| Map > Problem Definition > Data Preparation > Data Exploration > Bivariate |
Data Exploration - Bivariate Analysis |
| I. Data Preparation |
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1- Load libraries |
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library(data.table) library(formattable) library(plotrix) library(limma) |
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2- Read Expressions file |
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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) |
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3- Read Samples file |
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targets <- read.csv("GSE74763_rawlog_targets.csv") colnames(targets) dim(targets) |
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4- Merge Expressions with Samples |
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data <- cbind(expr, targets) colnames(data) dim(data) |
| II. Numerical & Numerical |
| 1- Linear Correlation |
| cor(data$P000001,data$P000002) |
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| 2- Scatter plot |
| pairs(~P000001+P000002, data=data, main="GSE74763_rawlog", col="darkgreen") |
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| III. Categorical & Categorical |
| 1- Count (crosstab) |
| xtabs(~target+gender, data=data) |
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| 2- CrossTab Plot |
| xt <-
xtabs(~target+gender, data=data) plot(xt, main="GSE74763_rawlog", col="darkgreen") |
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| 3- Chi2 test |
| tbl <-
table(data$target, data$gender) chisq.test(tbl) |
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| IV. Categorical & Numerical |
| 1- Box plot |
| boxplot(P000001~target, data=data, col="darkgreen", main="GSE74763_rawlog", ylab="P000001 - intensity") |
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| 2- t-test |
| t.test(data$P000002~data$target) |
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| 3- ANOVA |
| fit <-
aov(P000001~target, data=data) summary(fit) |
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| V. Bioada SmartArray |
| Watch this video to learn how you can perform bivariate analysis using Bioada SmartArray significantly faster and easier. |
