A brief example for RNA-seq data analysis
Firstly you should read the paper more than 3 times.
PLoS One. 2014 Jun 13;9(6):e99625. doi: 10.1371/journal.pone.0099625. eCollection 2014.
RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells.
In that paper, we can find the raw data of this experiment, which is located in : https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE52778
If you are familar with RNA-seq workflow , you can process those data from begin to end, get the expression matrix by your self, but you can also load the expression matrix by R package:
The readme for this package: https://bioconductor.org/packages/release/data/experiment/vignettes/airway/inst/doc/airway.html
check the correlation of expression matrix
As we can see, the samples in same group are more similar with each other than samples in different group.
It's very easy to understand, so I don't want to explain the codes one by one.
You can also read my tutorial
rnaseq-workflow.txt to study how to process the raw fastq data of RNA-seq.
The R code : DEG_rnsseq.R
Once we get the DEG list, we can annotate those genes.