Two groups | Adjusting for batch | Paired samples | More than two groups | A numeric covariate | Interaction | The effect within one group (simple effect) | Any effect of time (likelihood-ratio test) | What every run records | What rsemflow checks, and what it does not

What you need | Three commands | The same analysis in R | How rsemflow treats your design | Next steps

Requirements | Option 1: install the R package from GitHub | Option 2: install from source with the command-line launcher | Running the command line on Windows | Check the installation | MSigDB gene sets | Troubleshooting

0. The input | 1. Import | 2. Expression summaries | 3. Sample structure: PCA and correlation | 4. Normalized expression | 5. Differential expression | 6. Preranked GSEA | 7. GSVA and ssGSEA scores | 8. Gene annotation | Everything that was written

Structure | Length of observation | Do the expected RSEs hold?

Data and model | 1. Structure is not the obstacle | 2. What these data determine | 3. Which measurement would determine the rest

A model, step by step | The acceptance set | Reading the results