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GAA-EVAL.R
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library(ggplot2)
library(dplyr)
library(gridExtra)
library(grid)
library(lattice)
library(pROC)
library(ROCR)
require(reshape2)
library(ggrepel)
library(rlist)
library(gmodels)
######### This function needs to be more flexible, e.g., col 2 can be non-numberic
##################################################################################
#Validation
Validate.Submission.File <- function(infile.path, template.file,
header = TRUE, sep = "\t",
na.strings='', ...){
# #Inputs
# header = TRUE
# sep = "\t"
# na.strings=''
# template.file = "5-GAA_submission_template.txt"
# infile.path = "prediction/Group_1/Group_1-prediction_file_nocomments-1.txt"
# submission template
sub.template <- read.table(template.file, sep = sep, header = header,
na.strings = na.strings)
number.of.rows <- nrow(sub.template)
number.of.columns <- ncol(sub.template)
correct.header <- colnames(sub.template)
correct.column.1 <- sub.template[, 1]
#prediction file
cat('checking file', infile.path, '\n')
#check if file exists
if (!file.exists(infile.path)) {
stop("File ", infile," does not exist!")
}
#check permission to read file
if (file.access(infile.path, mode = 4) < 0) {
stop("No permission to read file ", infile.path, " !")
}
pred.table <- read.table(infile.path, sep = sep, header = header,
na.strings = na.strings)
#check file has correct number of lines
correct.num.row = TRUE
if (number.of.rows != nrow(pred.table)) {
cat('submission file ', infile.path,
' has not a correct number of lines','\n')
correct.num.row = FALSE
}
#check number of fields of each line is correct
correct.num.column = TRUE
if (number.of.columns != ncol(pred.table)) {
cat('submission file ', infile.path,
' has not a correct number of columns','\n')
correct.num.column = FALSE
}
#check if header is valid
correct.header = TRUE
if (any(correct.header != colnames(pred.table))) {
cat('submission file', infile.path,
' has not a correct header, or order may be different from template','\n') # check all element of header are correct
correct.header = FALSE
}
empty.cell = FALSE
#check file has not empty cells
if (any(grepl('NA', pred.table) == TRUE)){
cat('submission file ', infile.path, ' has a blank cell in column ',
match('TRUE',grepl('NA', pred.table)),'\n')
empty.cell = TRUE
}
#check each column contains valid character (for example: in some columns only numbers are accepted, etc....)
#check if column 1 contains valid character
#YuC: change this function since col1 not necessarily in order. First sort two list and then compare
# then get back the original row number of the prediction file with unmatched name
valid.column.1 = TRUE
if (any(sort(as.character(correct.column.1)) != sort(as.character(pred.table[, 1])))) { # escape sequences \d, \s and \w represent any decimal digit, space character and ‘word??? character, * means zero or one match, + means preceding item will match one or more times
cat("First column of ", infile.path," must contain valid alleles! Check row ",
which(as.character(pred.table[, 1]) == sort(pred.table[, 1])[sort(as.character(correct.column.1)) != sort(as.character(pred.table[, 1]))]),'\n')
valid.column.1 = FALSE
}
#check if column 2 is numerical value or star
valid.column.2 = TRUE
if (any(regexpr('[*]|\\d+', pred.table[, 2]) != 1)) {
cat("Second column of ", infile.path," must be numeric or \"*\"!", '\n')
valid.column.2 = FALSE
} else if (any(grepl('[*]', pred.table[, 2]) == grepl('\\d+', pred.table[, 2])) == TRUE) { # check both * and digits are not present in column 2
cat("Second column of ", infile.path, " must be numeric or \"*\", not both!",'\n')
valid.column.2 = FALSE
}
#check if column 3 is numerical value or star
valid.column.3 = TRUE
if (any(regexpr('[*]|\\d+', pred.table[, 3]) != 1)) {
cat("Third column of ", infile.path, " must be numeric or \"*\"!",'\n')
valid.column.3 = FALSE
} else if (any(grepl('[*]', pred.table[, 3]) == grepl('\\d+', pred.table[, 3])) == TRUE) { # check both * and digits are not present in column 3
cat("Third column of ", infile.path, " must be numeric or \"*\", not both!",'\n')
valid.column.3 = FALSE
}
# YuC: After this step, should implement new functions to determine the valid file's following
# check if column 1 is in the same order with template
ordered.rows = TRUE;
if (any(as.character(correct.column.1) != as.character(pred.table[, 1]))) {
ordered.rows = FALSE;
cat("lines are not ordered as in template",'\n')
}
# sd existence (if whole column 3 is *)
with_sd = TRUE;
if (all(regexpr('[*]', pred.table[, 3]) == 1)) {
cat("there's no standard deviation input",'\n')
with_sd = FALSE;
}
cat('submission file', infile.path, 'format check passed!', '\n')
validation.info = c(
correct.num.row = correct.num.row,
correct.num.column = correct.num.column,
correct.header = correct.header,
empty.cell = empty.cell,
valid.column.1 = valid.column.1,
valid.column.2 = valid.column.2,
valid.column.3 = valid.column.3,
ordered.rows = ordered.rows,
with_sd = with_sd)
return(validation.info)
}
Validate.Submission.Folder <- function(folder.name, template.file,
header = TRUE, sep = "\t", na.strings ='', ...){
#####################################
#inputs for debug
# folder.name = "prediction"
# template.file = "5-GAA_submission_template.txt"
# header = TRUE
# sep = "\t"
# na.strings =''
#######################
#read top folder
group.path <- paste(folder.name,list.files(folder.name), sep="/")
#obtain files in each sub-folder of the top folder
submission.files <- unname(unlist(sapply(group.path,function(x) paste(x, list.files(x), sep="/"))))
validation.info =sapply(submission.files,
function(x) Validate.Submission.File(x, template.file,
header = header, sep = sep,
na.strings = na.strings))
# YuC: Should unlist the tmp and output a table with
# |submission.files|order|col2_na|col3_na|with_sd|
validation.info = t(validation.info)
}
#######################################################################################
# Read
read.RealData <- function(file, col.id = 1, col.value = 2,
col.sd = 3, with.sd = TRUE,
sep = "\t", header = TRUE, na.character = '*', ...){
data <- read.table(file, sep = sep, header = header, na.strings = na.character, ...)
id <- data[, col.id[1]]
if(length(col.id)>1){
id <- sapply(2:length(col.id),function(x) paste(id,data[,col.id[x]],sep='.'))
}
value <- as.numeric(data[ , col.value])
names(value) <- id
sd <- NA
if(with.sd){
sd <- data[ , col.sd]
sd <- as.numeric(sd)
names(sd) <- id
}
return.data <- list(value, sd)
names(return.data) <- c("value", "sd")
return(return.data)
}
#define file-reading sub-function
read.Prediction.File <- function(file.name, col.id, col.value, col.sd,
with.sd = TRUE,
sep = "\t", header = TRUE,
na.character = na.character, ...){
data <- read.table(file.name, sep = sep, header = header, na.strings = na.character, ...)
id <- data[ , col.id[1]]
if(length(col.id) > 1){
id <- sapply(2:length(col.id),function(x) paste(id,data[,col.id[x]],sep='.'))
}
value <- as.numeric(as.character(data[ , col.value]))
names(value) <- id
if(with.sd){
sd <- as.numeric(as.character(data[ , col.sd]))
names(sd) = id
# if(is.na(sd[1])){
# sd <- rep(NA,length(sd))
# # YuC: don't get why to execute so, comment out as Zhiqiang suggests
# }
}
else{
sd <- NA
}
return.data <- list(value,sd)
names(return.data) <- c("value", "sd")
return(return.data)
}
# reorder the prediction submision value and sd column
validate.Submission.data <- function(real.data,pred.data){
pred.data$value=pred.data$value[names(real.data$value),]
pred.data$sd=pred.data$sd[names(real.data$sd),]
return(pred.data)
}
#adjust data structure function (convert file-centered data structure
#into value matrix and sd matrix)
adjustDataStructure <- function(data, with.sd){
order=names(data[[1]]$value)
value <- sapply(1:length(data), function(x) return(data[[x]]$value[order]))
colnames(value) <- names(data)
sd <- NA
if(with.sd){
sd <- sapply(1:length(data), function(x) return(data[[x]]$sd))
colnames(sd) <- names(data)
}
data <- list(value, sd)
names(data) = c("value", "sd")
return(data)
}
read.Submission.Folder <- function(folder.name, col.id = 1, col.value = 2,
col.sd = 3, with.sd = TRUE,
file.sep = "\t", file.header = TRUE,
chop.prefix = TRUE, chop.suffix = TRUE,
na.character = "*", real.data = NULL,...){
chopHeader <- function(x){
tmp <- unlist(strsplit(x,split="/"))
return(tmp[length(tmp)])
}
chopTail <- function(x){
tmp <- unlist(strsplit(x,split=".",fixed=TRUE))
if(length(tmp) > 1){
tmp <- substr(x,1,nchar(x)-nchar(tmp[length(tmp)])-1)
}
return(tmp)
}
# #for running ####################
exp.data <- read.RealData(file = "exp_data.csv", sep = ",",
col.id = 2, col.value = 5, col.sd = 6)
folder.name = "prediction"
col.id = 1
col.value = 2
col.sd = 3
with.sd = TRUE
file.sep = "\t"
file.header = TRUE
chop.prefix = TRUE
chop.suffix = TRUE
na.character = "*"
real.data = exp.data
##################################
#read top folder
group.path <- paste(folder.name,list.files(folder.name), sep="/")
#obtain files in each sub-folder of the top folder
file.path <- unname(unlist(sapply(group.path,function(x) paste(x, list.files(x), sep="/"))))
#read files to matrices
data <- lapply(file.path,function(x) read.Prediction.File(x, col.id = col.id,
col.value = col.value,
col.sd = col.sd,
sep = file.sep,
header = file.header,
na.character = na.character,
with.sd = with.sd))
#modify id by removing folder names
if(chop.prefix){
file.path <- sapply(file.path, chopHeader)
}
#modify id by removing suffix like ".txt"
if(chop.suffix){
file.path <- sapply(file.path, chopTail)
}
names(data) <- file.path
#adjst data structure
data <- adjustDataStructure(data = data, with.sd = with.sd)
if(!is.null(real.data)){
# Reordering of the rows
data <- validate.Submission.data(real.data,data)
}
tmp.group = sapply(colnames(data$value), function(x) unlist(strsplit(x,split="_"))[2])
tmp.group = sapply(1:length(tmp.group), function(x) unlist(strsplit(tmp.group[x],split="-"))[1])
tmp.group = as.numeric(tmp.group)
group = data.frame(colnames(data$value),tmp.group)
names(group) = c("file","group")
data$group = group
return(data)
}
# YuC: Add the Grouping functions
#' @decription Adding grouping information after parsing prediction files from folder
#' @param data the list with $value and $sd after parsing the prediction file folder
#' @param group_info a 1D integer vector specifying grouping of each column of value/sd matrix
#' @return original data with an addtional $group field as a list
#' with value as group and rownames as file names
#' @example to get all files of a group : group %>% filter(group == 2) %>% select(file)
addGroup = function(data,group_info) {
file.labels = data.frame(colnames(data$value),stringsAsFactors = FALSE)
group = cbind(file.labels,group_info)
names(group) = c("file","group")
data$group = group
return(data)
}
lmFitHelpher = function(real.data,pred.data,sub.name){
exp = real.data$value
sub = pred.data$value[,sub.name]
dat = as.data.frame(cbind(exp,sub))
lm.fit=lm(exp~sub,data=dat)
x = summary(lm.fit)
return(x$adj.r.squared)
}
# YuC: Add the Grouping functions
#' @decription fit each submission and the experimental value with linear value, then for each group,
#' keep the best fit submission based on adjusted R^2 statistics/pearson correlation
#' @name groupFilter
#' @param real.data the list with $value and $sd after parsing the prediction file folder
#' @param pred.data a 1D integer vector specifying grouping of each column of value/sd matrix
#' @return The original data set only keeping the best submission from each group
groupFilter = function(real.data, pred.data) {
######### debug input #####################
real.data <- read.RealData(file = "exp_data.csv", sep = ",",
col.id = 2, col.value = 5, col.sd = 6)
# pred.data <- read.Submission.Folder(folder.name = "prediction/",col.id = 1,
# col.value = 2, col.sd = 3, real.data = real.data)
# To change the name o unique identifiers
# real.data.2 <- read.RealData(file = "exp_data.csv", sep = ",",
# col.id = c(2,4), col.value = 5, col.sd = 6)
# names(pred.data$value) = names(real.data.2$value)
# names(pred.data$sd) = names(real.data.2$value)
# names(real.data$value) = names(real.data.2$value)
# names(real.data$sd) = names(real.data.2$value)
###################################################################
r_squares <- unlist(lapply(colnames(pred.data$value),
function(x) lmFitHelpher(real.data,pred.data,x))
)
r_squares_group = cbind(pred.data$group,r_squares)
max.r = r_squares_group %>% group_by(group) %>% summarise(max.r = max(r_squares))
selected.file = r_squares_group %>% filter(r_squares %in% max.r$max.r)
pred.data$value = pred.data$value[,selected.file$file]
pred.data$sd = pred.data$sd[,selected.file$file]
pred.data$group = selected.file
return (pred.data)
}
#####################################################################################################
# YuC: add a z-score transformation funcition to Normalize all the prediction samples to N(0,1) for cross-group
# comparison/distance calculation
# Since the standard deviation will also be changed, the sd.use input should be filtered before z-transformation
#' @decription normalize each of the column of the value to N(0,1)
#' @param real.data the experimental data from CAGI assessors
#' @param pred.data prediction data parsed in from submissions
#' @return z.real.data and z.pred.data
z.normalization = function(real.data, pred.data) {
z.Helper = function(column) {
std = sd(column, na.rm = TRUE)
avg = mean(column, na.rm = TRUE)
dat = (column - avg)/std
return(dat)
}
suppressWarnings(
z.pred.data <- sapply(1:ncol(pred.data$value),
function(x) z.Helper(pred.data$value[,x]))
)
z.real.data = z.Helper(real.data$value)
colnames(z.pred.data)=colnames(pred.data$value)
z = c()
z$real.data = z.real.data
z$pred.data = z.pred.data
return(z)
}
eval.Correlation <- function(real.data, pred.data, method = "pearson", sd.use = NA,z.transform = FALSE, boot = FALSE, ...){
if (z.transform) {
z_dat = z.normalization(real.data,pred.data)
real.data$value = z_dat$real.data
pred.data$value = z_dat$pred.data
}
sd.enable <- is.numeric(sd.use) & length(real.data$sd) >1
if(sd.enable){
keep <- (real.data$sd < sd.use) & (!is.na(real.data$sd))
real.data$value <- real.data$value[keep]
pred.data$value <- pred.data$value[keep, ]
}
n <- length(real.data$value)
suppressWarnings(
cor.coefficient <- sapply(1:ncol(pred.data$value),
function(x) cor.test(real.data$value, pred.data$value[,x],
method = method)$estimate))
suppressWarnings(
cor.p.value <- sapply(1:ncol(pred.data$value),
function(x) cor.test(real.data$value, pred.data$value[,x],
method = method)$p.value))
cor.result <- data.frame(cor.coefficient, cor.p.value)
#print(real.data$value)
# cor.to.plot <- cor(pred.data$value,real.data$value, method=method, use = "complete.obs")#tbd
# corrplot(cor.to.plot, method = "number") #tbd
colnames(cor.result) <- c(paste(method,".coefficient",".n=",n, sep=''), "p.value")
rownames(cor.result)=colnames(pred.data$value)
if(sd.enable){
colnames(cor.result)[1] <- paste(colnames(cor.result)[1], ".sd<", sd.use, sep="")
}
# add bootstrap
if (boot) {
# #####input################
# real.data <- read.RealData(file = "exp_data.csv", sep = ",",
# col.id = 2, col.value = 5, col.sd = 6)
# pred.data <- read.Submission.Folder(folder.name = "prediction/",col.id = 1,
# col.value = 2, col.sd = 3, real.data = exp.data)
# method = "pearson"
# sd.use = 0.3
# z.transform = TRUE
# ################################
rep.time = 500
rep.obj = bootstrap.Helper(real.data,pred.data,rep.time)
real.rep = rep.obj$real.rep
pred.rep = rep.obj$pred.rep
cor.rep = lapply(1:rep.time,function(x) eval.Correlation(real.data = real.rep[[x]], pred.data = pred.rep[[x]],
method, sd.use,z.transform,boot = F))
coefficient.rep = list.cbind(lapply(1:rep.time,function(x) cor.rep[[x]][,1]))
pval.rep = list.cbind(lapply(1:rep.time,function(x) cor.rep[[x]][,2]))
row.names(coefficient.rep) = row.names(cor.rep[[1]])
ci_low = list.rbind(lapply(1:nrow(coefficient.rep),function(x) quantile(coefficient.rep[x,],probs=0.05)))
ci_high = list.rbind(lapply(1:nrow(coefficient.rep),function(x) quantile(coefficient.rep[x,],probs=0.95)))
sd = list.rbind(lapply(1:nrow(coefficient.rep),function(x) sd(coefficient.rep[x,])))
avg = list.rbind(lapply(1:nrow(coefficient.rep),function(x) mean(coefficient.rep[x,])))
p.median = list.rbind(lapply(1:nrow(coefficient.rep),function(x) median(pval.rep[x,])))
cor.result = cbind(avg,ci_low,ci_high,sd,p.median)
row.names(cor.result) = row.names(cor.rep[[1]])
colnames(cor.result) = c("avg","low_ci","high_ci","sd","p.value")
}
return(as.data.frame(cor.result))
}
#' @decription The function takes in the distance matrix from the eval.RMSD function and output a normalized
#' version based on variance
#' For RMSD, the normalization for X1~N(V1,sd1^2), X2~N(V2,sd2^2) —>D = X1-X2~N(V1-V2,sd1^2 + sd2^2),
#' normalize all the variance for RMSD to 1
#' @name var.normalize
#' @param rmsd the rmsd result from the eval.RMSD function
#' @param real.data
#' @param pred.data
#' @return a normalized dis.matrix
var.normalize <- function(rmsd,real.data, pred.data) {
real.sd = sd(real.data$value, na.rm = TRUE)
pred.sd = sapply(1:ncol(pred.data$value),
function(x) sd(pred.data$value[,x], na.rm = TRUE))
new.sd = real.sd^2 + pred.sd^2
names(new.sd) = colnames(pred.data$value)
normalized.rmsd = sapply(rownames(rmsd),
function(x) rmsd[x,"RMSD"]/new.sd[x])
normalized.rmsd = as.data.frame(normalized.rmsd)
rownames(normalized.rmsd) = rownames(rmsd)
colnames(normalized.rmsd) = "RMSD"
return(normalized.rmsd)
}
eval.RMSD <- function(real.data, pred.data, sd.use = NA,
density.distance = FALSE, density.distance.adjust = FALSE, variance.normalization = TRUE,
boot = FALSE,...){
######input###################################################
# real.data <- read.RealData(file = "exp_data.csv", sep = ",",
# col.id = 2, col.value = 5, col.sd = 6)
# pred.data <- read.Submission.Folder(folder.name = "prediction/",col.id = 1,
# col.value = 2, col.sd = 3, real.data = exp.data)
# sd.use = NA
# density.distance = FALSE
# density.distance.adjust = FALSE
# variance.normalization = TRUE
##############################################################
sd.enable <- is.numeric(sd.use) & length(real.data$sd) >1
if(sd.enable){
keep <- (real.data$sd < sd.use) & (!is.na(real.data$sd))
real.data$value <- real.data$value[keep]
real.data$sd <- real.data$sd[keep]
pred.data$value <- pred.data$value[keep, ]
}
if(density.distance){
if (length(real.data$sd) == 1){
stop("This evaluation requires standard deviations for real data!")
}
}
n <- length(real.data$value)
dis.matrix <- sapply(1:ncol(pred.data$value),
function(x) pred.data$value[,x] - real.data$value)
if(density.distance){
if(density.distance.adjust){
dis.matrix <- Density.Distance(real.data = real.data,
pred.data = pred.data, adjusted = TRUE)
}
else{
dis.matrix <- Density.Distance(real.data = real.data,
pred.data = pred.data, adjusted = FALSE)
}
}
rmsd <- sapply(1:ncol(pred.data$value),
function(x) sqrt(sum((na.exclude(dis.matrix[,x]))^2)/n))
rmsd <-data.frame(rmsd)
colnames(rmsd) <- "RMSD"
rownames(rmsd) <- colnames(pred.data$value)
if(sd.enable){
colnames(rmsd)[1] <- paste(colnames(rmsd),".n=",n,".sd<", sd.use, sep="")
}
if (variance.normalization) {
rmsd = var.normalize(rmsd,real.data, pred.data)
}
if (boot) {
rep.time = 500
rep.obj = bootstrap.Helper(real.data,pred.data,rep.time)
real.rep = rep.obj$real.rep
pred.rep = rep.obj$pred.rep
rmsd.rep = lapply(1:rep.time,function(x) eval.RMSD(real.data = real.rep[[x]], pred.data = pred.rep[[x]], sd.use,
density.distance, density.distance.adjust, variance.normalization,
boot = FALSE))
rmsd.rep = list.cbind(lapply(1:rep.time,function(x) rmsd.rep[[x]][,1]))
row.names(rmsd.rep) = row.names(rmsd.rep[[1]])
ci_low = list.rbind(lapply(1:nrow(rmsd.rep),function(x) quantile(rmsd.rep[x,],probs=0.05)))
ci_high = list.rbind(lapply(1:nrow(rmsd.rep),function(x) quantile(rmsd.rep[x,],probs=0.95)))
sd = list.rbind(lapply(1:nrow(rmsd.rep),function(x) sd(rmsd.rep[x,])))
avg = list.rbind(lapply(1:nrow(rmsd.rep),function(x) mean(rmsd.rep[x,])))
rmsd.result = cbind(avg,ci_low,ci_high,sd)
row.names(rmsd.result) = colnames(pred.data$value)
colnames(rmsd.result) = c("RMSD","low_ci","high_ci","sd")
rmsd = rmsd.result
}
return(as.data.frame(rmsd))
}
AUC.build.class = function(dat,lower.positive,threshold = 0.5) {
if(lower.positive){
dat[dat <= threshold] = 1
dat[ dat > threshold] <- 0
}
else{
dat[dat > threshold] <- 1
dat[dat <= threshold] <- 0
}
return(dat)
}
# YuC: remove textbox and perform logistic regression
eval.AUC <- function(real.data, pred.data, threshold = 0.5, sd.use = NA, lower.positive=F){
sd.enable <- is.numeric(sd.use) & length(real.data$sd) >1
if(sd.enable){
keep <- (real.data$sd < sd.use) & (!is.na(real.data$sd))
real.data$value <- real.data$value[keep]
pred.data$value <- pred.data$value[keep, ]
}
n <- length(real.data$value)
realClass <- AUC.build.class(real.data$value,lower.positive,threshold)
AUC.Helper <- function(file.name,lower.positive){
#get predicted classes
predClass <- AUC.build.class(pred.data$value[,file.name],lower.positive,threshold)
#get positive
positive <- which(realClass == 1)
TP <- length( which(predClass[positive] == realClass[positive]))
FN <- length( which(predClass[positive] != realClass[positive]))
#get negative
neutral <- which(realClass == 0)
TN <- length( which(predClass[neutral] == realClass[neutral] ) )
FP <- length( which(predClass[neutral] != realClass[neutral] ) )
#get stats
sens <- TP / (TP + FN)
spec <- TN / (TN + FP)
bAccu <- (sens + spec) /2
accuracy = (TP+TN)/(TP+FP+FN+TN)
precision = TP/(TP + FP)
f1_score = 2*(sens*precision)/(sens + precision)
pred <<- prediction( predictions= pred.data$value[,file.name], labels= realClass)
perf <<- performance(pred, "tpr", "fpr")
auc <- performance(pred, "auc")
auc <- round(unlist(slot(auc, "y.values")), digits = 6)
result <- c(AUC = auc, sensitivity = sens, specificity = spec,accuracy = accuracy,precision = precision,f1_score = f1_score,bAccu = bAccu)
return(result)
}
auc.summary <- sapply(colnames(pred.data$value),
function(x) AUC.Helper(x,lower.positive))
auc.summary <- data.frame(t(auc.summary))
rownames(auc.summary) <- colnames(pred.data$value)
colnames(auc.summary)[1] <- paste("AUC", threshold, sep = '.')
if(sd.enable){
colnames(auc.summary)[1] <- paste(colnames(auc.summary)[1], ".n=",n,".sd<", sd.use, sep="")
}
return(round(auc.summary,4))
}
# YuC: A new plot function for plotting a single AUC curve with ggplot
#' @decription The function takes the output from eval.AUC function and plot one of the ROC plots for each submission file with
#' its respective AUC,sensitivity,specificity,accuracy,precision,f1_score,bAccu
#' @name plot.AUC.helper
#' @param real.data The real data object being parse in from experimental value
#' @param pred.data The real data object being parse in from submission folder
#' @param sd.use a sd threshold for filtering undescriptive samples
#' @param lower.positive whether lower than threshold is used as positive binary value
#' @return a ggplot object with all the ROC plots arranged
plot.AUC.helper = function(real.data,pred.data, file.name,threshold, sd.use, lower.positive,results) {
# render binary class data
realClass <- AUC.build.class(real.data$value,lower.positive,threshold)
# sd.enable dependent plot title
sd.enable <- is.numeric(sd.use) & length(real.data$sd) >1
if (sd.enable) {
title = paste0(file.name, "thresh=", threshold, "sd<", sd.use, "ROCcurve")
} else {
title = paste0(file.name, "thresh=", threshold, sd.use, "ROCcurve")
}
# get the evaluated statistics
rocobj <- roc(realClass,pred.data$value[,file.name])
result.label = paste(colnames(results) , results[file.name,],sep = ":")
g <- ggroc(rocobj,legacy.axes = TRUE)
g = g + theme_minimal(base_size = 5) + ggtitle(title) +
geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1), color="grey", linetype="dashed") +
annotate("text", x = 0.75, y = seq(0.125, 0.5, 3/64)[1:7], label = result.label,size = 2)
return(g)
}
# YuC: A new plot function for plotting AUC curve with ggplot
#' @decription The function takes the output from eval.AUC function and plot all the ROC plots for each submission file with
#' their respective AUC,sensitivity,specificity,accuracy,precision,f1_score,bAccu
#' @name plot.AUC
#' @param real.data The real data object being parse in from experimental value
#' @param pred.data The real data object being parse in from submission folder
#' @param sd.use a sd threshold for filtering undescriptive samples
#' @param lower.positive whether lower than threshold is used as positive binary value
#' @return a ggplot object with all the ROC plots arranged
plot.AUC = function(real.data, pred.data, threshold = 0.5, sd.use = NA, lower.positive=F) {
##########################################
# # for debugging input
# real.data <- read.RealData(file = "exp_data.csv", sep = ",",
# col.id = 2, col.value = 5, col.sd = 6)
# pred.data <- read.Submission.Folder(folder.name = "prediction/",col.id = 1,
# col.value = 2, col.sd = 3, real.data = exp.data)
# threshold = 0.5
# sd.use = NA
# lower.positive=F
###########################################
# get AUC related statistic values
sd.enable <- is.numeric(sd.use) & length(real.data$sd) >1
if(sd.enable){
keep <- (real.data$sd < sd.use) & (!is.na(real.data$sd))
real.data$value <- real.data$value[keep]
pred.data$value <- pred.data$value[keep, ]
}
results = eval.AUC(real.data, pred.data, threshold, sd.use, lower.positive)
plots <- lapply(colnames(pred.data$value),
function(x) plot.AUC.helper(real.data,pred.data,x,threshold,sd.use,lower.positive,results)
)
ml <- marrangeGrob(plots, nrow=2, ncol=2)
return(ml)
}
Density.Distance <- function(real.data, pred.data, sd.use = NA,
adjusted=FALSE){
if (length(real.data$sd) == 1){
stop("This evaluation requires standard deviations for real data!")
}
keep <- !is.na(real.data$sd)
if(is.numeric(sd.use) & length(real.data$sd) >1){
keep <- (real.data$sd < sd.use) & keep
}
real.data$value <- real.data$value[keep]
real.data$sd <- real.data$sd[keep]
pred.data$value <- pred.data$value[keep,]
averDis <-function(val,mu,sigma){
return(val*(2*pnorm(val,mean=mu,sd=sigma)-1)+
exp(-(val-mu)^2/(2*sigma^2))*(sigma*sqrt(2/pi))-
mu*(2*pnorm(val-mu,mean=0,sd=sigma)-1))
}
dis <- sapply(1:ncol(pred.data$value),function(x)
sapply(1:length(real.data$value),function(y)
averDis(pred.data$value[y,x],real.data$value[y],real.data$sd[y]+0.001)))
if(adjusted){
bacDis <- sapply(1:length(real.data$value),function(y)
averDis(real.data$value[y],real.data$value[y],real.data$sd[y]+0.001))
dis <- sapply(1:ncol(pred.data$value),function(x) dis[,x]-bacDis)
}
colnames(dis) <- colnames(pred.data$value)
row.names(dis) <- rownames(real.data$value)
return(as.data.frame(dis))
}
#lowest value should be -1. keep the colors for 1, 0, -1, provide parameter to change color, color_low, color_high, consistent for all the heatmap function calls
eval.Correlation.Between <- function(real.data, pred.data, method = "pearson",
sd.use = NA,z.transform = TRUE, grouped = FALSE, ...){
###############################################
# for debugging input
# real.data <- read.RealData(file = "exp_data.csv", sep = ",",
# col.id = 2, col.value = 5, col.sd = 6)
# pred.data <- read.Submission.Folder(folder.name = "prediction/",col.id = 1,
# col.value = 2, col.sd = 3, real.data = exp.data)
# sd.use = 0.01
# method = "pearson"
# z.transform = TRUE
###############################################
if (z.transform) {
z_dat = z.normalization(real.data,pred.data)
real.data$value = z_dat$real.data
pred.data$value = z_dat$pred.data
}
sd.enable <- is.numeric(sd.use) & length(real.data$sd) >1
if(sd.enable){
keep <- (real.data$sd < sd.use) & (!is.na(real.data$sd))
real.data$value <- real.data$value[keep]
pred.data$value <- pred.data$value[keep, ]
}
if(grouped) {
pred.data = groupFilter(real.data,pred.data)
}
mat <- cbind(real.data$value,pred.data$value)
colnames(mat)[1] <- "real.value"
suppressWarnings(
cor.coefficient <- sapply(1:ncol(mat), function(x)
sapply(1:ncol(mat), function(y)
cor.test(mat[,x], mat[,y], method = method)$estimate)))
suppressWarnings(
cor.p.value <- sapply(1:ncol(mat), function(x)
sapply(1:ncol(mat), function(y)
cor.test(mat[,x], mat[,y], method = method)$estimate)))
rownames(cor.coefficient) <- colnames(mat)
colnames(cor.coefficient) <- colnames(mat)
rownames(cor.p.value) <- colnames(mat)
colnames(cor.p.value) <- colnames(mat)
cor.result <- list(cor.coefficient, cor.p.value)
names(cor.result) <- c("coefficient", "p.value")
return(cor.result)
}
eval.Partial.Correlation <- function(real.data, pred.data, method = "pearson",
sd.use = NA, ...){
require(ppcor)
sd.enable <- is.numeric(sd.use) & length(real.data$sd) >1
if(sd.enable){
keep <- (real.data$sd < sd.use) & (!is.na(real.data$sd))
real.data$value <- real.data$value[keep]
pred.data$value <- pred.data$value[keep, ]
}
n <- length(real.data$value)
mat <- cbind(real.data$value,pred.data$value)
colnames(mat)[1] <- "real.value"
mat <-na.exclude(mat)
cor <- pcor(mat, method = "spearman", ...)
cor.coefficient=cor$estimate[2:nrow(cor$estimate),1]
cor.p.value=cor$p.value[2:nrow(cor$p.value),1]
cor.result <- data.frame(cor.coefficient, cor.p.value)
names(cor.result) <- c("coefficient", "p.value")
if(sd.enable){
colnames(cor.result)[1] <- paste(colnames(cor.result)[1], ".sd<", sd.use, sep="")
}
return(cor.result)
}
#' @decription The function call groupFilter to pick one submission from each group, then build
#' linear model excluding one group one at a time. R^2_tot - R^2_excl = unique contribution
#' @name eval.uniqueness
#' @param real.data The real data object being parse in from experimental value
#' @param pred.data The real data object being parse in from submission folder
#' @return a table with the uniqueness of each group
eval.uniqueness = function(real.data,pred.data,boot = FALSE) {
######### debug input #####################
# real.data <- read.RealData(file = "exp_data.csv", sep = ",",
# col.id = 2, col.value = 5, col.sd = 6)
# pred.data <- read.Submission.Folder(folder.name = "prediction/",col.id = 1,
# col.value = 2, col.sd = 3, real.data = real.data)
# # To change the name o unique identifiers
# real.data.2 <- read.RealData(file = "exp_data.csv", sep = ",",
# col.id = c(2,4), col.value = 5, col.sd = 6)
# names(pred.data$value) = names(real.data.2$value)
# names(pred.data$sd) = names(real.data.2$value)
# names(real.data$value) = names(real.data.2$value)
# names(real.data$sd) = names(real.data.2$value)
###################################################################
grouped.pred.data = groupFilter(real.data,pred.data)
input.group = lapply(1:length(colnames(grouped.pred.data$value)),function(x) colnames(grouped.pred.data$value)[-x] )
r.square.excl = unlist(lapply(1:length(input.group),function(x) lmFitHelpher(real.data,grouped.pred.data,input.group[[x]])))
r.square.tot = lmFitHelpher(real.data,grouped.pred.data,colnames(grouped.pred.data$value))
uniqueness = r.square.tot - r.square.excl
result = data.frame(uniqueness)
row.names(result) = grouped.pred.data$group$group
if (boot) {
rep.time = 500
rep.obj = bootstrap.Helper(real.data,pred.data,rep.time)
real.rep = rep.obj$real.rep
pred.rep = rep.obj$pred.rep
unique.rep = lapply(1:rep.time,function(x) eval.uniqueness(real.data = real.rep[[x]], pred.data = pred.rep[[x]]))
rname = row.names(unique.rep[[1]])
unique.rep = list.cbind(lapply(1:rep.time,function(x) unique.rep[[x]][,1]))
ci_low = list.rbind(lapply(1:nrow(unique.rep),function(x) quantile(unique.rep[x,],probs=0.05)))
ci_high = list.rbind(lapply(1:nrow(unique.rep),function(x) quantile(unique.rep[x,],probs=0.95)))
sd = list.rbind(lapply(1:nrow(unique.rep),function(x) sd(unique.rep[x,])))
avg = list.rbind(lapply(1:nrow(unique.rep),function(x) mean(unique.rep[x,])))
result = cbind(avg,ci_low,ci_high,sd)
row.names(result) = rname
colnames(result) = c("uniqueness","low_ci","high_ci","sd")
}
return(as.data.frame(result))
}
#' @decription This function perform the bootstrap resampling with replacement on the value table and perform
#' the specified function to access the accuracy of model more thoroughly
#' @name bootstrap.Helper
#' @param real.data The real data object being parse in from experimental value
#' @param pred.data The real data object being parse in from submission folder
bootstrap.Helper = function(real.data,pred.data,rep.time) {
# real.data <- read.RealData(file = "exp_data.csv", sep = ",",
# col.id = 2, col.value = 5, col.sd = 6)
# pred.data <- read.Submission.Folder(folder.name = "prediction/",col.id = 1,
# col.value = 2, col.sd = 3, real.data = exp.data)
# rep.time = 5
ind = lapply(1:rep.time,function(x) sample(1:length(real.data$value), length(real.data$value), replace=TRUE))
real.value.rep = lapply(1:rep.time,function(x) real.data$value[ind[[x]]])
real.sd.rep = lapply(1:rep.time,function(x) real.data$sd[ind[[x]]])
pred.value.rep = lapply(1:rep.time,function(x) pred.data$value[ind[[x]],])
pred.sd.rep = lapply(1:rep.time,function(x) pred.data$sd[ind[[x]],])
real.rep = lapply(1:rep.time,function(x) list(value = real.value.rep[[x]],sd = real.sd.rep[[x]]))
pred.rep = lapply(1:rep.time,function(x) list(value = pred.value.rep[[x]],sd = pred.sd.rep[[x]],group = pred.data$group))
result = list(real.rep = real.rep,pred.rep = pred.rep)
return(result)
}
#---------This function will plot the correlation given by the function "eval.Correlation" above! ---------------------------
plot.Correlation <- function(result.cor, method, pval=TRUE,boot = FALSE){
mydata <-data.frame(submissions=row.names(result.cor), Correlation=result.cor[,1])
plot <- ggplot(data=mydata, aes(x=submissions, y=Correlation)) +
geom_bar(stat="identity") + theme_classic() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
#If pval argument is given, we use geom_text (http://ggplot2.tidyverse.org/reference/geom_text.html) to plot appropriate number of stars
#if p smaller than 0.05 => *, p < 0.01 => **, p <0.001 => ***
stars1<-c()
if(pval) {
for (i in 1:nrow(result.cor)){
if(result.cor$p.value[i] < 0.001) {
stars1<-c(stars1, "***")
} else if(result.cor$p.value[i] < 0.01) {
stars1<-c(stars1, "**")
} else if(result.cor$p.value[i] < 0.05) {
stars1<-c(stars1, "*")
} else {
stars1<-c(stars1, " ")
}
}
}
plot2 <- plot + geom_text(aes(label = stars1), vjust = -0.5, position = position_dodge(0.9)) +
ggtitle(paste("Plot of", method, "Correlation vs. Predictor", sep=" ")) + theme(plot.title = element_text(hjust = 0.5))
# directly return the plot object instead of saving
#ggsave(filename=paste("plots/", method, "correlation",".pdf"), plot=plot2, width = 7, height = 7, units = "in",dpi = 300)
if (boot) {
plot2 <- plot2 + geom_errorbar(aes(ymin=result.cor$low_ci, ymax=result.cor$high_ci),
width=.2,position=position_dodge(.9))
}
return(plot2)
}
#---------This function will plot the correlation between given by the function "eval.Correlation.Between" above!, and return the plot object
#Reference: http://www.sthda.com/english/wiki/ggplot2-quick-correlation-matrix-heatmap-r-software-and-data-visualization
#(pairwise correlation with 1's down the diagonal, output is a matrix for heatmap)
plot.Correlation.Between <- function(result.cor, method, color_low="blue", color_high="red", color_mid="white") {
######### for running ############
# result.cor = cor.result
# color_low="blue"
# color_high="red"
# color_mid="white"
# method = "pearson"
#################################
result.cor.melted <- melt(result.cor)
p <- ggplot(data = result.cor.melted, aes(result.cor.melted$Var1, result.cor.melted$Var2, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = color_low, high = color_high, mid = color_mid,
midpoint = 0, limit = c(-2,2), space = "Lab",
name=paste(method, "\nCorrelation")) +
theme_classic() +
theme(text = element_text(size = 10), axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1), axis.title.x = element_blank(),
axis.title.y = element_blank()) + coord_fixed()
#ggsave(filename=paste("plots/", method, "correlationBetween",".pdf"), plot=p, width = 7, height = 7, units = "in",dpi = 300)
return(p)
}
#---------This function will plot the partial correlation given by the funciton "eval.Partial.Correlation" above!, and return plot object ------------
plot.Partial.Correlation <- function(result.cor, method, pval=TRUE){
mydata <-data.frame(method=labels(result.cor)[[1]], Correlation=result.cor[[1]])
head(mydata)
plot <- ggplot(data=mydata, aes(x=method, y=Correlation)) +
geom_bar(stat="identity") + theme_classic() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
#If pval argument is given, we use geom_text (http://ggplot2.tidyverse.org/reference/geom_text.html) to plot appropriate number of stars
#if p smaller than 0.05 => *, p < 0.01 => **, p <0.001 => ***
stars1<-c()
if(pval) {
for (i in 1:nrow(result.cor)){
if(result.cor[[2]][i] < 0.001) {
stars1<-c(stars1, "***")
} else if(result.cor[[2]][i] < 0.01) {
stars1<-c(stars1, "**")
} else if(result.cor[[2]][i] < 0.05) {
stars1<-c(stars1, "*")
} else {
stars1<-c(stars1, " ")
}
}
plot2 <- plot + geom_text(aes(label = stars1), vjust = -0.5, position = position_dodge(0.9)) +
ggtitle(paste("Plot of", method, "Partial Correlation vs. Predictor", sep=" ")) + theme(plot.title = element_text(hjust = 0.5))
#ggsave(filename=paste("plots/", method, "PartialCorrelation",".pdf"), plot=plot2, width = 7, height = 7, units = "in",dpi = 300)
return(plot2)
}
}
# YuC: A new plot function for the Prediction VS Experimental scatterplot, axies can be unified by the largest x and y value
#' @decription The function is a helper function for ploting all the scatter plots for Prediction VS Experimental values, but
#' can also be used as an independent function to plot a single scatterplot
#' @name plot_single_scatter
#' @param real.data The real data object being parse in from experimental value
#' @param pred.data The real data object being parse in from submission folder
#' @param file.name The submitted prediction name to be plotted
#' @return a ggplot object with the single scatterplot
plot_single_scatter = function(real.data,pred.data,file.name) {
################################
# #Debugging input
# real.data <- read.RealData(file = "exp_data.csv", sep = ",",
# col.id = 2, col.value = 5, col.sd = 6)
# pred.data <- read.Submission.Folder(folder.name = "prediction/",col.id = 1,