The Concept

I have an some RNA-seq data from an in vitro drug candidate screening experiment. I want to know:

The conventional way

Run data processing pipelines to turn sequencing reads into counts.
Differential gene expression analysis, make volcano plot and heatmap.
Pathway & gene ontology enrichment analysis, top pathways have 10 genes and 5 are common signal transduction genes.
Find an interesting gene set and do Gene Set Enrichment Analysis.
Maybe transcription factor enrichment analysis.
Maybe network analysis, if there's enough samples.
Read papers and hope that some of the differentially expressed genes may connect to a potential mechanism.

Run data processing pipelines to turn sequencing reads into counts.
Use transfer learning to learn representation of expression profile and infer functional changes based on similarity with previous observations.
Use generative models to deconvolute cell types and cell type-specific gene regulatory network changing between samples.
Multiple specialised models to identify metabolic states, surface protein interactome, signalling networks.
Models to extrapolate transcriptome to metabolome, proteome, epigenome, phenome etc. to generate hypothesis for the next experiment.