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functions.R
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# Installing CRAN packages:
if(!require(BiocManager)){install.packages("BiocManager")}
library(BiocManager)
if(!require(methods)){install.packages("methods")}
library(methods)
if(!require(colorspace)){install.packages("colorspace")}
library(colorspace)
if(!require(grid)){install.packages("grid")}
library(grid)
if(!require(utils)){install.packages("utils")}
library(utils)
if(!require(dplyr)){install.packages("dplyr")}
library(dplyr)
if(!require(ggplot2)){install.packages("ggplot2")}
library(ggplot2)
if(!require(VennDiagram)){install.packages("VennDiagram")}
library(VennDiagram)
if(!require(VIM)){install.packages("VIM")}
library(VIM)
if(!require(wrProteo)){install.packages("wrProteo")}
library(wrProteo)
if(!require(wrMisc)){install.packages("wrMisc")}
library(wrMisc)
if(!require(gplots)){install.packages("gplots")}
library(gplots)
if(!require(gprofiler2)){install.packages("gprofiler2")}
library(gprofiler2)
if(!require(writexl)){install.packages("writexl")}
library(writexl)
if(!require(igraph)){install.packages("igraph")}
library(igraph)
if(!require(plotly)){install.packages("plotly")}
library(plotly)
if(!require(matrixStats)){install.packages("matrixStats")}
library(matrixStats)
if(!require(ggExtra)){install.packages("ggExtra")}
library(ggExtra)
if(!require(corrplot)){install.packages("corrplot")}
library(corrplot)
if(!require(lme4)){install.packages("lme4")}
library(lme4)
if(!require(matrixStats)){install.packages("matrixStats")}
library(matrixStats)
if(!require(DEqMS)){install.packages("DEqMS")}
library(DEqMS)
if(!require(dplyr)){install.packages("dplyr")}
library(dplyr)
if(!require(devtools)){install.packages("devtools")}
library(devtools)
install_github("https://github.com/vdemichev/diann-rpackage")
library(diann)
#Installing bioconductor packages:
if(!require(rsconnect)){BiocManager::install("rsconnect",update=F,ask=F)}
library(rsconnect)
if(!require(BiocGenerics)){BiocManager::install("BiocGenerics",update=F,ask=F)}
library(BiocGenerics)
if(!require(Biobase)){BiocManager::install("Biobase",update=F,ask=F)}
library(Biobase)
if(!require(S4Vectors)){BiocManager::install("S4Vectors",update=F,ask=F)}
library(S4Vectors)
if(!require(vsn)){BiocManager::install("vsn",update=F,ask=F)}
library(vsn)
if(!require(IRanges)){BiocManager::install("IRanges",update=F,ask=F)}
if(!require(AnnotationDbi)){BiocManager::install("AnnotationDbi",update=F,ask=F)}
library(AnnotationDbi)
if(!require(limma)){BiocManager::install("limma",update=F,ask=F)}
library(limma)
if(!require(qvalue)){BiocManager::install("qvalue",update=F,ask=F)}
library(qvalue)
if(!require(clusterProfiler)){BiocManager::install("clusterProfiler",update=F,ask=F)}
library(clusterProfiler)
if(!require(enrichplot)){BiocManager::install("enrichplot",update=F,ask=F)}
library(enrichplot)
if(!require(DOSE)){BiocManager::install("DOSE",update=F,ask=F)}
library(DOSE)
if(!require(STRINGdb)){BiocManager::install("STRINGdb",update=F,ask=F)}
library(STRINGdb)
#################################### helper functions ####################################
##################################################################################
#Quick filtering
quick_filtering <- function(raw, input, platform, organism, condition1, condition2){
if (platform == 1){
df <- raw %>%
filter(Potential.contaminant != "+") %>% #Nos quedamos con todos aquellos que no tengan un +
filter(Reverse != "+") %>% #Nos quedamos con todos aquellos que no tengan un +
filter(Only.identified.by.site != "+") #Nos quedamos con todos aquellos que no tengan un +
if (organism == 1){
#Obtenemos los identificadores
regex <- regexpr(".*(?=.CGDID)", df$Fasta.headers, perl = TRUE)
df$Protein <- regmatches(df$Fasta.headers, regex)
df$Protein <- sub(" ", ";", df$Protein)
regex2 <- regexpr("(?<=;).*(?>[A-Z0-9])", df$Protein, perl = TRUE)
df$Protein <- regmatches(df$Protein, regex2)
#Obtenemos la descripci?n proteica
regex3 <- regexpr("(?<=;).*(?>;|[a-z])", df$Fasta.headers, perl = TRUE)
df$Protein_description <- regmatches(df$Fasta.headers, regex3)
} else if (organism == 2){
pattern <- regexpr("(?<=\\|)[A-Z0-9-]+(?=\\|)", df$Fasta.headers, perl = TRUE)
df$Protein <- regmatches(df$Fasta.headers, pattern)
match <- regexpr("(?<=\\s)(.*?)(?=\\sOS)", df$Fasta.headers, perl = TRUE)
df$Protein_description <- regmatches( df$Fasta.headers, match)
}
#Hacemos la log-transformaci?n
if (input == "lfq"){
intensity_names <- grep("LFQ.intensity", colnames(df), value = TRUE)
df[intensity_names] <- sapply(df[intensity_names], as.numeric) #convertimos los valores en numericos
LOG2.names <- sub("^LFQ.intensity", "LOG2", intensity_names)
df[LOG2.names] <- log2(df[intensity_names])
} else if (input == "int"){
intensity_names <- grep("^Intensity", colnames(df), value = TRUE)
df[intensity_names] <- sapply(df[intensity_names], as.numeric) #convertimos los valores en integers
LOG_names <- sub("^Intensity", "LOG2", intensity_names)
df[LOG_names] <- log2(df[intensity_names])
} else if (input == "spcount"){
spectral_names <- grep("MS.MS.count", colnames(df), value = TRUE)
df[spectral_names] <- sapply(df[spectral_names], as.numeric) #convertimos los valores en integers
LOG_names <- paste("LOG2", spectral_names)
df[LOG_names] <- log2(df[spectral_names])
}
return(df)
} else if (platform == 2){
df <- raw
if (organism == 1){
regex <- regexpr(".*(?=.CGDID)", df$Protein.ID, perl = TRUE)
df$Protein <- regmatches(df$Protein.ID, regex)
df$Protein <- sub(" ", ";", df$Protein)
regex2 <- regexpr("(?<=;).*(?>[A-Z0-9])", df$Protein, perl = TRUE)
df$Protein <- regmatches(df$Protein, regex2)
#Obtenemos la descripci?n proteica
regex3 <- regexpr("(?<=;).*(?>;|[a-z])", df$Protein.ID, perl = TRUE)
df$Protein_description <- regmatches(df$Protein.ID, regex3)
colnames(df)[1] <- "Protein"
} else if (organism == 2){
colnames(df)[1] <- "Protein.ID"
colnames(df)[2] <- "Protein"
colnames(df)[3] <- "Protein_description"
}
if (input == "lfq"){
intensity_names <- grep("MaxLFQ.Intensity", colnames(df), value = TRUE)
df[intensity_names] <- sapply(df[intensity_names], as.numeric) #convertimos los valores en numericos
LOG2.names <- sub("MaxLFQ.Intensity", "LOG2", intensity_names)
#df[LOG2.names] <- log2(df[intensity_names])
df[LOG2.names] <- lapply(df[intensity_names], log2)
} else if (input == "int"){
intensity_names <- grep("Intensity", colnames(df), value = TRUE)
df[intensity_names] <- sapply(df[intensity_names], as.numeric) #convertimos los valores en integers
LOG2.names <- sub("Intensity", "LOG2", intensity_names)
df[LOG2.names] <- log2(df[intensity_names])
} else if (input == "spcount"){
spectral_names <- grep("Total.Spectral.Count", colnames(df), value = TRUE)
df[spectral_names] <- sapply(df[spectral_names], as.numeric) #convertimos los valores en integers
LOG2.names <- sub("Total", "LOG2", spectral_names)
df[LOG2.names] <- log2(df[spectral_names])
}
return(df)
} else if (platform == 3){
df <- raw
colnames(df)[1] <- "Protein"
colnames(df)[3] <- "Protein_description"
intensity_columns <- grepl("\\.d", colnames(df)) #Nos quedamos con aquellas columnas que corresponden con muestras
intensity_columns <- colnames(df[intensity_columns])
#Le damos un lavado
new_names <- c()
for (i in intensity_columns){
new <- basename(i)
new_names <- c(new_names, new)
}
colnames(df)[length(colnames(df)) - (length(intensity_columns)-1):length(colnames(intensity_columns))] <- new_names
intensity_names <- grep("\\.d", colnames(df), value = TRUE)
df[intensity_names] <- sapply(df[intensity_names], as.numeric) #convertimos los valores en integers
LOG2.names <- sub("\\.d", ".LOG2", intensity_names)
df[LOG2.names] <- log2(df[intensity_names])
##############################################################################################################
DIANN_report <- diann_load("report.tsv")
samples <- unique(DIANN_report$Run)[order(unique(DIANN_report$Run))]
DIANN_report <- DIANN_report %>% #Filtramos por FDR del 1%
filter(Q.Value < 0.01)
#Obtenemos los peptidos y peptidos unicos de cada muestra almacenado en listas
process_subset <- function(df) {
colnames(df)[5] <- "Unique.Peptide.Count"
data <- unique(df[, c('Protein.Group', 'Stripped.Sequence')])
data.all <- df[, c('Protein.Group', 'Stripped.Sequence')]
unique_peptides_info <- as.data.frame(table(data$Protein.Group))
peptides_info <- as.data.frame(table(data.all$Protein.Group))
return(list(data = data, data_all = data.all, unique_peptides_info = unique_peptides_info, peptides_info = peptides_info))
}
subsets <- list()
processed_results <- list()
#Loop through each unique sample name and create a subset
for (sample in samples) {
# Subset the dataframe
subset_df <- DIANN_report[DIANN_report$Run == sample, ]
# Store the subset in the list with the sample name as the key
subsets[[sample]] <- subset_df
processed_results[[sample]] <- process_subset(subset_df)
}
#Extract the dataframes of peptide counts and rename columns accordingly
peptide_counts_list <- list()
unique_peptide_counts_list <- list()
for (sample_name in names(processed_results)) {
# Extract the unique_peptides_info dataframe
df_unique_peptides <- processed_results[[sample_name]]$unique_peptides_info #Nos quedamos con el dataframe de cada muestra
# Rename the columns to indicate the sample name
colnames(df_unique_peptides) <- c("Var", sample_name)
# Store the dataframe in the list
unique_peptide_counts_list[[sample_name]] <- df_unique_peptides
}
for (sample_name in names(processed_results)) {
# Extract the unique_peptides_info dataframe
df_peptides <- processed_results[[sample_name]]$peptides_info #Nos quedamos con el dataframe de cada muestra
# Rename the columns to indicate the sample name
colnames(df_peptides) <- c("Var", sample_name) #Renombramos
# Store the dataframe in the list
peptide_counts_list[[sample_name]] <- df_peptides #Guardamos en un dataframe mayor el dataframe de cada muestra
}
#Merge all dataframes by the "Var" column
merged_unique_peptide_counts <- Reduce(function(x, y) merge(x, y, by = "Var", all = TRUE), unique_peptide_counts_list) #Los juntamos todos por la columna Var
merged_peptide_counts <- Reduce(function(x, y) merge(x, y, by = "Var", all = TRUE), peptide_counts_list) #Los juntamos todos por la columna Var
# Modify the names
colnames(merged_unique_peptide_counts)[2:(length(LOG2.names) +1)] <- paste("Unique peptides", colnames(merged_unique_peptide_counts)[2:(length(LOG2.names) +1)], sep=" ")
colnames(merged_unique_peptide_counts)[1] <- "Protein"
colnames(merged_peptide_counts)[2:(length(LOG2.names) +1)] <- paste("Peptides", colnames(merged_peptide_counts)[2:(length(LOG2.names) +1)], sep=" ")
colnames(merged_peptide_counts)[1] <- "Protein"
merged_df <- merge(df, merged_unique_peptide_counts, by = "Protein", all = FALSE)
df <- merge(merged_df, merged_peptide_counts, by = "Protein", all = FALSE)
# Sample names
conditions1 <- c(condition1, condition2)
condition1_names <- grep(conditions1[1], samples, value = TRUE)
condition2_names <- grep(conditions1[2], samples, value = TRUE)
sample_names <- c(condition1_names,condition2_names)
# Identify columns for each condition
condition_1 <- grep(condition1, LOG2.names, value = TRUE)
condition_2 <- grep(condition2, LOG2.names, value = TRUE)
# Calculate non-NA counts for each condition separately
df <- df %>% #Se coge la variable del codigo
rowwise() %>%
mutate(
counts_condition1 = sum(!is.na(c_across(all_of(condition_1)))),
counts_condition2 = sum(!is.na(c_across(all_of(condition_2))))
) %>%
ungroup()
# Combine the counts into a single non_na_counts column if needed
df <- df %>%
mutate(counts = counts_condition1 + counts_condition2)
# If you need to replace only when non_na_counts is 0 in the original column
df <- df %>%
mutate(counts = if_else(counts == 0, counts_condition1 + counts_condition2, counts))
##############################################################################################################
return(df)
} else if (platform == 4){
df <- raw
abundance_names <- grep("Abundance:", colnames(df), value = TRUE)
df[abundance_names] <- sapply(df[abundance_names], as.numeric) #convertimos los valores en numericos
LOG2.names <- sub("^Abundance:", "LOG2", abundance_names)
df[LOG2.names] <- log2(df[abundance_names])
colnames(df)[colnames(df) == "Accession"] <- "Protein"
colnames(df)[colnames(df) == "Description"] <- "Protein_description"
if (organism == 1){
regex <- regexpr(".*(?=.CGDID)", df$Protein_description, perl = TRUE)
df$Protein <- regmatches(df$Protein_description, regex)
#Obtenemos la descripci?n proteica
regex3 <- regexpr("(?<=;).*(?>;|[a-z])", df$Protein_description, perl = TRUE)
df$Protein_description <- regmatches(df$Protein_description, regex3)
}
} else if (platform == 5){
df <- raw
colnames(df)[1] <- "Protein"
colnames(df)[4] <- "Protein_description"
if (input == "int"){
intensity_names <- grep(".Intensity", colnames(df), value = TRUE)
df[intensity_names] <- sapply(df[intensity_names], as.numeric) #convertimos los valores en numericos
LOG2.names <- sub(".Intensity", ".LOG2", intensity_names)
df[LOG2.names] <- log2(df[intensity_names])
}
return(df)
}
return(df)
}
##################################################################################
#Obtaining LOG2 names
obtain_LOG.names <- function(df){
LOG2.names <- grep("LOG2", colnames(df), value = TRUE)
return(LOG2.names)
}
##################################################################################
#Subseting unique proteins for each condition
obtain_unique_proteins <- function(df, conditions, LOG2.names, replicas_condicion1, replicas_condicion2){
cond.names <- lapply(conditions, function(x) grep(x, LOG2.names, value = TRUE, perl = TRUE))
condi_names <- unlist(cond.names)
df2 <- df[condi_names]
df2 <- as.matrix(df2)
total_replicas <- replicas_condicion1 + replicas_condicion2
finite_sums <- rowSums(is.finite(df2[, 1:replicas_condicion1]))
infinite_sums <- rowSums(!is.finite(df2[, (replicas_condicion1 + 1):total_replicas]))
cond1_exclusive <- Reduce(`|`, lapply(0:(replicas_condicion2 - 1), function(i) {
condition <- finite_sums == (replicas_condicion1 - i) & infinite_sums == replicas_condicion2
sufficient_presence <- finite_sums >= ceiling(replicas_condicion1 / 2)
condition & sufficient_presence
}))
cond2_exclusive <- Reduce(`|`, lapply(1:replicas_condicion2, function(i) {
condition <- finite_sums == 0 & infinite_sums == (replicas_condicion2 - i)
sufficient_presence <- infinite_sums < ceiling(replicas_condicion2 / 2)
condition & sufficient_presence
}))
df$cond1_exclusive <- cond1_exclusive
df$cond2_exclusive <- cond2_exclusive
cond1_unicas <- filter(df, df$cond1_exclusive)
cond2_unicas <- filter(df, df$cond2_exclusive)
return(list(cond1_unicas, cond2_unicas))
}
##################################################################################
#Proteins identified
identify_proteins <- function(raw, condi.names, platform, repcond1, repcond2) {
# Step 1: Initialization
long <- length(condi.names) # Number of conditions
filt <- list() # List to store filtered data frames
# Step 2: Filtering and assignment
if (platform == 1 | platform == 2 | platform == 4 | platform == 5){
for (i in 1:long) {
filt[[i]] <- raw[raw[, condi.names[i]] > 0, ]
}
} else if (platform == 3){
for (i in 1:long) {
filt[[i]] <- raw[complete.cases(raw[, condi.names[i]]), ]
}
}
# Step 3: Counting proteins
proteins <- sapply(filt, nrow)
# Step 4: Visualization
barplot(
proteins,
main = "Proteins identified",
xlab = "Samples",
ylab = "Total proteins",
cex.names = 0.5,
names.arg = condi.names,
col = c(rep("light green", repcond1), rep("light blue", repcond2)),
las = 2,
ylim = c(0, max(proteins) + 200)
)
}
#Venn Diagram
venn_diagram <- function(df, unique_proteins, label1, label2, color1, color2){
#Proteinas en muestras WT
cond1_set <- if (nrow(unique_proteins[[2]]) > 0) {
df %>% filter(Protein != unique_proteins[[2]]$Protein)
} else {
df # If unique_proteins[[2]] is empty, return the original df
}
for (i in unique_proteins[[2]]$Protein){
cond1_set <- cond1_set%>%
filter(cond1_set$Protein != i)
}
cond2_set <- if (nrow(unique_proteins[[1]]) > 0) {
df %>% filter(Protein != unique_proteins[[1]]$Protein)
} else {
df # If unique_proteins[[2]] is empty, return the original df
}
for (i in unique_proteins[[1]]$Protein){
cond2_set <- cond2_set %>%
filter(cond2_set$Protein != i)
}
x <- list( cond1_set$Protein, cond2_set$Protein)
names(x) <- c(label1, label2)
venn.plot <- venn.diagram(
x = x,
category.names = c(label1, label2),
filename = NULL,
output = TRUE,
imagetype = "png",
main = "Venn Diagram",
fill = c(color1, color2)
)
return(venn.plot)
}
##################################################################################
#Filtering
filter_valids <- function(df, unique_proteins, conditions1, min_count, at_least_one = FALSE, LOG2.names, labeltype) {
cond.names <- lapply(conditions1, # Sobre la lista conditions, aplicamos una funcion quedarnos con las columnas de las condiciones con datos en log
function(x) grep(x, LOG2.names, value = TRUE, perl = TRUE))
print(cond.names)
total_unique_proteins <- rbind(unique_proteins[[1]], unique_proteins[[2]])
common_df <- df%>%
filter(df$Protein != total_unique_proteins$Protein[1])
for (i in total_unique_proteins$Protein){
common_df <- common_df%>%
filter(common_df$Protein != i)
}
if (labeltype == 1){
cond.filter <- sapply(1:length(cond.names), function(i) { #En nuestro caso se itera del 1 al 2
df2 <- common_df[cond.names[[i]]] # Extrae las columnas de inter?s
df2 <- as.matrix(df2) # Lo convierte en matriz
sums <- rowSums(is.finite(df2)) # Cuenta el numero de valores validos para cada condicion
sums >= min_count[i] # Calculates whether min_count requirement is met si el n?mero de datos es igual o superior al elemento en min_count se establece true
})
if (at_least_one) {
common_df$KEEP <- apply(cond.filter, 1, any)
} else {
common_df$KEEP <- apply(cond.filter, 1, all)
}
common_df <- filter(common_df, KEEP)
common_df[LOG2.names] <- lapply(LOG2.names,
function(x) {
temp <- common_df[[x]]
temp[!is.finite(temp)] = NA
return(temp)
})
condition1_names <- cond.names[[1]] #Manejamos grupos
condition2_names <- cond.names[[2]]
for (i in 1:nrow(common_df)) { #Probar a poner el CV multiplicado por 100 al resultado y no al denominador
valuesA <- as.numeric(common_df[i, condition1_names])
valuesB <- as.numeric(common_df[i, condition2_names])
valuesC <- c(valuesA, valuesB)
cv1 <- (sd(valuesA, na.rm = TRUE) / mean(valuesA, na.rm = TRUE)) * 100
cv2 <- (sd(valuesB, na.rm = TRUE) / mean(valuesB, na.rm = TRUE)) * 100
cv3 <- (sd(valuesC, na.rm = TRUE) / mean(valuesC, na.rm = TRUE)) * 100
common_df$CV_Control[i] <- cv1
common_df$CV_Tratamiento[i] <- cv2
common_df$CV_TratamientoyControl[i] <- cv3
}
} else if (labeltype ==2){
common_df <- df
common_df <- remove_missing(common_df)
condition1_names <- cond.names[[1]] #Manejamos grupos
condition2_names <- cond.names[[2]]
for (i in 1:nrow(common_df)) { #Probar a poner el CV multiplicado por 100 al resultado y no al denominador
valuesA <- as.numeric(common_df[i, condition1_names])
valuesB <- as.numeric(common_df[i, condition2_names])
valuesC <- c(valuesA, valuesB)
cv1 <- (sd(valuesA, na.rm = TRUE) / mean(valuesA, na.rm = TRUE)) * 100
cv2 <- (sd(valuesB, na.rm = TRUE) / mean(valuesB, na.rm = TRUE)) * 100
cv3 <- (sd(valuesC, na.rm = TRUE) / mean(valuesC, na.rm = TRUE)) * 100
common_df$CV_Control[i] <- cv1
common_df$CV_Tratamiento[i] <- cv2
common_df$CV_TratamientoyControl[i] <- cv3
}
}
return(common_df)
}
##################################################################################
#Normalization
median_centering <- function(df, LOG2.names) {
df[, LOG2.names] <- lapply(LOG2.names, #A las columnas de inter?s, se procede a calcular el LOG2-mediana de cada
function(x) {
LOG2 <- df[[x]] #Se asigna el valor calculado
LOG2[!is.finite(LOG2)] <- NA # Excluimos los missing values del c?lculo de la mediana
gMedian <- median(LOG2, na.rm = TRUE)
LOG2 - gMedian #Se calcula el valor.
}
)
return(df)
}
normalization_func <- function(df, LOG2.names, method){
if (method == "trimMean"){
df[, LOG2.names] <- normalizeThis(dat = df[, LOG2.names], method = method, trimFa = 0.4)
} else if (method != "trimMean"){
df[, LOG2.names] <- normalizeThis(dat = df[, LOG2.names], method = method)
}
return(df)
}
##################################################################################
#Imputation
impute_data <- function(df, LOG2.names, width = 0.3, downshift = 1.8) {
impute.names <- sub("LOG2", "impute", LOG2.names)
# Create new columns indicating whether the values are imputed
df[impute.names] <- lapply(LOG2.names, function(x) !is.finite(df[, x]))
# Imputation
set.seed(1)
df[LOG2.names] <- lapply(LOG2.names,
function(x) {
temp <- df[[x]]
temp[!is.finite(temp)] = NA
temp.sd <- width * sd(temp[df$KEEP], na.rm = TRUE) # shrink sd width
temp.mean <- mean(temp[df$KEEP], na.rm = TRUE) -
downshift * sd(temp[df$KEEP], na.rm = TRUE) # shift mean of imputed values
n.missing <- sum(is.na(temp))
temp[is.na(temp)] <- rnorm(n.missing, mean = temp.mean, sd = temp.sd)
return(temp)
})
return(df)
}
impute_KNN_data <- function(df, LOG2.names, ...){
impute.names <- sub("LOG2", "impute", LOG2.names)
df[impute.names] <- lapply(LOG2.names, function(x) !is.finite(df[, x])) #Creamos columnas donde se ve si imputar o no
#Imputaci?n
df[LOG2.names] <- lapply(LOG2.names,
function(x) {
temp <- df[[x]]
temp[!is.finite(temp)] = NA
return(temp)
})
imp_knn <- kNN(df, variable = LOG2.names, ... )
return(imp_knn)
}
##################################################################################
#Processing check
plotCV2 <- function(y, trend = TRUE, main= "Imputation check", ...){
y <- na.omit(y)
A <- rowMeans(y, na.rm = TRUE) #Hago las medias de las filas
CV <- (matrixStats::rowSds(data.matrix(y), na.rm = TRUE)/A)^2 #Calculo de la desviaci?n estandar relativa invocando el paquete matrixStats y usando su funci?n rowSds
res <- data.frame(mean = A, CV = CV)
plot(A, CV , ylim = c(min(CV)-0.001, max(CV) +0.001), ...)
if(trend){
fit <- limma::loessFit(CV, A) #Invocamos la funcion loess del paquete limma, para hacer una regresi?n local
o <- order(A)
lines(A[o], fit$fitted[o], lwd =2, col = "red")
}
return(res)
}
boxplot_function <- function(df, cond.names, ...){
df <- na.omit(df[cond.names])
par(mfrow=c(1,1), font.lab=2, cex.lab=1, font.axis=2, cex.axis=1, cex.main=1, las = 1)
boxplot(df[, cond.names], names = cond.names, ylim = c(min(df[,cond.names])-1, max(df[,cond.names]) +1), main="Boxplot normalized Intensities", ylab = "Intensities", las=2, ...)
}
preimputation_state <- function(df, cond.names){
par(mfrow=c(2,2))
aggr(df[, cond.names], delimiter = "NA", labels = names(df[cond.names]), cex.axis = .8)
}
postimputation_state <- function(df, cond.names){
par(mfrow=c(2,2))
aggr(df[, cond.names], delimiter = "NA", labels = names(df[cond.names]), cex.axis = .8)
}
histogram <- function(df, colname, color, title){
hist(as.numeric(df[,colname]), main = title, xlab = 'Intensity-value', col = color)
}
scatterplot_function <- function(df, colname1, colname2){
corr_plot <- ggplot(df, aes(df[,colname1], df[,colname2])) +
geom_point() +
theme_minimal() +
labs(y = colname2, x = colname1)
ggMarginal(corr_plot, type = "densigram")
}
qqplot_function <- function(df, colname1, colname2, color){
p <- data.frame(qqnorm(x = df[,colname1], y = df[,colname2]))
ggplot(p, aes(x, y)) +
geom_point(pch = 21,
colour = color) +
theme_minimal() +
geom_qq_line(aes(sample = y)) +
ylab("Sample dist") +
xlab("Theoretical dist") +
ggtitle("Q-Q plot")
}
corrplot_function <- function(df, display, tl.col = "black", addCoef.col = "black"){
df_cor <- cor(df, use = "complete.obs", method = "pearson")
corrplot(df_cor, method=display, tl.col = "black", addCoef.col = "black")
}
##################################################################################
#Differential expression analysis
##################################################################################
#Limma function
statistical_analysis <- function(df, LOG2.names,test, paired = FALSE, replicas_condicion1, replicas_condicion2, condition1, condition2, logfcup, logfcdown, sig, adjval, statval, unique_proteins, way, psms, platform){
if (test == 2){
if (psms == TRUE){
condition1_names <- grep(condition1, LOG2.names, value = TRUE)
condition2_names <- grep(condition2, LOG2.names, value = TRUE)
#Control columns
for (i in 1:replicas_condicion1){
nam <- paste("control_sample", i, sep = "")
assign(nam, condition1_names[i])
} #LOG2.WT1,#LOG2.WT2,#LOG2.WT3,#LOG2.WT4
#Treatment columns
for (i in 1:replicas_condicion2){
nam <- paste("prob_column", i, sep = "")
assign(nam, condition2_names[i])
} #LOG2.WT_H2O2_1, #LOG2.WT_H2O2_2, #LOG2.WT_H2O2_3, #LOG2.WT_H2O2_4
ct <- c()
for (i in ls()[grep("control_sample", ls())]){
new_value_control <- get(i)
ct <- c(ct, new_value_control)
}
print(ct)
tr <- c()
for (i in ls()[grep("prob_column", ls())]){
new_value_prob <- get(i)
tr <- c(tr, new_value_prob)
}
print(tr)
if (paired == FALSE){
control <- rep(1, replicas_condicion1)
treatment <- rep(2, replicas_condicion2)
design <- model.matrix(~factor(c(control, treatment)))
} else if (paired == TRUE){
pairinfo = factor(rep(1:replicas_condicion1,2))
control <- rep(1, replicas_condicion1)
treatment <- rep(2, replicas_condicion2)
design <- model.matrix(~pairinfo+factor(c(control, treatment)))
}
dat <- df[, c(ct, tr)]
n <- dim(dat)[1]
fit <- lmFit(dat, design)
fit.eb <- eBayes(fit)
if (platform == 1){ #MaxQuant
count_columns = grep("Razor...unique.peptides", colnames(df)) #En el output que genera MaxQuant las columnas de PSMs son las indicadas en el script
df[count_columns] <- sapply(df[count_columns], as.numeric)
psm.count.table = data.frame(count = rowMins(
as.matrix(df[,count_columns])), row.names = df$Protein)
#psm.count.table[psm.count.table$count == 0] <- 1 # Añadimos solo un uno para los casos en los que el valor de PSMs es igual a 0
psm.count.table$count[psm.count.table$count == 0] <- 1
fit.eb$count <- psm.count.table$count
} else if (platform == 2){ #MSFragger
count_columns <- grep("Combined.Unique.Spectral.Count", colnames(df))
count_column <- as.numeric(df$Combined.Unique.Spectral.Count)
count_column <- replace(count_column, count_column==0, 1)
fit.eb$count <- count_column
} else if (platform == 3){ #DIA-NN #Antes Protein.Ids era Genes
DIANN_report <- diann_load("report.tsv") #Buscar la forma de cargar el report
data <- unique(DIANN_report[,c('Protein.Group','Stripped.Sequence')])
t_prueba <- table(data$Protein.Group)
unique_peptides_info <- as.data.frame(t_prueba) #Listado de peptidos unicos por proteina.
unique_peptides_info$Var1 <- as.character(unique_peptides_info$Var1)
df$count <- NA
match_indices <- match(df$Protein, unique_peptides_info$Var1)
fit.eb$count <- unique_peptides_info$Freq[match_indices]
} else if (platform == 4){ #Proteome Discoverer
count_columns <- as.numeric(df$`# PSMs`)
fit.eb$count <- count_columns
}
fit <- spectraCounteBayes(fit.eb)
coef_col <- 2 #Para acceder a la columna de LogFC y P value
results.eb = limma::topTable(fit,coef = coef_col,n= Inf)
p.value.column <- grep("P.Value", colnames(results.eb))
colnames(results.eb)[p.value.column] <- "p.value"
results.eb$adj.P.Val = p.adjust(results.eb$p.value,
method = adjval)
results.eb$Protein = as.numeric(rownames(results.eb))
#results.eb$count = fit$count[results.eb$Protein]
results.eb$sca.t = fit$sca.t[results.eb$Protein,coef_col]
results.eb$sca.P.Value = as.numeric(fit$sca.p[results.eb$Protein,coef_col])
results.eb$sca.adj.pval = as.numeric(p.adjust(results.eb$sca.P.Value,
method = "BH"))
results.eb = results.eb[order(results.eb$sca.P.Value), ]
if (statval == 1){
expression <- case_when(results.eb$logFC >= logfcup & -log10(results.eb$sca.P.Value) >= -log10(sig) ~ "Up-regulated",
results.eb$logFC <= logfcdown & -log10(results.eb$sca.P.Value) >= -log10(sig) ~ "Down-regulated",
TRUE ~ "Unchanged")#labels para expresion
} else if (statval == 2){
expression <- case_when(results.eb$logFC >= logfcup & -log10(results.eb$sca.adj.pval) >= -log10(sig) ~ "Up-regulated",
results.eb$logFC <= logfcdown & -log10(results.eb$sca.adj.pval) >= -log10(sig) ~ "Down-regulated",
TRUE ~ "Unchanged")
}
results.eb$expression <- expression
results_rownames <- rownames(results.eb)
results.eb$Protein <- df[results_rownames, "Protein"]
results.eb$Protein_description <- df[results_rownames, "Protein_description"]
row.names(results.eb) <- results.eb$Protein
#row.names(results.eb) <- make.unique(results.eb$Protein)
results.eb <- results.eb[-c(2,3,6,8)]
results.eb <- results.eb[c(1,2,3,4,8,6,5,7)]
if (way == 1){
return(results.eb)
} else if (way == 2){
#Empezamos a trabajar con las unicas control
unique.control <- as.data.frame(unique_proteins[1])
fusioncontrol.df <- data.frame(matrix(ncol = 8))
n <- c("logFC", "p.value", "adj.P.Val", "expression", "Protein", "Protein_description", "sca.P.Value", "sca.adj.pval")
colnames(fusioncontrol.df) <- n
for (i in unique.control$Protein){
new <- c(min(results.eb$logFC, na.rm = TRUE)-2, min(results.eb$p.value, na.rm = TRUE)/100, min(results.eb$adj.P.Val, na.rm = TRUE)/100, "Down-regulated", i, unique.control$Protein_description[unique.control$Protein==i], min(results.eb$sca.P.Value, na.rm = TRUE)/100, min(results.eb$sca.adj.pval, na.rm = TRUE)/100)
fusioncontrol.df <- rbind(fusioncontrol.df, new)
}
fusioncontrol.df <- fusioncontrol.df[-1,]
unique.treatment <- as.data.frame(unique_proteins[2])
fusiontreatment.df <- data.frame(matrix(ncol = 8))
n <- c("logFC", "p.value", "adj.P.Val", "expression", "Protein", "Protein_description", "sca.P.Value", "sca.adj.pval")
colnames(fusiontreatment.df) <- n
for (i in unique.treatment$Protein){
new <- c(max(results.eb$logFC, na.rm = TRUE)+2, min(results.eb$p.value, na.rm = TRUE)/100, min(results.eb$adj.P.Val, na.rm = TRUE)/100, "Up-regulated", i, unique.treatment$Protein_description[unique.treatment$Protein==i], min(results.eb$sca.P.Value, na.rm = TRUE)/100, min(results.eb$sca.adj.pval, na.rm = TRUE)/100)
fusiontreatment.df <- rbind(fusiontreatment.df, new)
}
fusiontreatment.df <- fusiontreatment.df[-1,]
unique.proteins.limma <- rbind(fusioncontrol.df, fusiontreatment.df)
row.names(unique.proteins.limma) <- unique.proteins.limma$Protein
unique.proteins.limma[c("logFC", "p.value", "adj.P.Val", "sca.P.Value", "sca.adj.pval")] <- sapply(unique.proteins.limma[c("logFC", "p.value", "adj.P.Val", "sca.P.Value", "sca.adj.pval")], as.numeric)
results.eb <- rbind(unique.proteins.limma, results.eb)
#row.names(results.eb) <- make.unique(results.eb$Protein)
return(results.eb)
}
} else if (psms == FALSE){
condition1_names <- grep(condition1, LOG2.names, value = TRUE)
condition2_names <- grep(condition2, LOG2.names, value = TRUE)
#Control columns
for (i in 1:replicas_condicion1){
nam <- paste("control_sample", i, sep = "")
assign(nam, condition1_names[i])
}
#Treatment columns
for (i in 1:replicas_condicion2){
nam <- paste("prob_column", i, sep = "")
assign(nam, condition2_names[i])
}
ct <- c()
for (i in ls()[grep("control_sample", ls())]){
new_value_control <- get(i)
ct <- c(ct, new_value_control)
}
print(ct)
tr <- c()
for (i in ls()[grep("prob_column", ls())]){
new_value_prob <- get(i)
tr <- c(tr, new_value_prob)
}
print(tr)
if (paired == FALSE){
control <- rep(1, replicas_condicion1)
treatment <- rep(2, replicas_condicion2)
design <- model.matrix(~factor(c(control, treatment)))
} else if (paired == TRUE){
pairinfo = factor(rep(1:replicas_condicion1,2))
control <- rep(1, replicas_condicion1)
treatment <- rep(2, replicas_condicion2)
design <- model.matrix(~pairinfo+factor(c(control, treatment)))
}
dat <- df[, c(ct, tr)]
n <- dim(dat)[1]
fit <- lmFit(dat, design)
fit.eb <- eBayes(fit)
print(colnames(fit.eb))
logFC <- fit.eb$coefficients[, 2] #Calculo del log fold-change
p.value <- fit.eb$p.value[, 2] # p-valor moderado correspondiente al estad?stico t moderado.
adj.P.Val <- p.adjust(p.value, method = adjval)
if (statval == 1){
expression <- case_when(logFC >= logfcup & -log10(p.value) >= -log10(sig) ~ "Up-regulated",
logFC <= logfcdown & -log10(p.value) >= -log10(sig) ~ "Down-regulated",
TRUE ~ "Unchanged")#labels para expresion
} else if (statval == 2){
expression <- case_when(logFC >= logfcup & -log10(adj.P.Val) >= -log10(sig) ~ "Up-regulated",
logFC <= logfcdown & -log10(adj.P.Val) >= -log10(sig) ~ "Down-regulated",
TRUE ~ "Unchanged")
}
results.eb <- data.frame(logFC, p.value, adj.P.Val, expression)
results_rownames <- rownames(results.eb) #Obtenemos el nombre de las filas
# Extract Protein and Protein_description directly using vectorized operations
results.eb$Protein <- df[results_rownames, "Protein"]
results.eb$Protein_description <- df[results_rownames, "Protein_description"]
#row.names(results.eb) <- make.unique(results.eb$Protein)
if (way == 1){
return(results.eb)
} else if (way == 2){
#Empezamos a trabajar con las unicas control
unique.control <- as.data.frame(unique_proteins[1])
fusioncontrol.df <- data.frame(matrix(ncol = 6))
n <- c("logFC", "p.value", "adj.P.Val", "expression", "Protein", "Protein_description")
colnames(fusioncontrol.df) <- n
for (i in unique.control$Protein){
new <- c(min(results.eb$logFC, na.rm = TRUE)-2, min(results.eb$p.value, na.rm = TRUE)/100, min(results.eb$adj.P.Val, na.rm = TRUE)/100, "Down-regulated", i, unique.control$Protein_description[unique.control$Protein==i] )
fusioncontrol.df <- rbind(fusioncontrol.df, new)
}
fusioncontrol.df <- fusioncontrol.df[-1,]
#Empezamos a trabajar con la unicas tratamiento
unique.treatment <- as.data.frame(unique_proteins[2])
fusiontreatment.df <- data.frame(matrix(ncol = 6))
n <- c("logFC", "p.value", "adj.P.Val", "expression", "Protein", "Protein_description")
colnames(fusiontreatment.df) <- n
for (i in unique.treatment$Protein){
new <- c(max(results.eb$logFC, na.rm = TRUE)+2, min(results.eb$p.value, na.rm = TRUE)/100, min(results.eb$adj.P.Val, na.rm = TRUE)/100, "Up-regulated", i, unique.treatment$Protein_description[unique.treatment$Protein==i] )
fusiontreatment.df <- rbind(fusiontreatment.df, new)
}
fusiontreatment.df <- fusiontreatment.df[-1,]
unique.proteins.limma <- rbind(fusioncontrol.df, fusiontreatment.df)
row.names(unique.proteins.limma) <- unique.proteins.limma$Protein
unique.proteins.limma[c("logFC", "p.value", "adj.P.Val")] <- sapply(unique.proteins.limma[c("logFC", "p.value", "adj.P.Val")], as.numeric)
results.eb <- rbind(unique.proteins.limma, results.eb)
#row.names(results.eb) <- make.unique(results.eb$Protein)
return(results.eb)
}
}
} else if (test == 1){
condition1_names <- grep(condition1, LOG2.names, value = TRUE)
condition2_names <- grep(condition2, LOG2.names, value = TRUE)
valuesA_cols <- df[, condition1_names]
valuesB_cols <- df[, condition2_names]
for (i in unique(seq_len(nrow(df)))) {
valuesA <- as.numeric(valuesA_cols[i, ])
valuesB <- as.numeric(valuesB_cols[i, ])
if (sum(is.finite(valuesA)) >= 2 && sum(is.finite(valuesB)) >= 2) {
testResults <- t.test(x = valuesA, y = valuesB, paired = paired)
df$pValue[i] <- testResults$p.value
df$tStat[i] <- testResults$statistic
df$logFC[i] <- mean(valuesB, na.rm = TRUE) - mean(valuesA, na.rm = TRUE)
# Storing results in a separate data frame
results.eb <- data.frame(df$logFC, df$pValue)
} else {
df$pValue[i] <- NA
df$tStat[i] <- NA
df$logFC[i] <- NA
}
}
}
colnames(results.eb) <- c("logFC", "p.value")
results.eb$adj.P.Val <- p.adjust(results.eb$p.value, method = adjval)
if (statval==1){