1 Main exercises
The following data will be used in the used for the exercises in this course. The data comes from a mock RNA sequencing data with 12 samples that are from cell-lines. As in any normal RNAseq analysis the counts of genes were normalized using different methods such as VST and CPM after filtering out the genes that were of very low expression. We would use these data for visualizations.
Download all of these files into your current working directory
You can download all the files together here.
Make a directory called data and unzip in that directory!
If workshop_on_plotting_in_R is the current working directory, the directory tree should look like this:
- ggplot_geneco_course
- data
- arch_newick.txt
- counts_deseq2.txt
- counts_filtered.txt
- counts_raw.txt
- counts_vst.txt
- human_biomaRt_annotation.csv
- metadata.csv
- Time_t24_vs_t0.txt
- Time_t2_vs_t0.txt
- Time_t6_vs_t0.txt
- tree_env.tsv
- tree_hmap.tsv
- shiny_app_data.csv
- data
You can find the information related to the important files below:
1.1 Counts table
- Table with gene counts after filtering: Filtered Counts
- Table with gene counts normalized with VST: VST counts
- Table with gene counts normalized with DESeq2: DESeq2 counts
1.2 Metadata
- Metadata of the samples: Sample Metadata
- Metadata of the genes with their functions: Gene Annotation
1.3 DE genes
Below are the lists of differentially expressed genes between different time points