ppt-6_stat.Rmd

---
title: "R for beginners Notebook"
output:
  ioslides_presentation: default
  slidy_presentation: default
  beamer_presentation: default
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


# R Biostat. and data analysis

## R Biostat. basics

Useful functions:

- sum(x)      --> Sums the elements in x
- prod(x)      --> Product of the elements in x
- max(x)      --> Maximum element in x
- min(x)      --> Minimum element in x
- range(x)  --> Range (min to max) of elements in x


## R Biostat. basics

Useful functions:

- mean(x)   --> Mean (average value) of elements in x.
- median(x) --> Median  (middle  value)  of  elements  in x
- var(x)    --> Variance of elements in x
- sd(x)        --> Standard deviation of element in x
- cor(x,y)    --> Correlation between x and y
- cov(x,y)    --> Covariance between x and y
- quantile(x,p)    --> The pth quantile of x


## R Biostat. basics

Example: patients data

```{r, results='hold'}
set.seed(21341)
patients.df <- data.frame(
  "ID"=paste0("p",1:100)
  ,"Gender"=factor(sample(c("F","M"),100,replace = T))
  ,"Stage"=factor(sample(c("I","II","III","IV"),100,replace = T))
  ,"Age"=as.integer(rgamma(100,shape = 50))
  ,"TumorVolume"=rgamma(100,shape = 10)
  ,stringsAsFactors = F)

head(patients.df)


```

## R Biostat. basics

```{r}
quantile(patients.df$Age)
summary(patients.df$Age)
```

## R Biostat. basics

```{r}
hist(patients.df$Age)
```


## R Biostat. basics

```{r}
summary(patients.df$Stage)
table(patients.df$Stage)
```

## R Biostat. basics

```{r}
boxplot(Age ~ Stage, patients.df
  , ylab="Age",xlab="Stage",main="Patients data")
```


## R Biostat. basics

T-test and Wilcoxon rank sum test

- T-test measures the difference in mean between two independent populations [assumes normal distribution]
- Wilcoxon rank sum test measures the difference in the median between two independent populations [non-parametric]


## R Biostat. basics

```{r}
boxplot(TumorVolume ~ Gender, patients.df
  , ylab="TumorVolume",xlab="Gender",main="Patients data")
```

## R Biostat. basics

Let's use the Wilcoxon rank sum test to see if female and male patients are significantly different in regards to Tumor Volume

```{r}
  wilcox.test(x = patients.df$TumorVolume[patients.df$Gender=="M"]
              ,y = patients.df$TumorVolume[patients.df$Gender=="F"]
              ,mu = 0,alternative = "two.sided",paired = F,T)
```


## R Biostat. basics
Let's check the correlation between Age and Tumor volume

```{r}
plot(patients.df$Age,patients.df$TumorVolume)
```


## R Biostat. basics

- Pearson product moment correlation: The Pearson correlation evaluates the linear relationship between two continuous variables. 
- Spearman rank-order correlation: The Spearman correlation evaluates the monotonic relationship between two continuous or ordinal variables.

## R Biostat. basics
Let's check the correlation between Age and Tumor volume

```{r}
cor.test(patients.df$Age,patients.df$TumorVolume)
```


# Project

## Project
1- Download the following file from: 
https://drive.google.com/drive/folders/1Jqa5KxKyfYqMMkk1IOwKihOZw-IeedxT

2- Find the if there is a significant difference between TNBC and nonTNBC samples based on these genes [ESR1, PGR, ERBB2] and show that in a plot.
3- Compute the pairwise correlation between all the samples and show that in a plot. 

Hint: Install and use package **corrplot**