From d777cc212ac18da9c14a05438abc18f76a0f2c11 Mon Sep 17 00:00:00 2001 From: laurenpmarazzi Date: Sun, 6 Nov 2022 20:28:02 -0500 Subject: [PATCH 1/4] data upload --- .DS_Store | Bin 0 -> 12292 bytes 1_individual_methods_analysis/.DS_Store | Bin 0 -> 10244 bytes 1_individual_methods_analysis/.RData | Bin 0 -> 214760 bytes 1_individual_methods_analysis/.Rhistory | 512 ++++ 1_individual_methods_analysis/README.md | 22 + 1_individual_methods_analysis/analysis.Rmd | 669 +++++ 1_individual_methods_analysis/analysis.html | 2641 +++++++++++++++++ .../Average_Combined_data_sums_37.csv | 42 + .../Average_Combined_data_sums_73.csv | 42 + .../Average_Combined_data_sums_equal.csv | 42 + .../Average_Combined_data_sums_onlyACC.csv | 42 + .../Average_Combined_data_sums_onlyAUC.csv | 42 + ...Average_Median_Combined_data_median_37.csv | 42 + ...Average_Median_Combined_data_median_73.csv | 42 + ...rage_Median_Combined_data_median_equal.csv | 42 + ...ge_Median_Combined_data_median_onlyACC.csv | 42 + 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z#5nD47A{LYV9lWP;wVd;tK`<U|QpV0=Kr-SH{Y| zzP}te*1n5N6x|PYJDOdw<6!%_R0Cyr@`}eO)kPT>y*rMSXY@H9VTIY9oc!$5NN!f4vQuC+k38eYCVqA{g zb+bpW&0Wi5v5V!HS6QdZ6E&NeY{1`QhMSy5clU&{;A2R2V9sLRq@sZU|BVq7Et!70 zg6G`N0~=AN+0x#ls$*b=1~(lmi&QwGp83R3qeI&&a(DVZdQ656cd?9<{y}+=BNGO_ z=iKY33*cPN&bdgV$ypVG31tyK`yWSwv(wQJ7^30xhzPoGf~75>S?~wndqp zQ?7c36QyaEu)al^ms2kP)c|VegIxFS&nMIe$!)8S^S}14kIHK5ORiiM9}sN)l{dXI zu?qY&3$7A8+men65On@C(4sWgl+NC{`e6PXgQ-0`-egEduD#kb|Ik*b{Q{;mBd5Ot}dVE@xMQF}vSiWBkCmX)p7Se!!gk1O%zOcLV-Qb&n?`3w| znULHEJ6f@M;kgbI5C0l{T%pMM3;9=-k8q|XfUikO8I5p+=H0e|7M6*rG#G4CU#G!o z+81ZgC~rLjosbidR=$u2S$bY7y#=|Uz#Hh?Ral>puuf>b=P`8+;zGu|w#uYsl5Lgz zQ(I%(woG~TuEYelCuPHwpUljU(W3J% filter(Method_name %in% methods) +f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f)))) +colnames(f)[3]<-c('time_series') +#if the counts object is empty just copy the f to m +if(length(d1) == 0){ +d1 <- f +} else +{ +#if the dataframe is not empty then merge the data +d1 <- rbind(d1,f) +} +} +d1$time_series<-gsub("sim","",d1$time_series) +d1$time_series<-str_pad(d1$time_series, 2, pad = "0") +colnames(d1)<-c("Method","Row Sum","Network") +d1$`Row Sum`<-d1$`Row Sum`/50 +return(d1) +} +pal<-c("#8dd3c7","#bebada","#fb8072","#80b1d3","#fdb462","#ffed6f","#b3de69","#d9d9d9","#ffffb3","#bc80bd","#ccebc5","#fccde5") +methods<-c("BCohF","BCorrU","BF_PFmean","PCohF","T_BCmean","T_BF_BCmean","T_BF_PF_BCmean","T_BF_PFmean","T_BFmean","T_PF_BCmean","T_PFmean","Tigress") +#onlyAUC +pattern<-"Combined_data_sums_onlyAUC.csv" +d1<-create_rowsum_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=1, WeightACC=0 for Top 12 Methods across Networks")+scale_color_manual(values=pal) +ggplotly(p) +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_AUConly.pdf") +#onlyACC +pattern<-"Combined_data_sums_onlyACC.csv" +d1<-create_rowsum_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0, WeightACC=1 for Top 12 Methods across Networks")+scale_color_manual(values=pal) +ggplotly(p) +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_ACConly.pdf") +#equal weights +pattern<-"Combined_data_sums_equal.csv" +d1<-create_rowsum_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.5, WeightACC=0.5 for Top 12 Methods across Networks")+scale_color_manual(values=pal) +ggplotly(p) +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_equal.pdf") +#AUC=0.3, ACC=0.7 weights +pattern<-"Combined_data_sums_37.csv" +d1<-create_rowsum_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.3, WeightACC=0.7 for Top 12 Methods across Networks")+scale_color_manual(values=pal) +ggplotly(p) +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_37.pdf") +#AUC=0.7, ACC=0.3 weights +pattern<-"Combined_data_sums_73.csv" +d1<-create_rowsum_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.7, WeightACC=0.3 for Top 12 Methods across Networks")+scale_color_manual(values=pal) +ggplotly(p) +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_73.pdf") +create_median_lineplot<-function(pattern,methods){ +count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE) +d1<-data.frame() +for (i in count_files) { +f <- read.csv(i,row.names = 1) %>% filter(Method_name %in% methods) +f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f)))) +colnames(f)[3]<-c('time_series') +#if the counts object is empty just copy the f to m +if(length(d1) == 0){ +d1 <- f +} else +{ +#if the dataframe is not empty then merge the data +d1 <- rbind(d1,f) +} +} +d1$time_series<-gsub("sim","",d1$time_series) +d1$time_series<-str_pad(d1$time_series, 2, pad = "0") +colnames(d1)<-c("Method","Median","Network") +return(d1) +} +#onlyAUC +pattern<-"Combined_data_median_onlyAUC.csv" +d1<-create_median_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=1, WeightACC=0 for Top 12 Methods across Networks")+scale_color_manual(values=pal) +ggplotly(p) +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_AUConly.pdf") +#onlyACC +pattern<-"Combined_data_median_onlyACC.csv" +d1<-create_median_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0, WeightACC=1 for Top 12 Methods across Networks")+scale_color_manual(values=pal) +ggplotly(p) +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_ACConly.pdf") +#equal weights +pattern<-"Combined_data_median_equal.csv" +d1<-create_median_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.5, WeightACC=0.5 for Top 12 Methods across Networks")+scale_color_manual(values=pal) +ggplotly(p) +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_equal.pdf") +#AUC=0.3, ACC=0.7 weights +pattern<-"Combined_data_sums_37.csv" +d1<-create_median_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.3, WeightACC=0.7 for Top 12 Methods across Networks")+scale_color_manual(values=pal) +ggplotly(p) +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_37.pdf") +#AUC=0.7, ACC=0.3 weights +pattern<-"Combined_data_sums_73.csv" +d1<-create_median_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.7, WeightACC=0.3 for Top 12 Methods across Networks")+scale_color_manual(values=pal) +ggplotly(p) +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_73.pdf") +create_rowsum_lineplot<-function(pattern){ +count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE) +d1<-data.frame() +for (i in count_files) { +f <- read.csv(i,row.names = 1) +f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f)))) +colnames(f)[3]<-c('time_series') +#if the counts object is empty just copy the f to m +if(length(d1) == 0){ +d1 <- f +} else +{ +#if the dataframe is not empty then merge the data +d1 <- rbind(d1,f) +} +} +d1$time_series<-gsub("sim","",d1$time_series) +d1$time_series<-str_pad(d1$time_series, 2, pad = "0") +colnames(d1)<-c("Method","Row Sum","Network") +d1$`Row Sum`<-d1$`Row Sum`/50 +return(d1) +} +create_median_lineplot<-function(pattern){ +count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE) +d1<-data.frame() +for (i in count_files) { +f <- read.csv(i,row.names = 1) +f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f)))) +colnames(f)[3]<-c('time_series') +#if the counts object is empty just copy the f to m +if(length(d1) == 0){ +d1 <- f +} else +{ +#if the dataframe is not empty then merge the data +d1 <- rbind(d1,f) +} +} +d1$time_series<-gsub("sim","",d1$time_series) +d1$time_series<-str_pad(d1$time_series, 2, pad = "0") +colnames(d1)<-c("Method","Median","Network") +return(d1) +} +pattern<-"Combined_data_sums_onlyAUC.csv" +d1<-create_rowsum_lineplot(pattern,methods) +pattern<-"Combined_data_sums_onlyAUC.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=1, WeightACC=0 for Top 12 Methods across Networks")+scale_color_manual(values=pal) +ggplotly(p) +pattern<-"Combined_data_sums_onlyAUC.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=1, WeightACC=0 for Top 12 Methods across Networks") +ggplotly(p) +p1<-ggplotly(p) +#onlyAUC +pattern<-"Combined_data_sums_onlyAUC.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=1, WeightACC=0 for Top 12 Methods across Networks") +ggplotly(p) +p1<-ggplotly(p) +#onlyACC +pattern<-"Combined_data_sums_onlyACC.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0, WeightACC=1 for Top 12 Methods across Networks") +ggplotly(p) +p1<-ggplotly(p) +#equal weights +pattern<-"Combined_data_sums_equal.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.5, WeightACC=0.5 for Top 12 Methods across Networks") +ggplotly(p) +p1<-ggplotly(p) +#AUC=0.3, ACC=0.7 weights +pattern<-"Combined_data_sums_37.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.3, WeightACC=0.7 for Top 12 Methods across Networks") +ggplotly(p) +p1<-ggplotly(p) +#AUC=0.7, ACC=0.3 weights +pattern<-"Combined_data_sums_73.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.7, WeightACC=0.3 for Top 12 Methods across Networks") +ggplotly(p) +p1<-ggplotly(p) +#onlyAUC +pattern<-"Combined_data_median_onlyAUC.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=1, WeightACC=0 for Top 12 Methods across Networks") +ggplotly(p) +p1<-ggplotly(p) +#onlyACC +pattern<-"Combined_data_median_onlyACC.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0, WeightACC=1 for Top 12 Methods across Networks") +ggplotly(p) +p1<-ggplotly(p) +#equal weights +pattern<-"Combined_data_median_equal.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.5, WeightACC=0.5 for Top 12 Methods across Networks") +ggplotly(p) +p1<-ggplotly(p) +#AUC=0.3, ACC=0.7 weights +pattern<-"Combined_data_sums_37.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.3, WeightACC=0.7 for Top 12 Methods across Networks") +ggplotly(p) +p1<-ggplotly(p) +#AUC=0.7, ACC=0.3 weights +pattern<-"Combined_data_sums_73.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.7, WeightACC=0.3 for Top 12 Methods across Networks") +ggplotly(p) +p1<-ggplotly(p) +knitr::opts_chunk$set( +echo = TRUE, +fig.show = "hold", +message = FALSE, +warning = FALSE +) +library(dplyr) +library(caret) +library(ggplot2) +library(plotly) +library(RColorBrewer) +library(heatmaply) +library(stringr) +?lhei +??lhei +knitr::opts_chunk$set(echo = TRUE) +getwd() +library(dplyr) +library(caret) +library(ggplot2) +library(plotly) +library(RColorBrewer) +library(heatmaply) +library(stringr) +pal<-rev(c("#FF0000","#000000","#031e33","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,scale_fill_gradient = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +pal<-rev(c("#FF0000","#000000","#031e33","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,limits = c(0,500),show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +pal<-rev(c("#FF0000","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,limits = c(0,500),show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +pal<-rev(c("#FF0000","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,limits = c(0,10000),show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +pal<-rev(c("#FF0000","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,limits = c(0,100),show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +pal<-rev(c("#FF0000","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,limits = c(0,1),show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +pal<-rev(c("#FF0000","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,limits = c(0,.92),show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +pal<-rev(c("#FF0000","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,limits = c(0,.97),show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +pal<-rev(c("#FF0000","#031e33","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,limits = c(0,.97),show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +pal<-rev(c("#FF0000","#031e33","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,limits = c(0,1.2),show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +pal<-rev(c("#FF0000","#031e33","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,limits = c(0,1),show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +pal<-rev(c("#FF0000","#031e33","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +pal<-rev(c("#FF0000","#031e33","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,limits = c(0,.94),show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +pal<-rev(c("#FF0000","#000000","#031e33","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) +patterns<-c("Combined_data_median_onlyAUC","Combined_data_median_onlyACC","Combined_data_median_equal","Combined_data_median_37","Combined_data_median_73") +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Median"),full.names=TRUE) +d1<-data.frame() +for (i in count_files){ +f<-read.csv(i,row.names = 1) +if(length(d1) == 0){ +d1<-f +} +else{ +d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") +} +} +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) +heatmaply(d1,colors = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Median Value across 28 Networks",file = "heatmap_medians.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) diff --git a/1_individual_methods_analysis/README.md b/1_individual_methods_analysis/README.md new file mode 100644 index 0000000..6ed0b92 --- /dev/null +++ b/1_individual_methods_analysis/README.md @@ -0,0 +1,22 @@ +# Individual and combined methods analysis + +This subfolder contains the code and data for analyzing the networks created by the 40 single methods inmplemented in MULAN and molecular biology biology methods + +The `sim` subfolders contain the following files for each simulated network: + +- Combined_Data_onlyAUC.csv: the area under the reciever operating characteristic (AUROC, abbreviated here as AUC) values for each method for the 50 time series simulations of the network calculated by MULAN +- Combined_Data_onlyACC.csv: the accuracy (ACC) values for each method for the 50 time series simulations of the network calculated by MULAN +- Combined_data_sums_onlyAUC: The Row Sums for each method across the 50 simulated time series using the weight Weight_{AUC=1}, Weight_{ACC=0} +- Combined_data_sums_onlyACC: The Row Sums for each method across the 50 simulated time series using the weight Weight_{AUC=0}, Weight_{ACC=1} +- Combined_data_median_onlyAUC: The Median value for each method across the 50 simulated time series using the weight Weight_{AUC=1}, Weight_{ACC=0} +- Combined_data_median_onlyACC: The Row Sums value for each method across the 50 simulated time series using the weight Weight_{AUC=0}, Weight_{ACC=1} +- Combined_data_sums_equal: The Row Sums for each method across the 50 simulated time series using the weight Weight_{AUC=0.5}, Weight_{ACC=0.5} +- Combined_data_median_equal: The Median value for each method across the 50 simulated time series using the weight Weight_{AUC=0.5}, Weight_{ACC=0.5} +- Combined_data_sums_73: The Row Sums for each method across the 50 simulated time series using the weight Weight_{AUC=0.7}, Weight_{ACC=0.3} +- Combined_data_median_73: The Median value for each method across the 50 simulated time series using the weight Weight_{AUC=0.7}, Weight_{ACC=0.3} +- Combined_data_sums_37: The Row Sums for each method across the 50 simulated time series using the weight Weight_{AUC=0.3}, Weight_{ACC=0.7} +- Combined_data_median_37: The Median value for each method across the 50 simulated time series using the weight Weight_{AUC=0.3}, Weight_{ACC=0.7} + +The `averages` subfolder contains the values of the averages for each method across all simulated networks for both the row sums and median values under the various weights of AUROC and ACC + +The heatmap and lineplots that display the row sums and median values can be found in the main folder. diff --git a/1_individual_methods_analysis/analysis.Rmd b/1_individual_methods_analysis/analysis.Rmd new file mode 100644 index 0000000..44eadb4 --- /dev/null +++ b/1_individual_methods_analysis/analysis.Rmd @@ -0,0 +1,669 @@ +--- +title: "Individual Methods Analysis" +author: "Lauren Marazzi" +date: "10/25/2022" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set( + echo = TRUE, + fig.show = "hold", + message = FALSE, + warning = FALSE +) +library(dplyr) +library(caret) +library(ggplot2) +library(plotly) +library(RColorBrewer) +library(heatmaply) +library(stringr) +``` + +# Step1: Compute AUROC and ACC Row Sums and Median values under different weights + +We identify the top methods for network structure prediction by computing the area under the receiver operating characteristic (AUROC) and accuracy (ACC) row sum and median values for each of the 28 networks. We weight these variables at 5 different ranges: + +* {Weight~AUC=1~,Weight~AUC=0~} +* {Weight~AUC=0~,Weight~AUC=1~} +* {Weight~AUC=0.5~,Weight~AUC=0.5~} +* {Weight~AUC=0.3~,Weight~AUC=0.7~} +* {Weight~AUC=0.7~,Weight~AUC=0.3~} + +```{r load data} +for (sim in (1:28)){ + AUC<-read.csv(paste0("sim",sim,"/Combined_Data_onlyAUC.csv"),row.names = 1) + ACC<-read.csv(paste0("sim",sim,"/Combined_Data_onlyACC.csv"),row.names = 1) + + #calculate AUC only sums and medians + AUC_sum<-cbind(AUC$Method_name,rowSums(AUC[,-1])) %>% as.data.frame() + colnames(AUC_sum)<-c("Method_name","Sum_of_avg_across_patients") + AUC_sum$Sum_of_avg_across_patients<-as.numeric(AUC_sum$Sum_of_avg_across_patients) + + AUC_median<-cbind(AUC$Method_name,apply(AUC[-1], 1, median)) %>% as.data.frame() + colnames(AUC_median)<-c("Method_name","Median_across_patients") + + write.csv(AUC_sum,paste0('sim',sim,'/Combined_data_sums_onlyAUC.csv'),quote=F) + write.csv(AUC_median,paste0('sim',sim,'/Combined_data_median_onlyAUC.csv'),quote=F) + + #calculate ACC only sums and medians + ACC_sum<-cbind(ACC$Method_name,rowSums(ACC[,-1])) %>% as.data.frame() + colnames(ACC_sum)<-c("Method_name","Sum_of_avg_across_patients") + ACC_sum$Sum_of_avg_across_patients<-as.numeric(ACC_sum$Sum_of_avg_across_patients) + + + ACC_median<-cbind(ACC$Method_name,apply(ACC[-1], 1, median)) %>% as.data.frame() + colnames(ACC_median)<-c("Method_name","Median_across_patients") + + write.csv(ACC_sum,paste0('sim',sim,'/Combined_data_sums_onlyACC.csv'),quote=F) + write.csv(ACC_median,paste0('sim',sim,'/Combined_data_median_onlyACC.csv'),quote=F) + + # calculate equal weights sums and medians + AUC_half<-cbind(sapply(AUC[,-1],"*",0.5)) + ACC_half<-cbind(sapply(ACC[,-1],"*",0.5)) + equal<-cbind( AUC_half + ACC_half) + + + equal_sum<-cbind(AUC$Method_name,rowSums(equal)) %>% as.data.frame() + colnames(equal_sum)<-c("Method_name","Sum_of_avg_across_patients") + equal_sum$Sum_of_avg_across_patients<-as.numeric(equal_sum$Sum_of_avg_across_patients) + + equal_median<-cbind(AUC$Method_name,apply(equal, 1, median)) %>% as.data.frame() + colnames(equal_median)<-c("Method_name","Median_across_patients") + + + write.csv(equal,paste0('sim',sim,'/Combined_data_equal.csv'),quote=F) + write.csv(equal_sum,paste0('sim',sim,'/Combined_data_sums_equal.csv'),quote=F) + write.csv(equal_median,paste0('sim',sim,'/Combined_data_median_equal.csv'),quote=F) + + + #calculate 0.3 AUROC and 0.7 ACC weights sums and medians + AUC_low<-cbind(sapply(AUC[,-1],"*",0.3)) + ACC_high<-cbind(sapply(ACC[,-1],"*",0.7)) + three_seven<-cbind( AUC_low + ACC_high) + + + three_seven_sum<-cbind(AUC$Method_name,rowSums(three_seven)) %>% as.data.frame() + colnames(three_seven_sum)<-c("Method_name","Sum_of_avg_across_patients") + three_seven_sum$Sum_of_avg_across_patients<-as.numeric(three_seven_sum$Sum_of_avg_across_patients) + + three_seven_median<-cbind(AUC$Method_name,apply(three_seven, 1, median)) %>% as.data.frame() + colnames(three_seven_median)<-c("Method_name","Median_across_patients") + + write.csv(three_seven,paste0('sim',sim,'/Combined_data_37.csv'),quote=F) + write.csv(three_seven_sum,paste0('sim',sim,'/Combined_data_sums_37.csv'),quote=F) + write.csv(three_seven_median,paste0('sim',sim,'/Combined_data_median_37.csv'),quote=F) + + #calculate 0.7 AUROC and 0.3 ACC weights sums and medianssums and medians} + AUC_high<-cbind(sapply(AUC[,-1],"*",0.7)) + ACC_low<-cbind(sapply(ACC[,-1],"*",0.3)) + seven_three<-cbind( AUC_low + ACC_high) + + seven_three_sum<-cbind(AUC$Method_name,rowSums(seven_three)) %>% as.data.frame() + colnames(seven_three_sum)<-c("Method_name","Sum_of_avg_across_patients") + seven_three_sum$Sum_of_avg_across_patients<-as.numeric(seven_three_sum$Sum_of_avg_across_patients) + + + seven_three_median<-cbind(AUC$Method_name,apply(seven_three, 1, median)) %>% as.data.frame() + colnames(seven_three_median)<-c("Method_name","Median_across_patients") + + write.csv(seven_three,paste0('sim',sim,'/Combined_data_73.csv'),quote=F) + write.csv(seven_three_sum,paste0('sim',sim,'/Combined_data_sums_73.csv'),quote=F) + write.csv(seven_three_median,paste0('sim',sim,'/Combined_data_median_73.csv'),quote=F) +} +``` + +# Step2: Compute Average Row Sums and Median values under different weights + +Next, we compute the average Row Sums and Median values under the 5 AUROC and ACC weights. + +```{r average of sums} +patterns<-c("Combined_data_sums_onlyAUC","Combined_data_sums_onlyACC","Combined_data_sums_equal","Combined_data_sums_37","Combined_data_sums_73") + +dir.create("averages") + +for (pattern in patterns){ + count_files <- list.files(recursive=TRUE,pattern=paste0("^",pattern,".csv"),full.names=TRUE) + d1<-data.frame() + for (i in count_files) { + # read file i as a data frame + f <- read.csv(i,row.names = 1) + if(length(d1) == 0){ + d1 <- f + }else + { + d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients+f$Sum_of_avg_across_patients + } + } + d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients/28 + d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients/50 + d1<- d1 %>% arrange(desc(Sum_of_avg_across_patients)) + write.csv(d1,paste0("averages/Average_",pattern,".csv"),quote=F) +} + + +``` + +```{r average of medians, message=FALSE, warning=FALSE} +patterns<-c("Combined_data_median_onlyAUC","Combined_data_median_onlyACC","Combined_data_median_equal","Combined_data_median_37","Combined_data_median_73") + +for (pattern in patterns){ + count_files <- list.files(recursive=TRUE,pattern=paste0("^",pattern,".csv"),full.names=TRUE) + count_files<-count_files[!grepl("all", count_files)] + d1<-data.frame() + for (i in count_files) { + # read file i as a data frame + f <- read.csv(i,row.names = 1) + if(length(d1) == 0){ + d1 <- f + }else + { + d1$Median_across_patients<-d1$Median_across_patients+f$Median_across_patients + } + } + d1$Median_across_patients<-d1$Median_across_patients/28 + d1<- d1 %>% arrange(desc(Median_across_patients)) + write.csv(d1,paste0("averages/Average_Median_",pattern,".csv"),quote=F) +} +rm(list = ls()) +``` + +# Step3: Generate heatmaps of Average Row Sums and Median values under different weights + +To identify the top performing network methods across all 28 networks and under different weights of the AUROC and ACC, we generate a heatmap with hierarchical clustering + +```{r heatmaps rowsums, message=FALSE, warning=FALSE} +pal<-rev(c("#FF0000","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) + +d1<-data.frame() +for (i in count_files){ + f<-read.csv(i,row.names = 1) + + if(length(d1) == 0){ + d1<-f + } + else{ + d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") + } + + } + + +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) + + +rowsums_top10<-rownames(d1 %>% arrange(desc(`AUC=0.5,ACC=0.5`)))[1:10] +rowsums_top4<-rownames(d1 %>% arrange(desc(`AUC=0.5,ACC=0.5`)))[1:4] + +heatmaply(d1,colors = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) + +``` + + +



















+```{r heatmaps median, message=FALSE, warning=FALSE} + +patterns<-c("Combined_data_median_onlyAUC","Combined_data_median_onlyACC","Combined_data_median_equal","Combined_data_median_37","Combined_data_median_73") + +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Median"),full.names=TRUE) + +d1<-data.frame() +for (i in count_files){ + f<-read.csv(i,row.names = 1) + + if(length(d1) == 0){ + d1<-f + } + else{ + d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") + } + + } + + +rownames(d1)<-d1$Method_name +colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") +d1<- d1 %>% select(-Method_name) + +medians_top10<-rownames(d1 %>% arrange(desc(`AUC=0.5,ACC=0.5`)))[1:10] +medians_top4<-rownames(d1 %>% arrange(desc(`AUC=0.5,ACC=0.5`)))[1:4] + +heatmaply(d1,colors = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Median Value across 28 Networks",file = "heatmap_medians.pdf",width=800,height=1000,margins = c(40, 50)) %>% + layout(height = 900) + +``` +



















+ +These heatmaps show that the top methods across all networks with all weights of AUROC and ACC are BCohF, BCorrU, PCohF, and Tigress + +# Step4: Generate line plots of Average Row Sums and Median AUROC and ACC values under different weights + +We can plot how the individual top methods perform in all 28 networks individually using line plots under different weights of AUROC and ACC + + +```{r rowsums top4 lineplot function} +create_rowsum_lineplot<-function(pattern){ + count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE) + d1<-data.frame() + for (i in count_files) { + f <- read.csv(i,row.names = 1) %>% filter(Method_name %in% rowsums_top4) + f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f)))) + colnames(f)[3]<-c('time_series') + + #if the counts object is empty just copy the f to m + if(length(d1) == 0){ + d1 <- f + + } else + { + #if the dataframe is not empty then merge the data + d1 <- rbind(d1,f) + } + } + + d1$time_series<-gsub("sim","",d1$time_series) + d1$time_series<-str_pad(d1$time_series, 2, pad = "0") + colnames(d1)<-c("Method","Row Sum","Network") + d1$`Row Sum`<-d1$`Row Sum`/50 + + return(d1) +} + + +``` + +```{r line plots top 4 rowsums, include=FALSE} +pal<-c("#8dd3c7","#bebada","#80b1d3","#fccde5") + +#onlyAUC +pattern<-"Combined_data_sums_onlyAUC.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=1, WeightACC=0 for Top 4 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_Top4_AUConly.pdf") +``` + +```{r plot 1, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r line plot 2, include=FALSE} +#onlyACC +pattern<-"Combined_data_sums_onlyACC.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0, WeightACC=1 for Top 4 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_Top4_ACConly.pdf") +``` + +```{r plot 2, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r line plot 3, include=FALSE} + +#equal weights +pattern<-"Combined_data_sums_equal.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.5, WeightACC=0.5 for Top 4 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_Top4_equal.pdf") + +``` + +```{r plot 3, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r line plot 4, include=FALSE} +#AUC=0.3, ACC=0.7 weights +pattern<-"Combined_data_sums_37.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.3, WeightACC=0.7 for Top 4 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_Top4_37.pdf") +``` + +```{r plot 4,echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r line plot 5,include=FALSE} +#AUC=0.7, ACC=0.3 weights +pattern<-"Combined_data_sums_73.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.7, WeightACC=0.3 for Top 4 Methods across Networks")+scale_color_manual(values=pal) +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_Top4_73.pdf") +``` + +```{r plot 5, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + + +```{r median top4 lineplot function} +create_median_lineplot<-function(pattern){ + count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE) + d1<-data.frame() + for (i in count_files) { + f <- read.csv(i,row.names = 1) %>% filter(Method_name %in% rowsums_top4) + f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f)))) + colnames(f)[3]<-c('time_series') + + #if the counts object is empty just copy the f to m + if(length(d1) == 0){ + d1 <- f + + } else + { + #if the dataframe is not empty then merge the data + d1 <- rbind(d1,f) + } + } + + d1$time_series<-gsub("sim","",d1$time_series) + d1$time_series<-str_pad(d1$time_series, 2, pad = "0") + colnames(d1)<-c("Method","Median","Network") + + return(d1) +} + + +``` + +```{r line plots top 4 median, include=FALSE} + +#onlyAUC +pattern<-"Combined_data_median_onlyAUC.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=1, WeightACC=0 for Top 4 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_Top4_AUConly.pdf") +``` + +```{r plot 6, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 7, include=FALSE} +#onlyACC +pattern<-"Combined_data_median_onlyACC.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0, WeightACC=1 for Top 4 Methods across Networks")+scale_color_manual(values=pal) +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_Top4_ACConly.pdf") +``` + +```{r plot 7, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 8, include=FALSE} +#equal weights +pattern<-"Combined_data_median_equal.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.5, WeightACC=0.5 for Top 4 Methods across Networks")+scale_color_manual(values=pal) +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_Top4_equal.pdf") +``` + +```{r plot 8, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + + +```{r lineplot 9, include=FALSE} +#AUC=0.3, ACC=0.7 weights +pattern<-"Combined_data_sums_37.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.3, WeightACC=0.7 for Top 4 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_Top4_37.pdf") +``` + +```{r plot 9, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + + +```{r lineplot 10, include=FALSE} +#AUC=0.7, ACC=0.3 weights +pattern<-"Combined_data_sums_73.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.7, WeightACC=0.3 for Top 4 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_Top4_73.pdf") +``` + +```{r plot 10, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + + +## We can plot how the top 4 methods perform against the other top 6 methods in all 28 networks individually using line plots under different weights of AUROC and ACC. + +```{r rowsums top10 lineplot function, include=FALSE} +create_rowsum_lineplot<-function(pattern){ + count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE) + d1<-data.frame() + for (i in count_files) { + f <- read.csv(i,row.names = 1) %>% filter(Method_name %in% rowsums_top10) + f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f)))) + colnames(f)[3]<-c('time_series') + + #if the counts object is empty just copy the f to m + if(length(d1) == 0){ + d1 <- f + + } else + { + #if the dataframe is not empty then merge the data + d1 <- rbind(d1,f) + } + } + + d1$time_series<-gsub("sim","",d1$time_series) + d1$time_series<-str_pad(d1$time_series, 2, pad = "0") + colnames(d1)<-c("Method","Row Sum","Network") + d1$`Row Sum`<-d1$`Row Sum`/50 + + return(d1) +} + + +``` + +```{r line plots top 10 rowsums, include=FALSE} +pal<-c("#8dd3c7","#fb8072","#bebada","#fdb462","#ffed6f","#b3de69","#d9d9d9","#80b1d3","#ffffb3","#fccde5") +#onlyAUC +pattern<-"Combined_data_sums_onlyAUC.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=1, WeightACC=0 for Top 10 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_Top10_AUConly.pdf") +``` + +```{r plot 11, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 12, include=FALSE} +#onlyACC +pattern<-"Combined_data_sums_onlyACC.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0, WeightACC=1 for Top 10 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_Top10_ACConly.pdf") +``` + +```{r plot 12, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 13, include=FALSE} +#equal weights +pattern<-"Combined_data_sums_equal.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.5, WeightACC=0.5 for Top 10 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_Top10_equal.pdf") + +``` + +```{r plot 13, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 14, include=FALSE} +#AUC=0.3, ACC=0.7 weights +pattern<-"Combined_data_sums_37.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.3, WeightACC=0.7 for Top 10 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_Top10_37.pdf") + +``` + +```{r plot 14, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + + +```{r lineplot 15, include=FALSE} +#AUC=0.7, ACC=0.3 weights +pattern<-"Combined_data_sums_73.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.7, WeightACC=0.3 for Top 10 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_Top10_73.pdf") +``` + + +```{r plot 15, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r median top10 lineplot function, include=FALSE} +create_median_lineplot<-function(pattern){ + count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE) + d1<-data.frame() + for (i in count_files) { + f <- read.csv(i,row.names = 1) %>% filter(Method_name %in% rowsums_top10) + f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f)))) + colnames(f)[3]<-c('time_series') + + #if the counts object is empty just copy the f to m + if(length(d1) == 0){ + d1 <- f + + } else + { + #if the dataframe is not empty then merge the data + d1 <- rbind(d1,f) + } + } + + d1$time_series<-gsub("sim","",d1$time_series) + d1$time_series<-str_pad(d1$time_series, 2, pad = "0") + colnames(d1)<-c("Method","Median","Network") + + return(d1) +} + + +``` + +```{r line plots top 10 median, include=FALSE} +#onlyAUC +pattern<-"Combined_data_median_onlyAUC.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=1, WeightACC=0 for Top 10 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_Top10_AUConly.pdf") +``` + +```{r plot 16, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 17, include=FALSE} + +#onlyACC +pattern<-"Combined_data_median_onlyACC.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0, WeightACC=1 for Top 10 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_Top10_ACConly.pdf") +``` + + +```{r plot 17, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 18, include=FALSE} + +#equal weights +pattern<-"Combined_data_median_equal.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.5, WeightACC=0.5 for Top 10 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_Top10_equal.pdf") +``` + + +```{r plot 18, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 19, include=FALSE} + +#AUC=0.3, ACC=0.7 weights +pattern<-"Combined_data_median_37.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.3, WeightACC=0.7 for Top 10 Methods across Networks")+scale_color_manual(values=pal) +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_Top10_37.pdf") + + +``` + + +```{r plot 19, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 20, include=FALSE} +#AUC=0.7, ACC=0.3 weights +pattern<-"Combined_data_median_73.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.7, WeightACC=0.3 for Top 10 Methods across Networks")+scale_color_manual(values=pal) +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_Top10_73.pdf") +``` + + +```{r plot 20,echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` \ No newline at end of file diff --git a/1_individual_methods_analysis/analysis.html b/1_individual_methods_analysis/analysis.html new file mode 100644 index 0000000..a81710c --- /dev/null +++ b/1_individual_methods_analysis/analysis.html @@ -0,0 +1,2641 @@ + + + + + + + + + + + + + + + +Individual Methods Analysis + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ + + + + + + +
+

Step1: Compute AUROC and ACC Row Sums and Median values under different weights

+

We identify the top methods for network structure prediction by computing the area under the receiver operating characteristic (AUROC) and accuracy (ACC) row sum and median values for each of the 28 networks. We weight these variables at 5 different ranges:

+
    +
  • {WeightAUC=1,WeightAUC=0}
  • +
  • {WeightAUC=0,WeightAUC=1}
  • +
  • {WeightAUC=0.5,WeightAUC=0.5}
  • +
  • {WeightAUC=0.3,WeightAUC=0.7}
  • +
  • {WeightAUC=0.7,WeightAUC=0.3}
  • +
+
for (sim in (1:28)){
+  AUC<-read.csv(paste0("sim",sim,"/Combined_Data_onlyAUC.csv"),row.names = 1)
+  ACC<-read.csv(paste0("sim",sim,"/Combined_Data_onlyACC.csv"),row.names = 1)
+
+  #calculate AUC only sums and medians
+  AUC_sum<-cbind(AUC$Method_name,rowSums(AUC[,-1])) %>% as.data.frame()
+  colnames(AUC_sum)<-c("Method_name","Sum_of_avg_across_patients")
+  AUC_sum$Sum_of_avg_across_patients<-as.numeric(AUC_sum$Sum_of_avg_across_patients)
+
+  AUC_median<-cbind(AUC$Method_name,apply(AUC[-1], 1, median)) %>% as.data.frame()
+  colnames(AUC_median)<-c("Method_name","Median_across_patients")
+
+  write.csv(AUC_sum,paste0('sim',sim,'/Combined_data_sums_onlyAUC.csv'),quote=F)
+  write.csv(AUC_median,paste0('sim',sim,'/Combined_data_median_onlyAUC.csv'),quote=F)
+
+  #calculate ACC only sums and medians
+  ACC_sum<-cbind(ACC$Method_name,rowSums(ACC[,-1])) %>% as.data.frame()
+  colnames(ACC_sum)<-c("Method_name","Sum_of_avg_across_patients")
+  ACC_sum$Sum_of_avg_across_patients<-as.numeric(ACC_sum$Sum_of_avg_across_patients)
+
+
+  ACC_median<-cbind(ACC$Method_name,apply(ACC[-1], 1, median)) %>% as.data.frame()
+  colnames(ACC_median)<-c("Method_name","Median_across_patients")
+
+  write.csv(ACC_sum,paste0('sim',sim,'/Combined_data_sums_onlyACC.csv'),quote=F)
+  write.csv(ACC_median,paste0('sim',sim,'/Combined_data_median_onlyACC.csv'),quote=F)
+
+  #  calculate equal weights sums and medians
+  AUC_half<-cbind(sapply(AUC[,-1],"*",0.5))
+  ACC_half<-cbind(sapply(ACC[,-1],"*",0.5))
+  equal<-cbind( AUC_half + ACC_half)
+
+
+  equal_sum<-cbind(AUC$Method_name,rowSums(equal)) %>% as.data.frame()
+  colnames(equal_sum)<-c("Method_name","Sum_of_avg_across_patients")
+  equal_sum$Sum_of_avg_across_patients<-as.numeric(equal_sum$Sum_of_avg_across_patients)
+
+  equal_median<-cbind(AUC$Method_name,apply(equal, 1, median)) %>% as.data.frame()
+  colnames(equal_median)<-c("Method_name","Median_across_patients")
+
+
+  write.csv(equal,paste0('sim',sim,'/Combined_data_equal.csv'),quote=F)
+  write.csv(equal_sum,paste0('sim',sim,'/Combined_data_sums_equal.csv'),quote=F)
+  write.csv(equal_median,paste0('sim',sim,'/Combined_data_median_equal.csv'),quote=F)
+
+
+  #calculate 0.3 AUROC and 0.7 ACC weights sums and medians
+  AUC_low<-cbind(sapply(AUC[,-1],"*",0.3))
+  ACC_high<-cbind(sapply(ACC[,-1],"*",0.7))
+  three_seven<-cbind( AUC_low + ACC_high)
+
+
+  three_seven_sum<-cbind(AUC$Method_name,rowSums(three_seven)) %>% as.data.frame()
+  colnames(three_seven_sum)<-c("Method_name","Sum_of_avg_across_patients")
+  three_seven_sum$Sum_of_avg_across_patients<-as.numeric(three_seven_sum$Sum_of_avg_across_patients)
+
+  three_seven_median<-cbind(AUC$Method_name,apply(three_seven, 1, median)) %>% as.data.frame()
+  colnames(three_seven_median)<-c("Method_name","Median_across_patients")
+
+  write.csv(three_seven,paste0('sim',sim,'/Combined_data_37.csv'),quote=F)
+  write.csv(three_seven_sum,paste0('sim',sim,'/Combined_data_sums_37.csv'),quote=F)
+  write.csv(three_seven_median,paste0('sim',sim,'/Combined_data_median_37.csv'),quote=F)
+
+  #calculate 0.7 AUROC and 0.3 ACC weights sums and medianssums and medians}
+  AUC_high<-cbind(sapply(AUC[,-1],"*",0.7))
+  ACC_low<-cbind(sapply(ACC[,-1],"*",0.3))
+  seven_three<-cbind( AUC_low + ACC_high)
+
+  seven_three_sum<-cbind(AUC$Method_name,rowSums(seven_three)) %>% as.data.frame()
+  colnames(seven_three_sum)<-c("Method_name","Sum_of_avg_across_patients")
+  seven_three_sum$Sum_of_avg_across_patients<-as.numeric(seven_three_sum$Sum_of_avg_across_patients)
+
+
+  seven_three_median<-cbind(AUC$Method_name,apply(seven_three, 1, median)) %>% as.data.frame()
+  colnames(seven_three_median)<-c("Method_name","Median_across_patients")
+
+  write.csv(seven_three,paste0('sim',sim,'/Combined_data_73.csv'),quote=F)
+  write.csv(seven_three_sum,paste0('sim',sim,'/Combined_data_sums_73.csv'),quote=F)
+  write.csv(seven_three_median,paste0('sim',sim,'/Combined_data_median_73.csv'),quote=F)
+}
+
+
+

Step2: Compute Average Row Sums and Median values under different weights

+

Next, we compute the average Row Sums and Median values under the 5 AUROC and ACC weights.

+
patterns<-c("Combined_data_sums_onlyAUC","Combined_data_sums_onlyACC","Combined_data_sums_equal","Combined_data_sums_37","Combined_data_sums_73")
+
+dir.create("averages")
+
+for (pattern in patterns){
+  count_files <- list.files(recursive=TRUE,pattern=paste0("^",pattern,".csv"),full.names=TRUE)
+  d1<-data.frame()
+  for (i in count_files) {
+    # read file i as a data frame
+    f <- read.csv(i,row.names = 1)
+    if(length(d1) == 0){
+      d1 <- f
+    }else
+    {
+     d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients+f$Sum_of_avg_across_patients
+    }
+  }
+    d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients/28
+    d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients/50
+    d1<- d1 %>% arrange(desc(Sum_of_avg_across_patients))
+    write.csv(d1,paste0("averages/Average_",pattern,".csv"),quote=F)
+}
+
patterns<-c("Combined_data_median_onlyAUC","Combined_data_median_onlyACC","Combined_data_median_equal","Combined_data_median_37","Combined_data_median_73")
+
+for (pattern in patterns){
+  count_files <- list.files(recursive=TRUE,pattern=paste0("^",pattern,".csv"),full.names=TRUE)
+  count_files<-count_files[!grepl("all", count_files)]
+  d1<-data.frame()
+  for (i in count_files) {
+    # read file i as a data frame
+    f <- read.csv(i,row.names = 1)
+    if(length(d1) == 0){
+      d1 <- f
+    }else
+    {
+     d1$Median_across_patients<-d1$Median_across_patients+f$Median_across_patients
+    }
+  }
+    d1$Median_across_patients<-d1$Median_across_patients/28
+    d1<- d1 %>% arrange(desc(Median_across_patients))
+    write.csv(d1,paste0("averages/Average_Median_",pattern,".csv"),quote=F)
+}
+rm(list = ls())
+
+
+

Step3: Generate heatmaps of Average Row Sums and Median values under different weights

+

To identify the top performing network methods across all 28 networks and under different weights of the AUROC and ACC, we generate a heatmap with hierarchical clustering

+
pal<-rev(c("#FF0000","#0b5394","#38761d","#ffe700","#FFFFFF"))
+count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE)
+
+d1<-data.frame()
+for (i in count_files){
+  f<-read.csv(i,row.names = 1)
+
+  if(length(d1) == 0){
+    d1<-f
+  }
+  else{
+    d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name")
+  }
+
+  }
+
+
+rownames(d1)<-d1$Method_name
+colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0")
+d1<- d1 %>% select(-Method_name)
+
+
+rowsums_top10<-rownames(d1 %>% arrange(desc(`AUC=0.5,ACC=0.5`)))[1:10]
+rowsums_top4<-rownames(d1 %>% arrange(desc(`AUC=0.5,ACC=0.5`)))[1:4]
+
+heatmaply(d1,colors = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("Weight<sub>AUC=0.3</sub>,Weight<sub>ACC=0.7</sub>","Weight<sub>AUC=0.7</sub>,Weight<sub>ACC=0.3</sub>","Weight<sub>AUC=0.5</sub>,Weight<sub>ACC=0.5</sub>","Weight<sub>AUC=0</sub>,Weight<sub>ACC=1</sub>","Weight<sub>AUC=1</sub>,Weight<sub>ACC=0</sub>"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>%
+layout(height = 900)
+
+ +





















+
patterns<-c("Combined_data_median_onlyAUC","Combined_data_median_onlyACC","Combined_data_median_equal","Combined_data_median_37","Combined_data_median_73")
+
+count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Median"),full.names=TRUE)
+
+d1<-data.frame()
+for (i in count_files){
+  f<-read.csv(i,row.names = 1)
+
+  if(length(d1) == 0){
+    d1<-f
+  }
+  else{
+    d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name")
+  }
+
+  }
+
+
+rownames(d1)<-d1$Method_name
+colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0")
+d1<- d1 %>% select(-Method_name)
+
+medians_top10<-rownames(d1 %>% arrange(desc(`AUC=0.5,ACC=0.5`)))[1:10]
+medians_top4<-rownames(d1 %>% arrange(desc(`AUC=0.5,ACC=0.5`)))[1:4]
+
+heatmaply(d1,colors = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("Weight<sub>AUC=0.3</sub>,Weight<sub>ACC=0.7</sub>","Weight<sub>AUC=0.7</sub>,Weight<sub>ACC=0.3</sub>","Weight<sub>AUC=0.5</sub>,Weight<sub>ACC=0.5</sub>","Weight<sub>AUC=0</sub>,Weight<sub>ACC=1</sub>","Weight<sub>AUC=1</sub>,Weight<sub>ACC=0</sub>"),main="Average Median Value across 28 Networks",file = "heatmap_medians.pdf",width=800,height=1000,margins = c(40, 50))  %>%
+  layout(height = 900)
+
+ +





















+

These heatmaps show that the top methods across all networks with all weights of AUROC and ACC are BCohF, BCorrU, PCohF, and Tigress

+
+
+

Step4: Generate line plots of Average Row Sums and Median AUROC and ACC values under different weights

+

We can plot how the individual top methods perform in all 28 networks individually using line plots under different weights of AUROC and ACC

+
create_rowsum_lineplot<-function(pattern){
+  count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE)
+  d1<-data.frame()
+  for (i in count_files) {
+    f <- read.csv(i,row.names = 1) %>% filter(Method_name %in% rowsums_top4)
+    f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f))))
+    colnames(f)[3]<-c('time_series')
+
+    #if the counts object is empty just copy the f to m
+    if(length(d1) == 0){
+      d1 <- f
+
+    } else
+    {
+      #if the dataframe is not empty then merge the data
+      d1 <- rbind(d1,f)
+    }
+  }
+
+  d1$time_series<-gsub("sim","",d1$time_series)
+  d1$time_series<-str_pad(d1$time_series, 2, pad = "0")
+  colnames(d1)<-c("Method","Row Sum","Network")
+  d1$`Row Sum`<-d1$`Row Sum`/50
+
+  return(d1)
+}
+
+ +
+ +
+ +
+ +
+ +
create_median_lineplot<-function(pattern){
+  count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE)
+  d1<-data.frame()
+  for (i in count_files) {
+    f <- read.csv(i,row.names = 1) %>% filter(Method_name %in% rowsums_top4)
+    f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f))))
+    colnames(f)[3]<-c('time_series')
+
+    #if the counts object is empty just copy the f to m
+    if(length(d1) == 0){
+      d1 <- f
+
+    } else
+    {
+      #if the dataframe is not empty then merge the data
+      d1 <- rbind(d1,f)
+    }
+  }
+
+  d1$time_series<-gsub("sim","",d1$time_series)
+  d1$time_series<-str_pad(d1$time_series, 2, pad = "0")
+  colnames(d1)<-c("Method","Median","Network")
+
+  return(d1)
+}
+
+ +
+ +
+ +
+ +
+ +
+

We can plot how the top 4 methods perform against the other top 6 methods in all 28 networks individually using line plots under different weights of AUROC and ACC.

+
+ +
+ +
+ +
+ +
+ +
+ +
+ +
+ +
+ +
+ +
+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/1_individual_methods_analysis/averages/Average_Combined_data_sums_37.csv b/1_individual_methods_analysis/averages/Average_Combined_data_sums_37.csv new file mode 100644 index 0000000..2d21f4e --- /dev/null +++ b/1_individual_methods_analysis/averages/Average_Combined_data_sums_37.csv @@ -0,0 +1,42 @@ +,Method_name,Sum_of_avg_across_patients +1,Tigress,0.929065723175239 +2,PCohF,0.923685105871964 +3,BCohF,0.903033625089022 +4,BCorrU,0.881940181491559 +5,Genie,0.841170736173594 +6,PCorrD,0.840750370345341 +7,BCorrD,0.840158352864321 +8,COH1,0.833850682470601 +9,pCOH2,0.830915915004072 +10,pCOH1,0.80169277322325 +11,COH2,0.800358005977161 +12,dDTF,0.800176756204355 +13,BH2U,0.793011581165244 +14,BTEU,0.785092822102274 +15,PMITD1,0.784514819247255 +16,PMITD2,0.783411220692518 +17,PTEU,0.780826040575117 +18,BMITD2,0.775926615292089 +19,BMITD1,0.775565941548156 +20,BH2D,0.774872673357147 +21,PTED,0.772110387732001 +22,PH2D,0.770582620479413 +23,Smvar,0.770508091847258 +24,BTED,0.768341985216255 +25,DC1,0.767962906657812 +26,GPDC,0.7667361867869 +27,ffDTF,0.763134704717043 +28,DTF,0.762836870432201 +29,AS,0.762590047013468 +30,Af,0.762126011539534 +31,MVAR,0.761867627463684 +32,PCorrU,0.761321540166803 +33,oPDCF,0.761145704423002 +34,PDC,0.761104508903728 +35,hmvar,0.759723527813019 +36,PMITU,0.754428653340289 +37,PH2U,0.745821751602767 +38,GGC,0.741002383010804 +39,BMITU,0.730378194576608 +40,BCohW,0 +41,PCohW,0 diff --git a/1_individual_methods_analysis/averages/Average_Combined_data_sums_73.csv b/1_individual_methods_analysis/averages/Average_Combined_data_sums_73.csv new file mode 100644 index 0000000..2d21f4e --- /dev/null +++ b/1_individual_methods_analysis/averages/Average_Combined_data_sums_73.csv @@ -0,0 +1,42 @@ +,Method_name,Sum_of_avg_across_patients +1,Tigress,0.929065723175239 +2,PCohF,0.923685105871964 +3,BCohF,0.903033625089022 +4,BCorrU,0.881940181491559 +5,Genie,0.841170736173594 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a/1_individual_methods_analysis/averages/Average_Combined_data_sums_equal.csv b/1_individual_methods_analysis/averages/Average_Combined_data_sums_equal.csv new file mode 100644 index 0000000..32171d3 --- /dev/null +++ b/1_individual_methods_analysis/averages/Average_Combined_data_sums_equal.csv @@ -0,0 +1,42 @@ +,Method_name,Sum_of_avg_across_patients +1,Tigress,0.929209115344974 +2,PCohF,0.924939134125231 +3,BCohF,0.905392930703925 +4,BCorrU,0.883100471798101 +5,Genie,0.835171353940117 +6,BCorrD,0.832116556361171 +7,PCorrD,0.831492479676097 +8,COH1,0.828779433747298 +9,pCOH2,0.822831921832183 +10,pCOH1,0.796770495054622 +11,COH2,0.79349409461802 +12,BH2U,0.784685883952655 +13,dDTF,0.770405133356464 +14,Smvar,0.756853761544372 +15,PMITD1,0.748989735782461 +16,PMITD2,0.747295568879065 +17,BTEU,0.747089243186329 +18,PCorrU,0.744589699219804 +19,PTEU,0.741845665508793 +20,PMITU,0.739005194720588 +21,BMITD2,0.736971342947133 +22,BMITD1,0.73675026236862 +23,BH2D,0.735561482050271 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a/1_individual_methods_analysis/averages/Average_Combined_data_sums_onlyAUC.csv b/1_individual_methods_analysis/averages/Average_Combined_data_sums_onlyAUC.csv new file mode 100644 index 0000000..4add1dd --- /dev/null +++ b/1_individual_methods_analysis/averages/Average_Combined_data_sums_onlyAUC.csv @@ -0,0 +1,42 @@ +,Method_name,Sum_of_avg_across_patients +1,Tigress,0.929567595769314 +2,PCohF,0.9280742047584 +3,BCohF,0.911291194741183 +4,BCorrU,0.886001197564456 +5,Genie,0.820172898356425 +6,COH1,0.81610131193904 +7,BCorrD,0.812012065103294 +8,PCorrD,0.808347753002988 +9,pCOH2,0.802621938902462 +10,pCOH1,0.784464799633054 +11,COH2,0.776334316220167 +12,BH2U,0.763871640921183 +13,Smvar,0.722717935787157 +14,PCorrU,0.702760096852307 +15,PMITU,0.700446548171334 +16,dDTF,0.695976076236737 +17,PH2U,0.686379616453667 +18,BMITU,0.677971463403508 +19,PMITD1,0.660177027120479 +20,PMITD2,0.657006439345431 +21,BTEU,0.652080295896468 +22,PTEU,0.644394727842984 +23,BMITD1,0.63971106441978 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a/1_individual_methods_analysis/averages/Average_Median_Combined_data_median_onlyACC.csv b/1_individual_methods_analysis/averages/Average_Median_Combined_data_median_onlyACC.csv new file mode 100644 index 0000000..7f05f0c --- /dev/null +++ b/1_individual_methods_analysis/averages/Average_Median_Combined_data_median_onlyACC.csv @@ -0,0 +1,42 @@ +,Method_name,Median_across_patients +1,Tigress,0.93120634920635 +2,PCohF,0.922360317460318 +3,BCohF,0.899217460317461 +4,BCorrU,0.884768253968254 +5,BCorrD,0.853587301587301 +6,Genie,0.852530158730159 +7,PCorrD,0.851801587301587 +8,BTEU,0.849842857142857 +9,pCOH2,0.844288888888889 +10,COH1,0.842066666666667 +11,dDTF,0.839842857142857 +12,PTEU,0.839485714285715 +13,PMITD2,0.832342857142857 +14,PMITD1,0.830914285714286 +15,BMITD2,0.828057142857143 +16,BMITD1,0.827342857142857 +17,Af,0.823771428571429 +18,AS,0.823771428571429 +19,DTF,0.823771428571429 +20,GPDC,0.823771428571429 +21,hmvar,0.823771428571429 +22,oPDCF,0.823771428571429 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0000000..a866245 --- /dev/null +++ b/2_individual_and_combined_methods_analysis/README.md @@ -0,0 +1,22 @@ +# Individual and combined methods analysis + +This subfolder contains the code and data for analyzing the networks created by the top singleton methods and combinations of those methods. + +The `sim` subfolders contain the following files for each simulated network: + +- Combined_Data_onlyAUC.csv: the area under the reciever operating characteristic (AUROC, abbreviated here as AUC) values for each method for the 50 time series simulations of the network calculated by MULAN +- Combined_Data_onlyACC.csv: the accuracy (ACC) values for each method for the 50 time series simulations of the network calculated by MULAN +- Combined_data_sums_onlyAUC: The Row Sums for each of the singleton or combined methods across the 50 simulated time series using the weight Weight_{AUC=1}, Weight_{ACC=0} +- Combined_data_sums_onlyACC: The Row Sums for each of the singleton or combined methods across the 50 simulated time series using the weight Weight_{AUC=0}, Weight_{ACC=1} +- Combined_data_median_onlyAUC: The Median value for each of the singleton or combined methods across the 50 simulated time series using the weight Weight_{AUC=1}, Weight_{ACC=0} +- Combined_data_median_onlyACC: The Row Sums value for each of the singleton or combined methods across the 50 simulated time series using the weight Weight_{AUC=0}, Weight_{ACC=1} +- Combined_data_sums_equal: The Row Sums for each of the singleton or combined methods across the 50 simulated time series using the weight Weight_{AUC=0.5}, Weight_{ACC=0.5} +- Combined_data_median_equal: The Median value for each of the singleton or combined methods across the 50 simulated time series using the weight Weight_{AUC=0.5}, Weight_{ACC=0.5} +- Combined_data_sums_73: The Row Sums for each of the singleton or combined methods across the 50 simulated time series using the weight Weight_{AUC=0.7}, Weight_{ACC=0.3} +- Combined_data_median_73: The Median value for each of the singleton or combined methods across the 50 simulated time series using the weight Weight_{AUC=0.7}, Weight_{ACC=0.3} +- Combined_data_sums_37: The Row Sums for each of the singleton or combined methods across the 50 simulated time series using the weight Weight_{AUC=0.3}, Weight_{ACC=0.7} +- Combined_data_median_37: The Median value for each of the singleton or combined methods across the 50 simulated time series using the weight Weight_{AUC=0.3}, Weight_{ACC=0.7} + +The `averages` subfolder contains the values of the averages for each single or combination method across all simulated networks for both the row sums and median values under the various weights of AUROC and ACC + +The heatmap and lineplots that display the row sums and median values can be found in the main folder. diff --git a/2_individual_and_combined_methods_analysis/analysis.Rmd b/2_individual_and_combined_methods_analysis/analysis.Rmd new file mode 100644 index 0000000..2023122 --- /dev/null +++ b/2_individual_and_combined_methods_analysis/analysis.Rmd @@ -0,0 +1,639 @@ +--- +title: "Individualand Combined Methods Analysis" +author: "Lauren Marazzi" +date: "10/25/2022" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set( + echo = TRUE, + fig.show = "hold", + message = FALSE, + warning = FALSE +) + +library(dplyr) +library(caret) +library(ggplot2) +library(plotly) +library(RColorBrewer) +library(heatmaply) +library(stringr) +``` + +# Step1: Compute AUROC and ACC Row Sums and Median values under different weights + +We identify the top methods for network structure prediction by computing the area under the receiver operating characteristic (AUROC) and accuracy (ACC) row sum and median values for each of the 28 networks. We weight these variables at 5 different ranges: + +* {Weight~AUC=1~,Weight~AUC=0~} +* {Weight~AUC=0~,Weight~AUC=1~} +* {Weight~AUC=0.5~,Weight~AUC=0.5~} +* {Weight~AUC=0.3~,Weight~AUC=0.7~} +* {Weight~AUC=0.7~,Weight~AUC=0.3~} + +```{r load data} +for (sim in (1:28)){ + AUC<-read.csv(paste0("sim",sim,"/Combined_Data_onlyAUC.csv"),row.names = 1) + ACC<-read.csv(paste0("sim",sim,"/Combined_Data_onlyACC.csv"),row.names = 1) + + #calculate AUC only sums and medians + AUC_sum<-cbind(AUC$Method_name,rowSums(AUC[,-1])) %>% as.data.frame() + colnames(AUC_sum)<-c("Method_name","Sum_of_avg_across_patients") + AUC_sum$Sum_of_avg_across_patients<-as.numeric(AUC_sum$Sum_of_avg_across_patients) + + AUC_median<-cbind(AUC$Method_name,apply(AUC[-1], 1, median)) %>% as.data.frame() + colnames(AUC_median)<-c("Method_name","Median_across_patients") + + write.csv(AUC_sum,paste0('sim',sim,'/Combined_data_sums_onlyAUC.csv'),quote=F) + write.csv(AUC_median,paste0('sim',sim,'/Combined_data_median_onlyAUC.csv'),quote=F) + + #calculate ACC only sums and medians + ACC_sum<-cbind(ACC$Method_name,rowSums(ACC[,-1])) %>% as.data.frame() + colnames(ACC_sum)<-c("Method_name","Sum_of_avg_across_patients") + ACC_sum$Sum_of_avg_across_patients<-as.numeric(ACC_sum$Sum_of_avg_across_patients) + + + ACC_median<-cbind(ACC$Method_name,apply(ACC[-1], 1, median)) %>% as.data.frame() + colnames(ACC_median)<-c("Method_name","Median_across_patients") + + write.csv(ACC_sum,paste0('sim',sim,'/Combined_data_sums_onlyACC.csv'),quote=F) + write.csv(ACC_median,paste0('sim',sim,'/Combined_data_median_onlyACC.csv'),quote=F) + + # calculate equal weights sums and medians + AUC_half<-cbind(sapply(AUC[,-1],"*",0.5)) + ACC_half<-cbind(sapply(ACC[,-1],"*",0.5)) + equal<-cbind( AUC_half + ACC_half) + + + equal_sum<-cbind(AUC$Method_name,rowSums(equal)) %>% as.data.frame() + colnames(equal_sum)<-c("Method_name","Sum_of_avg_across_patients") + equal_sum$Sum_of_avg_across_patients<-as.numeric(equal_sum$Sum_of_avg_across_patients) + + equal_median<-cbind(AUC$Method_name,apply(equal, 1, median)) %>% as.data.frame() + colnames(equal_median)<-c("Method_name","Median_across_patients") + + + write.csv(equal,paste0('sim',sim,'/Combined_data_equal.csv'),quote=F) + write.csv(equal_sum,paste0('sim',sim,'/Combined_data_sums_equal.csv'),quote=F) + write.csv(equal_median,paste0('sim',sim,'/Combined_data_median_equal.csv'),quote=F) + + + #calculate 0.3 AUROC and 0.7 ACC weights sums and medians + AUC_low<-cbind(sapply(AUC[,-1],"*",0.3)) + ACC_high<-cbind(sapply(ACC[,-1],"*",0.7)) + three_seven<-cbind( AUC_low + ACC_high) + + + three_seven_sum<-cbind(AUC$Method_name,rowSums(three_seven)) %>% as.data.frame() + colnames(three_seven_sum)<-c("Method_name","Sum_of_avg_across_patients") + three_seven_sum$Sum_of_avg_across_patients<-as.numeric(three_seven_sum$Sum_of_avg_across_patients) + + three_seven_median<-cbind(AUC$Method_name,apply(three_seven, 1, median)) %>% as.data.frame() + colnames(three_seven_median)<-c("Method_name","Median_across_patients") + + write.csv(three_seven,paste0('sim',sim,'/Combined_data_37.csv'),quote=F) + write.csv(three_seven_sum,paste0('sim',sim,'/Combined_data_sums_37.csv'),quote=F) + write.csv(three_seven_median,paste0('sim',sim,'/Combined_data_median_37.csv'),quote=F) + + #calculate 0.7 AUROC and 0.3 ACC weights sums and medianssums and medians} + AUC_high<-cbind(sapply(AUC[,-1],"*",0.7)) + ACC_low<-cbind(sapply(ACC[,-1],"*",0.3)) + seven_three<-cbind( AUC_low + ACC_high) + + seven_three_sum<-cbind(AUC$Method_name,rowSums(seven_three)) %>% as.data.frame() + colnames(seven_three_sum)<-c("Method_name","Sum_of_avg_across_patients") + seven_three_sum$Sum_of_avg_across_patients<-as.numeric(seven_three_sum$Sum_of_avg_across_patients) + + + seven_three_median<-cbind(AUC$Method_name,apply(seven_three, 1, median)) %>% as.data.frame() + colnames(seven_three_median)<-c("Method_name","Median_across_patients") + + write.csv(seven_three,paste0('sim',sim,'/Combined_data_73.csv'),quote=F) + write.csv(seven_three_sum,paste0('sim',sim,'/Combined_data_sums_73.csv'),quote=F) + write.csv(seven_three_median,paste0('sim',sim,'/Combined_data_median_73.csv'),quote=F) +} +``` + +# Step2: Compute Average Row Sums and Median values under different weights + +Next, we compute the average Row Sums and Median values under the 5 AUROC and ACC weights. +```{r average of sums} +patterns<-c("Combined_data_sums_onlyAUC","Combined_data_sums_onlyACC","Combined_data_sums_equal","Combined_data_sums_37","Combined_data_sums_73") + +dir.create("averages") + +for (pattern in patterns){ + count_files <- list.files(recursive=TRUE,pattern=paste0("^",pattern,".csv"),full.names=TRUE) + d1<-data.frame() + for (i in count_files) { + # read file i as a data frame + f <- read.csv(i,row.names = 1) + if(length(d1) == 0){ + d1 <- f + }else + { + d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients+f$Sum_of_avg_across_patients + } + } + d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients/28 + d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients/50 + d1<- d1 %>% arrange(desc(Sum_of_avg_across_patients)) + write.csv(d1,paste0("averages/Average_",pattern,".csv"),quote=F) +} + + +``` + +```{r average of medians} +patterns<-c("Combined_data_median_onlyAUC","Combined_data_median_onlyACC","Combined_data_median_equal","Combined_data_median_37","Combined_data_median_73") + +for (pattern in patterns){ + count_files <- list.files(recursive=TRUE,pattern=paste0("^",pattern,".csv"),full.names=TRUE) + count_files<-count_files[!grepl("all", count_files)] + d1<-data.frame() + for (i in count_files) { + # read file i as a data frame + f <- read.csv(i,row.names = 1) + if(length(d1) == 0){ + d1 <- f + }else + { + d1$Median_across_patients<-d1$Median_across_patients+f$Median_across_patients + } + } + d1$Median_across_patients<-d1$Median_across_patients/28 + d1<- d1 %>% arrange(desc(Median_across_patients)) + write.csv(d1,paste0("averages/Average_Median_",pattern,".csv"),quote=F) +} +rm(list = ls()) +``` + +# Step3: Generate heatmaps of Average Row Sums and Median values under different weights + +To identify the top performing network methods across all 28 networks and under different weights of the AUROC and ACC, we generate a heatmap with hierarchical clustering + +```{r heatmaps rowsums, message=FALSE, warning=FALSE} +pal<-rev(c("#FF0000","#000000","#031e33","#0b5394","#38761d","#ffe700","#FFFFFF")) +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE) + +d1<-data.frame() +for (i in count_files){ + f<-read.csv(i,row.names = 1) + + if(length(d1) == 0){ + d1<-f + } + else{ + d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") + } + + } + + + rownames(d1)<-d1$Method_name + colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") + d1<- d1 %>% select(-Method_name) + heatmaply(d1,colors = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>% +layout(height = 900) + +``` + + +



















+```{r heatmaps median, message=FALSE, warning=FALSE} + +patterns<-c("Combined_data_median_onlyAUC","Combined_data_median_onlyACC","Combined_data_median_equal","Combined_data_median_37","Combined_data_median_73") + +count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Median"),full.names=TRUE) + +d1<-data.frame() +for (i in count_files){ + f<-read.csv(i,row.names = 1) + + if(length(d1) == 0){ + d1<-f + } + else{ + d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name") + } + + } + + + rownames(d1)<-d1$Method_name + colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0") + d1<- d1 %>% select(-Method_name) + heatmaply(d1,colors = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("WeightAUC=0.3,WeightACC=0.7","WeightAUC=0.7,WeightACC=0.3","WeightAUC=0.5,WeightACC=0.5","WeightAUC=0,WeightACC=1","WeightAUC=1,WeightACC=0"),main="Average Median Value across 28 Networks",file = "heatmap_medians.pdf",width=800,height=1000,margins = c(40, 50)) %>% + layout(height = 900) + + +``` +



















+ + + + +# Step4: Generate line plots of Average Row Sums and Median AUROC and ACC values under different weights + +Using our heatmaps, we see that the top combined methods of network structure prediction are BF_PFmean, T_BCmean, T_BF_BCmean, T_BF_PF_BCmean, T_BF_PFmean, T_BFmean, T_PF_BCmean, T_PFmean. We can see how these compare to the individual methods (Tigress, BCorrU, BCohF, and PCohF) in all 28 networks individually using line plots + +```{r rowsums top12 lineplot function} +create_rowsum_lineplot<-function(pattern,methods){ + count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE) + d1<-data.frame() + for (i in count_files) { + f <- read.csv(i,row.names = 1) %>% filter(Method_name %in% methods) + f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f)))) + colnames(f)[3]<-c('time_series') + + #if the counts object is empty just copy the f to m + if(length(d1) == 0){ + d1 <- f + + } else + { + #if the dataframe is not empty then merge the data + d1 <- rbind(d1,f) + } + } + + d1$time_series<-gsub("sim","",d1$time_series) + d1$time_series<-str_pad(d1$time_series, 2, pad = "0") + colnames(d1)<-c("Method","Row Sum","Network") + d1$`Row Sum`<-d1$`Row Sum`/50 + + return(d1) +} + + +``` + +```{r line plots top 12 rowsums, include=FALSE} +pal<-c("#8dd3c7","#bebada","#fb8072","#80b1d3","#fdb462","#ffed6f","#b3de69","#d9d9d9","#ffffb3","#bc80bd","#ccebc5","#fccde5") +methods<-c("BCohF","BCorrU","BF_PFmean","PCohF","T_BCmean","T_BF_BCmean","T_BF_PF_BCmean","T_BF_PFmean","T_BFmean","T_PF_BCmean","T_PFmean","Tigress") + +#onlyAUC +pattern<-"Combined_data_sums_onlyAUC.csv" +d1<-create_rowsum_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=1, WeightACC=0 for Top 12 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_AUConly.pdf") +``` + +```{r plot 1, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot2, include=FALSE} +#onlyACC +pattern<-"Combined_data_sums_onlyACC.csv" +d1<-create_rowsum_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0, WeightACC=1 for Top 12 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_ACConly.pdf") +``` + +```{r plot 2, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot3, include=FALSE} +#equal weights +pattern<-"Combined_data_sums_equal.csv" +d1<-create_rowsum_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.5, WeightACC=0.5 for Top 12 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_equal.pdf") +``` + +```{r plot 3, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot4, include=FALSE} + +#AUC=0.3, ACC=0.7 weights +pattern<-"Combined_data_sums_37.csv" +d1<-create_rowsum_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.3, WeightACC=0.7 for Top 12 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_37.pdf") +``` + +```{r plot 4, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot5, include=FALSE} +#AUC=0.7, ACC=0.3 weights +pattern<-"Combined_data_sums_73.csv" +d1<-create_rowsum_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.7, WeightACC=0.3 for Top 12 Methods across Networks")+scale_color_manual(values=pal) +p1<-ggplotly(p) +export(p1, file = "lineplot_RowSums_73.pdf") +``` + +```{r plot 5, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + + +```{r median lineplot function} +create_median_lineplot<-function(pattern,methods){ + count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE) + d1<-data.frame() + for (i in count_files) { + f <- read.csv(i,row.names = 1) %>% filter(Method_name %in% methods) + f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f)))) + colnames(f)[3]<-c('time_series') + + #if the counts object is empty just copy the f to m + if(length(d1) == 0){ + d1 <- f + + } else + { + #if the dataframe is not empty then merge the data + d1 <- rbind(d1,f) + } + } + + d1$time_series<-gsub("sim","",d1$time_series) + d1$time_series<-str_pad(d1$time_series, 2, pad = "0") + colnames(d1)<-c("Method","Median","Network") + + return(d1) +} + + +``` + +```{r line plots top 12 median, include=FALSE} + +#onlyAUC +pattern<-"Combined_data_median_onlyAUC.csv" +d1<-create_median_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=1, WeightACC=0 for Top 12 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_AUConly.pdf") +``` + +```{r plot 6, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot7, include=FALSE} +#onlyACC +pattern<-"Combined_data_median_onlyACC.csv" +d1<-create_median_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0, WeightACC=1 for Top 12 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_ACConly.pdf") +``` + +```{r plot 7, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot8, include=FALSE} + +#equal weights +pattern<-"Combined_data_median_equal.csv" +d1<-create_median_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.5, WeightACC=0.5 for Top 12 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_equal.pdf") +``` + +```{r plot 8, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot9, include=FALSE} +#AUC=0.3, ACC=0.7 weights +pattern<-"Combined_data_sums_37.csv" +d1<-create_median_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.3, WeightACC=0.7 for Top 12 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_37.pdf") +``` + +```{r plot 9, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot10, include=FALSE} + +#AUC=0.7, ACC=0.3 weights +pattern<-"Combined_data_sums_73.csv" +d1<-create_median_lineplot(pattern,methods) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.7, WeightACC=0.3 for Top 12 Methods across Networks")+scale_color_manual(values=pal) + +p1<-ggplotly(p) +export(p1, file = "lineplot_Median_73.pdf") +``` + +```{r plot 10, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + + + +```{r all methods rowsums and median lineplot functions, include=FALSE} + +create_rowsum_lineplot<-function(pattern){ + count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE) + d1<-data.frame() + for (i in count_files) { + f <- read.csv(i,row.names = 1) + f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f)))) + colnames(f)[3]<-c('time_series') + + #if the counts object is empty just copy the f to m + if(length(d1) == 0){ + d1 <- f + + } else + { + #if the dataframe is not empty then merge the data + d1 <- rbind(d1,f) + } + } + + d1$time_series<-gsub("sim","",d1$time_series) + d1$time_series<-str_pad(d1$time_series, 2, pad = "0") + colnames(d1)<-c("Method","Row Sum","Network") + d1$`Row Sum`<-d1$`Row Sum`/50 + + return(d1) +} + +create_median_lineplot<-function(pattern){ + count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE) + d1<-data.frame() + for (i in count_files) { + f <- read.csv(i,row.names = 1) + f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f)))) + colnames(f)[3]<-c('time_series') + + #if the counts object is empty just copy the f to m + if(length(d1) == 0){ + d1 <- f + + } else + { + #if the dataframe is not empty then merge the data + d1 <- rbind(d1,f) + } + } + + d1$time_series<-gsub("sim","",d1$time_series) + d1$time_series<-str_pad(d1$time_series, 2, pad = "0") + colnames(d1)<-c("Method","Median","Network") + + return(d1) +} + +``` + + +We can also plot all 26 combined and individual methods on an interactive line plot + + +```{r all methods line plots, include=FALSE} +#onlyAUC +pattern<-"Combined_data_sums_onlyAUC.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=1, WeightACC=0 for Top 12 Methods across Networks") + +p1<-ggplotly(p) +``` + +```{r plot 11, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 12, include=FALSE} +#onlyACC +pattern<-"Combined_data_sums_onlyACC.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0, WeightACC=1 for Top 12 Methods across Networks") + +p1<-ggplotly(p) +``` + +```{r plot 12, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 13, include=FALSE} +#equal weights +pattern<-"Combined_data_sums_equal.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.5, WeightACC=0.5 for Top 12 Methods across Networks") + +p1<-ggplotly(p) +``` + +```{r plot 13, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 14, include=FALSE} + + +#AUC=0.3, ACC=0.7 weights +pattern<-"Combined_data_sums_37.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.3, WeightACC=0.7 for Top 12 Methods across Networks") + +p1<-ggplotly(p) +``` + +```{r plot 14, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 15, include=FALSE} + +#AUC=0.7, ACC=0.3 weights +pattern<-"Combined_data_sums_73.csv" +d1<-create_rowsum_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=`Row Sum`,group=Method,color=Method)) + geom_line() + ggtitle("Row Sums WeightAUC=0.7, WeightACC=0.3 for Top 12 Methods across Networks") +p1<-ggplotly(p) +``` + +```{r plot 15, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 16, include=FALSE} +#onlyAUC +pattern<-"Combined_data_median_onlyAUC.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=1, WeightACC=0 for Top 12 Methods across Networks") +p1<-ggplotly(p) +``` + +```{r plot 16, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 17, include=FALSE} +#onlyACC +pattern<-"Combined_data_median_onlyACC.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0, WeightACC=1 for Top 12 Methods across Networks") +p1<-ggplotly(p) +``` + +```{r plot 17, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 18, include=FALSE} +#equal weights +pattern<-"Combined_data_median_equal.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.5, WeightACC=0.5 for Top 12 Methods across Networks") + +p1<-ggplotly(p) +``` + +```{r plot 18, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 19, include=FALSE} +#AUC=0.3, ACC=0.7 weights +pattern<-"Combined_data_sums_37.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.3, WeightACC=0.7 for Top 12 Methods across Networks") +p1<-ggplotly(p) +``` + +```{r plot 19, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + +```{r lineplot 20, include=FALSE} +#AUC=0.7, ACC=0.3 weights +pattern<-"Combined_data_sums_73.csv" +d1<-create_median_lineplot(pattern) +p<-ggplot(d1, aes(x=Network, y=Median,group=Method,color=Method)) + geom_line() + ggtitle("Medians WeightAUC=0.7, WeightACC=0.3 for Top 12 Methods across Networks") +p1<-ggplotly(p) +``` + +```{r plot 20, echo=FALSE} +ggplotly(p) %>% layout(autosize = F, width = 900, height = 500) +``` + diff --git a/2_individual_and_combined_methods_analysis/analysis.html b/2_individual_and_combined_methods_analysis/analysis.html new file mode 100644 index 0000000..394035b --- /dev/null +++ b/2_individual_and_combined_methods_analysis/analysis.html @@ -0,0 +1,2629 @@ + + + + + + + + + + + + + + + +Individualand Combined Methods Analysis + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ + + + + + + +
+

Step1: Compute AUROC and ACC Row Sums and Median values under different weights

+

We identify the top methods for network structure prediction by computing the area under the receiver operating characteristic (AUROC) and accuracy (ACC) row sum and median values for each of the 28 networks. We weight these variables at 5 different ranges:

+
    +
  • {WeightAUC=1,WeightAUC=0}
  • +
  • {WeightAUC=0,WeightAUC=1}
  • +
  • {WeightAUC=0.5,WeightAUC=0.5}
  • +
  • {WeightAUC=0.3,WeightAUC=0.7}
  • +
  • {WeightAUC=0.7,WeightAUC=0.3}
  • +
+
for (sim in (1:28)){
+  AUC<-read.csv(paste0("sim",sim,"/Combined_Data_onlyAUC.csv"),row.names = 1) 
+  ACC<-read.csv(paste0("sim",sim,"/Combined_Data_onlyACC.csv"),row.names = 1) 
+
+  #calculate AUC only sums and medians
+  AUC_sum<-cbind(AUC$Method_name,rowSums(AUC[,-1])) %>% as.data.frame()
+  colnames(AUC_sum)<-c("Method_name","Sum_of_avg_across_patients")
+  AUC_sum$Sum_of_avg_across_patients<-as.numeric(AUC_sum$Sum_of_avg_across_patients)
+
+  AUC_median<-cbind(AUC$Method_name,apply(AUC[-1], 1, median)) %>% as.data.frame()
+  colnames(AUC_median)<-c("Method_name","Median_across_patients")
+
+  write.csv(AUC_sum,paste0('sim',sim,'/Combined_data_sums_onlyAUC.csv'),quote=F)
+  write.csv(AUC_median,paste0('sim',sim,'/Combined_data_median_onlyAUC.csv'),quote=F)
+
+  #calculate ACC only sums and medians
+  ACC_sum<-cbind(ACC$Method_name,rowSums(ACC[,-1])) %>% as.data.frame()
+  colnames(ACC_sum)<-c("Method_name","Sum_of_avg_across_patients")
+  ACC_sum$Sum_of_avg_across_patients<-as.numeric(ACC_sum$Sum_of_avg_across_patients)
+
+
+  ACC_median<-cbind(ACC$Method_name,apply(ACC[-1], 1, median)) %>% as.data.frame()
+  colnames(ACC_median)<-c("Method_name","Median_across_patients")
+
+  write.csv(ACC_sum,paste0('sim',sim,'/Combined_data_sums_onlyACC.csv'),quote=F)
+  write.csv(ACC_median,paste0('sim',sim,'/Combined_data_median_onlyACC.csv'),quote=F)
+
+  #  calculate equal weights sums and medians
+  AUC_half<-cbind(sapply(AUC[,-1],"*",0.5))
+  ACC_half<-cbind(sapply(ACC[,-1],"*",0.5))
+  equal<-cbind( AUC_half + ACC_half)
+
+
+  equal_sum<-cbind(AUC$Method_name,rowSums(equal)) %>% as.data.frame()
+  colnames(equal_sum)<-c("Method_name","Sum_of_avg_across_patients")
+  equal_sum$Sum_of_avg_across_patients<-as.numeric(equal_sum$Sum_of_avg_across_patients)
+
+  equal_median<-cbind(AUC$Method_name,apply(equal, 1, median)) %>% as.data.frame()
+  colnames(equal_median)<-c("Method_name","Median_across_patients")
+
+
+  write.csv(equal,paste0('sim',sim,'/Combined_data_equal.csv'),quote=F)
+  write.csv(equal_sum,paste0('sim',sim,'/Combined_data_sums_equal.csv'),quote=F)
+  write.csv(equal_median,paste0('sim',sim,'/Combined_data_median_equal.csv'),quote=F)
+
+
+  #calculate 0.3 AUROC and 0.7 ACC weights sums and medians
+  AUC_low<-cbind(sapply(AUC[,-1],"*",0.3))
+  ACC_high<-cbind(sapply(ACC[,-1],"*",0.7))
+  three_seven<-cbind( AUC_low + ACC_high)
+
+
+  three_seven_sum<-cbind(AUC$Method_name,rowSums(three_seven)) %>% as.data.frame()
+  colnames(three_seven_sum)<-c("Method_name","Sum_of_avg_across_patients")
+  three_seven_sum$Sum_of_avg_across_patients<-as.numeric(three_seven_sum$Sum_of_avg_across_patients)
+
+  three_seven_median<-cbind(AUC$Method_name,apply(three_seven, 1, median)) %>% as.data.frame()
+  colnames(three_seven_median)<-c("Method_name","Median_across_patients")
+
+  write.csv(three_seven,paste0('sim',sim,'/Combined_data_37.csv'),quote=F)
+  write.csv(three_seven_sum,paste0('sim',sim,'/Combined_data_sums_37.csv'),quote=F)
+  write.csv(three_seven_median,paste0('sim',sim,'/Combined_data_median_37.csv'),quote=F)
+
+  #calculate 0.7 AUROC and 0.3 ACC weights sums and medianssums and medians}
+  AUC_high<-cbind(sapply(AUC[,-1],"*",0.7))
+  ACC_low<-cbind(sapply(ACC[,-1],"*",0.3))
+  seven_three<-cbind( AUC_low + ACC_high)
+
+  seven_three_sum<-cbind(AUC$Method_name,rowSums(seven_three)) %>% as.data.frame()
+  colnames(seven_three_sum)<-c("Method_name","Sum_of_avg_across_patients")
+  seven_three_sum$Sum_of_avg_across_patients<-as.numeric(seven_three_sum$Sum_of_avg_across_patients)
+
+
+  seven_three_median<-cbind(AUC$Method_name,apply(seven_three, 1, median)) %>% as.data.frame()
+  colnames(seven_three_median)<-c("Method_name","Median_across_patients")
+
+  write.csv(seven_three,paste0('sim',sim,'/Combined_data_73.csv'),quote=F)
+  write.csv(seven_three_sum,paste0('sim',sim,'/Combined_data_sums_73.csv'),quote=F)
+  write.csv(seven_three_median,paste0('sim',sim,'/Combined_data_median_73.csv'),quote=F)
+}
+
+
+

Step2: Compute Average Row Sums and Median values under different weights

+

Next, we compute the average Row Sums and Median values under the 5 AUROC and ACC weights.

+
patterns<-c("Combined_data_sums_onlyAUC","Combined_data_sums_onlyACC","Combined_data_sums_equal","Combined_data_sums_37","Combined_data_sums_73")
+
+dir.create("averages")
+
+for (pattern in patterns){
+  count_files <- list.files(recursive=TRUE,pattern=paste0("^",pattern,".csv"),full.names=TRUE)
+  d1<-data.frame()
+  for (i in count_files) {
+    # read file i as a data frame
+    f <- read.csv(i,row.names = 1)
+    if(length(d1) == 0){
+      d1 <- f
+    }else
+    {
+     d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients+f$Sum_of_avg_across_patients
+    }
+  }
+    d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients/28
+    d1$Sum_of_avg_across_patients<-d1$Sum_of_avg_across_patients/50
+    d1<- d1 %>% arrange(desc(Sum_of_avg_across_patients))
+    write.csv(d1,paste0("averages/Average_",pattern,".csv"),quote=F)
+}
+
patterns<-c("Combined_data_median_onlyAUC","Combined_data_median_onlyACC","Combined_data_median_equal","Combined_data_median_37","Combined_data_median_73")
+
+for (pattern in patterns){
+  count_files <- list.files(recursive=TRUE,pattern=paste0("^",pattern,".csv"),full.names=TRUE)
+  count_files<-count_files[!grepl("all", count_files)]
+  d1<-data.frame()
+  for (i in count_files) {
+    # read file i as a data frame
+    f <- read.csv(i,row.names = 1)
+    if(length(d1) == 0){
+      d1 <- f
+    }else
+    {
+     d1$Median_across_patients<-d1$Median_across_patients+f$Median_across_patients
+    }
+  }
+    d1$Median_across_patients<-d1$Median_across_patients/28
+    d1<- d1 %>% arrange(desc(Median_across_patients))
+    write.csv(d1,paste0("averages/Average_Median_",pattern,".csv"),quote=F)
+}
+rm(list = ls())
+
+
+

Step3: Generate heatmaps of Average Row Sums and Median values under different weights

+

To identify the top performing network methods across all 28 networks and under different weights of the AUROC and ACC, we generate a heatmap with hierarchical clustering

+
pal<-rev(c("#FF0000","#000000","#031e33","#0b5394","#38761d","#ffe700","#FFFFFF"))
+count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Combined"),full.names=TRUE)
+
+d1<-data.frame()
+for (i in count_files){
+  f<-read.csv(i,row.names = 1)
+
+  if(length(d1) == 0){
+    d1<-f
+  }
+  else{
+    d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name")
+  }
+
+  }
+
+
+  rownames(d1)<-d1$Method_name
+  colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0")
+  d1<- d1 %>% select(-Method_name)
+  heatmaply(d1,colors = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("Weight<sub>AUC=0.3</sub>,Weight<sub>ACC=0.7</sub>","Weight<sub>AUC=0.7</sub>,Weight<sub>ACC=0.3</sub>","Weight<sub>AUC=0.5</sub>,Weight<sub>ACC=0.5</sub>","Weight<sub>AUC=0</sub>,Weight<sub>ACC=1</sub>","Weight<sub>AUC=1</sub>,Weight<sub>ACC=0</sub>"),main="Average Row Sums Value across 28 Networks",file = "heatmap_rowsums.pdf",width=800,height=1000,margins = c(40, 50)) %>%
+layout(height = 900)
+
+ +





















+
patterns<-c("Combined_data_median_onlyAUC","Combined_data_median_onlyACC","Combined_data_median_equal","Combined_data_median_37","Combined_data_median_73")
+
+count_files <- list.files(path="averages",recursive=TRUE,pattern=paste0("Average_Median"),full.names=TRUE)
+
+d1<-data.frame()
+for (i in count_files){
+  f<-read.csv(i,row.names = 1)
+
+  if(length(d1) == 0){
+    d1<-f
+  }
+  else{
+    d1<- merge(d1, f, by.x = "Method_name", by.y = "Method_name")
+  }
+
+  }
+
+
+  rownames(d1)<-d1$Method_name
+  colnames(d1)<-c("Method_name","AUC=0.3,ACC=0.7","AUC=0.7,ACC=0.3","AUC=0.5,ACC=0.5","AUC=0,ACC=1","AUC=1,ACC=0")
+  d1<- d1 %>% select(-Method_name)
+  heatmaply(d1,colors = pal,show_dendrogram = c(TRUE, FALSE),labCol=c("Weight<sub>AUC=0.3</sub>,Weight<sub>ACC=0.7</sub>","Weight<sub>AUC=0.7</sub>,Weight<sub>ACC=0.3</sub>","Weight<sub>AUC=0.5</sub>,Weight<sub>ACC=0.5</sub>","Weight<sub>AUC=0</sub>,Weight<sub>ACC=1</sub>","Weight<sub>AUC=1</sub>,Weight<sub>ACC=0</sub>"),main="Average Median Value across 28 Networks",file = "heatmap_medians.pdf",width=800,height=1000,margins = c(40, 50))  %>%
+  layout(height = 900)
+
+ +





















+
+
+

Step4: Generate line plots of Average Row Sums and Median AUROC and ACC values under different weights

+

Using our heatmaps, we see that the top combined methods of network structure prediction are BF_PFmean, T_BCmean, T_BF_BCmean, T_BF_PF_BCmean, T_BF_PFmean, T_BFmean, T_PF_BCmean, T_PFmean. We can see how these compare to the individual methods (Tigress, BCorrU, BCohF, and PCohF) in all 28 networks individually using line plots

+
create_rowsum_lineplot<-function(pattern,methods){
+  count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE)
+  d1<-data.frame()
+  for (i in count_files) {
+    f <- read.csv(i,row.names = 1) %>% filter(Method_name %in% methods)
+    f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f))))
+    colnames(f)[3]<-c('time_series')
+
+    #if the counts object is empty just copy the f to m
+    if(length(d1) == 0){
+      d1 <- f
+
+    } else
+    {
+      #if the dataframe is not empty then merge the data
+      d1 <- rbind(d1,f)
+    }
+  }
+
+  d1$time_series<-gsub("sim","",d1$time_series)
+  d1$time_series<-str_pad(d1$time_series, 2, pad = "0")
+  colnames(d1)<-c("Method","Row Sum","Network")
+  d1$`Row Sum`<-d1$`Row Sum`/50
+
+  return(d1)
+}
+
+ +
+ +
+ +
+ +
+ +
create_median_lineplot<-function(pattern,methods){
+  count_files <- list.files(recursive=T,pattern=paste0("^",pattern),full.names=TRUE)
+  d1<-data.frame()
+  for (i in count_files) {
+    f <- read.csv(i,row.names = 1) %>% filter(Method_name %in% methods)
+    f<- cbind(f,c(rep(unlist(strsplit(i,'/'))[2],nrow(f))))
+    colnames(f)[3]<-c('time_series')
+
+    #if the counts object is empty just copy the f to m
+    if(length(d1) == 0){
+      d1 <- f
+
+    } else
+    {
+      #if the dataframe is not empty then merge the data
+      d1 <- rbind(d1,f)
+    }
+  }
+
+  d1$time_series<-gsub("sim","",d1$time_series)
+  d1$time_series<-str_pad(d1$time_series, 2, pad = "0")
+  colnames(d1)<-c("Method","Median","Network")
+
+  return(d1)
+}
+
+ +
+ +
+ +
+ +
+ +

We can also plot all 26 combined and individual methods on an interactive line plot

+
+ +
+ +
+ +
+ +
+ +
+ +
+ +
+ +
+ +
+ +
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_37.csv b/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_37.csv new file mode 100644 index 0000000..6b67632 --- /dev/null +++ b/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_37.csv @@ -0,0 +1,27 @@ +,Method_name,Sum_of_avg_across_patients +1,T_PFmean,0.934173659689824 +2,T_BFmean,0.933356596469304 +3,T_BCmean,0.933012408163268 +4,T_BF_PFmean,0.932337033549236 +5,T_PF_BCmean,0.931938564942433 +6,T_BF_BCmean,0.931510052371559 +7,T_PFmedian,0.929758963303649 +8,T_BFmedian,0.929649496716938 +9,T_BF_PF_BCmean,0.929238663581096 +10,Tigress,0.929065723175239 +11,T_BCmedian,0.928248734344655 +12,PCohF,0.923685105871964 +13,T_PF_BCmedian,0.916548043080275 +14,T_BF_BCmedian,0.907924382944155 +15,BCohF,0.903033625089022 +16,T_BF_PF_BCmedian,0.893238863647351 +17,BCorrU,0.881940181491559 +18,T_BF_PFmedian,0.84829706952646 +19,BF_PFmean,0.846722251789206 +20,BF_PF_BCmean,0.846655128243819 +21,PF_BCmean,0.846012884524885 +22,BF_BCmean,0.843794994789101 +23,BF_PFmedian,0.820357361602473 +24,PF_BCmedian,0.819751902340175 +25,BF_BCmedian,0.819199102276833 +26,BF_PF_BCmedian,0.818485801077502 diff --git a/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_73.csv b/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_73.csv new file mode 100644 index 0000000..6b67632 --- /dev/null +++ b/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_73.csv @@ -0,0 +1,27 @@ +,Method_name,Sum_of_avg_across_patients +1,T_PFmean,0.934173659689824 +2,T_BFmean,0.933356596469304 +3,T_BCmean,0.933012408163268 +4,T_BF_PFmean,0.932337033549236 +5,T_PF_BCmean,0.931938564942433 +6,T_BF_BCmean,0.931510052371559 +7,T_PFmedian,0.929758963303649 +8,T_BFmedian,0.929649496716938 +9,T_BF_PF_BCmean,0.929238663581096 +10,Tigress,0.929065723175239 +11,T_BCmedian,0.928248734344655 +12,PCohF,0.923685105871964 +13,T_PF_BCmedian,0.916548043080275 +14,T_BF_BCmedian,0.907924382944155 +15,BCohF,0.903033625089022 +16,T_BF_PF_BCmedian,0.893238863647351 +17,BCorrU,0.881940181491559 +18,T_BF_PFmedian,0.84829706952646 +19,BF_PFmean,0.846722251789206 +20,BF_PF_BCmean,0.846655128243819 +21,PF_BCmean,0.846012884524885 +22,BF_BCmean,0.843794994789101 +23,BF_PFmedian,0.820357361602473 +24,PF_BCmedian,0.819751902340175 +25,BF_BCmedian,0.819199102276833 +26,BF_PF_BCmedian,0.818485801077502 diff --git a/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_equal.csv b/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_equal.csv new file mode 100644 index 0000000..13c40f2 --- /dev/null +++ b/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_equal.csv @@ -0,0 +1,27 @@ +,Method_name,Sum_of_avg_across_patients +1,T_PFmean,0.934152797895739 +2,T_BFmean,0.933331449141961 +3,T_BCmean,0.932491219954653 +4,T_BF_PFmean,0.932017362793701 +5,T_PF_BCmean,0.931414751094531 +6,T_BF_BCmean,0.930740076703921 +7,T_PFmedian,0.929569129315606 +8,Tigress,0.929209115344974 +9,T_BFmedian,0.929140589766325 +10,T_BF_PF_BCmean,0.928379656233043 +11,T_BCmedian,0.927031954066488 +12,PCohF,0.924939134125231 +13,T_PF_BCmedian,0.915888092964479 +14,T_BF_BCmedian,0.905808617076238 +15,BCohF,0.905392930703925 +16,T_BF_PF_BCmedian,0.890902529359341 +17,BCorrU,0.883100471798101 +18,BF_PFmean,0.843195477849736 +19,BF_PF_BCmean,0.842498081464567 +20,PF_BCmean,0.841534892197559 +21,BF_BCmean,0.837562028352205 +22,T_BF_PFmedian,0.836440364554682 +23,BF_PFmedian,0.801099771982957 +24,PF_BCmedian,0.799530503900292 +25,BF_BCmedian,0.798098292154509 +26,BF_PF_BCmedian,0.797383382748217 diff --git a/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_onlyACC.csv b/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_onlyACC.csv new file mode 100644 index 0000000..54519fe --- /dev/null +++ b/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_onlyACC.csv @@ -0,0 +1,27 @@ +,Method_name,Sum_of_avg_across_patients +1,T_PFmean,0.934204952380952 +2,T_BCmean,0.93379419047619 +3,T_BFmean,0.933394317460317 +4,T_BF_PFmean,0.93281653968254 +5,T_PF_BCmean,0.932724285714286 +6,T_BF_BCmean,0.932665015873016 +7,T_BF_PF_BCmean,0.930527174603175 +8,T_BFmedian,0.930412857142857 +9,T_BCmedian,0.930073904761905 +10,T_PFmedian,0.930043714285714 +11,Tigress,0.928850634920635 +12,PCohF,0.921804063492063 +13,T_PF_BCmedian,0.917537968253968 +14,T_BF_BCmedian,0.911098031746032 +15,BCohF,0.899494666666667 +16,T_BF_PF_BCmedian,0.896743365079365 +17,BCorrU,0.880199746031746 +18,T_BF_PFmedian,0.866082126984127 +19,BF_BCmean,0.853144444444444 +20,BF_PF_BCmean,0.852890698412698 +21,PF_BCmean,0.852729873015873 +22,BF_PFmean,0.852012412698413 +23,BF_BCmedian,0.850850317460317 +24,BF_PF_BCmedian,0.850139428571429 +25,PF_BCmedian,0.850084 +26,BF_PFmedian,0.849243746031746 diff --git a/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_onlyAUC.csv b/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_onlyAUC.csv new file mode 100644 index 0000000..b1411b0 --- /dev/null +++ b/2_individual_and_combined_methods_analysis/averages/Average_Combined_data_sums_onlyAUC.csv @@ -0,0 +1,27 @@ +,Method_name,Sum_of_avg_across_patients +1,T_PFmean,0.934100643410526 +2,T_BFmean,0.933268580823605 +3,T_BF_PFmean,0.931218185904861 +4,T_BCmean,0.931188249433115 +5,T_PF_BCmean,0.930105216474776 +6,Tigress,0.929567595769314 +7,T_PFmedian,0.929094544345496 +8,T_BF_BCmean,0.928815137534827 +9,PCohF,0.9280742047584 +10,T_BFmedian,0.927868322389793 +11,T_BF_PF_BCmean,0.926232137862911 +12,T_BCmedian,0.923990003371071 +13,T_PF_BCmedian,0.914238217674991 +14,BCohF,0.911291194741183 +15,T_BF_BCmedian,0.900519202406444 +16,BCorrU,0.886001197564456 +17,T_BF_PF_BCmedian,0.885061693639317 +18,BF_PFmean,0.834378543001059 +19,BF_PF_BCmean,0.832105464516436 +20,PF_BCmean,0.830339911379246 +21,BF_BCmean,0.821979612259966 +22,T_BF_PFmedian,0.806798602125237 +23,BF_PFmedian,0.752955797934169 +24,PF_BCmedian,0.748977007800585 +25,BF_BCmedian,0.745346266848702 +26,BF_PF_BCmedian,0.744627336925005 diff --git a/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_37.csv b/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_37.csv new file mode 100644 index 0000000..504b819 --- /dev/null +++ b/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_37.csv @@ -0,0 +1,27 @@ +,Method_name,Median_across_patients +1,T_PFmean,0.935549060357563 +2,T_BFmean,0.934475967879522 +3,T_BCmean,0.934418042998867 +4,T_PF_BCmean,0.932962562120387 +5,T_BF_PFmean,0.932834877888327 +6,T_BF_BCmean,0.932302820294716 +7,Tigress,0.931714070848948 +8,T_PFmedian,0.93143775633552 +9,T_BFmedian,0.931376591225937 +10,T_BF_PF_BCmean,0.93129100363841 +11,T_BCmedian,0.930418357710855 +12,PCohF,0.927035858756686 +13,T_PF_BCmedian,0.918811274753897 +14,T_BF_BCmedian,0.90848968363538 +15,BCohF,0.905726554782211 +16,T_BF_PF_BCmedian,0.893294123477295 +17,BCorrU,0.88621288625005 +18,BF_PFmean,0.849428114754803 +19,BF_PF_BCmean,0.848783729714666 +20,PF_BCmean,0.847988177625683 +21,T_BF_PFmedian,0.847977558840753 +22,BF_BCmean,0.845792418176752 +23,BF_PFmedian,0.823414683647411 +24,BF_BCmedian,0.82228274731585 +25,PF_BCmedian,0.822186676199032 +26,BF_PF_BCmedian,0.821170415358817 diff --git a/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_73.csv b/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_73.csv new file mode 100644 index 0000000..504b819 --- /dev/null +++ b/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_73.csv @@ -0,0 +1,27 @@ +,Method_name,Median_across_patients +1,T_PFmean,0.935549060357563 +2,T_BFmean,0.934475967879522 +3,T_BCmean,0.934418042998867 +4,T_PF_BCmean,0.932962562120387 +5,T_BF_PFmean,0.932834877888327 +6,T_BF_BCmean,0.932302820294716 +7,Tigress,0.931714070848948 +8,T_PFmedian,0.93143775633552 +9,T_BFmedian,0.931376591225937 +10,T_BF_PF_BCmean,0.93129100363841 +11,T_BCmedian,0.930418357710855 +12,PCohF,0.927035858756686 +13,T_PF_BCmedian,0.918811274753897 +14,T_BF_BCmedian,0.90848968363538 +15,BCohF,0.905726554782211 +16,T_BF_PF_BCmedian,0.893294123477295 +17,BCorrU,0.88621288625005 +18,BF_PFmean,0.849428114754803 +19,BF_PF_BCmean,0.848783729714666 +20,PF_BCmean,0.847988177625683 +21,T_BF_PFmedian,0.847977558840753 +22,BF_BCmean,0.845792418176752 +23,BF_PFmedian,0.823414683647411 +24,BF_BCmedian,0.82228274731585 +25,PF_BCmedian,0.822186676199032 +26,BF_PF_BCmedian,0.821170415358817 diff --git a/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_equal.csv b/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_equal.csv new file mode 100644 index 0000000..386d440 --- /dev/null +++ b/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_equal.csv @@ -0,0 +1,27 @@ +,Method_name,Median_across_patients +1,T_PFmean,0.936643730838713 +2,T_BFmean,0.935848760918052 +3,T_BCmean,0.935632282405222 +4,T_PF_BCmean,0.93417452299556 +5,T_BF_PFmean,0.933999920798732 +6,T_BF_BCmean,0.933325076534356 +7,Tigress,0.933025555394293 +8,T_PFmedian,0.932797398142791 +9,T_BFmedian,0.932790396347848 +10,T_BF_PF_BCmean,0.932348636356087 +11,T_BCmedian,0.930955620423156 +12,PCohF,0.930363859491851 +13,T_PF_BCmedian,0.919562655621879 +14,BCohF,0.910009145728096 +15,T_BF_BCmedian,0.907505979305886 +16,T_BF_PF_BCmedian,0.892762204463754 +17,BCorrU,0.887766333410274 +18,BF_PFmean,0.846800773822549 +19,BF_PF_BCmean,0.84533262659496 +20,PF_BCmean,0.844712214428481 +21,BF_BCmean,0.840270547886457 +22,T_BF_PFmedian,0.838117180723611 +23,BF_PFmedian,0.804350769855334 +24,PF_BCmedian,0.801858135442522 +25,BF_BCmedian,0.801732165944961 +26,BF_PF_BCmedian,0.80060192275238 diff --git a/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_onlyACC.csv b/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_onlyACC.csv new file mode 100644 index 0000000..880c83c --- /dev/null +++ b/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_onlyACC.csv @@ -0,0 +1,27 @@ +,Method_name,Median_across_patients +1,T_BCmean,0.935957142857143 +2,T_PFmean,0.935300793650794 +3,T_BFmean,0.933807142857143 +4,T_BF_BCmean,0.932742857142858 +5,T_PF_BCmean,0.93185 +6,T_BCmedian,0.93165 +7,Tigress,0.93120634920635 +8,T_BF_PFmean,0.931121428571429 +9,T_BF_PF_BCmean,0.93095 +10,T_PFmedian,0.930055555555556 +11,T_BFmedian,0.929685714285715 +12,PCohF,0.922360317460318 +13,T_PF_BCmedian,0.916098412698413 +14,T_BF_BCmedian,0.911596825396826 +15,BCohF,0.899217460317461 +16,T_BF_PF_BCmedian,0.895107936507937 +17,BCorrU,0.884768253968254 +18,T_BF_PFmedian,0.860024603174603 +19,BF_BCmean,0.854301587301587 +20,BF_BCmedian,0.854301587301587 +21,BF_PF_BCmean,0.854301587301587 +22,BF_PF_BCmedian,0.854301587301587 +23,BF_PFmean,0.854301587301587 +24,BF_PFmedian,0.854301587301587 +25,PF_BCmean,0.854301587301587 +26,PF_BCmedian,0.854301587301587 diff --git a/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_onlyAUC.csv b/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_onlyAUC.csv new file mode 100644 index 0000000..4737cba --- /dev/null +++ b/2_individual_and_combined_methods_analysis/averages/Average_Median_Combined_data_median_onlyAUC.csv @@ -0,0 +1,27 @@ +,Method_name,Median_across_patients 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zh+HZM!ww;B=$(#C%e^8;{3;rRWrUKbeTTMSO%p}1sqd1^i1v?-hODq>)AsR78$6NU za@&!(@0ptHGZ}k(`+TCyvv*F?SIw;Ci})ucr%pfaaBCcB!)bH>Nr?kHbX_li%xasu9N)i&J_p0)-|G)MFlz2{@NnUd+ uhFvPFt`8~hf$5f7ecpdN*;++g=E@rNIPNz7#K>w6_*s}(8{gdT5%XUrI5w*Q literal 0 HcmV?d00001 diff --git a/2_individual_and_combined_methods_analysis/sim1/.DS_Store b/2_individual_and_combined_methods_analysis/sim1/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 Date: Sun, 6 Nov 2022 20:31:57 -0500 Subject: [PATCH 2/4] Update README.md --- README.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 295c4f1..44e9808 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,8 @@ # Ensemble-Functional-Brain-Networks -This repository contains the code and the computations of the Koshy et al. 2022 paper. This includes the modified MULAN code with the additional regression inference methods and the ACC metrics +This repository contains the code and the computations of the Koshy et al. 2022 paper. This includes the analysis of the modified MULAN output for the 28 simulated networks. + +## `1_individual_methods_analysis` +This subfolder contains the code for analysis of the 40 network inference methods found in MULAN and two additional molecular biology methods. + +## `2_individual_and_combined_methods_analysiss` +This subfolder contains the code for analysis of the top individual inference methods and their combinations. From 54c1c2b35287b0706dc41d4819dbad252edc92ad Mon Sep 17 00:00:00 2001 From: Lauren P Marazzi Date: Sun, 6 Nov 2022 20:33:53 -0500 Subject: [PATCH 3/4] Update README.md --- 1_individual_methods_analysis/README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/1_individual_methods_analysis/README.md b/1_individual_methods_analysis/README.md index 6ed0b92..9b31a49 100644 --- a/1_individual_methods_analysis/README.md +++ b/1_individual_methods_analysis/README.md @@ -1,6 +1,9 @@ # Individual and combined methods analysis -This subfolder contains the code and data for analyzing the networks created by the 40 single methods inmplemented in MULAN and molecular biology biology methods +This subfolder contains the code and data for analyzing the networks created by the 40 single methods inmplemented in MULAN and molecular biology methods + + +The code to perform the analysis can be found in `analysis.Rmd`. An rmarkdown .html file is also found within this folder (`analysis.html`) The `sim` subfolders contain the following files for each simulated network: From 721064b55a903a6c58762cdbae4b024220028727 Mon Sep 17 00:00:00 2001 From: Lauren P Marazzi Date: Sun, 6 Nov 2022 20:34:07 -0500 Subject: [PATCH 4/4] Update README.md --- 2_individual_and_combined_methods_analysis/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/2_individual_and_combined_methods_analysis/README.md b/2_individual_and_combined_methods_analysis/README.md index a866245..50fcf07 100644 --- a/2_individual_and_combined_methods_analysis/README.md +++ b/2_individual_and_combined_methods_analysis/README.md @@ -2,6 +2,8 @@ This subfolder contains the code and data for analyzing the networks created by the top singleton methods and combinations of those methods. +The code to perform the analysis can be found in `analysis.Rmd`. An rmarkdown .html file is also found within this folder (`analysis.html`) + The `sim` subfolders contain the following files for each simulated network: - Combined_Data_onlyAUC.csv: the area under the reciever operating characteristic (AUROC, abbreviated here as AUC) values for each method for the 50 time series simulations of the network calculated by MULAN

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