquoise', 'TCXbrown', 'STGturquoise', 'PHGblue'))) # g <- ggplot2::ggplot(dummyDf, # ggplot2::aes(x = Module, # y = category, # size = fisherOR, # color = -log10(adj.pval))) # g <- g+ggplot2::geom_count() # #g <- g + ggplot2::scale_y_log10() # #g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) # g <- g + ggplot2::theme(axis.text.x=ggplot2::element_blank(), # axis.ticks.x=ggplot2::element_blank()) # g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1), breaks = c(1.5, 3,8,20,50,110,300),trans='log') # #g <- g + ggplot2::coord_flip() # g <- g + ggplot2::ggtitle('Enrichment for AD DEG Signatures') g <- ggplot2::ggplot(dummyDf, ggplot2::aes(x = Module, y = category, size = fisherOR, color = Cluster)) g <- g + ggplot2::geom_count() #g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) g <- g + ggplot2::labs(y = 'DEG Sex Specific Meta Analysis', x = 'AD Coexpression Module') g <- g + AMPAD::cowplot_rotated(11) g ggplot2::ggsave('figure2D.tiff',device='tiff',units='mm',width=85,height=85,scale=1.8) #ggplot2::ggsave('deg_signature_enrichments.png')
#modMeta <- AMPAD::getModuleMetainfo('syn11932957') dummyDf <- dplyr::filter(targetedEnrichment$targetPathways,adj.pval<=0.05) dummyDf <- dplyr::left_join(dummyDf,modMeta) dummyDf$ModuleBrainRegion <- factor(dummyDf$ModuleBrainRegion,levels = unique(dummyDf$ModuleBrainRegion)) g <- ggplot2::ggplot(dummyDf, ggplot2::aes(x = Module, y = category, size = fisherOR, color = -log10(adj.pval))) g <- g + ggplot2::geom_count() #g <- g + ggplot2::scale_y_log10() g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) g <- g + ggplot2::scale_color_gradientn(colours = c(viridis::viridis(2)[2], viridis::viridis(2)[1]),values = c(0,1)) g <- g + ggplot2::coord_flip() g
Reformate DEG results for visualization
tcx_keep <- intersect(grep('TCX',targetedEnrichment$deg$ModuleNameFull), grep('TCX',targetedEnrichment$deg$category)) phg_keep <- intersect(grep('PHG',targetedEnrichment$deg$ModuleNameFull), grep('PHG',targetedEnrichment$deg$category)) cbe_keep <- intersect(grep('CBE',targetedEnrichment$deg$ModuleNameFull), grep('CBE',targetedEnrichment$deg$category)) dlpfc_keep <- intersect(grep('DLPFC',targetedEnrichment$deg$ModuleNameFull), grep('DLPFC',targetedEnrichment$deg$category)) stg_keep <- intersect(grep('STG',targetedEnrichment$deg$ModuleNameFull), grep('STG',targetedEnrichment$deg$category)) ifg_keep <- intersect(grep('IFG',targetedEnrichment$deg$ModuleNameFull), grep('IFG',targetedEnrichment$deg$category)) fp_keep <- intersect(grep('FP',targetedEnrichment$deg$ModuleNameFull), grep('FP',targetedEnrichment$deg$category)) deg <- targetedEnrichment$deg[c(tcx_keep,phg_keep,cbe_keep,dlpfc_keep,stg_keep,ifg_keep,fp_keep),] deg$adj.pval <- p.adjust(deg$fisherPval,method='fdr') deg <- dplyr::filter(deg,adj.pval<=0.05) dumfun <- function(x){ y<-grep('UP',x) y2 <- grep('DOWN',x) z <- x z[y] <- 'Up' z[y2] <- 'Down' return(z) } sexfun <- function(x){ y<-grep('\\.FEMALE',x) y2 <- grep('\\.MALE',x) z <- rep('All',length(x)) z[y] <- 'Female' z[y2] <- 'Male' return(z) } deg <- dplyr::mutate(deg,direction = dumfun(category)) deg <- dplyr::mutate(deg,sex = sexfun(category)) deg <- dplyr::mutate(deg,dirbysex = paste0(direction,'.',sex)) deg <- dplyr::left_join(deg,modMeta)
Plot DEG results
g <- ggplot2::ggplot(deg,ggplot2::aes(x = Module, y = fisherOR, fill = direction)) g <- g + ggplot2::geom_col(position = 'dodge') g <- g + ggplot2::facet_grid(. ~ sex) g <- g + ggplot2::scale_y_log10() g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) g <- g + ggplot2::coord_flip() g
degMeta <- targetedEnrichment$degMeta degMeta$adj.pval <- p.adjust(degMeta$fisherPval,method='fdr') degMeta <- dplyr::filter(degMeta,adj.pval<=0.05) dumfun <- function(x){ y<-grep('UP',x) y2 <- grep('DOWN',x) z <- x z[y] <- 'Up' z[y2] <- 'Down' return(z) } sexfun <- function(x){ y<-grep('\\.FEMALE',x) y2 <- grep('\\.MALE',x) z <- rep('All',length(x)) z[y] <- 'Female' z[y2] <- 'Male' return(z) } degMeta <- dplyr::mutate(degMeta,direction = dumfun(category)) degMeta <- dplyr::mutate(degMeta,sex = sexfun(category)) degMeta <- dplyr::mutate(degMeta,dirbysex = paste0(direction,'.',sex)) degMeta <- dplyr::left_join(degMeta,modMeta)
ind <- grep('fixed',degMeta$category) g <- ggplot2::ggplot(degMeta[ind,],ggplot2::aes(x = Module, y = fisherOR, fill = category)) g <- g + ggplot2::geom_col(position = 'dodge') g <- g + ggplot2::facet_grid(. ~ sex) g <- g + ggplot2::scale_y_log10() g <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) g <- g + ggplot2::coord_flip() g
Overlap plot
AMPAD::pairwiseMatrixOfEnrichments('syn11932957')
Make PC statistics
AMPAD::makePcStatistics('March 2 2018')
Run pathway enrichments and push to synapse
pathwayGeneSets <- AMPAD::collatePathways() fullPathwayEnrichmentsList <- mapply(AMPAD::run_amp_ad_enrichment, pathwayGeneSets, names(pathwayGeneSets), MoreArgs = list(hgnc = TRUE, manifestId = 'syn11932957'), SIMPLIFY = FALSE) fullPathwayEnrichments <- do.call(rbind,fullPathwayEnrichmentsList) rSynapseUtilities::makeTable(fullPathwayEnrichments, "Aggregate Module Pathway Enrichments March 7 2018", 'syn2370594')
Diagnose DEG differences
load(synapseClient::synGet('syn10496554',version = 10)@filePath) amp.ad.de.geneSetsNew <- amp.ad.de.geneSets load(synapseClient::synGet('syn10496554',version = 9)@filePath) setdiff(amp.ad.de.geneSetsNew$`Diagnosis.Sex.DLPFC.AD-CONTROL.MALE.DOWN`,amp.ad.de.geneSets$`Diagnosis.Sex.DLPFC.AD-CONTROL.MALE.DOWN`)
look at glymphatic flow in tcx vs cbe
foobar <- AMPAD::pullExpressionAndPhenoWinsorized() cbe_diag <- foobar$mayoCER$logitDiagnosis redCBEMatrix <- foobar$mayoCER[,colnames(foobar$mayoCER)%in%targetedGeneSets$targetedPathways$WP1877] ab <- svd(scale(redCBEMatrix)) plot(as.factor(cbe_diag),ab$u[,2]) summary(lm(ab$u[,3] ~ cbe_diag)) pheatmap::pheatmap(cbind(cbe_diag,redCBEMatrix)) tcx_diag <- foobar$mayoTCX$logitDiagnosis redTCXMatrix <- foobar$mayoTCX[,colnames(foobar$mayoTCX)%in%targetedGeneSets$targetedPathways$WP1877] ab2 <- svd(scale(redTCXMatrix)) plot(as.factor(tcx_diag),ab2$u[,1]) summary(lm(ab2$u[,3] ~ tcx_diag)) pheatmap::pheatmap(cbind(tcx_diag,redTCXMatrix))
summarize pathway enrichments for neuronal modules down in women
neuron <- list() neuron$cbeyellow <- AMPAD::summarizeModulesNew('aggregateCBEyellowCBE') neuron$dlpfcyellow <- AMPAD::summarizeModulesNew('aggregateDLPFCyellowDLPFC') neuron$ifgbrown <- AMPAD::summarizeModulesNew('aggregateIFGbrownIFG') neuron$phgbrown <- AMPAD::summarizeModulesNew('aggregatePHGbrownPHG') neuron$stgbrown <- AMPAD::summarizeModulesNew('aggregateSTGbrownSTG') neuron$tcxgreen <- AMPAD::summarizeModulesNew('aggregateTCXgreenTCX') neuron$fpyellow <- AMPAD::summarizeModulesNew('aggregateFPyellowFP') neuron2 <- do.call(rbind,neuron)
group by and summarize fisher odd's ratios across the 7 tissue types
neuron3 <- neuron2 neuron3$fisherOR[neuron3$fisherOR==Inf] <- 55 foobar2 <- dplyr::group_by(neuron3,category) foobar3 <- dplyr::summarise(foobar2,nObserved = length(ModuleNameFull),meanOR = mean(fisherOR))
summarize neuroinfllmatory modules
infl <- list() infl$tcxblue <- AMPAD::summarizeModulesNew('aggregateTCXblueTCX') infl$ifgyellow <- AMPAD::summarizeModulesNew('aggregateIFGyellowIFG') infl$phgyellow <- AMPAD::summarizeModulesNew('aggregatePHGyellowPHG') infl$dlpfcblue<- AMPAD::summarizeModulesNew('aggregateDLPFCblueDLPFC') infl$cbeturquoise <- AMPAD::summarizeModulesNew('aggregateCBEturquoiseCBE') infl$stgblue<- AMPAD::summarizeModulesNew('aggregateSTGblueSTG') infl$phgturquoise <- AMPAD::summarizeModulesNew('aggregatePHGturquoisePHG') infl$ifgturquoise <- AMPAD::summarizeModulesNew('aggregateIFGturquoiseIFG') infl$tcxturquoise <- AMPAD::summarizeModulesNew('aggregateTCXturquoiseTCX') infl2 <- do.call(rbind,infl) infl3 <- infl2 infl3$fisherOR[infl3$fisherOR==Inf] <- 55 foobar2 <- dplyr::group_by(infl3,category) foobar3 <- dplyr::summarise(foobar2,nObserved = length(ModuleNameFull),meanOR = mean(fisherOR))
summary oligodendrocyte pathways
oligo <- list() oligo$dlpfcbrown <- AMPAD::summarizeModulesNew('aggregateDLPFCbrownDLPFC') oligo$stgyellow <- AMPAD::summarizeModulesNew('aggregateSTGyellowSTG') oligo$phggreen <- AMPAD::summarizeModulesNew('aggregatePHGgreenPHG') oligo$cbebrown<- AMPAD::summarizeModulesNew('aggregateCBEbrownCBE') oligo$tcxyellow <- AMPAD::summarizeModulesNew('aggregateTCXyellowTCX') oligo$ifgblue <- AMPAD::summarizeModulesNew('aggregateIFGblueIFG') oligo$fpblue <- AMPAD::summarizeModulesNew('aggregateFPblueFP') oligo2 <- do.call(rbind,oligo) oligo3 <- oligo2 oligo3$fisherOR[oligo3$fisherOR==Inf] <- 55 foobar2 <- dplyr::group_by(oligo3,category) foobar3 <- dplyr::summarise(foobar2,nObserved = length(ModuleNameFull),meanOR = mean(fisherOR))
Male specific modules
proteo <- list() proteo$cbeblue <- AMPAD::summarizeModulesNew('aggregateCBEblueCBE') proteo$dlpfcturquoise <- AMPAD::summarizeModulesNew('aggregateDLPFCturquoiseDLPFC') proteo$tcxbrown <- AMPAD::summarizeModulesNew('aggregateTCXbrownTCX') proteo$stgturquoise<- AMPAD::summarizeModulesNew('aggregateSTGturquoiseSTG') proteo$phgblue <- AMPAD::summarizeModulesNew('aggregatePHGbluePHG') proteo2 <- do.call(rbind,proteo) proteo3 <- proteo2 proteo3$fisherOR[proteo3$fisherOR==Inf] <- 55 foobar2 <- dplyr::group_by(proteo3,category) foobar3 <- dplyr::summarise(foobar2,nObserved = length(ModuleNameFull),meanOR = mean(fisherOR))
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