R/optimize.R

In permubiome: A Permutation Based Test for Biomarker Discovery in Microbiome Data

optimize <-
function()
{
#Loading data
load("permubiome.RData")
Class<-NULL
df_norm<-df_norm
REFERENCE<-REFERENCE
classes<-levels(as.factor(df_norm$Class))
if (REFERENCE == ""){
REFERENCE <- classes[1]
} else if (REFERENCE == classes[2]){
classes[2] <- classes[1]
classes[1] <- REFERENCE
}

#Splitting data into classes
df_norm$Class <- factor(df$Class, levels=(c(classes[1],classes[2])))
group1<-subset(df_norm, Class == classes[1])
group2<-subset(df_norm, Class == classes[2])

#Calculating distances intra and inter classes - Log(counts)
categories<-colnames(df_norm)
distance_matrix<-matrix(, nrow = ncol(df_norm)-2, ncol = 7, byrow=T)
colnames(distance_matrix)<-c("Category", paste("SumDist ","[",classes[1],"]",sep="") , paste("SumDist ","[",classes[2],"]",sep=""), "ClassDist","RatioDist","Delta-Log", "Z-score")
for (i in 3:(ncol(group1))){
mydata1<-group1[,i]
sumdist1<-log10(sum(abs(apply(combn(mydata1,2), 2, diff))))
distance_matrix[(i-2),1]<-categories[i]
distance_matrix[(i-2),2]<-sumdist1
}
for (j in 3:(ncol(group2))){
mydata2<-group2[,j]
sumdist2<-log10(sum(abs(apply(combn(mydata2,2), 2, diff))))
distance_matrix[(j-2),3]<-sumdist2
}
classes_matrix<-matrix(, nrow = ncol(group1)-2, ncol = (nrow(group1)*nrow(group2)), byrow=T)
rownames(classes_matrix) <- colnames(group1[3:ncol(group1)])
features <- colnames(group1)
for (k in 3:(ncol(group1))){
classdist <- vector()
for (l in 1:nrow(group1[k])){
classdist_tmp <- as.list(abs(group2[k] - group1[l,k]))
classdist <- c(classdist , classdist_tmp[[features[k]]])
}
classes_matrix[(k-2),] <- classdist
}
inter.class.dist<-as.list(rowSums(classes_matrix))
for (m in 3:(ncol(group1))){
distance_matrix[(m-2),4] <- log10(inter.class.dist[[features[m]]])
}

#Ratio test for early detection of larger variation intra classes
distance_matrix[,5] <- as.numeric(distance_matrix[,4]) / ((as.numeric(distance_matrix[,3]) / as.numeric(distance_matrix[,2])))

#Delta scaling
distance_matrix[,6] <- abs(as.numeric(distance_matrix[,5]) - as.numeric(distance_matrix[,4]))

#Z-score calculation
distance_matrix[,7] <- (as.numeric(distance_matrix[,6]) - mean(as.numeric(distance_matrix[,6]))) / sd(as.numeric(distance_matrix[,6]))

#Selection for upper and lower quartiles for permutation test
selected_features<-subset(distance_matrix, abs(as.numeric(distance_matrix[,6])) < quantile(as.numeric(distance_matrix[,6]),0.91))
save(df, df_norm, REFERENCE, classes, distance_matrix, selected_features, file="permubiome.RData")
}