update README

Author: sreichl

README.md

@@ -39,7 +39,7 @@ This project wouldn't be possible without the following software and their depen
 | Snakemake      | https://doi.org/10.12688/f1000research.29032.2    |
 
 # 🔬 Methods
-This is a template for the Methods section of a scientific publication and is intended to serve as a starting point. Only retain paragraphs relevant to your analysis. References [ref] to the respective publications are curated in the software table above. Versions (ver) have to be read out from the respective conda environment specifications (.yaml file) or post execution. Parameters that have to be adapted depending on the data or workflow configurations are denoted in squared brackets e.g. [X].
+This is a template for the Methods section of a scientific publication and is intended to serve as a starting point. Only retain paragraphs relevant to your analysis. References [ref] to the respective publications are curated in the software table above. Versions (ver) have to be read out from the respective conda environment specifications (`workflow/envs/*.yaml file`) or post-execution in the result directory (`dea_limma/envs/*.yaml`). Parameters that have to be adapted depending on the data or workflow configurations are denoted in squared brackets e.g., [X].
 
 __Differential Expression Analysis (DEA).__ DEA was performed on the quality-controlled filtered [raw/normalized] counts using the _limma_ (ver) [ref] workflow for fitting a linear model [formula] to identify features (genes/regions) that statistically significantly change with [comparisons] compared to the control group [reference levels] (intercept). Briefly, we determined normalization factors with edgeR::calcNormFactors (optional) using method [X], then applied voom (optional) to estimate the mean-variance relationship of the log-counts. We used blocking on (optional) variable [X] to account for repeated measurements, lmFit to fit the model to the data, and finally eBayes (optional) with the robust (and trend flag – optional for normalized data) flag to compute (moderated/ordinary) t-statistics. For each comparison we used topTable to extract feature-wise average expression, effect sizes (log2 fold change) and their statistical significance as adjusted p-values, determined using the Benjamini-Hochberg method. Furthermore, we calculated feature scores, for each feature in all comparisons, using the formula [score_formula] for downstream ranked enrichment analyses. Next, these results were filtered for relevant features based on the following criteria: statistical significance (adjusted p-value < [X]), effect size (absolute log2 fold change > [X]), and expression (average expression > [X]). Finally, we performed hierarchical clustering on the effect sizes (log2 fold changes) of the union of all relevant features and comparison groups.