diff --git a/rmd_analysis/expressionPC_covariate_r2.Rmd b/rmd_analysis/expressionPC_covariate_r2.Rmd deleted file mode 100644 index e0723e0..0000000 --- a/rmd_analysis/expressionPC_covariate_r2.Rmd +++ /dev/null @@ -1,112 +0,0 @@ ---- -title: "expressionPC_covariate_R2" -output: html_notebook ---- - - -```{r} -library(recount) -library(ggplot2) -library(RColorBrewer) -library(reshape2) - -load("../data/pc_loadings.Rdata") -load("../data/raw_protein_coding_withGC.Rdata") - -## none of the covariates have NA --> total sum of squares can be computed before hand -dat.cov <- lapply(gtex.rse, function(tiss) { - cov.dat <- tiss@colData[,c(10, 11, 21:80, 83)] - cov.dat <- cov.dat[,-which(sapply(cov.dat, function(x) length(unique(x))) == 1)] - cov.dat <- cov.dat[, -which(colnames(cov.dat) %in% c("smunpdrd","smpthnts", "smtstptref", "smnabtch", "smnabtchd", "smgebtch"))] ## not enought unique entries in more than one category, < 50 - cov.dat -}) - -pve.tissue <- mapply(function(x,y,z){ - pve.eachtiss <- matrix(ncol = z, nrow = ncol(y)) - for(i in 1:ncol(y)){ - pve.eachtiss[i,] <- sapply(summary(lm(x[,1:z]~y[,i])), function(p) p$r.squared) - } - colnames(pve.eachtiss) <- 1:z - rownames(pve.eachtiss) <- colnames(y) - pve.eachtiss -}, pc.loadings, dat.cov, num.pc.estimates, SIMPLIFY = FALSE) - -plot.r2 <- lapply(pve.tissue, melt) -``` - -```{r} -myPalette <- colorRampPalette(brewer.pal(9,'Blues'), space = "Lab") - -tiss<- "Subcutaneous" - -ggplot(plot.r2[[tiss]], aes(Var2, Var1)) + geom_tile(aes(fill = value), - colour = "white") + - # geom_text(size = 2, aes(label = prettyNum(value, digits=3, width=4, format="fg"))) + - scale_fill_gradientn(colours = myPalette(10)[1:6] - , limits = c(0,1) - , values = c(0, 0.1, 0.2, 0.3, 1) - ) + - xlab("") + ylab("Principal Components") + theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))+ - ggtitle(tiss) -``` -```{r} -tiss<- "Lung" - -ggplot(plot.r2[[tiss]], aes(Var2, Var1)) + geom_tile(aes(fill = value), - colour = "white") + - # geom_text(size = 2, aes(label = prettyNum(value, digits=3, width=4, format="fg"))) + - scale_fill_gradientn(colours = myPalette(10)[1:6] - , limits = c(0,1) - , values = c(0, 0.1, 0.2, 0.3, 1) - ) + - xlab("") + ylab("Principal Components") + theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))+ - ggtitle(tiss) -``` - -```{r} -tiss<- "Thyroid" - -ggplot(plot.r2[[tiss]], aes(Var2, Var1)) + geom_tile(aes(fill = value), - colour = "white") + - # geom_text(size = 2, aes(label = prettyNum(value, digits=3, width=4, format="fg"))) + - scale_fill_gradientn(colours = myPalette(10)[1:6] - , limits = c(0,1) - , values = c(0, 0.1, 0.2, 0.3, 1) - ) + - xlab("") + ylab("Principal Components") + theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))+ - ggtitle(tiss) -``` - -```{r} -tiss<- "Muscle" -ggplot(plot.r2[[tiss]], aes(Var2, Var1)) + geom_tile(aes(fill = value), - colour = "white") + - # geom_text(size = 2, aes(label = prettyNum(value, digits=3, width=4, format="fg"))) + - scale_fill_gradientn(colours = myPalette(10)[1:6] - , limits = c(0,1) - , values = c(0, 0.1, 0.2, 0.3, 1) - ) + - xlab("") + ylab("Principal Components") + theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))+ - ggtitle(tiss) -``` - -```{r} -tiss<- "Blood" - -ggplot(plot.r2[[tiss]], aes(Var2, Var1)) + geom_tile(aes(fill = value), - colour = "white") + - # geom_text(size = 2, aes(label = prettyNum(value, digits=3, width=4, format="fg"))) + - scale_fill_gradientn(colours = myPalette(10)[1:6] - , limits = c(0,1) - , values = c(0, 0.1, 0.2, 0.3, 1) - ) + - xlab("") + ylab("Principal Components") + theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))+ - ggtitle(tiss) -``` - -Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*. - -When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file). - -The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed. - diff --git a/rmd_analysis/expressionPC_covariate_r2.nb.html b/rmd_analysis/expressionPC_covariate_r2.nb.html deleted file mode 100644 index 6d7f92c..0000000 --- a/rmd_analysis/expressionPC_covariate_r2.nb.html +++ /dev/null @@ -1,345 +0,0 @@ - - - - - - - - - - - - - -expressionPC_covariate_R2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - - - - - - - - - - - - - - - -
library(recount)
-library(ggplot2)
-library(RColorBrewer)
-library(reshape2)
-load("../data/pc_loadings.Rdata")
-load("../data/raw_protein_coding_withGC.Rdata")
-## none of the covariates have NA --> total sum of squares can be computed before hand
-dat.cov <- lapply(gtex.rse, function(tiss) {
-  cov.dat <- tiss@colData[,c(10, 11, 21:80, 83)]
-  cov.dat <- cov.dat[,-which(sapply(cov.dat, function(x) length(unique(x))) == 1)]
-  cov.dat <- cov.dat[, -which(colnames(cov.dat) %in% c("smunpdrd","smpthnts", "smtstptref", "smnabtch", "smnabtchd", "smgebtch"))] ## not enought unique entries in more than one category, < 50
-  cov.dat
-})
-pve.tissue <- mapply(function(x,y,z){
-  pve.eachtiss <- matrix(ncol = z, nrow = ncol(y))
-  for(i in 1:ncol(y)){
-    pve.eachtiss[i,] <- sapply(summary(lm(x[,1:z]~y[,i])), function(p) p$r.squared)
-  }
-  colnames(pve.eachtiss) <- 1:z
-  rownames(pve.eachtiss) <- colnames(y)
-  pve.eachtiss
-}, pc.loadings, dat.cov, num.pc.estimates, SIMPLIFY = FALSE)
-plot.r2 <- lapply(pve.tissue, melt)
- - - - - - -
myPalette <- colorRampPalette(brewer.pal(9,'Blues'), space = "Lab")
-tiss<- "Subcutaneous"
-ggplot(plot.r2[[tiss]], aes(Var2, Var1)) + geom_tile(aes(fill = value),
-         colour = "white") + 
-        # geom_text(size = 2, aes(label = prettyNum(value, digits=3, width=4, format="fg"))) +
-        scale_fill_gradientn(colours = myPalette(10)[1:6]
-            , limits = c(0,1)
-        , values = c(0, 0.1, 0.2, 0.3, 1)
-        ) + 
-        xlab("") + ylab("Principal Components") + theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))+
-        ggtitle(tiss)
- - -

- - - - -
tiss<- "Lung"
-ggplot(plot.r2[[tiss]], aes(Var2, Var1)) + geom_tile(aes(fill = value),
-         colour = "white") + 
-        # geom_text(size = 2, aes(label = prettyNum(value, digits=3, width=4, format="fg"))) +
-        scale_fill_gradientn(colours = myPalette(10)[1:6]
-            , limits = c(0,1)
-        , values = c(0, 0.1, 0.2, 0.3, 1)
-        ) + 
-        xlab("") + ylab("Principal Components") + theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))+
-        ggtitle(tiss)
- - -

- - - - - - -
tiss<- "Thyroid"
-ggplot(plot.r2[[tiss]], aes(Var2, Var1)) + geom_tile(aes(fill = value),
-         colour = "white") + 
-        # geom_text(size = 2, aes(label = prettyNum(value, digits=3, width=4, format="fg"))) +
-        scale_fill_gradientn(colours = myPalette(10)[1:6]
-            , limits = c(0,1)
-        , values = c(0, 0.1, 0.2, 0.3, 1)
-        ) + 
-        xlab("") + ylab("Principal Components") + theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))+
-        ggtitle(tiss)
- - -

- - - - - - -
tiss<- "Muscle"
-ggplot(plot.r2[[tiss]], aes(Var2, Var1)) + geom_tile(aes(fill = value),
-         colour = "white") + 
-        # geom_text(size = 2, aes(label = prettyNum(value, digits=3, width=4, format="fg"))) +
-        scale_fill_gradientn(colours = myPalette(10)[1:6]
-            , limits = c(0,1)
-        , values = c(0, 0.1, 0.2, 0.3, 1)
-        ) + 
-        xlab("") + ylab("Principal Components") + theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))+
-        ggtitle(tiss)
- - -

- - - - - - -
tiss<- "Blood"
-ggplot(plot.r2[[tiss]], aes(Var2, Var1)) + geom_tile(aes(fill = value),
-         colour = "white") + 
-        # geom_text(size = 2, aes(label = prettyNum(value, digits=3, width=4, format="fg"))) +
-        scale_fill_gradientn(colours = myPalette(10)[1:6]
-            , limits = c(0,1)
-        , values = c(0, 0.1, 0.2, 0.3, 1)
-        ) + 
-        xlab("") + ylab("Principal Components") + theme(axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))+
-        ggtitle(tiss)
- - -

- - - -

Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Cmd+Option+I.

-

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Cmd+Shift+K to preview the HTML file).

-

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

- - -
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b3V0cHV0IHdpbGwgYmUgc2F2ZWQgYWxvbmdzaWRlIGl0IChjbGljayB0aGUgKlByZXZpZXcqIGJ1dHRvbiBvciBwcmVzcyAqQ21kK1NoaWZ0K0sqIHRvIHByZXZpZXcgdGhlIEhUTUwgZmlsZSkuIAoKVGhlIHByZXZpZXcgc2hvd3MgeW91IGEgcmVuZGVyZWQgSFRNTCBjb3B5IG9mIHRoZSBjb250ZW50cyBvZiB0aGUgZWRpdG9yLiBDb25zZXF1ZW50bHksIHVubGlrZSAqS25pdCosICpQcmV2aWV3KiBkb2VzIG5vdCBydW4gYW55IFIgY29kZSBjaHVua3MuIEluc3RlYWQsIHRoZSBvdXRwdXQgb2YgdGhlIGNodW5rIHdoZW4gaXQgd2FzIGxhc3QgcnVuIGluIHRoZSBlZGl0b3IgaXMgZGlzcGxheWVkLgoK
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- - - - - - - - diff --git a/rmd_analysis/genes_assigned_plots.Rmd b/rmd_analysis/genes_assigned_plots.Rmd deleted file mode 100644 index b144950..0000000 --- a/rmd_analysis/genes_assigned_plots.Rmd +++ /dev/null @@ -1,52 +0,0 @@ ---- -title: "WGCNA - # genes assigned to a module (non-gray)" -output: html_notebook ---- - -```{r "setup", include=FALSE} -require("knitr") -opts_knit$set(root.dir = "/work-zfs/abattle4/parsana/networks_correction_v/networks/") -``` - -```{r} -rm(list = ls()) -library(reshape2) -library(ggplot2) -load("raw/wgcna_networks.Rdata") -plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(which(x$colors!=0)))) -plot.mod <- melt(do.call(cbind, plot.mod)) -ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("Raw")+ - xlab("cut-height") + ylab("# genes assigned to modules") -``` - -```{r} -load("rin/wgcna_networks.Rdata") -plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(which(x$colors!=0)))) -plot.mod <- melt(do.call(cbind, plot.mod)) -ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("RIN")+ - xlab("cut-height") + ylab("# genes assigned to modules") -``` - -```{r} -load("quarterpc/wgcna_networks.Rdata") -plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(which(x$colors!=0)))) -plot.mod <- melt(do.call(cbind, plot.mod)) -ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("quarter-PC")+ - xlab("cut-height") + ylab("# genes assigned to modules") -``` - -```{r} -load("halfpc//wgcna_networks.Rdata") -plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(which(x$colors!=0)))) -plot.mod <- melt(do.call(cbind, plot.mod)) -ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("halfPC")+ - xlab("cut-height") + ylab("# genes assigned to modules") -``` - -```{r} -load("pc//wgcna_networks.Rdata") -plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(which(x$colors!=0)))) -plot.mod <- melt(do.call(cbind, plot.mod)) -ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("PC")+ - xlab("cut-height") + ylab("# genes assigned to modules") -``` diff --git a/rmd_analysis/genes_assigned_plots.nb.html b/rmd_analysis/genes_assigned_plots.nb.html deleted file mode 100644 index 9efc696..0000000 --- a/rmd_analysis/genes_assigned_plots.nb.html +++ /dev/null @@ -1,292 +0,0 @@ - - - - - - - - - - - - - -WGCNA - # genes assigned to a module (non-gray) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - - - - - - - - - - - - - - - - - -
rm(list = ls())
-library(reshape2)
-library(ggplot2)
-load("raw/wgcna_networks.Rdata")
-plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(which(x$colors!=0))))
-plot.mod <- melt(do.call(cbind, plot.mod))
-ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("Raw")+
-  xlab("cut-height") + ylab("# genes assigned to modules")
- - -

- - - - - - -
load("rin/wgcna_networks.Rdata")
-plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(which(x$colors!=0))))
-plot.mod <- melt(do.call(cbind, plot.mod))
-ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("RIN")+
-  xlab("cut-height") + ylab("# genes assigned to modules")
- - -

- - - - - - -
load("quarterpc/wgcna_networks.Rdata")
-plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(which(x$colors!=0))))
-plot.mod <- melt(do.call(cbind, plot.mod))
-ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("quarter-PC")+
-  xlab("cut-height") + ylab("# genes assigned to modules")
- - -

- - - - - - -
load("halfpc//wgcna_networks.Rdata")
-plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(which(x$colors!=0))))
-plot.mod <- melt(do.call(cbind, plot.mod))
-ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("halfPC")+
-  xlab("cut-height") + ylab("# genes assigned to modules")
- - -

- - - - - - -
load("pc//wgcna_networks.Rdata")
-plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(which(x$colors!=0))))
-plot.mod <- melt(do.call(cbind, plot.mod))
-ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("PC")+
-  xlab("cut-height") + ylab("# genes assigned to modules")
- - -

- - - -
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- - - - - - - - diff --git a/rmd_analysis/module_count_bytissue.Rmd b/rmd_analysis/module_count_bytissue.Rmd deleted file mode 100644 index 0079a9e..0000000 --- a/rmd_analysis/module_count_bytissue.Rmd +++ /dev/null @@ -1,85 +0,0 @@ ---- -title: "WGCNA - # genes assigned to a module (non-gray) by tissue" -output: html_notebook ---- - -```{r "setup", include=FALSE} -require("knitr") -opts_knit$set(root.dir = "/work-zfs/abattle4/parsana/networks_correction/networks/") -``` - -```{r} -rm(list = ls()) -library(reshape2) -library(ggplot2) -load("raw/wgcna_networks.Rdata") -tiss <- "Thyroid" -raw <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -load("rin/wgcna_networks.Rdata") -rin <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -load("quarterpc/wgcna_networks.Rdata") -quarterPC <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -load("halfpc//wgcna_networks.Rdata") -halfPC <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -load("pc//wgcna_networks.Rdata") -pc <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -plot.mod <- melt(cbind(raw, rin, quarterPC, halfPC, pc)) -plot.mod$Var2 <- factor(plot.mod$Var2, levels = c("pc", "halfPC", "quarterPC", "rin", "raw"), labels = c("PC", "half-PC", "quarter-PC", "RIN", "uncorrected")) - -ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle(tiss)+ - xlab("cut-height") + ylab("# genes assigned to modules") -``` - -```{r} -tiss <- "Subcutaneous" -load("raw/wgcna_networks.Rdata") -raw <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -load("rin/wgcna_networks.Rdata") -rin <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -load("quarterpc/wgcna_networks.Rdata") -quarterPC <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -load("halfpc//wgcna_networks.Rdata") -halfPC <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -load("pc//wgcna_networks.Rdata") -pc <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -plot.mod <- melt(cbind(raw, rin, quarterPC, halfPC, pc)) -plot.mod$Var2 <- factor(plot.mod$Var2, levels = c("pc", "halfPC", "quarterPC", "rin", "raw"), labels = c("PC", "half-PC", "quarter-PC", "RIN", "uncorrected")) - -ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle(tiss)+ - xlab("cut-height") + ylab("# genes assigned to modules") -``` - -```{r} -tiss <- "Lung" -load("raw/wgcna_networks.Rdata") -raw <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -load("rin/wgcna_networks.Rdata") -rin <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -load("quarterpc/wgcna_networks.Rdata") -quarterPC <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -load("halfpc//wgcna_networks.Rdata") -halfPC <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -load("pc//wgcna_networks.Rdata") -pc <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0))) - -plot.mod <- melt(cbind(raw, rin, quarterPC, halfPC, pc)) -plot.mod$Var2 <- factor(plot.mod$Var2, levels = c("pc", "halfPC", "quarterPC", "rin", "raw"), labels = c("PC", "half-PC", "quarter-PC", "RIN", "uncorrected")) - -ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle(tiss)+ - xlab("cut-height") + ylab("# genes assigned to modules") -``` - diff --git a/rmd_analysis/module_count_bytissue.nb.html b/rmd_analysis/module_count_bytissue.nb.html deleted file mode 100644 index 9f3f483..0000000 --- a/rmd_analysis/module_count_bytissue.nb.html +++ /dev/null @@ -1,296 +0,0 @@ - - - - - - - - - - - - - -WGCNA - # genes assigned to a module (non-gray) by tissue - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - - - - - - - - - - - - - - - - - -
rm(list = ls())
-library(reshape2)
-library(ggplot2)
-load("raw/wgcna_networks.Rdata")
-tiss <- "Thyroid"
-raw <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-load("rin/wgcna_networks.Rdata")
-rin <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-load("quarterpc/wgcna_networks.Rdata")
-quarterPC <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-load("halfpc//wgcna_networks.Rdata")
-halfPC <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-load("pc//wgcna_networks.Rdata")
-pc <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-plot.mod <- melt(cbind(raw, rin, quarterPC, halfPC, pc))
-plot.mod$Var2 <- factor(plot.mod$Var2, levels = c("pc", "halfPC", "quarterPC", "rin", "raw"), labels = c("PC", "half-PC", "quarter-PC", "RIN", "uncorrected"))
-ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle(tiss)+
-  xlab("cut-height") + ylab("# genes assigned to modules")
- - -

- - - - - - -
tiss <- "Subcutaneous"
-load("raw/wgcna_networks.Rdata")
-raw <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-load("rin/wgcna_networks.Rdata")
-rin <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-load("quarterpc/wgcna_networks.Rdata")
-quarterPC <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-load("halfpc//wgcna_networks.Rdata")
-halfPC <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-load("pc//wgcna_networks.Rdata")
-pc <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-plot.mod <- melt(cbind(raw, rin, quarterPC, halfPC, pc))
-plot.mod$Var2 <- factor(plot.mod$Var2, levels = c("pc", "halfPC", "quarterPC", "rin", "raw"), labels = c("PC", "half-PC", "quarter-PC", "RIN", "uncorrected"))
-ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle(tiss)+
-  xlab("cut-height") + ylab("# genes assigned to modules")
- - -

- - - - - - -
tiss <- "Lung"
-load("raw/wgcna_networks.Rdata")
-raw <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-load("rin/wgcna_networks.Rdata")
-rin <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-load("quarterpc/wgcna_networks.Rdata")
-quarterPC <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-load("halfpc//wgcna_networks.Rdata")
-halfPC <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-load("pc//wgcna_networks.Rdata")
-pc <- sapply(dat.net[[tiss]], function(x) length(which(x$colors!=0)))
-plot.mod <- melt(cbind(raw, rin, quarterPC, halfPC, pc))
-plot.mod$Var2 <- factor(plot.mod$Var2, levels = c("pc", "halfPC", "quarterPC", "rin", "raw"), labels = c("PC", "half-PC", "quarter-PC", "RIN", "uncorrected"))
-ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle(tiss)+
-  xlab("cut-height") + ylab("# genes assigned to modules")
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- - - - - -
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- - - - - - - - diff --git a/rmd_analysis/module_count_plots.Rmd b/rmd_analysis/module_count_plots.Rmd deleted file mode 100644 index 1a88a22..0000000 --- a/rmd_analysis/module_count_plots.Rmd +++ /dev/null @@ -1,55 +0,0 @@ ---- -title: "WGCNA module distribution" -output: html_notebook ---- - -```{r "setup", include=FALSE} -require("knitr") -opts_knit$set(root.dir = "/work-zfs/abattle4/parsana/networks_correction_v/networks/") -``` - -```{r} -rm(list = ls()) -library(reshape2) -library(ggplot2) -load("raw/wgcna_networks.Rdata") -plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1)) -plot.mod <- melt(do.call(cbind, plot.mod)) -ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("Raw")+ - xlab("cut-height") + ylab("# modules") -``` - -```{r} -load("rin/wgcna_networks.Rdata") -plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1)) -plot.mod <- melt(do.call(cbind, plot.mod)) -ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("RIN")+ - xlab("cut-height") + ylab("# modules") -``` - -```{r} -load("quarterpc/wgcna_networks.Rdata") -plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1)) -plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1)) -plot.mod <- melt(do.call(cbind, plot.mod)) -ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("quarter-PC")+ - xlab("cut-height") + ylab("# modules") -``` - -```{r} -load("halfpc//wgcna_networks.Rdata") -plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1)) -plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1)) -plot.mod <- melt(do.call(cbind, plot.mod)) -ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("halfPC")+ - xlab("cut-height") + ylab("# modules") -``` - -```{r} -load("pc//wgcna_networks.Rdata") -plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1)) -plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1)) -plot.mod <- melt(do.call(cbind, plot.mod)) -ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("PC")+ - xlab("cut-height") + ylab("# modules") -``` diff --git a/rmd_analysis/module_count_plots.nb.html b/rmd_analysis/module_count_plots.nb.html deleted file mode 100644 index 83ceb42..0000000 --- a/rmd_analysis/module_count_plots.nb.html +++ /dev/null @@ -1,285 +0,0 @@ - - - - - - - - - - - - - -WGCNA module distribution - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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load("rin/wgcna_networks.Rdata")
-plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1))
-plot.mod <- melt(do.call(cbind, plot.mod))
-ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("RIN")+
-  xlab("cut-height") + ylab("# modules")
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load("quarterpc/wgcna_networks.Rdata")
-plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1))
-plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1))
-plot.mod <- melt(do.call(cbind, plot.mod))
-ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("quarter-PC")+
-  xlab("cut-height") + ylab("# modules")
- - -

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load("halfpc//wgcna_networks.Rdata")
-plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1))
-plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1))
-plot.mod <- melt(do.call(cbind, plot.mod))
-ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("halfPC")+
-  xlab("cut-height") + ylab("# modules")
- - -

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load("pc//wgcna_networks.Rdata")
-plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1))
-plot.mod <- lapply(dat.net, function(y) sapply(y, function(x) length(unique(x$colors))-1))
-plot.mod <- melt(do.call(cbind, plot.mod))
-ggplot(plot.mod, aes(x = Var1, y = value, col = Var2)) + geom_point()+ggtitle("PC")+
-  xlab("cut-height") + ylab("# modules")
- - -

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files a/rmd_analysis/plots/zoomed_correlation_networks_Subcutaneous.png and /dev/null differ diff --git a/rmd_analysis/plots/zoomed_correlation_networks_Thyroid.png b/rmd_analysis/plots/zoomed_correlation_networks_Thyroid.png deleted file mode 100644 index 4ab904b..0000000 Binary files a/rmd_analysis/plots/zoomed_correlation_networks_Thyroid.png and /dev/null differ diff --git a/rmd_analysis/thyroid_pc_foxe1.Rmd b/rmd_analysis/thyroid_pc_foxe1.Rmd deleted file mode 100644 index 091a558..0000000 --- a/rmd_analysis/thyroid_pc_foxe1.Rmd +++ /dev/null @@ -1,56 +0,0 @@ ---- -title: "Thyroid" -output: - pdf_document: default - html_notebook: default ---- - -Association of PC with FOXE1. Ensembl ID for FOXE1 = ENSG00000178919 -```{r} -rm(list = ls()) -tiss <- "Thyroid" -ensid <- "ENSG00000178919" -## load subsetted raw expression data for thyroid -load("../data/raw_subset.Rdata") - -## we only care about thyroid for this analysis - set dat.expr to thyroid -dat.tiss.raw <- dat.expr[[tiss]] - -## which row is FOXE1? -grep(ensid, rownames(dat.tiss.raw)) -ensid.dat <- rownames(dat.tiss.raw)[grep(ensid, rownames(dat.tiss.raw))] - -## load pc corrected data -load("../data/pc_corrected.Rdata") -## we only care about thyroid for this analysis - set dat.expr to thyroid -dat.tiss.pc <- dat.expr[[tiss]] - -## which row is FOXE1? -grep(ensid, rownames(dat.tiss.pc)) -print(ensid) -print(rownames(dat.tiss.pc)[grep(ensid, rownames(dat.tiss.pc))]) -print(ensid.dat) -``` - -```{r} -foxe.raw <- dat.tiss.raw[ensid.dat,]@assays$data$counts -foxe.pc <- dat.tiss.pc[ensid.dat, ]@assays$data$counts -``` -Association between raw foxe expression and PC -```{r} -# load PC loadings -load("../data/pc_loadings.Rdata") -raw.pve <- sapply(summary(lm(pc.loadings[[tiss]][,1:num.pc.estimates[[tiss]]]~t(foxe.raw))), function(x) x$r.squared) -plot(c(1:num.pc.estimates[[tiss]]), raw.pve, col = "blue", pch = "*", xlab = "PC") -``` -```{r} -pc.pve <- sapply(summary(lm(pc.loadings[[tiss]][,1:num.pc.estimates[[tiss]]]~t(foxe.pc))), function(x) x$r.squared)*100 -plot(c(1:num.pc.estimates[[tiss]]), pc.pve, col = "red", pch = "*", xlab = "PC") -``` - -Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*. - -When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file). - -The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed. - diff --git a/rmd_analysis/thyroid_pc_foxe1.nb.html b/rmd_analysis/thyroid_pc_foxe1.nb.html deleted file mode 100644 index f7d69f0..0000000 --- a/rmd_analysis/thyroid_pc_foxe1.nb.html +++ /dev/null @@ -1,292 +0,0 @@ - - - - - - - - - - - - - -Thyroid - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - - - - - - - - - - - - -

Association of PC with FOXE1. Ensembl ID for FOXE1 = ENSG00000178919

- - - -
rm(list = ls())
-tiss <- "Thyroid"
-ensid <- "ENSG00000178919"
-## load subsetted raw expression data for thyroid
-load("../data/raw_subset.Rdata")
-## we only care about thyroid for this analysis - set dat.expr to thyroid
-dat.tiss.raw <- dat.expr[[tiss]]
-## which row is FOXE1?
-grep(ensid, rownames(dat.tiss.raw))
- - -
[1] 3000
- - -
ensid.dat <- rownames(dat.tiss.raw)[grep(ensid, rownames(dat.tiss.raw))]
-## load pc corrected data
-load("../data/pc_corrected.Rdata")
-## we only care about thyroid for this analysis - set dat.expr to thyroid
-dat.tiss.pc <- dat.expr[[tiss]]
-## which row is FOXE1?
-grep(ensid, rownames(dat.tiss.pc))
- - -
[1] 3000
- - -
print(ensid)
- - -
[1] "ENSG00000178919"
- - -
print(rownames(dat.tiss.pc)[grep(ensid, rownames(dat.tiss.pc))])
- - -
[1] "ENSG00000178919.8"
- - -
print(ensid.dat)
- - -
[1] "ENSG00000178919.8"
- - - - - - -
foxe.raw <- dat.tiss.raw[ensid.dat,]@assays$data$counts
-foxe.pc <- dat.tiss.pc[ensid.dat, ]@assays$data$counts
- - - -

Association between raw foxe expression and PC

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- - - - -

- - - -

Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Cmd+Option+I.

-

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Cmd+Shift+K to preview the HTML file).

-

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

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- - - - - - - - diff --git a/rmd_analysis/total_expressionPVE_allcovariates.Rmd b/rmd_analysis/total_expressionPVE_allcovariates.Rmd deleted file mode 100644 index d6bf381..0000000 --- a/rmd_analysis/total_expressionPVE_allcovariates.Rmd +++ /dev/null @@ -1,53 +0,0 @@ ---- -title: "Total PVE by all covariates" -output: - html_notebook: default - pdf_document: default ---- - - -```{r} -rm(list = ls()) -library(RcppEigen) -load("../data/raw_protein_coding_withGC.Rdata") - -## none of the covariates have NA --> total sum of squares can be computed before hand -dat.cov <- lapply(gtex.rse, function(tiss) { - cov.dat <- tiss@colData[,c(10, 11, 21:80, 83)] - cov.dat <- cov.dat[,-which(sapply(cov.dat, function(x) length(unique(x))) == 1)] - cov.dat <- cov.dat[, -which(colnames(cov.dat) %in% c("smunpdrd","smpthnts", "smtstptref", "smnabtch", "smnabtchd", "smgebtch"))] ## not enought unique entries in more than one category, < 50 - cov.dat - }) -dat.expr <- lapply(gtex.rse, function(tiss) tiss@assays$data$counts) - -## compute the residuals -residual.expr.dof <- mapply(function(x,y){ - x <- t(x) - res.expr <- matrix(ncol = ncol(x), nrow = nrow(x)) - print(paste("begin")) - for(i in 1:ncol(x)){ - lm.out <- fastLm(x[,i]~1+. , data = y) - res.expr[,i] <- lm.out$residuals - } - list(residual_expression = res.expr, dof = lm.out$df.residual) -}, dat.expr, dat.cov) - -residual.expr <- lapply(residual.expr.dof[1,], function(x) x) -model.dof <- lapply(residual.expr.dof[2,], function(x) x) - -``` -Now that we have the expression data, residuals from the regression and dof - we can compute the adjusted r^2. -```{r} -tss <- lapply(dat.expr, function(x) (norm(x - rowMeans(x), type = "F"))^2) -rss <- lapply(residual.expr, function(x) (norm(x, type = "F"))^2) - -total.covariate.pve <- vector("numeric", length = length(dat.expr)) -names(total.covariate.pve) <- names(dat.expr) -for(i in 1:length(total.covariate.pve)){ - n <- ncol(dat.expr[[i]]) - r2 <- (tss[[i]] - rss[[i]])/tss[[i]] - total.covariate.pve[i] <- 1 - ((1-r2) * ((n - 1) / model.dof[[i]])) -} - -total.covariate.pve -``` diff --git a/rmd_analysis/total_expressionPVE_allcovariates.nb.html b/rmd_analysis/total_expressionPVE_allcovariates.nb.html deleted file mode 100644 index 643a398..0000000 --- a/rmd_analysis/total_expressionPVE_allcovariates.nb.html +++ /dev/null @@ -1,264 +0,0 @@ - - - - - - - - - - - - - -Total PVE by all covariates - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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rm(list = ls())
-library(RcppEigen)
-load("../data/raw_protein_coding_withGC.Rdata")
-
-## none of the covariates have NA --> total sum of squares can be computed before hand
-dat.cov <- lapply(gtex.rse, function(tiss) {
-  cov.dat <- tiss@colData[,c(10, 11, 21:80, 83)]
-  cov.dat <- cov.dat[,-which(sapply(cov.dat, function(x) length(unique(x))) == 1)]
-  cov.dat <- cov.dat[, -which(colnames(cov.dat) %in% c("smunpdrd","smpthnts", "smtstptref", "smnabtch", "smnabtchd", "smgebtch"))] ## not enought unique entries in more than one category, < 50
-  cov.dat
-  })
-dat.expr <- lapply(gtex.rse, function(tiss) tiss@assays$data$counts)
-
-## compute the residuals
-residual.expr.dof <- mapply(function(x,y){
-  x <- t(x)
-  res.expr <- matrix(ncol = ncol(x), nrow = nrow(x))
-  print(paste("begin"))
-  for(i in 1:ncol(x)){
-    lm.out <- fastLm(x[,i]~1+. , data = y)
-    res.expr[,i] <- lm.out$residuals
-  }
-  list(residual_expression = res.expr, dof = lm.out$df.residual)
-}, dat.expr, dat.cov)
-
-residual.expr <- lapply(residual.expr.dof[1,], function(x) x)
-model.dof <- lapply(residual.expr.dof[2,], function(x) x)
-
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Now that we have the expression data, residuals from the regression and dof - we can compute the adjusted r^2.

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total.covariate.pve
-
- - -
Subcutaneous         Lung      Thyroid       Muscle        Blood 
-   0.2675448    0.3670305    0.2696517    0.3518515    0.6266195
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