This app was developed by the Bioinformatics Core of Cancer Research Uk Cambridge Institute to accompany a training course. On the course webpage you will find lecture notes from the course and practical exercises that use this app Introduction to Statistical Analysis




View source Code for app

Data Import Parameters

Are your samples paired?

If your two groups are dependent, you should choose a paired test. If your groups are independant, leave this box un-ticked

Direction of comparison

If A vs B is selected, the difference will be the first column minus the second. Selecting B vs A will compute the second column minus the first You can choose to transform the data prior to statistical testing

            

Display Parameters

You can use the algorithm in R to guess how many bins to use in the histogram
Otherwise, you can choose your own number of bins
The boxplot and histogram of the data are shown below

Basic Summary



            
If you have selected a paired analysis, you will be able to assess the distribution of the differences here

Display Parameters

You can use the algorithm in R to guess how many bins to use in the histogram
Otherwise, you can choose your own number of bins
Use the histograms and boxplot to judge whether you need to use a parametric, or non-parametric test

Variances

Inspect the histograms and boxplots, or use the result of the F-test to judge whether the variances of each group are approximately the same
Note that changing this option will have no effect for a non-parametric test

Two-sample test


            If you have chosen a Parametric test, the comparison of the calculated test-statistic to the reference distribution will be shown here
            

F test for differences in variance

F test to compare the variances of two samples from normal populations WARNING: Please use the result of this test with caution. Sometimes you can better judge differences in variance by inspecting the data distribution

          

Report Parameters

R Script

You will be able to re-run this analysis in R by downloading the R code below We recommend RStudio to run the R code and compile a pdf or HTML report that will show the results of your analysis along with the code used
RStudio
The input file that you are analysing must be in your R working directory in order for the script to run In order to compile the report in RStudio, you will need to install the tidyverse package install.packages('tidyverse'))

Download R Script

Download R Markdown file