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Script_MScThesis.R
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Script_MScThesis.R
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#########################################################
# Script for Analysis of transformations for automated, #
# high-throughput clustering of CyTOF data #
#########################################################
## Required packages
library(flowCore)
library(MASS)
library(forecast)
library(AID)
library(scales)
library(FlowSOM)
library(ggplot2)
library(reshape2)
library(gridExtra)
library(pheatmap)
## Required scripts
source("run_FlowSOM.R")
source("helper_match_evaluate_multiple.R")
###########################
# LOAD BENCHMARK DATASETS #
###########################
## Function for loading the .fcs files given a path
load_data <- function(path){
fcs_data <- read.FCS(path, transformation = FALSE, truncate_max_range = FALSE)
data <- exprs(fcs_data)
dim(data)
return(data)
}
## Paths of .fcs files
files <- list(
Levine32 = "data/Levine_32dim_notransform.fcs",
Levine13 = "data/Levine_13dim_notransform.fcs",
Samusik39 = "data/Samusik_all_notransform.fcs"
)
## Load data
data <- lapply(files, load_data)
# lapply(data, dim)
## Select marker columns only
marker_cols <- list(
Levine32 = 5:36,
Levine13 = 1:13,
Samusik39 = 9:47
)
## "data.s" is a list of 5 datasets (only marker columns)
data.s <- data
for (i in 1:length(data)){
data.s[[i]] <- data[[i]][ , marker_cols[[i]] ]
}
## Function to retrieve (a list of) all clusters of a given "dataset"
## (Clusters ordered decreasingly by size)
## If "useNaN = TRUE", non-assigned cells (NaN) are included as clusters in the list
## If "transform = TRUE", returned clusters are TRANSFORMED with ASINH (cofactor = 5)
get_clusters <- function(index, transform = TRUE, useNaN = FALSE){
dataset <- data.s[[index]]
labels <- data[[index]][, "label"]
if (useNaN == TRUE){
# Get decreasing order (including "NaN" and excluding "NA")
indexes <- names(table(labels, exclude = NA))[order( table(labels, exclude = NA), decreasing = TRUE )]
} else{
# Get decreasing order (only assigned cells)
indexes <- order( table(labels), decreasing = TRUE)
}
clusters <- lapply( indexes, function(u) { dataset[which(labels == u),]} )
names(clusters) <- indexes
cof <- 5 # CyTOF data
if (transform == TRUE)
clusters <- lapply( clusters, function(u) asinh(u/cof) )
return(clusters)
}
## Function to retrieve the clusters in a given dataset, given the labels
## The order of events in dataset and their labels must match
return_clusters <- function(dataset, labels, orderit = FALSE){
# Only for flow cytometry (rare) datasets (just one cluster each):
# Change label of unassigned cells (label = 0) to "NaN"
if ( names(table(labels)[1]) == "0" ){
labels[labels == "0"] <- NaN
}
if (orderit == FALSE){
indexes <- names(table(labels))
} else{
indexes <- order( table(labels), decreasing = TRUE)
}
clusters <- lapply( indexes, function(u) { dataset[which(labels == u),]} )
names(clusters) <- indexes
return(clusters)
}
#####################
# ESTIMATION METHOD #
#####################
## This function is used because, for now, goal is to only estimate lambda
make_positive <- function(dataset){
p.dataset <- dataset
eps <- 0
if (sum(dataset < 0)){
min <- abs(min(dataset))
eps <- min + 1e-10 # data have to be strictly positive so add a small increment
# to avoid min(dataset) = zero
p.dataset <- dataset + min
}
p.dataset[p.dataset == 0] <- 1e-10 # data have to be strictly positive so add a small increment
# to avoid min(dataset) = zero
return(list(data = p.dataset, eps = eps))
}
## Function to estimate the parameters (lambda, epsilon) of the
## BoxCox transformation necessary to achieve normality of "dataset"
## using a specific "method" for the estimation
## Possible methods:
## - "loglik": maximize profile log-likelihood
## - "sw": maximize Shapiro-Wilk test statistic
## to use this method, size of "dataset" has to be between 3 and 5000
estimate_trans <- function(dataset, method){
lambda <- NA
r <- make_positive(dataset) # make data positive for the BoxCox estimation
p.dataset <- r$data # if data are already positive, p.dataset = dataset
eps <- r$eps # and eps = 0
if (method == "loglik"){
out.fore <- BoxCox.lambda(p.dataset, method = "loglik",
lower = 0, upper = 3) # we only consider positive lambdas (for now?)
lambda <- out.fore
} else if (method == "sw"){
if ( length(p.dataset) > 5000 ){ # size of "dataset" has to be between 3 and 5000
p.dataset <- p.dataset[sample(4500)] # so draw a sample of size = 4500
}
out.aid <- boxcoxnc(p.dataset,
lam = seq(-4,4,0.1) # such a large range to avoid error "Enlarge the range
# of the lambda in a positive/negative direction"
)#plotit = FALSE, verbose = FALSE)
lambda <- out.aid$lambda.hat
}
return( c(lambda = lambda, eps = eps) )
}
### MAPPING BETWEEN BOXCOX AND ASINH
## Calculate sum of absolute deviation between transformations
## for a specific cofactor (in asinh) and lambda (in x^lambda)
sumsq <- function(cofactor, lambda, x) {
# sum( abs(asinh(x/cofactor) - BoxCox(x, lambda = lambda) ) )
# sum( abs(asinh(x/cofactor) - (x^lambda)) )
if (lambda == 0 ){
sum( abs(asinh(x/cofactor) - log(x)) )
} else{
sum( abs(asinh(x/cofactor) - (x^lambda)) )
}
}
## Given a lambda, map it to the "closest" cofactor in asinh(x/cofactor)
## Mapping through min of the absolute deviation between both transformations
map_cofactor <- function(lambda, cofactors=1:200, x, plot=FALSE){
res <- sapply(cofactors, sumsq, lambda = lambda, x = x)
the_cof <- cofactors[which.min(res)]
if (plot == TRUE){
plot(cofactors, res, xlab = paste0("Cofactor (Min=",the_cof,")"),
ylab = "Abs. Deviation", main = paste0("Cofactor2 from est. Lambda = ",lambda))
abline(v=the_cof,col="blue")
}
return( c(cofactor = the_cof, min = min(res)) )
}
#############################
# SIMULATION TO TEST METHOD #
#############################
## Applies the inverse of the BoxCox transformation (with parameters lambda and epsilon [lambda2 or shift])
## To apply the "original" BoxCox transformation use eps = 0
## This function is taken from and almost identical to InvBoxCox() {forecast}
InvBoxCoxEps <- function(x, lambda, eps){
if (lambda < 0) x[x > -1/lambda] <- NA # Not sure how to deal with this??
if (lambda == 0){
out <- exp(x) - eps
} else if (lambda > 0) {
xx <- x * lambda + 1
out <- ( sign(xx) * abs(xx)^(1/lambda) ) - eps
}
return(out)
}
## Outputs a dataset of size "n" following a normal distribution with
## a specified "mean" and "sd"
get_sample <- function(n, mean, sd){
dataset <- rnorm(n, mean = mean, sd = sd)
return(dataset)
}
## SIMULATIONS
# Possible methods ( see description below in estimate_trans() ):
# "loglik",
# "sw"
# Choose values
method <- "loglik"
lambda <- 0.25 # testing for one lambda
eps <- 0 # for now, estimate only lambda
nrep <- 1000
set.seed(123)
### APPLY ESTIMATION
df <- data.frame(lambda=rep(NA,nrep),eps=NA,n=NA,mean=NA,sd=NA,nneg=NA,nzer=NA,
nneg.t=NA,lower.t=NA,upper.t=NA,lower=NA,upper=NA)
for (i in 1:nrep){
if( i %% 100 == 0 )
cat("i:",i,"\n")
## Get dataset
n <- 10000
mean <- sample( seq(0,6, length = 5), 1 )
sd <- sample( seq(0.1,2, length = 5), 1 )
true <- get_sample(n, mean, sd)
lower.t <- range(true)[1] # min of sample dataset
upper.t <- range(true)[2] # max of sample dataset
nneg.t <- sum(true < 0) # number of negatives of sample dataset
## Inverse-transform dataset
dataset <- InvBoxCoxEps(true, lambda, 0)
nneg <- sum(dataset < 0)
nzer <- sum(dataset == 0)
lower <- range(dataset)[1]
upper <- range(dataset)[2]
## Estimate
r <- estimate_trans(dataset, method)
df$lambda[i] <- r[1]
df$eps[i] <- r[2]
df$mean[i] <- mean
df$sd[i] <- sd
df$n[i] <- n
df$nneg[i] <- nneg
df$nneg.t[i] <- nneg.t
df$nzer[i] <- nzer
df$lower[i] <- lower
df$upper[i] <- upper
df$lower.t[i] <- lower.t
df$upper.t[i] <- upper.t
}
df$mean <- as.factor(round(df$mean,3))
df$sd <- as.factor(round(df$sd,3))
df$nneg <- as.factor(df$nneg)
dff <- df
dff$group <- with(dff, interaction(df$mean, df$sd, df$n))
dff$meanneg <- ave(dff$nneg.t, dff$group, mean)
dff$meanneg <- as.factor(round(dff$meanneg,0))
p1 <- ggplot(dff, aes(x = sd, y = lambda)) + geom_violin() + facet_grid(~mean) + coord_cartesian(ylim = c(0, 1))
plot(p1)
#######################################
# RUN AND EVALUATE FLOWSOM CLUSTERING #
#######################################
clus_and_eval <- function(x, labels_true) {
F1 <- matrix( NA, nrow = length(table(labels_true)), ncol = nruns )
pre <- matrix( NA, nrow = length(table(labels_true)), ncol = nruns )
rec <- matrix( NA, nrow = length(table(labels_true)), ncol = nruns )
labmat <- matrix( NA, nrow = length(labels_true), ncol = nruns )
for (i in 1:nruns) {
cat('Run',i,'\n')
labels <- run_FlowSOM(x, k)
scores <- helper_match_evaluate_multiple(clus_algorithm = labels, clus_truth = labels_true)
labmat[,i] <- labels
F1[,i] <- scores$F1
pre[,i] <- scores$pr
rec[,i] <- scores$re
}
return(list(labels = labmat,
F1 = F1,
pre = pre,
rec = rec))
}
###################
# APPLYING METHOD #
###################
## ESTIMATE TRANSFORMATIONS
df.list <- list()
for (j in 1:3){
clusters <- get_clusters(j, transform = TRUE, useNaN = FALSE) # transformed
o.clusters <- get_clusters(j, transform = FALSE, useNaN = FALSE) # raw
cof <- 5
## Get marker-wise means for each cluster
## 'means' is a 'clusters x markers' matrix (after transposing) containing the mean value
## of each marker for each cluster
means <- sapply(clusters, function(u){
apply(u, 2, mean)
})
means <- t(means)
## Re-order columns of 'means' dataframe (markers) by decreasing maximum mean
new <- means[, order(apply(means,2,max), decreasing = TRUE)]
hmarkers <- colnames(new) # Examine all markers
df <- data.frame(lambda=rep(NA,length(hmarkers)),eps=NA,# lambda.abs=NA,
cofactor=NA,minsq=NA,cluster=NA,marker=NA,mean=NA,n=NA)
for (i in 1:length(hmarkers)){
marker <- hmarkers[i] # For each marker
maxmean <- max(means[,marker]) # Select the maximum mean
pp <- names(which.max(means[,marker])) # Pick the cluster correspoding
# to maxmean (positive population)
## Positive population for that marker
cluster <- clusters[[ pp ]][ ,marker] # Select "cluster" by name of cluster ("pp")
# and name of marker ("marker")
n <- length(cluster)
o.cluster <- sinh(cluster)*cof # Inverse of asinh(x / cof); i.e. original datavalues
r <- estimate_trans(o.cluster, "loglik") # Estimate transf introducing a shift
m <- map_cofactor(lambda=r[1],x=abs(o.cluster),plot=FALSE)
df$lambda[i] <- r[1]
df$eps[i] <- r[2]
df$cofactor[i] <- m[1]
df$minsq[i] <- m[2]
df$mean[i] <- maxmean
df$cluster[i] <- pp
df$marker[i] <- marker
df$n[i] <- n
}
df.list[[j]] <- df
}
## APPLY TRANSFORMATIONS
dt.list <- list()
for (j in 1:3){
cof <- 5
dataset <- data.s[[j]] # raw dataset without labels
df <- df.list[[j]]
labels <- data[[j]][,"label"]
data_transf <- list()
## Raw data
data_transf[[1]] <- dataset
## Standard transform (asinh/cof)
data_transf[[2]] <- asinh(dataset/cof)
## Get range of asinh (original) dataset to normalize the scales of the other transformations
z <- data_transf[[2]]
r.dat <- apply(z,2,function(u){
round(range(u),1)
})
rownames(r.dat) <- c("min","max")
## TRANSFORM + NO SCALING
## Shift + Power transform (est. lambda)
data_transf[[3]] <- sapply(1:nrow(df),function(u){
dataset <- data.s[[j]][,u]
lambda <- df$lambda[ df$marker == colnames(data.s[[j]])[u] ]
p.dataset <- make_positive(dataset)$data # if data are already positive, p.dataset = dataset
t.dataset <- p.dataset^lambda
if (lambda == 0)
t.dataset <- log(p.dataset)
return(t.dataset)
})
## Asinh with mapped cofactor from est. lambda
data_transf[[4]] <- sapply(1:nrow(df),function(u){
dataset <- data.s[[j]][,u]
marker <- colnames(data.s[[j]])[u]
cofactor <- df$cofactor[ df$marker == marker ]
t.dataset <- asinh(dataset/cofactor)
return(t.dataset)
})
## Shift + BoxCox transform
data_transf[[5]] <- sapply(1:nrow(df),function(u){
dataset <- data.s[[j]][,u]
marker <- colnames(data.s[[j]])[u]
lambda <- df$lambda[ df$marker == marker ]
p.dataset <- make_positive(dataset)$data # if data are already positive, p.dataset = dataset
t.dataset <- BoxCox(p.dataset, lambda = lambda)
return(t.dataset)
})
## TRANSFORM + SCALING WITH ASINH 5 RANGE
## Shift + Power transform (est. lambda)
data_transf[[6]] <- sapply(1:nrow(df),function(u){
marker <- colnames(data.s[[j]])[u]
dataset <- data_transf[[3]][,u]
## for scaling with range
ra <- as.numeric(r.dat[,colnames(r.dat) == marker])
t.dataset <- rescale( dataset, to = ra )
if (sum(ra != round(range(t.dataset),1)) > 0){
# cat("Dataset",j,"Power","Asinh5:",ra,marker,":",range(t.dataset),
# "\n",file = "ranges.txt", append = TRUE)
}
return(t.dataset)
})
## Asinh with mapped cofactor from est. lambda
data_transf[[7]] <- sapply(1:nrow(df),function(u){
marker <- colnames(data.s[[j]])[u]
dataset <- data_transf[[4]][,u]
## for scaling with range
ra <- as.numeric(r.dat[,colnames(r.dat) == marker])
t.dataset <- rescale( dataset, to = ra )
if (sum(ra != round(range(t.dataset),1)) > 0){
# cat("Dataset",j,"AsinhCof","Asinh5:",ra,marker,":",range(t.dataset),
# "\n",file = "ranges.txt", append = TRUE)
}
return(t.dataset)
})
## Shift + BoxCox transform
data_transf[[8]] <- sapply(1:nrow(df),function(u){
marker <- colnames(data.s[[j]])[u]
dataset <- data_transf[[5]][,u]
## for scaling with range
ra <- as.numeric(r.dat[,colnames(r.dat) == marker])
t.dataset <- rescale( dataset, to = ra )
if (sum(ra != round(range(t.dataset),1)) > 0){
# cat("Dataset",j,"BoxCox","Asinh5:",ra,marker,":",range(t.dataset),
# "\n",file = "ranges.txt", append = TRUE)
}
return(t.dataset)
})
for (u in 2:length(data_transf))
dimnames(data_transf[[u]]) <- dimnames(data_transf[[1]])
names(data_transf) <- c("Raw", "Asinh5", "Power", "AsinhCof", "BoxCox",
"PowerSc", "AsinhCofSc", "BoxCoxSc")
dt.list[[j]] <- data_transf
}
## EVALUATE ON FLOWSOM CLUSTERING
# number of meta-clusters k
k <- 40
# number of repetitions of the analysis
nruns <- 10
output <- list()
for (j in 1:3){
data_transf <- dt.list[[j]]
labels <- data[[j]][, "label"] # true labels of dataset
set.seed(123)
res <- list()
cat( system.time( res <- lapply(data_transf, function(x) { clus_and_eval(x, labels) }) ), '\n' )
output[[j]] <- res
}
names(output) <- c("Levine32", "Levine13", "Samusik39")
save(output, file = paste0("output_",nruns,"run_",k,"k_scaled.RData"))
load("output_10run_40k_scaled.RData")
##########################################
# COMPARE RESULTS AND PLOT DISTRIBUTIONS #
##########################################
names <- c("Levine32", "Levine13", "Samusik39")
## Barplots of results (lambdas and cofactors) from estimation method
pdf("plots/est-barplots.pdf")
par(mfrow = c(3,1))
for (i in 1:3){
df <- df.list[[i]]
barplot(table(df$lambda), xlab = "Lambda", ylab = "Frequency", main = names[i])
}
for(i in 1:3){
df <- df.list[[i]]
barplot(table(df$cofactor), xlab = "Cofactor", ylab = "Frequency", main = names[i])
}
dev.off()
## Plot comparing lambdas with positive population (log) size and mean
pdf("plots/lambda-mean-size.pdf")
par(mfrow = c(2,3))
for (i in 1:3){
df <- df.list[[i]]
plot(df$mean, df$lambda, pch = 20, ylab = "Lambda", xlab = "Pop. Mean", main = names[i])
}
for (i in 1:3){
df <- df.list[[i]]
plot(log(df$n), df$lambda, pch = 20, ylab = "Lambda", xlab = "Log(Pop. Size)", main = names[i])
}
dev.off()
## Plots comparing effect of all transformations on clusters for each marker
filenames <- list(
Levine32 = "plots/Levine32-transf-marker-sc.pdf",
Levine13 = "plots/Levine13-transf-marker-sc.pdf",
Samusik39 = "plots/Samusik39-transf-marker-sc.pdf"
)
for (j in 1:3){
plots.l <- list()
df <- df.list[[j]]
data_transf <- dt.list[[j]] # get all transformed forms for the given dataset
labels <- data[[j]][, "label"] # get true gates for the given dataset
# events in data_transf must be in same
# order as in labels
## For each transformed set, get all clusters
cdata_transf <- lapply(data_transf, function(u){
return_clusters(u, labels, orderit = FALSE)
})
m.data <- melt(cdata_transf)
colnames(m.data) <- c("Cell", "Marker", "Value", "Cluster", "Transf")
n <- length(cdata_transf[[1]])
m.data$Cluster <- factor(m.data$Cluster, levels = 1:n)
m.data$Transf <- factor(m.data$Transf)
# m.data$Marker <- factor(m.data$Marker)
## reorder to better display comparisons between transformations
m.data$Transf <- factor(m.data$Transf, levels(m.data$Transf)[c(8,1,2,4,6,3,5,7)])
col.vec <- c("#F8766D", "#E38900", "#C49A00", "#99A800", "#53B400", "#00BC56",
"#00C094", "#00BFC4", "#00B6EB", "#06A4FF", "#A58AFF", "#DF70F8",
"#FB61D7", "#FF66A8")
if (j > 1){}
col.vec <- c("#F8766D", "#ED813E", "#DE8C00", "#CD9600", "#B79F00", "#9DA700",
"#7CAE00", "#49B500", "#00BA38", "#00BE67", "#00C08B", "#00C1A9",
"#00BFC4", "#00BBDC", "#00B4F0", "#00A9FF", "#619CFF", "#9F8CFF",
"#C77CFF", "#E36EF6", "#F564E3", "#FF61CC", "#FF64B0", "#FF6C91")
## Plot one marker per page (all transformations)
plots.l <- lapply(colnames(data_transf[[1]]), function(u) {
lambda <- df$lambda[ df$marker == u ]
cofactor <- df$cofactor[ df$marker == u ]
m <- m.data[m.data$Marker == u,]
maxc <- as.numeric(df$cluster[ df$marker == u ])
col.vec[maxc] <- "black"
p <- ggplot(m, aes(x = Value, color = Cluster, group = Cluster)) +
geom_density() + facet_wrap(~ Transf, scales = "free") +
scale_color_manual(values=col.vec) +
theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle(paste0(u, " Lambda=", lambda, " Cofactor=", cofactor))
})
plots <- marrangeGrob(plots.l, nrow=1, ncol=1)
ggsave(file = filenames[[j]], plots)
}
## Plots comparing effect of all transformations on cluster used for estimation
## for each marker
filenames2 <- list(
Levine32 = "plots/Levine32-transf-marker-cluster.pdf",
Levine13 = "plots/Levine13-transf-marker-cluster.pdf",
Samusik39 = "plots/Samusik39-transf-marker-cluster.pdf"
)
for (j in 1:3){
plots.l <- list()
df <- df.list[[j]]
data_transf <- dt.list[[j]] # get all transformed forms for the given dataset
labels <- data[[j]][, "label"] # get true gates for the given dataset
# events in data_transf must be in same
# order as in labels
## For each transformed set, get all clusters
cdata_transf <- lapply(data_transf, function(u){
return_clusters(u, labels, orderit = FALSE)
})
m.data <- melt(cdata_transf)
colnames(m.data) <- c("Cell", "Marker", "Value", "Cluster", "Transf")
n <- length(cdata_transf[[1]])
m.data$Cluster <- factor(m.data$Cluster, levels = 1:n)
m.data$Transf <- factor(m.data$Transf)
## Plot one marker per page (all transformations)
plots.l <- lapply(colnames(data_transf[[1]]), function(u) {
lambda <- df$lambda[ df$marker == u ]
cofactor <- df$cofactor[ df$marker == u ]
maxc <- as.numeric(df$cluster[ df$marker == u ])
m <- m.data[m.data$Marker == u & m.data$Cluster == maxc, ]
p <- ggplot(m, aes(x = Value, color = Transf)) +
geom_density() + coord_cartesian(xlim = c(-5, 50)) +
theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle(paste0(u, " Cluster=", maxc, " Lambda=", lambda, " Cofactor=", cofactor))
})
plots <- marrangeGrob(plots.l, nrow=1, ncol=1)
ggsave(file = filenames2[[j]], plots)
}
## Plot differences in F1 scores (single) relative to arcsinh(x/5)
## Violin plots + Points
plots.l <- list()
for (j in 1:3){
diffs <- lapply(output[[j]],function(u){
diff <- u$F1 - output[[j]][[2]]$F1
})
names(diffs) <- c("Raw", "Asinh5", "Power", "AsinhCof", "BoxCox",
"PowerSc", "AsinhCofSc", "BoxCoxSc")
labels <- data[[j]][, "label"] # true labels
# m.data <- melt(diffs[-2])
m.data <- melt(diffs[3:8])
colnames(m.data) <- c("Cluster", "Run", "F1_Deviation", "Transf")
m.data <- cbind(m.data, "Scale" = 1)
m.data$Scale[grep("Sc", m.data$Transf)] <- "Scaled"
m.data$Scale[m.data$Scale == 1] <- "NotScaled"
m.data$Transf <- sub("Sc", "", m.data$Transf)
m.data$Cluster <- factor(m.data$Cluster)
m.data$Transf <- factor(m.data$Transf)
m.data$Scale <- factor(m.data$Scale)
## Ordering by cluster number (1,2,3...)
# p <- ggplot(m.data, aes(x = Cluster, y = F1_Deviation, color = Scale, fill = Scale)) +
# geom_violin() + geom_point(aes(color = Scale),
# position = position_dodge(width = 0.9),
# size = 1, alpha = 0.6) +
# scale_color_manual(values=c("tomato", "limegreen", "dodgerblue"),
# labels=c("NotScaled", "Asinh5", "Scaled")) +
# facet_wrap(~ Transf, scales = "free") + coord_cartesian(ylim = c(-1, 1)) +
# geom_hline(aes(yintercept = 0, col = "red"), show.legend = FALSE) +
# theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
# ggtitle(names[j]) + guides(fill=FALSE)
# plots.l <- c(plots.l, list(p))
## To order by cluster size
ord_clus <- order(table(labels)) # order by increasing cluster size
m.data$Cluster <- factor(m.data$Cluster, levels(m.data$Cluster)[ord_clus])
p2 <- ggplot(m.data, aes(x = Cluster, y = F1_Deviation, color = Scale, fill = Scale)) +
geom_violin() + geom_point(aes(color = Scale),
position = position_dodge(width = 0.9),
size = 1, alpha = 0.6) +
scale_color_manual(values=c("tomato", "limegreen", "dodgerblue"),
labels=c("NotScaled", "Asinh5", "Scaled")) +
facet_wrap(~ Transf, scales = "free") + coord_cartesian(ylim = c(-1, 1)) +
geom_hline(aes(yintercept = 0, col = "red"), show.legend = FALSE) +
theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill=FALSE) +
ggtitle(names[j])
# ggtitle(paste0(names[j], " ordered by incr. Cluster Size"))
plots.l <- c(plots.l, list(p2))
}
plots <- marrangeGrob(plots.l, nrow=1, ncol=1)
ggsave(file = "plots/clustering-performance-single.pdf", plots)
## Plot differences in F1 scores (averages) relative to arcsinh(x/5)
## Violin plots + Points
plots.l <- list()
for (i in 1:3){
diffs <- sapply(output[[i]],function(u){
diff <- apply(u$F1,1,mean) - apply(output[[i]][[2]]$F1,1,mean)
})
diffs <- diffs[, 2:8]
colnames(diffs) <- c("Asinh5", "Power", "AsinhCof", "BoxCox",
"PowerSc", "AsinhCofSc", "BoxCoxSc")
labels <- data[[i]][, "label"] # true labels
row.names(diffs) <- as.character(1:nrow(diffs))
m.diff <- melt(diffs[,-1])
colnames(m.diff) <- c("Cluster", "Transf", "MeanF1_Deviation")
m.diff$Cluster <- factor(m.diff$Cluster)
m.diff$Transf <- factor(m.diff$Transf)
## Ordering by cluster number (1,2,3...)
# p <- ggplot(m.diff, aes(x = Cluster, y = MeanF1_Deviation, group = Cluster)) +
# geom_violin() + geom_point(aes(color = Transf),
# size = 2, alpha = 0.6) +
# coord_cartesian(ylim = c(-1, 0.5)) +
# scale_color_manual(values=c("firebrick", "gold3", "forestgreen", "dodgerblue",
# "darkorchid", "hot pink", "black"),
# labels=c("AsinhCof", "AsinhCofSc", "BoxCox",
# "BoxCoxSc", "Power", "PowerSc", "Asinh5")) +
# geom_hline(aes(yintercept = 0, col = "red"), show.legend = FALSE) +
# theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
# ggtitle(names[i])
# plots.l <- c(plots.l, list(p))
## To order by cluster size
ord_clus <- order(table(labels)) # order by increasing cluster size
m.diff$Cluster <- factor(m.diff$Cluster, levels(m.diff$Cluster)[ord_clus])
p2 <- ggplot(m.diff, aes(x = Cluster, y = MeanF1_Deviation, group = Cluster)) +
geom_violin() + geom_point(aes(color = Transf),
size = 2, alpha = 0.6) +
coord_cartesian(ylim = c(-1, 0.5)) +
scale_color_manual(values=c("firebrick", "gold3", "forestgreen", "dodgerblue",
"darkorchid", "hot pink", "black"),
labels=c("AsinhCof", "AsinhCofSc", "BoxCox",
"BoxCoxSc", "Power", "PowerSc", "Asinh5")) +
geom_hline(aes(yintercept = 0, col = "red"), show.legend = FALSE) +
theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle(names[i])
# ggtitle(paste0(names[i], " ordered by incr. Cluster Size"))
plots.l <- c(plots.l, list(p2))
}
plots <- marrangeGrob(plots.l, nrow=1, ncol=1)
ggsave(file = "plots/clustering-performance-mean.pdf", plots)
# Heatmap of deviations from asinh5 reference
# plots.l <- list()
pdf(file = "plots/clustering-performance-heatmap.pdf")
for (i in 1:3){
diffs <- sapply(output[[i]],function(u){
diff <- apply(u$F1,1,mean) - apply(output[[i]][[2]]$F1,1,mean)
})
diffs <- diffs[,3:8]
colnames(diffs) <- c("Power", "AsinhCof", "BoxCox",
"PowerSc", "AsinhCofSc", "BoxCoxSc")
rownames(diffs) <- 1:nrow(diffs)
# m.diff <- melt(diffs)
# colnames(m.diff) <- c("Cluster", "Transf", "MeanF1_Deviation")
# m.diff$Cluster <- factor(m.diff$Cluster)
## Heatmap with ggplot
# p <- ggplot(m.diff, aes(x = Transf, y = Cluster)) +
# geom_tile(aes(fill = MeanF1_Deviation)) +
# scale_fill_gradient2(low = "dark blue", mid = "white", high = "red",
# limits = c(-1,1)) +
# geom_text(aes(label = round(MeanF1_Deviation, 2))) +
# theme(axis.text.x = element_text(angle = 90, size = 9)) +
# ggtitle(names[i])
# plots.l <- c(plots.l, list(p))
paletteLength <- 200
myColor <- colorRampPalette(c("blue", "white", "red"))(paletteLength)
pheatmap(diffs, cluster_rows = TRUE, cluster_cols = TRUE,
color = myColor, breaks = seq(-1, 1, by = 0.01),
display_numbers = TRUE, number_format = "%.3f",
number_color = "black", fontsize = 8, fontsize_number = 6,
main = names[i])
}
dev.off()
# plots <- marrangeGrob(plots.l, nrow=1, ncol=1)
# ggsave(file = "plots/clustering-performance-heatmap.pdf", plots)
## Plot of mean deviations from asinh5 reference
# plots.l <- list()
meandiffs <- matrix(nrow = 3, ncol = 6)
rownames(meandiffs) <- c("Levine32", "Levine13", "Samusik39")
colnames(meandiffs) <- c("Power", "AsinhCof", "BoxCox",
"PowerSc", "AsinhCofSc", "BoxCoxSc")
for (i in 1:3){
diffs <- sapply(output[[i]],function(u){
diff <- apply(u$F1,1,mean) - apply(output[[i]][[2]]$F1,1,mean)
})
diffs <- diffs[,3:8]
meandiffs[i,] <- apply(diffs, 2, mean)
}
m.data <- melt(meandiffs)
colnames(m.data) <- c("Dataset", "Transf", "Mean_Deviation")
p <- ggplot(m.data, aes(x = Transf, y = Mean_Deviation, col = Dataset)) + geom_point() +
coord_cartesian(ylim = c(-0.4,0))
ggsave(file = "plots/clustering-performance-meandev-comparison.pdf", p)
#######################
# VLOG IMPLEMENTATION #
#######################
tmax <- function(a, b, xmax){
tmax <- log( ( sqrt( xmax^2 + a*xmax + b^2 ) + xmax + a/2 ) / ( a/2 + b ) )
return(tmax)
}
## VLog function
vlog <- function(x, a, b, zmax){
xmax <- max(x)
tmax <- tmax(a,b,xmax)
z <- sign(x) * (zmax/tmax) * log( ( sqrt( x^2 + a*abs(x) + b^2 ) + abs(x) + a/2 ) / (a/2 + b) )
return(z)
}
## Inverse VLog function
ivlog <- function(z, a, b, xmax){
zmax <- max(z)
tmax <- tmax(a,b,xmax)
x <- sign(z) * a * sinh( (z/2)*(tmax/zmax) )^2 + b*sinh( z*(tmax/zmax) )
return(x)
}
########################
# APPLY TRANSFORMATION #
########################
## Apply transformation with paramters a = 0, b = 10, zmax = asinhmax
t.data <- list()
for (j in 1:3){
cof <- 5
dataset <- data.s[[j]] # raw dataset without labels
ref <- asinh(dataset/cof)
mdat <- apply(ref, 2, function(u){
round(max(u),1)
})
t.dataset <- sapply(1:ncol(dataset),function(u){
data <- dataset[,u]
zmax <- mdat[u]
vlog(x = data, a = 0, b = 10, zmax = zmax)
})
dimnames(t.dataset) <- dimnames(ref)
t.data[[j]] <- list(asinh = ref, vlog = t.dataset)
}
## Plotting resulting distributions
## Facetting by cluster
filenames <- list(
Levine32 = "plots/Levine32-vlog-marker.pdf",
Levine13 = "plots/Levine13-vlog-marker.pdf",
Samusik39 = "plots/Samusik39-vlog-marker.pdf"
)
plots.l <- list()
for (j in 1:3){
data_transf <- t.data[[j]] # get transformed dataset (zmax = custom)
labels <- data[[j]][, "label"] # get true gates for the given dataset
# events in data_transf must be in same
# order as in labels
## For each transformed set, get all clusters
cdata_transf <- lapply(data_transf, function(u){
return_clusters(u, labels, orderit = FALSE)
})
m.data <- melt(cdata_transf)
colnames(m.data) <- c("Cell", "Marker", "Value", "Cluster", "Transf")
n <- length(cdata_transf[[1]])
m.data$Cluster <- factor(m.data$Cluster, levels = 1:n)
m.data$Transf <- factor(m.data$Transf)
## Facetting by Cluster (one marker/page)
plots.l <- lapply(colnames(data_transf[[1]]), function(u) {
m <- m.data[m.data$Marker == u,]
p <- ggplot(m, aes(x = Value, color = Transf, group = Transf)) +
geom_density() + facet_wrap(~ Cluster, scales = "free") +
theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle(u)
})
plots <- marrangeGrob(plots.l, nrow=1, ncol=1)
ggsave(file = filenames[[j]], plots)
}
## EVALUATE ON FLOWSOM CLUSTERING
# number of meta-clusters k
k <- 40
# number of repetitions of the analysis
nruns <- 10
output <- list()
for (j in 1:3){
data_transf <- t.data[[j]]
labels <- data[[j]][, "label"] # true labels of dataset
set.seed(123)
res <- list()
cat( system.time( res <- lapply(data_transf, function(x) { clus_and_eval(x, labels) }) ), '\n' )
output[[j]] <- res
}
names(output) <- c("Levine32", "Levine13", "Samusik39")
save(output, file = paste0("vlog_z=c_",nruns,"run_",k,"k.RData"))
load("vlog_z=c_10run_40k.RData")
###################################
# COMPARE RESULTS ASINH5 VS. VLOG #
###################################
## Deviation values
names <- c("Levine32", "Levine13", "Samusik39")
plots.l <- list()
for (j in 1:3){
diffs <- lapply(output[[j]],function(u){
diff <- u$F1 - output[[j]][[1]]$F1
})
labels <- data[[j]][, "label"] # true labels
m.data <- melt(diffs[-1])
colnames(m.data) <- c("Cluster", "Run", "F1", "Transf")
m.data$Cluster <- factor(m.data$Cluster)
m.data$Transf <- factor(m.data$Transf)
## to order by cluster size
ord_clus <- order(table(labels)) # order by increasing cluster size
m.data$Cluster <- factor(m.data$Cluster, levels(m.data$Cluster)[ord_clus])
p <- ggplot(m.data, aes(x = Cluster, y = F1, group = Cluster)) +
geom_violin() + geom_point(size = 1.5, alpha = 0.6) +
coord_cartesian(ylim = c(-1, 1)) +
stat_summary(aes(group=Cluster), fun.y=mean, geom="point",
size=1.5, color = "dodgerblue") +
geom_hline(aes(yintercept = 0, col = "red"), show.legend = FALSE) +
theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle(names[j])