add Quickstart (#75)

* quickstart

* delete wrong readme

* better readme

* add time quickstart in readme

Author: martin5555555555

README.md

@@ -7,6 +7,19 @@ Get 80% of all standard (biomedical) data science analyses done semi-automated w
 > [!IMPORTANT]  
 > If you use **MrBiomics**, please don't forget to give credit to the authors by citing this original repository and the respective **Modules** and **Recipes**.
 
+# ⚡ Getting started quickly !
+To get everything set up from scratch for the built-in quickstart, run:
+
+```bash
+conda create -n snakemake -c conda-forge -c bioconda snakemake
+git clone https://github.com/epigen/MrBiomics.git
+cd MrBiomics
+conda activate snakemake
+snakemake --software-deployment-method conda --cores 1
+```
+
+This creates a Snakemake environment, clones the repository, activates the environment, and executes the current default quickstart workflow. It will take around 10 minutes to run because it will download conda environments. If you want to learn more about the underlying analysis, start with the [Quickstart wiki page](../../wiki/Quickstart).
+
 # ⏳ TL;DR - More Time for Science!
 > _"Programming is about trying to make the future less painful. It’s about making things easier for our teammates."_ from _The Pragmatic Programmer_ by Andy Hunt & Dave Thomas
 - **Why**: Time is the most precious resource. By taking care of efficiency (i.e., maximum output with limited resources) scientists can re-distribute their time to focus on effectiveness (i.e., the biggest impact possible).

config/quickstart/quickstart_enrichment_analysis_annotation.csv

@@ -1,2 +1,5 @@
-name,features_path,background_name,background_path,group
-GDS289,docs/quickstart/GDS289_scores.csv,,,COPD
+name,features_path,background_name,background_path,group
+Bcell,docs/quickstart/data/Bcell_up_features.bed,all_regions,docs/quickstart/data/ALL_features.bed,hematopoietic
+Ery,docs/quickstart/data/Ery_up_features.bed,all_regions,docs/quickstart/data/ALL_features.bed,hematopoietic
+Mono,docs/quickstart/data/Mono_up_features.bed,all_regions,docs/quickstart/data/ALL_features.bed,hematopoietic
+CD8,docs/quickstart/data/CD8Tcell_up_features.bed,all_regions,docs/quickstart/data/ALL_features.bed,hematopoietic

config/quickstart/quickstart_enrichment_analysis_config.yaml

@@ -1,6 +1,6 @@
 
 ##### RESOURCES #####
-mem: '4000'
+mem: 4000
 threads: 1
 
 ##### GENERAL #####
@@ -21,9 +21,7 @@ genome: 'hg38'
 #     Enrichr (https://maayanlab.cloud/Enrichr/#libraries)
 # JSON example content: { "MyDB_Term1": ["geneA","geneB","geneC"],"MyDB_Term2": ["geneX","geneY","geneZ"]}
 local_databases:
-    GO_Biological_Process_2023: "docs/quickstart/GO_Biological_Process_2023.gmt"
-    GO_Cellular_Component_2023: "docs/quickstart/GO_Cellular_Component_2023.gmt"
-    GO_Molecular_Function_2023: "docs/quickstart/GO_Molecular_Function_2023.gmt"
+    Azimuth_2023: "resources/Azimuth_2023.gmt"
 
 ## LOLA compatible region set databases
 # loaded using loadRegionDB() (https://code.databio.org/LOLA/reference/loadRegionDB.html)
@@ -102,9 +100,9 @@ column_names:
         term: 'Term'
     GREAT:
         top_n: 25
-        p_value: "p_value_hyper" # or binomial test result: p_value
-        adj_pvalue: "p_adjust_hyper" # or binomial test result: p_adjust
-        effect_size: "fold_enrichment_hyper" # or binomial test result: fold_enrichment
+        p_value: "p_value" # or binomial test result: p_value
+        adj_pvalue: "p_adjust" # or binomial test result: p_adjust
+        effect_size: "fold_enrichment" # or binomial test result: fold_enrichment
         overlap: "observed_region_hits" # or binomial test result: observed_gene_hits
         term: "description"
     LOLA:
@@ -135,13 +133,13 @@ column_names:
 adjp_th:
     ORA_GSEApy: 0.05
     preranked_GSEApy: 0.05
-    GREAT: 0.01
+    GREAT: 0.05
     LOLA: 0.01
     pycisTarget: 5 # keep results greater(!) than provided threshold
     RcisTarget: 5 # keep results greater(!) than provided threshold
 
 # number of top terms per feature set within each group for all overview plots (adjusted p-value, effect-size and bubble-heatmap)
-top_terms_n: 5
+top_terms_n: 1
 
 # cap for adjusted p-value plotting: -log10(adjusted p-value) > adjp_cap -> adjp_cap
 adjp_cap: 4
@@ -154,4 +152,4 @@ or_cap: 5
 nes_cap: 5
 
 # hierarchical cluster flag for summary plots (0=no; 1=yes)
-cluster_summary: 1
+cluster_summary: 1

docs/quickstart/data/Bcell_up_features.bed

@@ -11148,4 +11148,4 @@ chr1	145828289	145829119	CONS00000012863
 chrX	79408643	79409378	CONS00000289835
 chr16	1469575	1470343	CONS00000243904
 chr11	908174	908720	CONS00000189193
-chr2	47652192	47652735	CONS00000029976
+chr2	47652192	47652735	CONS00000029976