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Evaluation - Regression
I. Data Preparation

1- Load libraries
library(data.table)
library(formattable)
library(plotrix)
library(limma)
library(dplyr)
library(Rtsne)
library(MASS)
library(xgboost)
 

2- Read Expressions file
df <- read.csv("GSE74763_rawlog_expr.csv")
df2 <- df[,-1]
rownames(df2) <- df[,1]
expr <- transpose(df2)
rownames(expr) <- colnames(df2)
colnames(expr) <- rownames(df2)
dim(expr)
 

3- Read Samples file
targets <- read.csv("GSE74763_rawlog_targets.csv")
colnames(targets)
dim(targets)
 

4- Merge Expressions with Samples
data <- cbind(expr, targets)
colnames(data)
dim(data)
 
II. Splitting Data into Training and Test Sets
set.seed(101)

sample = sample.split(data$P000833, SplitRatio = .8)
train = subset(d1, sample == TRUE)
test = subset(d1, sample == FALSE)

dim(train)
dim(test)
 
II. MLR Model
mlr <- lm(P000833 ~ P000001+P007414+P002449, data = train)
summary(mlr)

#Residual plot
res = resid(mlr)
plot(train$P000833, res, ylab="Residuals", xlab="P000833", main="Residual Plot", col="darkred")
abline(0, 0)

#Q-Q plot
stdres = rstandard(mlr)
qqnorm(stdres, ylab="Standardized Residuals", xlab="Normal Scores", main="QQ Plot")
qqline(stdres)

#Test
pred <- predict(mlr, newdata=test)
errors <- test$P000833 - pred
rmse <- sqrt(mean((errors^2)))
print(rmse)

#Erros histogram
hist(errors, main="P000833", sub="(Actual-Predicted)", xlab="Error", breaks=10, col="darkred")

 
IV. Bioada Xarang
Watch this video to learn how you can build, test and deploy regression models using Bioada Xarang significantly faster and easier.