Map > Problem Definition > Data Preparation > Data Exploration > Modeling > Regression
 

Modeling - 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. MLR (Multiple Linear Regression)
mlr <- lm(P000833 ~ P000001+P007414+P002449, data = data)
summary(mlr)

 
III. XGBoost (Extreme Gradient Boosting)

d1 <- data
d1$geo_accession <- NULL
d1$age <- NULL
d1$gender <- NULL
d1$target <- NULL
d1$P000833 <- NULL

bst <- xgboost(data = as.matrix(d1), label = data$P000833, max.depth = 5, eta = 1, nthread = 2, nrounds = 100
          , metrics = list("rmse"), objective = "reg:linear")


var_importance <- xgb.importance(model = bst)
print(var_importance)
xgb.plot.importance(importance_matrix = head(var_importance), main="Variable Importance", col="blue")
 

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