diff --git a/Postre_app.zip b/Postre_app.zip index e2284c1..4b09d75 100644 Binary files a/Postre_app.zip and b/Postre_app.zip differ diff --git a/Postre_app/.Rhistory b/Postre_app/.Rhistory index b1b2e24..134a286 100644 --- a/Postre_app/.Rhistory +++ b/Postre_app/.Rhistory @@ -1,512 +1,512 @@ -genesWithSignal<-genesWithSignal$longitudPicos -nroObservacion_limiteSuperior_RegionKneePoint<-elbowPoint[1,1] ##Pq va en decreasing -nroObservacion_limiteInferior_RegionKneePoint<-elbowPoint[1,2] ##Pq va en decreasing -kneePoint_ejeX<-elbowPoint[1,3] -elbowPoint<-elbowPoint[1,1]##ESE -genesWithSignal[elbowPoint] -targetLongitudPico<-genesWithSignal[elbowPoint] -plot(x=1:length(genesWithSignal), y=genesWithSignal, type = "l", lwd=3, main = "H3K27me3 peak size associated to TSS \n in ESCs", xlab = "TSS", ylab="H3K27me3 peak size") -abline(h=c(targetLongitudPico),col="red", lwd=2, lty=2) -abline(v=nroObservacion_limiteSuperior_RegionKneePoint,col="red", lwd=2, lty=2) -abline(v=kneePoint_ejeX, col="blue", lwd=2, lty=2) -abline(v=nroObservacion_limiteInferior_RegionKneePoint, col="green", lwd=2, lty=2) -text(x= 20000, y=12000, paste0(targetLongitudPico, " bp"), col="red") -thresh<-targetLongitudPico -##################################################################### -##### seleccion genes (TSS) de desarrollo, aquellos cuyo overalapped H3K27me3 peak > thresh -##################################################################### -genesDesarrollo_allInfo<-developForPlot[developForPlot$longitudPicos>=thresh,] -selectedDevelopGenes<-sort(unique(as.character(genesDesarrollo_allInfo$gene))) -######################################################################## -##### no nos interesan ni los LINC ni los LOC asi que vamos a quitarlos -######################################################################## -AllGenes<-as.character(unique(developForPlot$gene))#27090 -LINCs<-AllGenes[grep("^LINC.*", AllGenes)] -LOCs<-AllGenes[grep("^LOC[0-9].*", AllGenes)] -NotRelevant<-c(LINCs,LOCs)#LINCs and LOCs -########################################################################## -###### seleccion de los TSS de interes -########################################################################## -selectedDevelopGenes<-selectedDevelopGenes[!(selectedDevelopGenes %in% NotRelevant)] -print(thresh) -print(length(selectedDevelopGenes)) -#################### -## Saving results -#################### -hESC_polyCombGenes<-selectedDevelopGenes -save(hESC_polyCombGenes, file = "~/Dropbox/Cantabria/PhD_Project/Resultados/genesDesarrollo/Robjects/hESC_polyCombGenes.RData") -tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/Revision_NAR/Manuscrito_New_Version/SupplementaryMaterial/SupplementaryFigures/Supplementary_Figure_1/ESC.tiff", units="in", +for(pheno in allPhenos){ +print(pheno) +for(tMode in runningMode_options){ +print(tMode) +##subset() does not work well with a==a, important to know... +filt_AllInfo<-subset(All_InfoPredictions, runMode==tMode & Phenotype==pheno) +iterInfo<-backBoneMat +#Get statistics +iterInfo$N_Patients<-nrow(filt_AllInfo) +iterInfo$PercentagePredictions<-round((sum(filt_AllInfo$PathogenicScore>=0.8)/nrow(filt_AllInfo))*100, digits = 0) +iterInfo$Phenotype<-pheno +iterInfo$Mode<-tMode +#Adding info +summaryInfo<-rbind(summaryInfo, iterInfo) +} +} +summaryInfo$Phenotype<-factor(summaryInfo$Phenotype, +levels=allPhenos, +ordered = TRUE)##For the ggplot +lapply(summaryInfo, class) +##Quiero el de multiple groups +# http://www.sthda.com/english/wiki/ggplot2-barplots-quick-start-guide-r-software-and-data-visualization +library(ggplot2) +# x11()##TO AVOID GGPLOT2 MASSIVE COLLAPSE, OR USE GGSAVE +##He hecho un recorte del grafico, luego al insertarlo en office, he quitado las labels y las he puesto manualmente con mucha mejor resolución, asi como la leyenda +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/Revision_NAR/Manuscrito_New_Version/figures_and_tables/Figures/Figure4/Fig4a.tiff", units="in", +width=10, height=5, res=300) +p<-ggplot(data=summaryInfo, aes(x=Phenotype, y=PercentagePredictions, fill=Mode)) + +geom_bar(stat="identity", position=position_dodge()) +coord_flip() +p + theme_classic() +dev.off() +rm(list=ls())##Cleaning variables +#################################################################### +## Analyisng frequency of Long-Range vs Direct +# panel b) +#################################################################### +load(file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/multiplePatientAnalysis/StandardMode_ParsedResults_MultiplePatientAnalyses.RData") +InfoPredictions<-cohort_results_standard$candidateGenesInfo +##Withouth Filt +allPhenoAllSV<-prop.table(colSums(InfoPredictions[,c("N_LR_Mech","N_Direct_Mech")])) +##Filt for Inv and Transloc +filt_InfoPred<-subset(InfoPredictions, TypeSV=="Inversion" | TypeSV=="Translocation") +allPheno_InvTrans<-prop.table(colSums(filt_InfoPred[,c("N_LR_Mech","N_Direct_Mech")])) +##Filt for Deletions Duplications +filt_InfoPred<-subset(InfoPredictions, TypeSV=="Deletion" | TypeSV=="Duplication") +allPheno_DelDup<-prop.table(colSums(filt_InfoPred[,c("N_LR_Mech","N_Direct_Mech")])) +########################### +## Hacer Stacked Barplots +########################### +library(ggplot2) +#Differences between phenos probably arised due to differenceson the depth of enhancer maps annotation +#Stacked barplot for all +df_stacked<-data.frame("percentages"=c(allPhenoAllSV,allPheno_DelDup, allPheno_InvTrans), +"typeMech"=factor(rep.int(x=c("LongRange","Direct"),times = 3), +levels = c("LongRange","Direct"), ordered = TRUE), +"groupObserv"=factor(c(rep.int(x="allPhenoAllSV", times=2), +rep.int(x="allPhenoDelDup", times=2), +rep.int(x="allPhenoInvTrans", times=2)), +levels = c("allPhenoAllSV","allPhenoDelDup","allPhenoInvTrans"), ordered = TRUE) +) +###Generating StackedBarplot +library(ggplot2) +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/Revision_NAR/Manuscrito_New_Version/figures_and_tables/Figures/Figure4/Fig4c.tiff", units="in", +width=5, height=5, res=300) +ggplot(df_stacked, aes(x=groupObserv, y=percentages, fill=typeMech))+ +geom_bar(stat = "identity", width = .7) + +ggtitle("Distribution Pathological mechanisms") + +theme(axis.text=element_text(size=12)) + +scale_fill_manual(values = c("#e699ff","#fce94f")) + theme_classic() +dev.off() +# 660066 +#fce94f +###e699ff +rm(list = ls()) +#################################################################################### +## New panel, comparing % pathogenic predictions +#################################################################################### +##Pathogenic SVs 270 patients +load(file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/multiplePatientAnalysis/StandardMode_ParsedResults_MultiplePatientAnalyses.RData") +InfoPredictions_Standard<-cohort_results_standard$candidateGenesInfo +load(file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/multiplePatientAnalysis/HighSpecificityMode_ParsedResults_MultiplePatientAnalyses.RData") +InfoPredictions_HighSpecificity<-cohort_results_hiSpe$candidateGenesInfo +#For Standard Mode +sum(InfoPredictions_Standard$PathogenicScore>=0.8)/nrow(InfoPredictions_Standard) +#For High Specificity Mode +sum(InfoPredictions_HighSpecificity$PathogenicScore>=0.8)/nrow(InfoPredictions_HighSpecificity) +##################################### +##Loading healthy svs predictions +##################################### +##Standard Mode +load(file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/Robjects/candidateGenesInfo_10000_HealthyOriginal_SVs.RData") +postre_10000SVs<-originalHealthy_candidateGenesInfo +load(file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/Robjects/candidateGenesInfo_2980_HealthyOriginal_SVs.RData") +postre_2980SVs<-originalHealthy_candidateGenesInfo +postre_healthy_standard<-unique(rbind(postre_10000SVs, postre_2980SVs))##unique to avoid duplicated info counted twice +rm(postre_10000SVs, postre_2980SVs) +sum(postre_healthy_standard$PathogenicScore>=0.8)/nrow(postre_healthy_standard)*100#4.3% +##High specificity mode +load(file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/Robjects/candidateGenesInfo_10000_HighSpecificity_HealthyOriginal_SVs.RData") +postre_10000SVs<-originalHealthy_candidateGenesInfo +load(file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/Robjects/candidateGenesInfo_2980_HighSpecificity_HealthyOriginal_SVs.RData") +postre_2980SVs<-originalHealthy_candidateGenesInfo +postre_healthy_standard<-unique(rbind(postre_10000SVs, postre_2980SVs))##unique to avoid duplicated info counted twice +rm(postre_10000SVs, postre_2980SVs) +sum(postre_healthy_standard$PathogenicScore>=0.8)/nrow(postre_healthy_standard)*100#1% +# For standard mode +# 59% and 4% +# For high specificity mode +# 23% and 1% +dataPlot<-data.frame("percentages"=c(59,4, +23,1), +"Mode"=factor(x=c("St","St","Hs","Hs"), levels = c("St","Hs"), ordered = TRUE), +"groupSV"=factor(x=c("Patho","Healthy", "Patho","Healthy"), +levels = c("Patho","Healthy"), ordered = TRUE)) +library(ggplot2) +ggplot(data = dataPlot, aes(x = Mode, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.4, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() +# theme_set(theme_classic()) +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", width=5, height=5, res=300) -plot(x=1:length(genesWithSignal), y=genesWithSignal, type = "l", lwd=3, main = "H3K27me3 peak size associated to TSS \n in ESCs", xlab = "TSS", ylab="H3K27me3 peak size") -abline(h=c(targetLongitudPico),col="red", lwd=2, lty=2) -abline(v=nroObservacion_limiteSuperior_RegionKneePoint,col="red", lwd=2, lty=2) -abline(v=kneePoint_ejeX, col="blue", lwd=2, lty=2) -abline(v=nroObservacion_limiteInferior_RegionKneePoint, col="green", lwd=2, lty=2) -text(x= 20000, y=12000, paste0(targetLongitudPico, " bp"), col="red") +ggplot(data = dataPlot, aes(x = Mode, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.4, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() + coord_flip() +dev.off() +# theme_set(theme_classic()) +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=10, height=10, res=300) +ggplot(data = dataPlot, aes(x = Mode, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.4, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() +dev.off() +# theme_set(theme_classic()) +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=10, height=6, res=300) +ggplot(data = dataPlot, aes(x = Mode, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.4, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() dev.off() -tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/Revision_NAR/Manuscrito_New_Version/SupplementaryMaterial/SupplementaryFigures/Supplementary_Figure_1/ESC.tiff", units="in", +# theme_set(theme_classic()) +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=10, height=8, res=300) +ggplot(data = dataPlot, aes(x = Mode, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.4, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() +dev.off() +# theme_set(theme_classic()) +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=10, height=8, res=300) +ggplot(data = dataPlot, aes(x = Mode, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.4, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() +dev.off() +# theme_set(theme_classic()) +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=10, height=8, res=300) +ggplot(data = dataPlot, aes(x = Mode, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.2, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() +dev.off() +# theme_set(theme_classic()) +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", width=8, height=4, res=300) -plot(x=1:length(genesWithSignal), y=genesWithSignal, type = "l", lwd=3, main = "H3K27me3 peak size associated to TSS \n in ESCs", xlab = "TSS", ylab="H3K27me3 peak size") -abline(h=c(targetLongitudPico),col="red", lwd=2, lty=2) -abline(v=nroObservacion_limiteSuperior_RegionKneePoint,col="red", lwd=2, lty=2) -abline(v=kneePoint_ejeX, col="blue", lwd=2, lty=2) -abline(v=nroObservacion_limiteInferior_RegionKneePoint, col="green", lwd=2, lty=2) -text(x= 20000, y=12000, paste0(targetLongitudPico, " bp"), col="red") +ggplot(data = dataPlot, aes(x = Mode, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.2, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() dev.off() -tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/Revision_NAR/Manuscrito_New_Version/SupplementaryMaterial/SupplementaryFigures/Supplementary_Figure_1/ESC.tiff", units="in", -width=7, height=4, res=300) -plot(x=1:length(genesWithSignal), y=genesWithSignal, type = "l", lwd=3, main = "H3K27me3 peak size associated to TSS \n in ESCs", xlab = "TSS", ylab="H3K27me3 peak size") -abline(h=c(targetLongitudPico),col="red", lwd=2, lty=2) -abline(v=nroObservacion_limiteSuperior_RegionKneePoint,col="red", lwd=2, lty=2) -abline(v=kneePoint_ejeX, col="blue", lwd=2, lty=2) -abline(v=nroObservacion_limiteInferior_RegionKneePoint, col="green", lwd=2, lty=2) -text(x= 20000, y=12000, paste0(targetLongitudPico, " bp"), col="red") +# theme_set(theme_classic()) +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=8, height=4, res=300) +ggplot(data = dataPlot, aes(x = Mode, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.2, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() + coord_flip() dev.off() -tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/Revision_NAR/Manuscrito_New_Version/SupplementaryMaterial/SupplementaryFigures/Supplementary_Figure_1/ESC.tiff", units="in", -width=6, height=3.8, res=300) -plot(x=1:length(genesWithSignal), y=genesWithSignal, type = "l", lwd=3, main = "H3K27me3 peak size associated to TSS \n in ESCs", xlab = "TSS", ylab="H3K27me3 peak size") -abline(h=c(targetLongitudPico),col="red", lwd=2, lty=2) -abline(v=nroObservacion_limiteSuperior_RegionKneePoint,col="red", lwd=2, lty=2) -abline(v=kneePoint_ejeX, col="blue", lwd=2, lty=2) -abline(v=nroObservacion_limiteInferior_RegionKneePoint, col="green", lwd=2, lty=2) -text(x= 20000, y=12000, paste0(targetLongitudPico, " bp"), col="red") +# theme_set(theme_classic()) +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=8, height=4, res=300) +ggplot(data = dataPlot, aes(x = Mode, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.8, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() + coord_flip() dev.off() -############################################################################## -##Master Regulators, those above the inflection point of the H3K27me3 curve -############################################################################## -###### filtering OverlapResults -## si los transcritos comparten TSS nos quedaremos con los 2 como un único -## pero si no lo comparten los trataremos por separado -load(file = "~/Dropbox/Cantabria/PhD_Project/Resultados/genesDesarrollo/bofsMetilacion/pi2_findingDevelopGenes.RData") -#vamos a trabajar con cada TSS por separado -#de forma que si los dos tienen un pico de H3K27me3, trabajaremos con los 2 -# y si solo hay uno, nos quedaremos con ese solo -#no obstante si que nos quedaremos con una única fila, si los dos transcritos presentan el mismo TSS y estan mapeados con el mismo pico -dim(developmentMatrix) -developmentMatrix<-unique(developmentMatrix) # de 34513 a 34513 (expected) -dim(developmentMatrix) -developmentMatrix<-as.data.frame(developmentMatrix) -#### anyadir columna lengths -#las pos picos estan como factores, las pasamos a numerico -developmentMatrix$peakStart<-as.numeric(as.character(developmentMatrix$peakStart)) -developmentMatrix$peakEnd<-as.numeric(as.character(developmentMatrix$peakEnd)) -developmentMatrix$longitudPicos<-developmentMatrix$peakEnd - developmentMatrix$peakStart -min(developmentMatrix$longitudPicos) #0 as expected -developForPlot<-developmentMatrix -rm(developmentMatrix) -developForPlot<-developForPlot[order(developForPlot$longitudPicos,decreasing = F),] -################################### -## Computing line inflexion point -################################### -library(inflection) -######################### -##con el metodo ESE -##Ahora si quitamos todos los que tienen 0 bases de H3K27me3 -##Reordenamos de mayor a menor para tenerlo como un scree plot -genesWithSignal<-developForPlot[order(developForPlot$longitudPicos, decreasing = TRUE),] -#To compute inflection point excluding the 0 values -genesWithSignal<-subset(genesWithSignal, longitudPicos>0)$longitudPicos -##Tal y como indican en el paper para el scree plot, que es el mismo concepto -elbowPoint<-findiplist(x = 1:length(genesWithSignal), y = genesWithSignal, -index = 0## -) -##Check if method EDE is applicable -if(is.na(elbowPoint[2,3])){ -print("method EDE not applicable") -} -##Loading again data of genes without exluding those without signal (peak length 0) -genesWithSignal<-developForPlot[order(developForPlot$longitudPicos, decreasing = TRUE),] -genesWithSignal<-genesWithSignal$longitudPicos -nroObservacion_limiteSuperior_RegionKneePoint<-elbowPoint[1,1] ##Pq va en decreasing -nroObservacion_limiteInferior_RegionKneePoint<-elbowPoint[1,2] ##Pq va en decreasing -kneePoint_ejeX<-elbowPoint[1,3] -elbowPoint<-elbowPoint[1,1]##ESE -genesWithSignal[elbowPoint] -targetLongitudPico<-genesWithSignal[elbowPoint] -plot(x=1:length(genesWithSignal), y=genesWithSignal, type = "l", lwd=3, main = "H3K27me3 peak size associated to TSS \n in NCCs", xlab = "TSS", ylab="H3K27me3 peak size") -abline(h=c(targetLongitudPico),col="red", lwd=2, lty=2) -abline(v=nroObservacion_limiteSuperior_RegionKneePoint,col="red", lwd=2, lty=2) -abline(v=kneePoint_ejeX, col="blue", lwd=2, lty=2) -abline(v=nroObservacion_limiteInferior_RegionKneePoint, col="green", lwd=2, lty=2) -text(x= 20000, y=16000, paste0(targetLongitudPico, " bp"), col="red") -thresh<-targetLongitudPico -##################################################################### -##### seleccion genes (TSS) de desarrollo, aquellos cuyo overalapped H3K27me3 peak > thresh -##################################################################### -genesDesarrollo_allInfo<-developForPlot[developForPlot$longitudPicos>=thresh,] -selectedDevelopGenes<-sort(unique(as.character(genesDesarrollo_allInfo$gene))) -######################################################################## -##### no nos interesan ni los LINC ni los LOC asi que vamos a quitarlos -######################################################################## -AllGenes<-as.character(unique(developForPlot$gene))#27090 -LINCs<-AllGenes[grep("^LINC.*", AllGenes)] -LOCs<-AllGenes[grep("^LOC[0-9].*", AllGenes)] -NotRelevant<-c(LINCs,LOCs)#LINCs and LOCs -########################################################################## -###### seleccion de los TSS de interes -########################################################################## -selectedDevelopGenes<-selectedDevelopGenes[!(selectedDevelopGenes %in% NotRelevant)] -print(thresh) -print(length(selectedDevelopGenes)) -#################### -## Saving results -#################### -hs_pi2_polyCombGenes<-selectedDevelopGenes -save(hs_pi2_polyCombGenes, file = "~/Dropbox/Cantabria/PhD_Project/Resultados/genesDesarrollo/Robjects/hs_pi2_polyCombGenes.RData") -tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/Revision_NAR/Manuscrito_New_Version/SupplementaryMaterial/SupplementaryFigures/Supplementary_Figure_1/NCCs.tiff", units="in", -width=6, height=3.8, res=300) -plot(x=1:length(genesWithSignal), y=genesWithSignal, type = "l", lwd=3, main = "H3K27me3 peak size associated to TSS \n in NCCs", xlab = "TSS", ylab="H3K27me3 peak size") -abline(h=c(targetLongitudPico),col="red", lwd=2, lty=2) -abline(v=nroObservacion_limiteSuperior_RegionKneePoint,col="red", lwd=2, lty=2) -abline(v=kneePoint_ejeX, col="blue", lwd=2, lty=2) -abline(v=nroObservacion_limiteInferior_RegionKneePoint, col="green", lwd=2, lty=2) -text(x= 20000, y=16000, paste0(targetLongitudPico, " bp"), col="red") +# theme_set(theme_classic()) +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=8, height=4, res=300) +ggplot(data = dataPlot, aes(x = Mode, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.5, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() + coord_flip() dev.off() -############################################################################## -##Master Regulators, those above the inflection point of the H3K27me3 curve -############################################################################## -###### filtering OverlapResults -## si los transcritos comparten TSS nos quedaremos con los 2 como un único -## pero si no lo comparten los trataremos por separado -load(file = "~/Dropbox/Cantabria/PhD_Project/Resultados/genesDesarrollo/cardiomiocitos/heart_findingDevelopGenes.RData") -#vamos a trabajar con cada TSS por separado -#de forma que si los dos tienen un pico de H3K27me3, trabajaremos con los 2 -# y si solo hay uno, nos quedaremos con ese solo -#no obstante si que nos quedaremos con una única fila, si los dos transcritos presentan el mismo TSS y estan mapeados con el mismo pico -dim(developmentMatrix) -developmentMatrix<-unique(developmentMatrix) # de 34513 a 34513 (expected) -dim(developmentMatrix) -developmentMatrix<-as.data.frame(developmentMatrix) -#### anyadir columna lengths -#las pos picos estan como factores, las pasamos a numerico -developmentMatrix$peakStart<-as.numeric(as.character(developmentMatrix$peakStart)) -developmentMatrix$peakEnd<-as.numeric(as.character(developmentMatrix$peakEnd)) -developmentMatrix$longitudPicos<-developmentMatrix$peakEnd - developmentMatrix$peakStart -min(developmentMatrix$longitudPicos) #0 as expected -developForPlot<-developmentMatrix -rm(developmentMatrix) -developForPlot<-developForPlot[order(developForPlot$longitudPicos,decreasing = F),] -################################### -## Computing line inflexion point -################################### -library(inflection) -######################### -##con el metodo ESE -##Ahora si quitamos todos los que tienen 0 bases de H3K27me3 -##Reordenamos de mayor a menor para tenerlo como un scree plot -genesWithSignal<-developForPlot[order(developForPlot$longitudPicos, decreasing = TRUE),] -#To compute inflection point excluding the 0 values -genesWithSignal<-subset(genesWithSignal, longitudPicos>0)$longitudPicos -##Tal y como indican en el paper para el scree plot, que es el mismo concepto -elbowPoint<-findiplist(x = 1:length(genesWithSignal), y = genesWithSignal, -index = 0## -) -##Check if method EDE is applicable -if(is.na(elbowPoint[2,3])){ -print("method EDE not applicable") -} -##Loading again data of genes without exluding those without signal (peak length 0) -genesWithSignal<-developForPlot[order(developForPlot$longitudPicos, decreasing = TRUE),] -genesWithSignal<-genesWithSignal$longitudPicos -nroObservacion_limiteSuperior_RegionKneePoint<-elbowPoint[1,1] ##Pq va en decreasing -nroObservacion_limiteInferior_RegionKneePoint<-elbowPoint[1,2] ##Pq va en decreasing -kneePoint_ejeX<-elbowPoint[1,3] -elbowPoint<-elbowPoint[1,1]##ESE -genesWithSignal[elbowPoint] -targetLongitudPico<-genesWithSignal[elbowPoint] -plot(x=1:length(genesWithSignal), y=genesWithSignal, type = "l", lwd=3, main = "H3K27me3 peak size associated to TSS \n in Cardiomyocites", xlab = "TSS", ylab="H3K27me3 peak size") -abline(h=c(targetLongitudPico),col="red", lwd=2, lty=2) -abline(v=nroObservacion_limiteSuperior_RegionKneePoint,col="red", lwd=2, lty=2) -abline(v=kneePoint_ejeX, col="blue", lwd=2, lty=2) -abline(v=nroObservacion_limiteInferior_RegionKneePoint, col="green", lwd=2, lty=2) -text(x= 20000, y=13000, paste0(targetLongitudPico, " bp"), col="red") -thresh<-targetLongitudPico -##################################################################### -##### seleccion genes (TSS) de desarrollo, aquellos cuyo overalapped H3K27me3 peak > thresh -##################################################################### -genesDesarrollo_allInfo<-developForPlot[developForPlot$longitudPicos>=thresh,] -selectedDevelopGenes<-sort(unique(as.character(genesDesarrollo_allInfo$gene))) -######################################################################## -##### no nos interesan ni los LINC ni los LOC asi que vamos a quitarlos -######################################################################## -AllGenes<-as.character(unique(developForPlot$gene))#27090 -LINCs<-AllGenes[grep("^LINC.*", AllGenes)] -LOCs<-AllGenes[grep("^LOC[0-9].*", AllGenes)] -NotRelevant<-c(LINCs,LOCs)#LINCs and LOCs -########################################################################## -###### seleccion de los TSS de interes -########################################################################## -selectedDevelopGenes<-selectedDevelopGenes[!(selectedDevelopGenes %in% NotRelevant)] -print(thresh) -print(length(selectedDevelopGenes)) -#################### -## Saving results -#################### -hs_cardiomiocytes_polyCombGenes<-selectedDevelopGenes -save(hs_cardiomiocytes_polyCombGenes, file = "~/Dropbox/Cantabria/PhD_Project/Resultados/genesDesarrollo/Robjects/hs_cardiomiocytes_polyCombGenes.RData") -tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/Revision_NAR/Manuscrito_New_Version/SupplementaryMaterial/SupplementaryFigures/Supplementary_Figure_1/Cardiomyocytes.tiff", units="in", -width=6, height=3.8, res=300) -plot(x=1:length(genesWithSignal), y=genesWithSignal, type = "l", lwd=3, main = "H3K27me3 peak size associated to TSS \n in Cardiomyocites", xlab = "TSS", ylab="H3K27me3 peak size") -abline(h=c(targetLongitudPico),col="red", lwd=2, lty=2) -abline(v=nroObservacion_limiteSuperior_RegionKneePoint,col="red", lwd=2, lty=2) -abline(v=kneePoint_ejeX, col="blue", lwd=2, lty=2) -abline(v=nroObservacion_limiteInferior_RegionKneePoint, col="green", lwd=2, lty=2) -text(x= 20000, y=13000, paste0(targetLongitudPico, " bp"), col="red") +# theme_set(theme_classic()) +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=8, height=4, res=300) +ggplot(data = dataPlot, aes(x = Mode, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.4, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() + coord_flip() +dev.off() +View(dataPlot) +subset(dataPlot,Mode=="St") +gg<-dataPlot +gg<-dataPlot +subset(gg,Mode=="St") +gg +gg<-subset(gg,Mode=="St") +gg +gg<-subset(gg,Mode=="St") +gg$Mode<-NULL +gg[2,1]<-3.2 +gg +gg<-dataPlot +gg<-subset(gg,Mode=="St") +gg$Mode<-NULL +gg[2,1]<-3.2 +ggplot(data = gg, aes(x = group, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.4, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() + coord_flip() +gg +ggplot(data = gg, aes(x = groupSV, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.4, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() + coord_flip() +# theme_set(theme_classic()) +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=8, height=4, res=300) +gg<-dataPlot +gg<-subset(gg,Mode=="St") +gg$Mode<-NULL +gg[2,1]<-3.2 +ggplot(data = gg, aes(x = groupSV, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.4, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() + coord_flip() +dev.off() +##For presentations plot +gg<-dataPlot +gg<-subset(gg,Mode=="St") +gg$Mode<-NULL +gg +##For presentations plot +gg<-dataPlot +gg<-subset(gg,Mode=="St") +gg$Mode<-NULL +gg[2,1]<-3.2 +gg +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=8, height=4, res=300) +ggplot(data = gg, aes(x = groupSV, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.4, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() + coord_flip() +dev.off() +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=8, height=4, res=300) +ggplot(data = gg, aes(x = groupSV, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.8, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() + coord_flip() +dev.off() +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=8, height=2, res=300) +ggplot(data = gg, aes(x = groupSV, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.8, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() + coord_flip() +dev.off() +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=8, height=2, res=300) +ggplot(data = gg, aes(x = groupSV, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.8, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() + coord_flip() +dev.off() +gg +gg$groupSV +gg$groupSV<-factor(as.character(gg$groupSV), levels = c("Healthy","Patho"), ordered = TRUE) +gg +gg$groupSV +##For presentations plot +gg<-dataPlot +gg<-subset(gg,Mode=="St") +gg$Mode<-NULL +gg[2,1]<-3.2 +gg$groupSV<-factor(as.character(gg$groupSV), levels = c("Healthy","Patho"), ordered = TRUE) +gg +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=8, height=2, res=300) +ggplot(data = gg, aes(x = groupSV, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.8, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#00ccff","#cc66ff"))+theme(legend.position = "none") + theme_classic() + coord_flip() dev.off() -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -shiny::runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -shiny::runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") -shiny::runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -############################# -## PCA-Initial exploration -## FOR fpkms -############################# -load(file = "/home/victor/Documentos/phD/manyFolders/colaboraciones/maria/rna_seq/processedFiles/maria_rnaSeq_2021_fpkm.RData") +tiff("/home/victor/Documentos/phD/ArticlesWritting/Postre_manuscript/FiguresPresentationTool/FigPercentagesPathoVsHealthy.tiff", units="in", +width=8, height=2, res=300) +ggplot(data = gg, aes(x = groupSV, y = percentages, fill = groupSV )) + +geom_bar(stat = "identity", width = 0.8, +position=position_dodge(width = 0.5))+ +scale_fill_manual(values = c("#cc66ff","#00ccff"))+theme(legend.position = "none") + theme_classic() + coord_flip() +dev.off() +##Loading Table, Manually Generated +##Info obtained from script: ~/Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/SV_LocalAnalysis/MultiplePatientAnalysis/evaluating_Sensitivity_OnPatientsData/3_ParsingPatientsPredictions.R +infoPredictions<-read.delim(file = "~/Dropbox/Cantabria/PhD_Project/DatosPHD/software_SV/dataManuscript/PredictionsPercentages.tab", +sep="\t", +header = TRUE, +stringsAsFactors = TRUE) ##This true, buecause for GGPLOT better +infoPredictions<-subset(infoPredictions, Mode=="Standard") +infoPredictions$Mode<-"Pathogenic" +################################################ +##Meter datos de %s predichos para controles ############################################### -##Loading relevant genes in counts script -#load(file = "/home/victor/Documentos/phD/colaboraciones/maria/rna_seq/processedFiles/test_genesSelectedCountsAnalyses.RData") -#fpkm_masterMatrix<-fpkm_masterMatrix[fpkm_masterMatrix$gene_id %in% genesCountsAnalyses,] -#################### -##remvoing character columns -num_fpkm<-fpkm_masterMatrix[,-c(1,2)] -num_fpkm<-log2(num_fpkm+1) ##log2 transformation essential to get meaningful results on plots, reducing effect of hugely expressed genes -filteringCols<-c("WT_1A_D0","WT_1B_D0","WT_2A_D0","WT_2B_D0", -"KO_1A_D0", "KO_1B_D0", "KO_2A_D0", "KO_2B_D0", "WT_1A_D3", "WT_1B_D3", -"WT_2A_D3", "WT_2B_D3", "KO_1A_D3", "KO_1B_D3", "KO_2A_D3", "KO_2B_D3" , -"RNA_WT_D6_1", "RNA_WT_D6_2", "RNA_WT_D6_3", "RNA_Z2KO_1_D6_1", -"RNA_Z2KO_1_D6_2", "RNA_Z2KO_1_D6_3") -num_fpkm<-num_fpkm[,filteringCols] -# ################################## -# ##Doing some clustering analyses -# -# library(rafalib) -# -# ##En primer lugar vamosa calcular al distancia entre cada par de observaciones -# ##La funcion dist, calcula la distancia entre las filas de la matriz. Por tanto, pensad si teneis que transponer o no la matriz... -# ##Por defecto calcula la distancia euclidea -# d <- dist(t(num_fpkm)) -# -# #View(as.matrix(d)) -# -# ##Realizamos el clustering jerarquico atendiendo a las distancias calculadas entre cada par de observaciones -# hc_analysis <- hclust(d) -# hc_analysis -# -# ##Representamos el arbol pintado por colores -# # plot(hc_analysis,labels=colnames(num_fpkm),cex=0.5) -# -# -# ## -# tissuesColors<-c(rep.int(x = "red",times=4), -# rep.int(x = "darkred",times=4), -# rep.int(x = "green",times=4), -# rep.int(x = "darkgreen",times=4), -# rep.int(x = "blue",times=4), -# rep.int(x = "darkblue",times=4), -# rep.int(x = "orange",times=3) -# ) -# -# -# ##Plot pintando por colores -# myplclust(hc_analysis, labels=colnames(num_fpkm), lab.col=tissuesColors, cex=1) -# -# -# ###Remove for pca study -# # fpkm_masterMatrix<-fpkm_masterMatrix[!genesNotExpNever,] -# # num_fpkm<-num_fpkm[!genesNotExpNever,] -# -# ##doing pca -# fun_doingPca<-function(targetMatrix, center, scale, PC_tag1, PC_tag2){ -# targetRes<-prcomp(x=t(targetMatrix), center = center, scale. = scale) -# -# plot(x=targetRes$x[,PC_tag1], -# y=targetRes$x[,PC_tag2], -# pch = 19, -# col=tissuesColors, -# -# xlab=PC_tag1, -# ylab=PC_tag2 -# -# ) -# -# # text(x=targetRes$x[,PC_tag1], -# # y=targetRes$x[,PC_tag2], -# # labels = colnames(targetMatrix), -# # col=tissuesColors) -# } -# -# fun_doingPca(targetMatrix = num_fpkm, center = TRUE, scale = FALSE, -# PC_tag1 = "PC1", PC_tag2 = "PC2") -############# -## PCA NICE -tissuesColors<-c(rep.int(x = "red",times=4), -rep.int(x = "darkred",times=4), -rep.int(x = "green",times=4), -rep.int(x = "darkgreen",times=4), -rep.int(x = "blue",times=4), -rep.int(x = "darkblue",times=4), -rep.int(x = "dodgerblue",times=3), -rep.int(x = "dodgerblue3",times=3), -rep.int(x = "orange",times=3) -) -tissuesColorsUnique<-c(rep.int(x = "red",times=1), -rep.int(x = "darkred",times=1), -rep.int(x = "green",times=1), -rep.int(x = "darkgreen",times=1), -rep.int(x = "blue",times=1), -rep.int(x = "darkblue",times=1), -rep.int(x = "dodgerblue",times=1), -rep.int(x = "dodgerblue3",times=1), -rep.int(x = "orange",times=1) -) -expDesignGroups<-c(rep.int(x = "WT_D0",times=4), -rep.int(x = "KO_D0",times=4), -rep.int(x = "WT_D3",times=4), -rep.int(x = "KO_D3",times=4), -rep.int(x = "WT_D6",times=3), -rep.int(x = "KO_D6",times=3) -) -##Metadatos, expDesignMatrix, en este caso un dataframe -expDesignMat<-data.frame("condition"= expDesignGroups) -rownames(expDesignMat)<-colnames(num_fpkm)##Mismo da el 1 que el 2 -##Check expDesign object -expDesignMat -##PCA -library(PCAtools) -x11() -data1_pca<-PCAtools::pca(mat = num_fpkm, -transposed = FALSE, -center = TRUE, -scale = FALSE, -metadata = expDesignMat -)#Se espera que las variables, en este caso los genes esten en las filas, como ya estan, no transponemos la matriz -screeplot(data1_pca, axisLabSize = 18, titleLabSize = 22, components = 1:10) -##Plot per a components 1 i 2 -biplot(data1_pca, colby = "condition", x="PC1", y="PC2", labSize = 0, title = "ZIC2 PCA") -ggg<-read.delim(file = "/home/victor/Documentos/phD/testing_CaptureC/GSE129378_Human_Capture.txt") -View(ggg) -227156 - 226254 -228156 - 225254 -223352 - 222450 -224352 - 221450 -128748439 - 128748253 -128749439 - 128747253 -227156 - 226254 -23386686 - 23385780 -5256556 - 5255391 -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") -shiny::runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -runApp('Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app') -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") +info_Healthy<-infoPredictions +info_Healthy$N_Patients<-10000 +info_Healthy$Mode<-"Healthy" +info_Healthy$PercentagePredictions<-c(5.32,3.41,1.47,2.76)#c(2.7,2.6,1.5,2.2) +infoPredictions<-rbind(infoPredictions, info_Healthy) +lapply(infoPredictions, class) +p<-ggplot(data=infoPredictions, aes(x=Phenotype, y=PercentagePredictions, fill=Mode)) + +geom_bar(stat="identity", position=position_dodge()) +coord_flip() #+ theme_classic() +p ######################################### -## Performing Predictions Approach 2 +## Parsing Predictions Patients ######################################### -##Loading controls data -load(file = "~/Dropbox/Cantabria/PhD_Project/DatosPHD/fichasPacientes/healthyControls_approach2.RData") -View(healthyControls_readyForSoftware) -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") -#################################################################### -## Script for HTML generation for Web with Patients Cohort Study -#################################################################### -##Use maybe the Standard info only -##Loading Standard Mode PARSED results -load(file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/multiplePatientAnalysis/StandardMode_ParsedResults_MultiplePatientAnalyses.RData") -######################################## -##HTML generation with cohort results -######################################## -###Setwd in the folder where all the app info is hosted -setwd("~/Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app/") -##Required functions -source("functions/multiple_SV_Functions/multipleStats_ExplorePreviousPat_htmlGeneration.R") -##AllPatientsInfo table, required for the Patients Info section +##CHECK PATIENTS WITH ERRORS RISED IF ANY +##TAKE AS REFERENCE SCRIPT: ~/Documentos/phD/SV_app_backup/SV_LocalAnalysis_backup_21Dic_2021/MultiplePatientAnalysis/evaluatingSpecificity_OnHealthyIndividuals/Considering_Most_Of_SVs/3_ParsingResults.R +############ +## Loading prediction results +load("~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/multiplePatientAnalysis/results_MultiplePatientAnalyses.RData") +## Loading SVs information load("~/Dropbox/Cantabria/PhD_Project/DatosPHD/fichasPacientes/AllPatientsInfo.RData") +######################## +# NOT TOUCHING FROM HERE. Use the remaining script above for all kind of prediction, either controls or pathogenic +# When doing them locally +######################## ##Phenotypes to be considered consideredPheno<-c("head_neck", "cardiovascular", "limbs", "neurodevelopmental")##As more phenos considered they will appear here -ExplorePreviousPatients_html<-multipleStats_htmlGeneration(cohort_results = cohort_results_standard, -consideredPheno = consideredPheno, -ids_append="PreviousPat",##to avoid conflicts with tables generated in multiples submission option -AllPatientsInfo=AllPatientsInfo, -explPreviousPatSection = TRUE) -########################################################################################### -##This is the html that is going to be loaded on the Explore Previous Patients Section -##Wrapp in html tags to avoid issues AND save html -########################################################################################### -ExplorePreviousPatients_html<-paste("", -ExplorePreviousPatients_html, -"", -sep="") -fileConn<-file("~/Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app/html_scripts/ExplorePreviousPatients.html") -writeLines(ExplorePreviousPatients_html, fileConn) -close(fileConn) -#################################################################### -## Script for HTML generation for Web with Patients Cohort Study -#################################################################### -##Use maybe the Standard info only -##Loading Standard Mode PARSED results -load(file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/multiplePatientAnalysis/StandardMode_ParsedResults_MultiplePatientAnalyses.RData") -######################################## -##HTML generation with cohort results -######################################## ###Setwd in the folder where all the app info is hosted setwd("~/Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app/") +############################################## +##Loading Required Function +source("functions/multiple_SV_Functions/cohortResults_Parser.R") +############# +#Parsing predictions +cohort_results_hiSpe<-cohortResults_Parser(minScore = 0.8, all_patientResults = resultsPerMode$`High-Specificity`, +consideredPheno =consideredPheno,##Improve as more Phenos can be processed##consideredPheno,##Multiple Option, for now stick with head_neck +discardRelevantByBrokenGene = FALSE, +AllPatientsInfo = AllPatientsInfo ) +cohort_results_standard<-cohortResults_Parser(minScore = 0.8, all_patientResults = resultsPerMode$Standard, +consideredPheno =consideredPheno,##Improve as more Phenos can be processed##consideredPheno,##Multiple Option, for now stick with head_neck +discardRelevantByBrokenGene = FALSE, +AllPatientsInfo = AllPatientsInfo ) +################################################### +## Saving parsed results, Used to generate HTML +################################################### +save(cohort_results_standard, +file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/multiplePatientAnalysis/StandardMode_ParsedResults_MultiplePatientAnalyses.RData") +save(cohort_results_hiSpe, +file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/multiplePatientAnalysis/HighSpecificityMode_ParsedResults_MultiplePatientAnalyses.RData") +##Save info candidate genes for attempt of matching +originalPathogenic_candidateGenesInfo<-cohort_results_standard$candidateGenesInfo +save(originalPathogenic_candidateGenesInfo, +file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/Robjects/candidateGenesInfo_Pathogenic_SVs.RData") +################################### +## Exploring cohort Parsed results +################################### +targetPhenos<-c("head_neck", +"cardiovascular", +"limbs", +"neurodevelopmental")##As more phenos considered they will appear here ##Required functions -source("functions/multiple_SV_Functions/multipleStats_ExplorePreviousPat_htmlGeneration.R") -##AllPatientsInfo table, required for the Patients Info section -load("~/Dropbox/Cantabria/PhD_Project/DatosPHD/fichasPacientes/AllPatientsInfo.RData") +source("/home/victor/Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/SV_LocalAnalysis/MultiplePatientAnalysis/functions/multiSV_analysis_stats_functions.R", +local = TRUE) +##Para High specificity +multiSV_analysis_stats(targetPhenos = targetPhenos, targetResults = cohort_results_hiSpe) +#Para Standard +multiSV_analysis_stats(targetPhenos = targetPhenos, targetResults = cohort_results_standard) +######################################### +## Parsing Predictions Approach 2 +######################################### +##CHECK PATIENTS WITH ERRORS RISED IF ANY +##TAKE AS REFERENCE SCRIPT: ~/Documentos/phD/SV_app_backup/SV_LocalAnalysis_backup_21Dic_2021/MultiplePatientAnalysis/evaluatingSpecificity_OnHealthyIndividuals/Considering_Most_Of_SVs/3_ParsingResults.R +############ +## Loading prediction results approach 2 +load("~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/Robjects/results_PredictionsHealthyControls_approach1.RData") +## Loading SVs information +load(file = "~/Dropbox/Cantabria/PhD_Project/DatosPHD/fichasPacientes/healthyControls_approach1.RData") +######## +#Renaming input data +AllPatientsInfo<-healthyControls_readyForSoftware +rm(healthyControls_readyForSoftware) +################################################################ +## Applying filtering for DelDup or INv to stratify results +################################################################ +##Required functions +source("/home/victor/Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/SV_LocalAnalysis/MultiplePatientAnalysis/functions/multiSV_analysis_stats_functions.R", +local = TRUE) +## It can be "DELDUP" "INV" or "NONE" (doing nothing) +filteringCriteria<-"NONE" ##Also to analyze with TADA +# filteredInfo<-filtering_PatientsInfo(filteringCriteria = filteringCriteria, +# AllPatientsInfo = AllPatientsInfo, +# resultsPerMode = resultsPerMode) +# +# AllPatientsInfo<-filteredInfo$AllPatientsInfo +# resultsPerMode<-filteredInfo$resultsPerMode +length(resultsPerMode$Standard)==nrow(AllPatientsInfo) +length(resultsPerMode$`High-Specificity`)==nrow(AllPatientsInfo) +######################## +# NOT TOUCHING FROM HERE. Use the remaining script above for all kind of prediction, either controls or pathogenic +# When doing them locally +######################## ##Phenotypes to be considered consideredPheno<-c("head_neck", "cardiovascular", "limbs", "neurodevelopmental")##As more phenos considered they will appear here -ExplorePreviousPatients_html<-multipleStats_htmlGeneration(cohort_results = cohort_results_standard, -consideredPheno = consideredPheno, -ids_append="PreviousPat",##to avoid conflicts with tables generated in multiples submission option -AllPatientsInfo=AllPatientsInfo, -explPreviousPatSection = TRUE) -########################################################################################### -##This is the html that is going to be loaded on the Explore Previous Patients Section -##Wrapp in html tags to avoid issues AND save html -########################################################################################### -ExplorePreviousPatients_html<-paste("", -ExplorePreviousPatients_html, -"", -sep="") -fileConn<-file("~/Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app/html_scripts/ExplorePreviousPatients.html") -writeLines(ExplorePreviousPatients_html, fileConn) -close(fileConn) +###Setwd in the folder where all the app info is hosted +setwd("~/Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app/") +############################################## +##Loading Required Function +source("functions/multiple_SV_Functions/cohortResults_Parser.R") +############# +#Parsing predictions +# cohort_results_hiSpe<-cohortResults_Parser(minScore = 0.8, all_patientResults = resultsPerMode$`High-Specificity`, +# consideredPheno =consideredPheno,##Improve as more Phenos can be processed##consideredPheno,##Multiple Option, for now stick with head_neck +# discardRelevantByBrokenGene = FALSE, +# AllPatientsInfo = AllPatientsInfo ) +cohort_results_standard<-cohortResults_Parser(minScore = 0.8, all_patientResults = resultsPerMode$Standard, +consideredPheno =consideredPheno,##Improve as more Phenos can be processed##consideredPheno,##Multiple Option, for now stick with head_neck +discardRelevantByBrokenGene = FALSE, +AllPatientsInfo = AllPatientsInfo ) +shiny::runApp() +?tags +runApp() +runApp() +runApp() +runApp() +runApp() +runApp() +runApp() +runApp() +runApp() +runApp() +runApp() +runApp() runApp() +masterWrapperSinglePrediction(patientInfo = patientData , minScore = minScore, +highScore = highScore, runMode = runMode_single, +user_tadMapInfo = user_tadMapInfo, +MultiDataList = MultiDataList) runApp() +tagEnhancersLabel +yPos_chr_WT<-13 ##variable to hold the position of the chr text in the WT situation in the Y axis +##Adjust image, to exclude spaces outside canvas (drawing area) +par(mar = c(0,0,0,0)) +##OPTION REMOVING AXIS +#UPON COMPLETION USING THIS +plot(x=0:20, y=0:20, type = "n", +ylim = yAxisLim, +xlim = xAxisLim, +xaxt = 'n', yaxt = 'n', bty = 'n', pch = '', ylab = '', xlab = '') ##Upon completion, REMOVE AXIS +texSizeChr<-2 +#Adding plot header +text(x=20, y=30, label=paste0("Graphical summary of ", gene, " regulatory domain in ", phase), cex = 1.7) +text(x=20,y=26, label="Simplification",cex=1.7) +#WT allelle label +text(x=20, y=20, label="Control Scenario", cex = 1.5) +##Coord for tads over X axis (Depending on the situation either two TADs or just one will be painted) +#Let's try make them wider two cm per side to ensure everything fits on the re-arrangement plots +tad_XCoord_OnLeftSide<-c(3,17,10)#c(5,15,10) ##c(3,17,10) +tad_XCoord_OnRightSide<-c(tad_XCoord_OnLeftSide[1]+10+11, +tad_XCoord_OnLeftSide[2]+10+11, +tad_XCoord_OnLeftSide[3]+10+11) +tad_XCoord_OnCenter<-c(13,27,20) +#Over Y axis, WT line +tad_YCoord_WildTypeLine<-c(0,0,15) +situation +text(x=5, y=yPos_chr_WT, label=chr_gene, cex = texSizeChr) +tad_X_cord<-tad_XCoord_OnLeftSide ##c(5,15,10) +info_drawingGENE_TAD<-paintGene_WT_TAD(tad_X_cord = tad_X_cord, +tad_Y_cord = tad_YCoord_WildTypeLine, +nEnh_initial_left = nEnh_initial_left, +nEnh_initial_right = nEnh_initial_right, +gene = gene, +gene_breakp_line_type = gene_breakp_line_type, +situation = situation, +patientResults = patientResults) runApp() runApp() +################################################################ +##Test en mi usuario antiguo, para que sea la zona de pruebas +################################################################ +rsconnect::deployApp('/home/victor/Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app/', +account = 'vicsanga') runApp() +################################################################ +##Test en mi usuario antiguo, para que sea la zona de pruebas +################################################################ +rsconnect::deployApp('/home/victor/Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app/', +account = 'vicsanga') runApp() -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") -source("https://raw.githubusercontent.com/vicsanga/Postre/main/Postre_wrapper.R") diff --git a/Postre_app/app.R b/Postre_app/app.R index 910acf3..f4cbfba 100644 --- a/Postre_app/app.R +++ b/Postre_app/app.R @@ -2,13 +2,6 @@ ### POSTRE (Prediction of STRuctural variant Effects) ######################################################## -##Do not repeat devStages or phases Names! Even for different phenotypes!! - -##To consider bioconductor repos -##To avoid this error: https://community.rstudio.com/t/deployment-error-unable-to-determine-the-location-for-some-packages/102312 -# options(repos = BiocManager::repositories()) -#options('repos') - library(shiny) library(waiter) library(shinybusy) @@ -19,6 +12,13 @@ library(plotrix)##For enhancers, ellipse shape representation library(shape)##For curve arrows representation library(diagram)##For curve arrows representation +##To consider bioconductor repos +##To avoid this error: https://community.rstudio.com/t/deployment-error-unable-to-determine-the-location-for-some-packages/102312 +# options(repos = BiocManager::repositories()) +#options('repos') + + +########################################################### ###Setwd in the folder where all the app info is hosted # setwd("~/Dropbox/Cantabria/PhD_Project/ScriptsPhd/ScriptsParaUsoLocal/Postre/Postre_app") @@ -53,188 +53,12 @@ relevantChr<-c(paste("chr",1:22,sep = ""), "chrX")##chrY excluded not all data a #To avoid navbar collapse in smaller screens # https://stackoverflow.com/questions/21738417/bootstrap-remove-responsive-from-navbar -navbar_js<-"@media (max-width: 768px) { - .navbar-header { - float: left; - } - - .navbar { - border-radius: 4px; - min-width: 400px; - } - - .nav-tabs-justified > li > a { - border-bottom: 1px solid #ddd; - border-radius: 4px 4px 0 0; - } - .nav-tabs-justified > .active > a, - .nav-tabs-justified > .active > a:hover, - .nav-tabs-justified > .active > a:focus { - border-bottom-color: #fff; - } - - .nav-justified > li { - display: table-cell; - width: 1%; - } - .nav-justified > li > a { - margin-bottom: 0; - } - - .nav-tabs.nav-justified > li > a { - border-bottom: 1px solid #ddd; - border-radius: 4px 4px 0 0; - } - .nav-tabs.nav-justified > .active > a, - .nav-tabs.nav-justified > .active > a:hover, - .nav-tabs.nav-justified > .active > a:focus { - border-bottom-color: #fff; - } - - .nav-tabs.nav-justified > li { - display: table-cell; - width: 1%; - } - .nav-tabs.nav-justified > li > a { - margin-bottom: 0; - } - - .navbar-right .dropdown-menu { - right: 0; - left: auto; - } - .navbar-right .dropdown-menu-left { - right: auto; - left: 0; - } - .container { - min-width: 400px; - } - - .navbar-collapse { - width: auto; - border-top: 0; - box-shadow: none; - } - .navbar-collapse.collapse { - display: block !important; - height: auto !important; - padding-bottom: 0; - overflow: visible !important; - } - .navbar-collapse.in { - overflow-y: visible; - } - .navbar-fixed-top .navbar-collapse, - .navbar-static-top .navbar-collapse, - .navbar-fixed-bottom .navbar-collapse { - padding-right: 0; - padding-left: 0; - } - - .container > .navbar-header, - .container-fluid > .navbar-header, - .container > .navbar-collapse, - .container-fluid > .navbar-collapse { - margin-right: 0; - margin-left: 0; - } - - .navbar-static-top { - border-radius: 0; - } - - .navbar-fixed-top, - .navbar-fixed-bottom { - border-radius: 0; - } - - .navbar-toggle { - display: none; - } - - .navbar-nav { - float: left; - margin: 0; - } - .navbar-nav > li { - float: left; - } - .navbar-nav > li > a { - padding-top: 15px; - padding-bottom: 15px; - } - .navbar-nav.navbar-right:last-child { - margin-right: -15px; - } - - .navbar-left { - float: left !important; - } - .navbar-right { - float: right !important; - } - - .navbar-form .form-group { - display: inline-block; - margin-bottom: 0; - vertical-align: middle; - } - .navbar-form .form-control { - display: inline-block; - width: auto; - vertical-align: middle; - } - .navbar-form .control-label { - margin-bottom: 0; - vertical-align: middle; - } - .navbar-form .radio, - .navbar-form .checkbox { - display: inline-block; - padding-left: 0; - margin-top: 0; - margin-bottom: 0; - vertical-align: middle; - } - .navbar-form .radio input[type='radio'], - .navbar-form .checkbox input[type='checkbox'] { - float: none; - margin-left: 0; - } - .navbar-form .has-feedback .form-control-feedback { - top: 0; - } - - .navbar-form { - width: auto; - padding-top: 0; - padding-bottom: 0; - margin-right: 0; - margin-left: 0; - border: 0; - -webkit-box-shadow: none; - box-shadow: none; - } - .navbar-form.navbar-right:last-child { - margin-right: -15px; - } - - .navbar-text { - float: left; - margin-right: 15px; - margin-left: 15px; - } - .navbar-text.navbar-right:last-child { - margin-right: 0; - } -}" +# navbar_js<-"" ui <-function(req){ return(div( class="container", - ##Mirar esto del title que no me acaba lo de meterlo como si no existiera nada div(class="titleBrowser", titlePanel(title="POSTRE: Prediction Of STRuctural variant Effects") ), @@ -271,24 +95,7 @@ ui <-function(req){ div(class="inp2", ##Patient Phenotype wellPanel( - # selectInput( - # inputId = "phenoPatient", - # label = "Phenotype", - # ##Choices full name is then matched & renamed in GenomicData_Loader.R - # ##Alphabet order - # choices = c("Cardiovascular", - # "Head & Neck", - # "Limbs", - # "Neurodevelopmental" - # ), - # selected ="Head & Neck" ) - - # https://shiny.rstudio.com/reference/shiny/1.6.0/checkboxGroupInput.html checkboxGroupInput(inputId = "phenoPatient", label = "Phenotype", - # c("Head & Neck" = "head_neck", - # "Cardiovascular" = "cardiovascular", - # "Limbs" = "limbs", - # "Neurodevelopmental" = "neurodevelopmental"), c("Cardiovascular" = "cardiovascular", "Head & Neck" = "head_neck", "Limbs" = "limbs", @@ -345,14 +152,6 @@ ui <-function(req){ ################################# ##Adding Advanced Features Menu ################################# - ##Selecting Running Mode - ##Drop down menu shiny - #https://rdrr.io/cran/shinyWidgets/man/dropdown.html - ##No me acaba, hacer tipo webGestalt - # https://www.w3schools.com/howto/howto_js_collapsible.asp - ##https://www.w3schools.com/howto/tryit.asp?filename=tryhow_js_collapsible - ##Con la parte de AddIcons - ##Y pillar el html que genera Shiny, yeah div(class="inp6", wellPanel(HTML(' @@ -494,12 +293,8 @@ for (i = 0; i < coll.length; i++) { ## Multiple SV Input Panel ###################### div(class="sideBarClassMultiple", - ##If I put here on the html, h2 tag it gets styled as the header, so style features - ##are connected - - #START MULTIPLE SUBMISSION div(class="formAndTitle_patient_Multiple_Input", - ##Patient SV type + HTML('

Upload Multiple Structural Variants with Phenotypes

'), div(class="patientMultipleFormulary", @@ -509,12 +304,14 @@ for (i = 0; i < coll.length; i++) { label="Select File", multiple = FALSE, accept = "*" - ))#,style = "padding: 5px; padding-bottom:0px;") + )) ), - ##Selecting Running Mode + ################################# + ##Adding Advanced Features Menu + ################################# div(class="multipleSVRunMode", - ##Meter aqui la info del panel de Advanced Features + wellPanel(HTML('
@@ -665,8 +462,6 @@ for (i = 0; i < coll.length; i++) { includeHTML("html_scripts/UserGuide_page.html") ) ) - , - tags$head(tags$style(HTML(navbar_js))) ) ), ##################### @@ -681,7 +476,7 @@ for (i = 0; i < coll.length; i++) { server <- function(input, output, session){ - ##Defining phenotypes currenlty accepted by POSTRE, + ##Defining phenotypes currently accepted by POSTRE, ##Variable used in downstream computations ##When doing multiple patient and pheno analysis, considered pheno ##If not here, not do prediction @@ -1001,14 +796,13 @@ server <- function(input, output, session){ userTadProcessed<-TRUE } - ##To track wehter user_tadMapInfo must be used or not in downstream functions + ##To track wether user_tadMapInfo must be used or not in downstream functions patientData$userTADmap<-"yes" } ##If there is an error the following instruction will not be terminated - # browser() patientResults_singlePhenoPrediction<-masterWrapperSinglePrediction(patientInfo = patientData , minScore = minScore, highScore = highScore, runMode = runMode_single, user_tadMapInfo = user_tadMapInfo, @@ -1044,9 +838,6 @@ server <- function(input, output, session){ } ###Merge results from the different phenotypes to create the master Output HTML - ##input$phenoPatient and patientResults, objects to be passed as parameters - # heatmapSummary - # patientResults$`Head & Neck`$heatmapSummary patientResults<-mergingMultiPhenoPredictions(patientResults = patientResults, selectedPheno = input$phenoPatient, allPostreAvailablePheno = consideredPheno) ##Stoping waiter @@ -1056,7 +847,6 @@ server <- function(input, output, session){ ######################################################################### ## Single SV submission From User Guide - ## Here, as a proof of concept, only dealing with 1 phenotype per patient patientResults_2<-eventReactive(input$click_SingleSubmissionUserGuide, { ###Adding-Initializing waiter show_modal_spinner(text = HTML("Running Prediction
@@ -1136,8 +926,7 @@ server <- function(input, output, session){ ##Stoping waiter remove_modal_spinner() - #removeModal() - + return(patientResults) }) @@ -1174,16 +963,8 @@ server <- function(input, output, session){ ##Trimws to avoid problems with spaces before or after the word targetPatient<-trimws(input$aggregatedResults_svID_ExplorePreviousPat) - ##Capturing selected phenotype - # targetPhenoSV<-input$aggregatedResults_phenoId_ExplorePreviousPat - ##All phenotypes associated with the patient will be considered - - ##Filtering database patients information filt_InfoDBsvs<-subset(AllPatientsInfo,patientID == targetPatient ) - ##Ahora mismo los phenos estan entrando aqui con los nombres para filtrar la tabla - ##Check pheno that user specified is indeed associated with the SV of interest - #sv_has_The_Pheno<-grepl(pattern = targetPhenoSV, x = filt_InfoDBsvs$Phenotype) ##Checking data is correct if(nrow(filt_InfoDBsvs)!=1){ @@ -1328,16 +1109,9 @@ server <- function(input, output, session){ ##Trimws to avoid problems with spaces before or after the word targetPatient<-trimws(input$aggregatedResults_svID_MultipleSVSubmission) - ##Capturing selected phenotype - # targetPhenoSV<-input$aggregatedResults_phenoId_MultipleSVSubmission - ##All phenotypes associated with the patient will be considered - ##Filtering database patients information filt_InfoDBsvs<-subset(multiSV_uploadedFile_AllPatientsInfo,patientID == targetPatient ) - ##Ahora mismo los phenos estan entrando aqui con los nombres para filtrar la tabla - ##Check pheno that user specified is indeed associated with the SV of interest - # sv_has_The_Pheno<-grepl(pattern = targetPhenoSV, x = filt_InfoDBsvs$Phenotype) - + ##Checking data is correct if(nrow(filt_InfoDBsvs)!=1){ ##Apparently the relation sv-pheno does not exist in the database, or for any reason there is a repeated SVID or the SVid is wrongly introduced @@ -1349,12 +1123,9 @@ server <- function(input, output, session){ ### ##Running configuration as runMode etc is already recorded on the patient Info from the multipleSV submission - ##Regarding refGenome it was necessary liftover, here the coordinates already liftovered - #browser() - patientInfo$runMode - patientInfo$genePhenoConsideration - patientInfo$userTADmap - + ## eg in patientInfo$runMode or patientInfo$genePhenoConsideration + ##Regarding refGenome ifit was necessary liftover, here the coordinates already liftovered + ##Changing patientID by SV_ID for the html table output. colnames(patientInfo)[colnames(patientInfo)=="patientID"]<-"SV_ID" @@ -1386,8 +1157,6 @@ server <- function(input, output, session){ }) - - ############################################ #If status error, generate the error html #We can be more specific in the future if we are interested @@ -1521,11 +1290,6 @@ server <- function(input, output, session){ multiData$chr_Break2<-as.character(multiData$chr_Break2) multiData$coord_Break2<-as.character(multiData$coord_Break2) - ####################################### - #multiple_patientResults<-list() - - # multiple_patientResults$patientsInfo<-multiData ##At the end, with the resulting df upon editing patients info if liftover required - ##Capturing runMode runMode_Multiple<-as.character(input$runMode_Multiple) @@ -1711,19 +1475,8 @@ server <- function(input, output, session){ # browser() ##To store some table output # candidateGenesInfo<-cohort_results$candidateGenesInfo + # And now save object - #Esta para guardar positive controls del table2 con Standard Mode - # save(candidateGenesInfo, - # file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/Robjects/candidateGenesInfo_PositiveControls_Table2_StandardMode.RData") - - #Esta para guardar positive controls del table2 con High Specificity Mode - # save(candidateGenesInfo, - # file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/Robjects/candidateGenesInfo_PositiveControls_Table2_HighSpecificityMode.RData") - - #Esta fue para guardar los positive controls table 1 and 2 - # save(candidateGenesInfo, - # file = "~/Dropbox/Cantabria/PhD_Project/Resultados/softwareObjects/Robjects/candidateGenesInfo_PositiveControls_Table1and2.RData") - ######################################### ## Generating HTML from Parsed results ######################################### diff --git a/Postre_app/functions/aux_functions_forGraphicalSummary.R b/Postre_app/functions/aux_functions_forGraphicalSummary.R index ea729ac..128f123 100644 --- a/Postre_app/functions/aux_functions_forGraphicalSummary.R +++ b/Postre_app/functions/aux_functions_forGraphicalSummary.R @@ -2,11 +2,12 @@ ## Auxiliar Functions developed for Graphical Summary plotting ## Maybe this functions are only valid for Inversions And Translocations Between TADs ###################################################################################### +#tagEnhancersLabel #varComesFromMainEnvironment,based on if NeoTad painted to avoid overlap of enhancers text ##NOTE: To place text labels, take as reference the center point of interest, ##since the label is centered over that coordinate -paintGene_WT_TAD<-function(tad_X_cord, tad_Y_cord, nEnh_initial_left, nEnh_initial_right, gene, gene_breakp_line_type, situation, patientResults){ +paintGene_WT_TAD<-function(tad_X_cord, tad_Y_cord, nEnh_initial_left, nEnh_initial_right, gene, gene_breakp_line_type, situation, patientResults, tagEnhancersLabel){ ######################################################## ## Function to paint GENE WT TAD Initial representation ######################################################## @@ -349,10 +350,10 @@ paintGene_WT_TAD<-function(tad_X_cord, tad_Y_cord, nEnh_initial_left, nEnh_initi y=enh_y_positions,a=0.1,col="#b2d235") #enhancers Label - #text(x=enhXpos + 0.4, y=enh_y_positions[1]-3, label="enhancers", cex = 0.8) + #text(x=enhXpos + 0.4, y=enh_y_positions[1]-3, label=tagEnhancersLabel, cex = 0.8) boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1]-distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -389,10 +390,10 @@ paintGene_WT_TAD<-function(tad_X_cord, tad_Y_cord, nEnh_initial_left, nEnh_initi y=enh_y_positions,a=0.1,col="#b2d235") #enhancers Label - #text(x=enhXpos + 0.4, y=enh_y_positions[1]-3, label="enhancers", cex = 0.8) + #text(x=enhXpos + 0.4, y=enh_y_positions[1]-3, label=tagEnhancersLabel, cex = 0.8) boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1]-distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -447,7 +448,7 @@ paintGene_WT_TAD<-function(tad_X_cord, tad_Y_cord, nEnh_initial_left, nEnh_initi } -paint_Enhancer_WT_Secondary_TAD<-function(tad_X_cord, tad_Y_cord, nEnh_other_domain, geneCenter,otherDomain_breakp_line_type, situation, geneBreakP_Position_respectToTSS, patientResults){ +paint_Enhancer_WT_Secondary_TAD<-function(tad_X_cord, tad_Y_cord, nEnh_other_domain, geneCenter,otherDomain_breakp_line_type, situation, geneBreakP_Position_respectToTSS, patientResults, tagEnhancersLabel){ ##geneCenter needs to be known to paint the arrow orientations, towards the left or right @@ -805,10 +806,10 @@ paint_Enhancer_WT_Secondary_TAD<-function(tad_X_cord, tad_Y_cord, nEnh_other_dom y=enh_y_positions,a=0.1,col="#b2d235") #enhancers Label - #text(x=enhXpos + 0.4, y=-3, label="enhancers", cex = 0.8) + #text(x=enhXpos + 0.4, y=-3, label=tagEnhancersLabel, cex = 0.8) boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_EnhLabel, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1.1 #To allow the text to breath diff --git a/Postre_app/functions/graphicalSummary_generation.R b/Postre_app/functions/graphicalSummary_generation.R index 075c115..45675e5 100644 --- a/Postre_app/functions/graphicalSummary_generation.R +++ b/Postre_app/functions/graphicalSummary_generation.R @@ -563,19 +563,22 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ ###Creating canvas for plotting yAxisLim<-c(-50,30) + ##tagEnhancersLabel to avoid enhancershancers when neoTAD painting, to avoid overlap + + tagEnhancersLabel<-"enhancers" + ##Defining xAxis Width - if((gene_mechanism == "LongRange_geneDuplication") && (SV_landing == "InterTAD")){ + if(((gene_mechanism == "LongRange_geneDuplication") || (gene_mechanism == "Direct_LongRange_geneDuplication") ) && (SV_landing == "InterTAD")){ ##xAxiss wider if NEO TAD because a third tad will be painted ##Usar un lienzo mas grande, due to NEO-TAD situation xAxisLim<-c(-15,55) + tagEnhancersLabel<-"Enh." }else{ xAxisLim<-c(0,40) } - - yPos_chr_WT<-13 ##variable to hold the position of the chr text in the WT situation in the Y axis ##Adjust image, to exclude spaces outside canvas (drawing area) @@ -652,7 +655,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ gene = gene, gene_breakp_line_type = gene_breakp_line_type, situation = situation, - patientResults = patientResults) + patientResults = patientResults, tagEnhancersLabel = tagEnhancersLabel) geneCenter<-info_drawingGENE_TAD$geneCenter @@ -676,7 +679,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ otherDomain_breakp_line_type = otherDomain_breakp_line_type, situation = situation, geneBreakP_Position_respectToTSS = geneBreakP_Position_respectToTSS, - patientResults = patientResults) + patientResults = patientResults, tagEnhancersLabel = tagEnhancersLabel) }else if(situation == "primaryTAD_Dextral"){ ######################## @@ -696,7 +699,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ gene = gene, gene_breakp_line_type = gene_breakp_line_type, situation = situation, - patientResults = patientResults) + patientResults = patientResults, tagEnhancersLabel = tagEnhancersLabel) geneCenter<-info_drawingGENE_TAD$geneCenter @@ -721,7 +724,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ otherDomain_breakp_line_type = otherDomain_breakp_line_type, situation = situation, geneBreakP_Position_respectToTSS = geneBreakP_Position_respectToTSS, - patientResults = patientResults) + patientResults = patientResults, tagEnhancersLabel = tagEnhancersLabel) } }else if(situation == "primaryTAD_Central"){ @@ -751,7 +754,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ gene = gene, gene_breakp_line_type = gene_breakp_line_type, situation = situation, - patientResults = patientResults) + patientResults = patientResults, tagEnhancersLabel = tagEnhancersLabel) geneCenter<-info_drawingGENE_TAD$geneCenter @@ -828,7 +831,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ tad_XCoord_OnLeftSide = tad_XCoord_OnLeftSide, tad_XCoord_OnRightSide = tad_XCoord_OnRightSide, tad_YCoord_Rearrangements = tad_YCoord_Rearrangements, - geneBreakP_Position_respectToTSS = geneBreakP_Position_respectToTSS) + geneBreakP_Position_respectToTSS = geneBreakP_Position_respectToTSS, tagEnhancersLabel = tagEnhancersLabel) #Paint SecondaryTAD SV re-arranged #The one where the gene of interest is not located @@ -846,7 +849,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ tad_XCoord_OnLeftSide = tad_XCoord_OnLeftSide, tad_XCoord_OnRightSide = tad_XCoord_OnRightSide, tad_YCoord_Rearrangements = tad_YCoord_Rearrangements, - infoDrawing_gene_sv_tad = infoDrawing_gene_sv_tad) + infoDrawing_gene_sv_tad = infoDrawing_gene_sv_tad, tagEnhancersLabel = tagEnhancersLabel) }else if(sv_type=="Deletion"){ ##For Deletions, by long-range... so TAD disrupted... in any case intraTAD or betweenTADs only one TAD painted @@ -863,7 +866,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ info_drawingSecondaryTAD = info_drawingSecondaryTAD, tad_X_cord = tad_XCoord_OnCenter , tad_YCoord_Rearrangements = tad_YCoord_Rearrangements, - geneBreakP_Position_respectToTSS = geneBreakP_Position_respectToTSS) + geneBreakP_Position_respectToTSS = geneBreakP_Position_respectToTSS, tagEnhancersLabel = tagEnhancersLabel) }else if(sv_type == "Duplication"){ @@ -900,7 +903,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ tad_X_cord = tad_XCoord_OnCenter , tad_YCoord_Rearrangements = tad_YCoord_Rearrangements, geneBreakP_Position_respectToTSS = geneBreakP_Position_respectToTSS, - patientResults = patientResults) + patientResults = patientResults, tagEnhancersLabel = tagEnhancersLabel) }else{ ##Error, no concibo esta situacion ahora mismo @@ -910,7 +913,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ } - }else if(gene_mechanism == "LongRange_geneDuplication"){ + }else if((gene_mechanism == "LongRange_geneDuplication") || (gene_mechanism == "Direct_LongRange_geneDuplication")){ ##Here it either can occur, NeoTAD if SV interTAD or be intraTAD, but for sure gene Duplicated ##So two genes need to be painted @@ -960,7 +963,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ gene = gene, gene_breakp_line_type = gene_breakp_line_type, situation = situation, - patientResults = patientResults) + patientResults = patientResults, tagEnhancersLabel = tagEnhancersLabel) geneCenter<-info_drawingGENE_TAD$geneCenter @@ -982,7 +985,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ otherDomain_breakp_line_type = otherDomain_breakp_line_type, situation = situation, geneBreakP_Position_respectToTSS = geneBreakP_Position_respectToTSS, - patientResults = patientResults) + patientResults = patientResults, tagEnhancersLabel = tagEnhancersLabel) }else if(situation == "primaryTAD_Dextral"){ ######################## @@ -1003,7 +1006,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ gene = gene, gene_breakp_line_type = gene_breakp_line_type, situation = situation, - patientResults = patientResults) + patientResults = patientResults, tagEnhancersLabel = tagEnhancersLabel) geneCenter<-info_drawingGENE_TAD$geneCenter @@ -1026,7 +1029,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ otherDomain_breakp_line_type = otherDomain_breakp_line_type, situation = situation, geneBreakP_Position_respectToTSS = geneBreakP_Position_respectToTSS, - patientResults = patientResults) + patientResults = patientResults, tagEnhancersLabel = tagEnhancersLabel) } ######################## @@ -1045,7 +1048,7 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ info_drawingSecondaryTAD = info_drawingSecondaryTAD, tad_X_cord = tad_XCoord_OnCenter , tad_YCoord_Rearrangements = tad_YCoord_Rearrangements, - geneBreakP_Position_respectToTSS = geneBreakP_Position_respectToTSS) + geneBreakP_Position_respectToTSS = geneBreakP_Position_respectToTSS, tagEnhancersLabel = tagEnhancersLabel) @@ -1059,13 +1062,9 @@ graphicalSummary_generation<-function(patientResults, minPathogenicScore){ - }else if((gene_mechanism == "Direct_geneDuplication") || (gene_mechanism == "Direct_LongRange_geneDuplication")){ + }else if(gene_mechanism == "Direct_geneDuplication"){ ##We paint two times only the gene, and enough... - ##Direc_LongRange_geneDuplication - ##Means that equal likely that upreg by enh dup, that by gene expression...This is sth tricky - ##If this is the case, just paint the gene duplicated - paint_Gene_Duplicated(gene = gene, xAxisLim = xAxisLim, tad_X_cord = tad_XCoord_OnCenter, diff --git a/Postre_app/functions/heatmap_SummaryResults.R b/Postre_app/functions/heatmap_SummaryResults.R index 6b2e9fe..38221da 100644 --- a/Postre_app/functions/heatmap_SummaryResults.R +++ b/Postre_app/functions/heatmap_SummaryResults.R @@ -588,7 +588,7 @@ heatmap_summaryResults<-function(patientResults, minRequiredScore, highScore){ ##I'm going to apply same style as for heatmap numbers collapsible sections, so we are going to repeat the button classes ##And so on because we want them to look the same - ##Adding heatmap numbers section + ##Adding SV considered information whole_html<-paste(whole_html, "
", ##The first class, collapsible_subsectionMainResults a secas, es para el estilo del boton, la segunda ,que integra el fenotipo para el responsiveness @@ -601,10 +601,33 @@ heatmap_summaryResults<-function(patientResults, minRequiredScore, highScore){ patientInfo_html, "
", "
", + "
", + sep="", + collapse="") + + + ##Adding Info for each of the cell types or tissues considered + whole_html<-paste(whole_html, + "
", + ##The first class, collapsible_subsectionMainResults a secas, es para el estilo del boton, la segunda ,que integra el fenotipo para el responsiveness + "", + "
", + "If you want to know more about the different cell types/tissues considered for each phenotype, check the + Cell types/tissues per phenotype File .", + "
", + "
", + "
", sep="", collapse="") + + + ########################################## ## Creating gene-phase report section ########################################## diff --git a/Postre_app/functions/makingGraphs/aux_fun_Plots_Rearranged_TADs.R b/Postre_app/functions/makingGraphs/aux_fun_Plots_Rearranged_TADs.R index e519ddf..55dd894 100644 --- a/Postre_app/functions/makingGraphs/aux_fun_Plots_Rearranged_TADs.R +++ b/Postre_app/functions/makingGraphs/aux_fun_Plots_Rearranged_TADs.R @@ -1,7 +1,7 @@ ############################################################### ## Script to hold the functions to plot the SV rearrangements ############################################################### - +#tagEnhancersLabel #varComesFromMainEnvironment,based on if NeoTad painted to avoid overlap of enhancers text ######################################################## ## Function to paint GENE SV re-arranged TAD @@ -14,7 +14,7 @@ paintGene_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, gene_bre info_drawingGENE_TAD, info_drawingSecondaryTAD, tad_XCoord_OnLeftSide, tad_XCoord_OnRightSide, tad_YCoord_Rearrangements, - geneBreakP_Position_respectToTSS){ + geneBreakP_Position_respectToTSS, tagEnhancersLabel){ ######################################################## ## Function to paint GENE SV re-arranged TAD @@ -399,11 +399,11 @@ paintGene_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, gene_bre y=rep.int(x = enh_y_positions[1],times = length(enh_x_positions)),a=0.1,col="#b2d235") #enhancers Label - #text(x=enhXpos + 0.4, y=enh_y_positions[1]-distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, label="enhancers", cex = 0.8) + #text(x=enhXpos + 0.4, y=enh_y_positions[1]-distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, label=tagEnhancersLabel, cex = 0.8) boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -456,7 +456,7 @@ paintGene_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, gene_bre boxed.labels(x=enhXpos + 0.15, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -513,7 +513,7 @@ paintGene_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, gene_bre boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -567,7 +567,7 @@ paintGene_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, gene_bre boxed.labels(x=enhXpos + 0.15, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -635,7 +635,7 @@ paintGene_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, gene_bre boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -685,7 +685,7 @@ paintGene_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, gene_bre boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -733,7 +733,7 @@ paintGene_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, gene_bre boxed.labels(x=enhXpos + 0.15, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -781,11 +781,11 @@ paintGene_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, gene_bre y=enh_y_positions,a=0.1,col="#b2d235") #enhancers Label - #text(x=enhXpos + 0.4, y=enh_y_positions[1]-distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, label="enhancers", cex = 0.8) + #text(x=enhXpos + 0.4, y=enh_y_positions[1]-distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, label=tagEnhancersLabel, cex = 0.8) boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -832,7 +832,7 @@ paintGene_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, gene_bre boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -881,7 +881,7 @@ paintGene_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, gene_bre boxed.labels(x=enhXpos + 0.15, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -1004,7 +1004,7 @@ paintGene_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, gene_bre -distance_Yaxis_EnhGene -distance_Yaxis_EnhLabel_EnhNumber -distance_Yaxis_EnhLabel_EnhNumber, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -1081,7 +1081,7 @@ paintSecondaryDomain_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gen info_drawingGENE_TAD, info_drawingSecondaryTAD, tad_XCoord_OnLeftSide, tad_XCoord_OnRightSide, tad_YCoord_Rearrangements, - infoDrawing_gene_sv_tad){ + infoDrawing_gene_sv_tad, tagEnhancersLabel){ ########################################################################################## #We are coloring in this function the re-arranged domain where the gen of interest is not @@ -1461,7 +1461,7 @@ paintSecondaryDomain_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gen #Below the level of the other enhancers, to avoid overlaps boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_EnhLabel, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -1593,7 +1593,7 @@ paintSecondaryDomain_SV_TAD<-function(nEnh_initial_left, nEnh_initial_right, gen -distance_Yaxis_EnhLabel -distance_Yaxis_EnhLabel_EnhNumber -distance_Yaxis_EnhLabel_EnhNumber, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -1820,7 +1820,7 @@ paintGene_SV_Deletion_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, info_drawingGENE_TAD, info_drawingSecondaryTAD, tad_X_cord, tad_YCoord_Rearrangements, - geneBreakP_Position_respectToTSS){ + geneBreakP_Position_respectToTSS, tagEnhancersLabel){ ######################################################## ## Function to paint GENE SV re-arranged TAD @@ -2266,11 +2266,11 @@ paintGene_SV_Deletion_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, y=enh_y_positions,a=0.1,col="#b2d235") #enhancers Label - #text(x=enhXpos + 0.4, y=enh_y_positions[1]-distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, label="enhancers", cex = 0.8) + #text(x=enhXpos + 0.4, y=enh_y_positions[1]-distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, label=tagEnhancersLabel, cex = 0.8) boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -2323,7 +2323,7 @@ paintGene_SV_Deletion_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, boxed.labels(x=enhXpos + 0.15, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -2380,7 +2380,7 @@ paintGene_SV_Deletion_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -2434,7 +2434,7 @@ paintGene_SV_Deletion_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, boxed.labels(x=enhXpos + 0.15, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -2535,7 +2535,7 @@ paintGene_SV_Deletion_TAD<-function(nEnh_initial_left, nEnh_initial_right, gene, -distance_Yaxis_EnhGene -distance_Yaxis_EnhLabel_EnhNumber -distance_Yaxis_EnhLabel_EnhNumber, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -2612,7 +2612,7 @@ paintGene_SV_Duplication_OnlyLongRange_Intra_TAD<-function(nEnh_initial_left, nE tad_X_cord, tad_YCoord_Rearrangements, geneBreakP_Position_respectToTSS, - patientResults){ + patientResults, tagEnhancersLabel){ ############################################################################################################# ##We are dealing with intraTAD SV in this context (if not intraTAD this function is unreachable in the code) @@ -2906,11 +2906,11 @@ paintGene_SV_Duplication_OnlyLongRange_Intra_TAD<-function(nEnh_initial_left, nE y=rep.int(x = enh_y_positions[1],times = length(enh_x_positions)),a=0.1,col="#b2d235") #enhancers Label - #text(x=enhXpos + 0.4, y=enh_y_positions[1]-distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, label="enhancers", cex = 0.8) + #text(x=enhXpos + 0.4, y=enh_y_positions[1]-distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, label=tagEnhancersLabel, cex = 0.8) boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -2958,7 +2958,7 @@ paintGene_SV_Duplication_OnlyLongRange_Intra_TAD<-function(nEnh_initial_left, nE boxed.labels(x=enhXpos + (enh_x_positions[length(enh_x_positions)] - enh_x_positions[1])/2, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -3001,7 +3001,7 @@ paintGene_SV_Duplication_OnlyLongRange_Intra_TAD<-function(nEnh_initial_left, nE boxed.labels(x=enhXpos + (enh_x_positions[length(enh_x_positions)] - enh_x_positions[1])/2, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -3051,7 +3051,7 @@ paintGene_SV_Duplication_OnlyLongRange_Intra_TAD<-function(nEnh_initial_left, nE boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -3102,7 +3102,7 @@ paintGene_SV_Duplication_OnlyLongRange_Intra_TAD<-function(nEnh_initial_left, nE boxed.labels(x=enhXpos + (enh_x_positions[length(enh_x_positions)] - enh_x_positions[1])/2, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -3156,7 +3156,7 @@ paintGene_SV_Duplication_OnlyLongRange_Intra_TAD<-function(nEnh_initial_left, nE boxed.labels(x=enhXpos + (enh_x_positions[length(enh_x_positions)] - enh_x_positions[1])/2, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -3301,7 +3301,7 @@ paintGene_SV_Duplication_NEO_TAD<-function(nEnh_initial_left, nEnh_initial_right info_drawingGENE_TAD, info_drawingSecondaryTAD, tad_X_cord, tad_YCoord_Rearrangements, - geneBreakP_Position_respectToTSS){ + geneBreakP_Position_respectToTSS, tagEnhancersLabel){ ##Coded taking as reference paintGene_SV_Deletion_TAD() ##La unica diferencia es ... apuntar si hay alguna @@ -3744,11 +3744,11 @@ paintGene_SV_Duplication_NEO_TAD<-function(nEnh_initial_left, nEnh_initial_right y=enh_y_positions,a=0.1,col="#b2d235") #enhancers Label - #text(x=enhXpos + 0.4, y=enh_y_positions[1]-distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, label="enhancers", cex = 0.8) + #text(x=enhXpos + 0.4, y=enh_y_positions[1]-distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, label=tagEnhancersLabel, cex = 0.8) boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -3801,7 +3801,7 @@ paintGene_SV_Duplication_NEO_TAD<-function(nEnh_initial_left, nEnh_initial_right boxed.labels(x=enhXpos + 0.15, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -3856,7 +3856,7 @@ paintGene_SV_Duplication_NEO_TAD<-function(nEnh_initial_left, nEnh_initial_right boxed.labels(x=enhXpos + 0.4, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -3910,7 +3910,7 @@ paintGene_SV_Duplication_NEO_TAD<-function(nEnh_initial_left, nEnh_initial_right boxed.labels(x=enhXpos + 0.15, y=enh_y_positions[1] -distance_Yaxis_geneLabel -distance_Yaxis_EnhGene, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath @@ -4008,7 +4008,7 @@ paintGene_SV_Duplication_NEO_TAD<-function(nEnh_initial_left, nEnh_initial_right -distance_Yaxis_EnhGene -distance_Yaxis_EnhLabel_EnhNumber -distance_Yaxis_EnhLabel_EnhNumber, - labels = "enhancers", cex=0.8, + labels = tagEnhancersLabel, cex=0.8, border = NA, bg ="white", xpad=1, ypad=1 #To allow the text to breath diff --git a/Postre_app/html_scripts/MainInterfaceStyling.html b/Postre_app/html_scripts/MainInterfaceStyling.html index dc004b5..22efe15 100644 --- a/Postre_app/html_scripts/MainInterfaceStyling.html +++ b/Postre_app/html_scripts/MainInterfaceStyling.html @@ -1220,6 +1220,184 @@ } +/*To avoid navbar collapse in smaller screens*/ +/*https://stackoverflow.com/questions/21738417/bootstrap-remove-responsive-from-navbar*/ +@media (max-width: 768px) { + .navbar-header { + float: left; + } + + .navbar { + border-radius: 4px; + min-width: 400px; + } + + .nav-tabs-justified > li > a { + border-bottom: 1px solid #ddd; + border-radius: 4px 4px 0 0; + } + .nav-tabs-justified > .active > a, + .nav-tabs-justified > .active > a:hover, + .nav-tabs-justified > .active > a:focus { + border-bottom-color: #fff; + } + + .nav-justified > li { + display: table-cell; + width: 1%; + } + .nav-justified > li > a { + margin-bottom: 0; + } + + .nav-tabs.nav-justified > li > a { + border-bottom: 1px solid #ddd; + border-radius: 4px 4px 0 0; + } + .nav-tabs.nav-justified > .active > a, + .nav-tabs.nav-justified > .active > a:hover, + .nav-tabs.nav-justified > .active > a:focus { + border-bottom-color: #fff; + } + + .nav-tabs.nav-justified > li { + display: table-cell; + width: 1%; + } + .nav-tabs.nav-justified > li > a { + margin-bottom: 0; + } + + .navbar-right .dropdown-menu { + right: 0; + left: auto; + } + .navbar-right .dropdown-menu-left { + right: auto; + left: 0; + } + .container { + min-width: 400px; + } + + .navbar-collapse { + width: auto; + border-top: 0; + box-shadow: none; + } + .navbar-collapse.collapse { + display: block !important; + height: auto !important; + padding-bottom: 0; + overflow: visible !important; + } + .navbar-collapse.in { + overflow-y: visible; + } + .navbar-fixed-top .navbar-collapse, + .navbar-static-top .navbar-collapse, + .navbar-fixed-bottom .navbar-collapse { + padding-right: 0; + padding-left: 0; + } + + .container > .navbar-header, + .container-fluid > .navbar-header, + .container > .navbar-collapse, + .container-fluid > .navbar-collapse { + margin-right: 0; + margin-left: 0; + } + + .navbar-static-top { + border-radius: 0; + } + + .navbar-fixed-top, + .navbar-fixed-bottom { + border-radius: 0; + } + + .navbar-toggle { + display: none; + } + + .navbar-nav { + float: left; + margin: 0; + } + .navbar-nav > li { + float: left; + } + .navbar-nav > li > a { + padding-top: 15px; + padding-bottom: 15px; + } + .navbar-nav.navbar-right:last-child { + margin-right: -15px; + } + + .navbar-left { + float: left !important; + } + .navbar-right { + float: right !important; + } + + .navbar-form .form-group { + display: inline-block; + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .form-control { + display: inline-block; + width: auto; + vertical-align: middle; + } + .navbar-form .control-label { + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .radio, + .navbar-form .checkbox { + display: inline-block; + padding-left: 0; + margin-top: 0; + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .radio input[type='radio'], + .navbar-form .checkbox input[type='checkbox'] { + float: none; + margin-left: 0; + } + .navbar-form .has-feedback .form-control-feedback { + top: 0; + } + + .navbar-form { + width: auto; + padding-top: 0; + padding-bottom: 0; + margin-right: 0; + margin-left: 0; + border: 0; + -webkit-box-shadow: none; + box-shadow: none; + } + .navbar-form.navbar-right:last-child { + margin-right: -15px; + } + + .navbar-text { + float: left; + margin-right: 15px; + margin-left: 15px; + } + .navbar-text.navbar-right:last-child { + margin-right: 0; + } +} diff --git a/Postre_app/html_scripts/Multiple_SV_Submission_Interface.html b/Postre_app/html_scripts/Multiple_SV_Submission_Interface.html index fd1d558..fd95178 100644 --- a/Postre_app/html_scripts/Multiple_SV_Submission_Interface.html +++ b/Postre_app/html_scripts/Multiple_SV_Submission_Interface.html @@ -4,7 +4,6 @@
-
diff --git a/Postre_app/html_scripts/Single_SV_Submission_Interface.html b/Postre_app/html_scripts/Single_SV_Submission_Interface.html index d6a7b24..e760018 100644 --- a/Postre_app/html_scripts/Single_SV_Submission_Interface.html +++ b/Postre_app/html_scripts/Single_SV_Submission_Interface.html @@ -4,7 +4,6 @@
-
diff --git a/Postre_app/www/DevStages_Metadata.html b/Postre_app/www/DevStages_Metadata.html deleted file mode 100644 index f84fe94..0000000 --- a/Postre_app/www/DevStages_Metadata.html +++ /dev/null @@ -1,135 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
PhenotypeDevelopmental Stages ConsideredStage Additional InformationChip-Seq Source (for enhancer annotation)Enhancer annotation, Main ProcedureRNA-Seq SourceHiC-Source – TAD maps
CardiovascularDay 5 Day 5 cardiac mesodermal cells, from 80 days ESC differentiation towards ventricular cardiomocyteshttps://doi.org/10.1038/s41588-019-0479-7H3K27ac peak with distance > 10Kb to protein coding TSShttps://doi.org/10.1038/s41588-019-0479-7TAD map generated upon processing Heart day 5 HiC data from: https://doi.org/10.1038/s41588-019-0479-7
Day 7Day 7 cardiac progenitors, from 80 days ESC differentiation towards ventricular cardiomocyteshttps://doi.org/10.1038/s41588-019-0479-7H3K27ac peak with distance > 10Kb to protein coding TSShttps://doi.org/10.1038/s41588-019-0479-7TAD map generated upon processing Heart day 7 HiC data from: https://doi.org/10.1038/s41588-019-0479-7
Day 15Day 15 primitive cardiomyocytes, from 80 days ESC differentiation towards ventricular cardiomocyteshttps://doi.org/10.1038/s41588-019-0479-7H3K27ac peak with distance > 10Kb to protein coding TSShttps://doi.org/10.1038/s41588-019-0479-7TAD map generated upon processing Heart day 15 HiC data from: https://doi.org/10.1038/s41588-019-0479-7
Day 80Day 80 ventricular cardiomiocytes, from 80 days ESC differentiation towards ventricular cardiomocyteshttps://doi.org/10.1038/s41588-019-0479-7H3K27ac peak with distance > 10Kb to protein coding TSShttps://doi.org/10.1038/s41588-019-0479-7TAD map generated upon processing Heart day 80 HiC data from: https://doi.org/10.1038/s41588-019-0479-7
Head-NeckNeural Crest EarlyNeural Crest Data of hNCC differentiationhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28876p300 peak, intersecting H3K27ac peak, with distance > 10Kb to protein coding TSShttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28876ESC TAD map provided in 3D genome browser: http://3dgenome.fsm.northwestern.edu/
TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip
Neural Crest LateNeural Crest Data of hNCC differentiation , check GEO links for detailed explanation?¿ https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70751p300 peak, intersecting H3K27ac peak, with distance > 10Kb to protein coding TSShttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70751ESC TAD map provided in 3D genome browser: http://3dgenome.fsm.northwestern.edu/
TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip
PalateCS20 Embryonic Palate Carnagie Stage 20https://pubmed.ncbi.nlm.nih.gov/29719267/H3K27ac peak with distance > 10Kb to protein coding TSShttps://elifesciences.org/articles/15657ESC TAD map provided in 3D genome browser: http://3dgenome.fsm.northwestern.edu/
TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip
LimbsEmbryonicLimb1Corresponds with embryonic Lower Limb datahttps://www.nature.com/articles/s41467-020-17305-2H3K27ac peak with distance > 10Kb to protein coding TSShttps://elifesciences.org/articles/15657
ESC TAD map provided in 3D genome browser: http://3dgenome.fsm.northwestern.edu/
TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip
EmbryonicLimb2Corresponds with embryonic Upper Limb datahttps://www.nature.com/articles/s41467-020-17305-2H3K27ac peak with distance > 10Kb to protein coding TSShttps://elifesciences.org/articles/15657
ESC TAD map provided in 3D genome browser: http://3dgenome.fsm.northwestern.edu/
TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip
NeurodevelopmentalPfcGw15Brain prefrontal cortex Gestational Week 15 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149268H3K27ac peak with distance > 10Kb to protein coding TSSFrom database: https://www.brainspan.org/static/home
Expression file considered (summarized to genes file)
Expression value as the average of the pcw13 samples: VFC (ventrolateral prefrontal cortex), MFC[anterior (rostral) cingulate (medial prefrontal) cortex] and DFC(dorsolateral prefrontal cortex)		Brain Prefrontal Cortex TAD map, generated from CO (prefrontal cortex) boundary map provided in:
https://www.cell.com/cell-reports/fulltext/S2211-1247(16)31481-4?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2211124716314814%3Fshowall%3Dtrue
Excel file containing boundary maps: https://www.cell.com/cms/10.1016/j.celrep.2016.10.061/attachment/c3954472-3378-4fd7-aa33-9a2d085f7bbd/mmc4.xlsx
PfcGw18Brain prefrontal cortex Gestation Week 18https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149268H3K27ac peak with distance > 10Kb to protein coding TSSFrom database: https://www.brainspan.org/static/home
Expression file considered (summarized to genes file)
Expression value as the average of the pcw16 samples: VFC (ventrolateral prefrontal cortex), MFC[anterior (rostral) cingulate (medial prefrontal) 		Brain Prefrontal Cortex TAD map, generated from CO (prefrontal cortex) boundary map provided in:
https://www.cell.com/cell-reports/fulltext/S2211-1247(16)31481-4?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2211124716314814%3Fshowall%3Dtrue
Excel file containing boundary maps: https://www.cell.com/cms/10.1016/j.celrep.2016.10.061/attachment/c3954472-3378-4fd7-aa33-9a2d085f7bbd/mmc4.xlsx
- - - - diff --git a/Postre_app/www/ImagenParaGithub_Tutorial.png b/Postre_app/www/ImagenParaGithub_Tutorial.png deleted file mode 100644 index 7b2b471..0000000 Binary files a/Postre_app/www/ImagenParaGithub_Tutorial.png and /dev/null differ diff --git a/Postre_app/www/Supplementary_Data_1.html b/Postre_app/www/Supplementary_Data_1.html index a1aebf8..ee21d4d 100644 --- a/Postre_app/www/Supplementary_Data_1.html +++ b/Postre_app/www/Supplementary_Data_1.html @@ -1,5 +1,5 @@ -Cell Types/Tissues per Phenotype

Phenotype

  Cell Types/Tissues  

 Cell Types/Tissues additional information

Chip-Seq Source for enhancer annotation

Enhancer annotation (based on Chip-Seq), Main Procedure

RNA-Seq Source

Gene Expression quantification main procedure

HiC – TAD maps Source

TAD maps main procedure

  

Cardiovascular

Day 5

Day 5 cardiac mesodermal cells, from 80 days ESC differentiation towards ventricular cardiomocytes

For + info: https://www.nature.com/articles/s41588-019-0479-7

https://doi.org/10.1038/s41588-019-0479-7

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called after reads mapping with Bowtie2 with Macs2)

https://doi.org/10.1038/s41588-019-0479-7

FPKMs directly obtained from source

https://doi.org/10.1038/s41588-019-0479-7

TAD map generated upon processing Day 5 cardiomyocyte differentiation HiC data.

TADs were called with the usage of the tool DomainCaller by considering the 50kb contact matrices in the .hic files

  

Day 7

Day 7 cardiac progenitors, from 80 days ESC differentiation towards ventricular cardiomocytes

For + info: https://www.nature.com/articles/s41588-019-0479-7

https://doi.org/10.1038/s41588-019-0479-7

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called after reads mapping with Bowtie2 with Macs2)

https://doi.org/10.1038/s41588-019-0479-7

FPKMs directly obtained from source

https://doi.org/10.1038/s41588-019-0479-7

TAD map generated upon processing Day 7 cardiomyocyte differentiation HiC data.

TADs were called with the usage of the tool DomainCaller by considering the 50kb contact matrices in the .hic files

  

Day 15

Day 15 primitive cardiomyocytes, from 80 days ESC differentiation towards ventricular cardiomocytes

For + info: https://www.nature.com/articles/s41588-019-0479-7

https://doi.org/10.1038/s41588-019-0479-7

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called after reads mapping with Bowtie2 with Macs2)

https://doi.org/10.1038/s41588-019-0479-7

FPKMs directly obtained from source

https://doi.org/10.1038/s41588-019-0479-7

TAD map generated upon processing Day 15 cardiomyocyte differentiation HiC data.

TADs were called with the usage of the tool DomainCaller by considering the 50kb contact matrices in the .hic files

  

Day 80

Day 80 ventricular cardiomiocytes, from 80 days ESC differentiation towards ventricular cardiomocytes

For + info: https://www.nature.com/articles/s41588-019-0479-7

https://doi.org/10.1038/s41588-019-0479-7

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called after reads mapping with Bowtie2 with Macs2)

https://doi.org/10.1038/s41588-019-0479-7

FPKMs directly obtained from source

https://doi.org/10.1038/s41588-019-0479-7

TAD map generated upon processing Day 80 cardiomyocyte differentiation HiC data.

TADs were called with the usage of the tool DomainCaller by considering the 50kb contact matrices in the .hic files

  

Head-Neck

Neural Crest Early

Neural Crest Data of hNCC differentiation

This NC data corresponds with an early and heterogeneous stage of NC differentiation

For +info: https://pubmed.ncbi.nlm.nih.gov/29719267/

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28876

p300 peak, intersecting H3K27ac peak, with distance > 10Kb to protein coding TSS

(Peaks were called upon reads mapping with Bowtie2 with Macs2)

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28876

FPKMs obtained upon reads mapping with Tophat2 and gene expression quantification with Cufflinks

3D genome browser: http://3dgenome.fsm.northwestern.edu/

TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

ESC TAD map directly obtained from: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

  

Neural Crest Late

Neural Crest Data of hNCC differentiation

P4 differentiation stage, it corresponds with a later and more homogeneous stage of NC differentation in comparison with Neural Crest Early

For +info: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70751

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70751

p300 peak, intersecting H3K27ac peak, with distance > 10Kb to protein coding TSS

(Peaks were called upon reads mapping with Bowtie2 with Macs2)

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70751

FPKMs obtained upon reads mapping with Tophat2 and  gene expression quantification with Cufflinks

3D genome browser: http://3dgenome.fsm.northwestern.edu/

TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

ESC TAD map directly obtained from: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

  

PalateCS20

Embryonic Palate Carnagie Stage 20

For +info: https://pubmed.ncbi.nlm.nih.gov/29719267/

https://pubmed.ncbi.nlm.nih.gov/29719267/

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called with Macs2 using the provided alignment files (tagAlign))

https://elifesciences.org/articles/15657

To obtain the FPKMs, first, the bedfiles containing the reads mapping coordinates (.unique.bed12) for Palate1 sample were converted into bam files with bedToBam tool.

Next FPKMs were computed with Cufflinks.

3D genome browser: http://3dgenome.fsm.northwestern.edu/

TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

ESC TAD map directly obtained from: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

  

Limbs

EmbryonicLimb1

Embryonic Lower Limb data of entire limb buds, Carnegie Stages 14-19

For +info: https://www.nature.com/articles/s41467-020-17305-2

https://www.nature.com/articles/s41467-020-17305-2

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called after reads mapping with Bowtie2 with Macs2)

https://elifesciences.org/articles/15657

 

To obtain the FPKMs, first, the bedfiles containing the reads mapping coordinates (.unique.bed12) for Lower Limb samples were converted into bam files with bedToBam tool.

Next FPKMs were computed with Cufflinks.

3D genome browser: http://3dgenome.fsm.northwestern.edu/

TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

ESC TAD map directly obtained from: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

  

EmbryonicLimb2

Embryonic Upper Limb data of entire limb buds, Carnegie Stages 14-19

For +info: https://www.nature.com/articles/s41467-020-17305-2

https://www.nature.com/articles/s41467-020-17305-2

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called after reads mapping with Bowtie2 with Macs2)

https://elifesciences.org/articles/15657

 

To obtain the FPKMs, first, the bedfiles containing the reads mapping coordinates (.unique.bed12) for Upper Limb samples were converted into bam files with bedToBam tool.

Next FPKMs were computed with Cufflinks.

3D genome browser: http://3dgenome.fsm.northwestern.edu/

TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

ESC TAD map directly obtained from: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

  

Neurodevelopmental

PfcGw15

Brain prefrontal cortex Gestation Week 15

For +info: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149268

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149268

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were directly obtained from source)

From database: https://www.brainspan.org/static/home

 

Expression values obtained from file: summarized to genes file

Given that gestational age is 2 weeks longer than conceptional age, post conception week 13 (pcw13) samples information was considered to match with chip-seq gestation week 15 data

Expression value computed as the average of the pcw13 samples: VFC (ventrolateral prefrontal cortex), MFC[anterior (rostral) cingulate (medial prefrontal) cortex] and DFC(dorsolateral prefrontal cortex)

 

https://doi.org/10.1016/j.celrep.2016.10.061

Brain Prefrontal Cortex TAD map, generated from CO (prefrontal cortex) boundary map provided in the referenced source.

The Excelfile containing the boundary maps can be found here:

https://www.cell.com/cms/10.1016/j.celrep.2016.10.061/attachment/c3954472-3378-4fd7-aa33-9a2d085f7bbd/mmc4.xlsx

  

PfcGw18

Brain prefrontal cortex Gestation Week 18

For +info: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149268

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149268

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were directly obtained from source)

From database: https://www.brainspan.org/static/home

 

Expression values obtained from file: summarized to genes file

Given that gestational age is 2 weeks longer than conceptional age, post conception week 16 (pcw16) samples information was considered to match with chip-seq gestation week 18 data

Expression value computed as the average of the pcw16 samples: VFC (ventrolateral prefrontal cortex), MFC[anterior (rostral) cingulate (medial prefrontal) cortex] and DFC(dorsolateral prefrontal cortex)

 

https://doi.org/10.1016/j.celrep.2016.10.061

Brain Prefrontal Cortex TAD map, generated from CO (prefrontal cortex) boundary map provided in the referenced source.

The Excelfile containing the boundary maps can be found here:

https://www.cell.com/cms/10.1016/j.celrep.2016.10.061/attachment/c3954472-3378-4fd7-aa33-9a2d085f7bbd/mmc4.xlsx

  
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
\ No newline at end of file +

Phenotype

  Cell Types/Tissues  

 Cell Types/Tissues additional information

Chip-Seq Source for enhancer annotation

Enhancer annotation (based on Chip-Seq), Main Procedure

RNA-Seq Source

Gene Expression quantification main procedure

HiC – TAD maps Source

TAD maps main procedure

  

Cardiovascular

Day 5

Day 5 cardiac mesodermal cells, from 80 days ESC differentiation towards ventricular cardiomyocytes

For + info: https://www.nature.com/articles/s41588-019-0479-7

https://doi.org/10.1038/s41588-019-0479-7

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called after reads mapping with Bowtie2 with Macs2)

https://doi.org/10.1038/s41588-019-0479-7

FPKMs directly obtained from source

https://doi.org/10.1038/s41588-019-0479-7

TAD map generated upon processing Day 5 cardiomyocyte differentiation HiC data.

TADs were called with the usage of the tool DomainCaller by considering the 50kb contact matrices in the .hic files

  

Day 7

Day 7 cardiac progenitors, from 80 days ESC differentiation towards ventricular cardiomyocytes

For + info: https://www.nature.com/articles/s41588-019-0479-7

https://doi.org/10.1038/s41588-019-0479-7

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called after reads mapping with Bowtie2 with Macs2)

https://doi.org/10.1038/s41588-019-0479-7

FPKMs directly obtained from source

https://doi.org/10.1038/s41588-019-0479-7

TAD map generated upon processing Day 7 cardiomyocyte differentiation HiC data.

TADs were called with the usage of the tool DomainCaller by considering the 50kb contact matrices in the .hic files

  

Day 15

Day 15 primitive cardiomyocytes, from 80 days ESC differentiation towards ventricular cardiomyocytes

For + info: https://www.nature.com/articles/s41588-019-0479-7

https://doi.org/10.1038/s41588-019-0479-7

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called after reads mapping with Bowtie2 with Macs2)

https://doi.org/10.1038/s41588-019-0479-7

FPKMs directly obtained from source

https://doi.org/10.1038/s41588-019-0479-7

TAD map generated upon processing Day 15 cardiomyocyte differentiation HiC data.

TADs were called with the usage of the tool DomainCaller by considering the 50kb contact matrices in the .hic files

  

Day 80

Day 80 ventricular cardiomiocytes, from 80 days ESC differentiation towards ventricular cardiomyocytes

For + info: https://www.nature.com/articles/s41588-019-0479-7

https://doi.org/10.1038/s41588-019-0479-7

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called after reads mapping with Bowtie2 with Macs2)

https://doi.org/10.1038/s41588-019-0479-7

FPKMs directly obtained from source

https://doi.org/10.1038/s41588-019-0479-7

TAD map generated upon processing Day 80 cardiomyocyte differentiation HiC data.

TADs were called with the usage of the tool DomainCaller by considering the 50kb contact matrices in the .hic files

  

Head-Neck

Neural Crest Early

Neural Crest Data of hNCC differentiation

This NC data corresponds with an early and heterogeneous stage of NC differentiation

For +info: https://pubmed.ncbi.nlm.nih.gov/22981823/

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28876

p300 peak, intersecting H3K27ac peak, with distance > 10Kb to protein coding TSS

(Peaks were called upon reads mapping with Bowtie2 with Macs2)

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28876

FPKMs obtained upon reads mapping with Tophat2 and gene expression quantification with Cufflinks

3D genome browser: http://3dgenome.fsm.northwestern.edu/

TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

ESC TAD map directly obtained from: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

  

Neural Crest Late

Neural Crest Data of hNCC differentiation

P4 differentiation stage, it corresponds with a later and more homogeneous stage of NC differentation in comparison with Neural Crest Early

For +info: https://pubmed.ncbi.nlm.nih.gov/26365491/

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70751

p300 peak, intersecting H3K27ac peak, with distance > 10Kb to protein coding TSS

(Peaks were called upon reads mapping with Bowtie2 with Macs2)

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70751

FPKMs obtained upon reads mapping with Tophat2 and  gene expression quantification with Cufflinks

3D genome browser: http://3dgenome.fsm.northwestern.edu/

TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

ESC TAD map directly obtained from: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

  

PalateCS20

Embryonic Palate Carnagie Stage 20

For +info: https://pubmed.ncbi.nlm.nih.gov/29719267/

https://pubmed.ncbi.nlm.nih.gov/29719267/

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called with Macs2 using the provided alignment files (tagAlign))

https://elifesciences.org/articles/15657

To obtain the FPKMs, first, the bedfiles containing the reads mapping coordinates (.unique.bed12) for Palate1 sample were converted into bam files with bedToBam tool.

Next FPKMs were computed with Cufflinks.

3D genome browser: http://3dgenome.fsm.northwestern.edu/

TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

ESC TAD map directly obtained from: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

  

Limbs

EmbryonicLimb1

Embryonic Lower Limb data of entire limb buds, Carnegie Stages 14-19

For +info: https://www.nature.com/articles/s41467-020-17305-2

https://www.nature.com/articles/s41467-020-17305-2

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called after reads mapping with Bowtie2 with Macs2)

https://elifesciences.org/articles/15657

 

To obtain the FPKMs, first, the bedfiles containing the reads mapping coordinates (.unique.bed12) for Lower Limb samples were converted into bam files with bedToBam tool.

Next FPKMs were computed with Cufflinks.

3D genome browser: http://3dgenome.fsm.northwestern.edu/

TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

ESC TAD map directly obtained from: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

  

EmbryonicLimb2

Embryonic Upper Limb data of entire limb buds, Carnegie Stages 14-19

For +info: https://www.nature.com/articles/s41467-020-17305-2

https://www.nature.com/articles/s41467-020-17305-2

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were called after reads mapping with Bowtie2 with Macs2)

https://elifesciences.org/articles/15657

 

To obtain the FPKMs, first, the bedfiles containing the reads mapping coordinates (.unique.bed12) for Upper Limb samples were converted into bam files with bedToBam tool.

Next FPKMs were computed with Cufflinks.

3D genome browser: http://3dgenome.fsm.northwestern.edu/

TAD maps: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

ESC TAD map directly obtained from: http://3dgenome.fsm.northwestern.edu/downloads/hg19.TADs.zip

  

Neurodevelopmental

PfcGw15

Brain prefrontal cortex Gestation Week 15

For +info: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149268

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149268

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were directly obtained from source)

From database: https://www.brainspan.org/static/home

 

Expression values obtained from file: summarized to genes file

Given that gestational age is 2 weeks longer than conceptional age, post conception week 13 (pcw13) samples information was considered to match with chip-seq gestation week 15 data

Expression value computed as the average of the pcw13 samples: VFC (ventrolateral prefrontal cortex), MFC[anterior (rostral) cingulate (medial prefrontal) cortex] and DFC(dorsolateral prefrontal cortex)

 

https://doi.org/10.1016/j.celrep.2016.10.061

Brain Prefrontal Cortex TAD map, generated from CO (prefrontal cortex) boundary map provided in the referenced source.

The Excelfile containing the boundary maps can be found here:

https://www.cell.com/cms/10.1016/j.celrep.2016.10.061/attachment/c3954472-3378-4fd7-aa33-9a2d085f7bbd/mmc4.xlsx

  

PfcGw18

Brain prefrontal cortex Gestation Week 18

For +info: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149268

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149268

H3K27ac peak with distance > 10Kb to protein coding TSS

(Peaks were directly obtained from source)

From database: https://www.brainspan.org/static/home

 

Expression values obtained from file: summarized to genes file

Given that gestational age is 2 weeks longer than conceptional age, post conception week 16 (pcw16) samples information was considered to match with chip-seq gestation week 18 data

Expression value computed as the average of the pcw16 samples: VFC (ventrolateral prefrontal cortex), MFC[anterior (rostral) cingulate (medial prefrontal) cortex] and DFC(dorsolateral prefrontal cortex)

 

https://doi.org/10.1016/j.celrep.2016.10.061

Brain Prefrontal Cortex TAD map, generated from CO (prefrontal cortex) boundary map provided in the referenced source.

The Excelfile containing the boundary maps can be found here:

https://www.cell.com/cms/10.1016/j.celrep.2016.10.061/attachment/c3954472-3378-4fd7-aa33-9a2d085f7bbd/mmc4.xlsx

  
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
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