4 Main lab
4.1 Data
In most of the exercises, we will use RNA-seq data (Illumina short reads) from the human A431 cell line. It is an epidermoid carcinoma cell line which is often used to study cancer and the cell cycle, and as a sort of positive control of epidermal growth factor receptor (EGFR) expression. A431 cells express very high levels of EGFR, in contrast to normal human fibroblasts.
The A431 cells were treated with gefinitib, which is an EGFR inhibitor and is used (under the trade name Iressa) as a drug to treat cancers with mutated and overactive EGFR. In the experiment, RNA was extracted at four time points: before the gefinitib treatment (t=0), and two, six and twenty-four hours after treatment (t=2, t=6, t=24, respectively), and sequenced using an Illumina HiSeq instrument in triplicates (thus there are 3x4=12 samples).
This data is part of the transcriptomics course that is also given by NBIS. We will use some of the counts table that was generated in the course after different transformations like manual filtering for low counts, VST and DESeq2. You don’t have to know what these exactly mean to do these exercises :) These are basically the same data at different stages of the transcriptomics analysis.
4.2 ggplot part 1
Below is the link for the basic plotting exercise. there will be some basic plotting exercises first using R base graphics and comparing them to the grid graphics using ggplot2. Followed by practicing the first basics in ggplot: geoms, colors and aesthetics.
4.3 ggplot part 2
Below is the link for the second exercise where you will look into facets, barplots and errorbars.
4.4 ggplot part 3
Below is the link for the third exercise where you will look into facets, barplots and errorbars.
4.5 Combining plots
In this part of the lab, we will look into: how one can combine different plots that we have made using different tools. Also to look into some of the advantages of cowplot and ggpubr.
4.6 PCA and Heatmaps
Here, we look into building PCA plots with ggplot. We will also look into making heatmaps with both pheatmap and geom_tile in ggplot.
4.7 Rshiny Part I
Here, we get introduced to how to start writing a Rshiny code. So, naturally we start to play around with the UI mainly to see what kinds of different UIs that one can use.
4.8 Rshiny Part II
Here, we look into some of the important Rshiny functions that one can use in the server part of the shiny code like reactivity, Error validation and Download options in the server.
4.9 Rshiny Covid App
In this part, we learn how to write an app from scratch! So, we take the visualization of the covid data as an example from few countries that we would like to plot!