I'm learning to use R in data analysis. I'm getting fluent in baseR and the tidyverse, but thus far only dealt with medium throughput plate-based experiments.
I am currently trying to learn how to work with a data set of normalized RNAseq results. I have a matrix consisting of tens of thousands of rows corresponding to genes. The first two columns identify 'gene_ID' and 'gene_name'. The remaining 96 columns are different samples (different treatments, timepoints, replicates, ...).
I would like to analyze this dataset to identify differentially expressed gene based on the different conditions, perform some cluster analysis, run GESA on these clusters, and generate heatmaps at various stages of the process.
Can anybody recommend the best libraries to look into in order to achieve these goals? I started looking at several tutorials, but much of them deal with the earlier steps of RNAseq data analysis and don't really focus on these later steps once the data is already processed and cleaned up.