resolve conflict in readme

README.md

@@ -54,20 +54,22 @@ Once done you can launch the pipeline as:
     nextflow run jass_pipeline.nf --ref_panel {ABSOLUTE_PATH_TO_PIPELINE_FOLDER}/Ref_panel --gwas_folder {ABSOLUTE_PATH_TO_PIPELINE_FOLDER}/test_data/hg_ -with-report jass_report.html
 ```
 
+If all went well, you have cleaned the three summary statistic files, aligned them on the reference panel, and integrated them in one database. The database was used to perform a multi-trait GWAS on the three traits. 
+
 ## Required Input
 
 The following Item are necessary to run JASS pipeline on real data
 
 1. --meta_data : A path toward a meta-data file describing GWAS (see example file in ./input_files/test1.csv and [jass_preprocessing documentation](http://statistical-genetics.pages.pasteur.fr/jass_preprocessing/))
 2. --gwas_folder : A path toward a folder containing the summary statistics to analyze
-3. --ref_panel :A folder containing a Reference Panel in the .bim, .bed, .fam format
+3. --ref_panel_WG : a path toward a reference panel (all genome as 1 file). See below to download curated reference panels by ancestries derived from 1000G
 4. --ld-folder : A path toward a folder containing LD matrices (that can be generated from the reference panel with the raiss package as described here : http://statistical-genetics.pages.pasteur.fr/raiss/#precomputation-of-ld-correlation)
 5. --group If you wish to compute joint analyses with the pipeline, a group file with the each phenotype group written on a separated line
 
 ## Optional parameters
 
 * --output_folder : A path toward a folder to write pipeline results (inittable, worktable...). by default results will be publish in the workflow directory.
-
+* --ref_panel : A folder containing a Reference Panel in the .bim, .bed, .fam format for imputation with RAISS
 ## Available reference panels
 
 To make reference panel readily available, we use git lfs.

jass_pipeline.nf

@@ -16,7 +16,13 @@ params.meta_data = "${projectDir}"+"/input_files/Data_test_EAS.csv" // file desc
 params.gwas_folder = "${projectDir}"+'/test_data/hg38_EAS/' 
 params.ref_panel = '/pasteur/zeus/projets/p02/GGS_JASS/jass_analysis_pipeline/Ref_panel_by_chr/'
 params.region = "${projectDir}"+"/input_files/All_Regions_ALL_ensemble_1000G_hg38_EAS.bed"
+<<<<<<< HEAD
 param.ref_panel_wg_channel = "${projectDir}"+"/Ref_Panel/1000G_EAS_0_01_chr22_21.csv"
+=======
+
+params.ref_panel_WG = "${projectDir}"+"/Ref_Panel/1000G_EAS_0_01_chr22_21.csv"//"${projectDir}/Ref_Panel/1000G_EAS_0_01_chr22.csv"
+
+>>>>>>> 1e9f87b9950df3c765cf738e14fac767e4ecba8a
 params.ancestry="EAS"
 params.prefix="ALL_ensemble_1000G_hg38_EAS_chr"
 params.prefix_Impute_GWAS="ALL_ensemble_1000G_hg38_EAS_"
@@ -52,7 +58,10 @@ chr_channel = Channel.from(1..22)
 ref_chr_channel=Channel.fromPath(params.ref_panel+"/ALL_ensemble_1000G_hg38_EAS_chr*.bim")
 ld_channel=Channel.fromPath("/pasteur/zeus/projets/p02/GGS_WKD/DATA_1000G/Panels/Matrix_LD_RAISS/EAS/*.ld")
 
+<<<<<<< HEAD
 //"${projectDir}/Ref_Panel/1000G_EAS_0_01_chr22.csv"
+=======
+>>>>>>> 1e9f87b9950df3c765cf738e14fac767e4ecba8a
 extract_sample_size_script_channel = Channel.fromPath("${projectDir}/bin/extract_sample_size.py")
 generate_trait_pairs_channel = Channel.fromPath("${projectDir}/bin/generate_trait_pairs.py")
 parse_correlation_channel = Channel.fromPath("${projectDir}/bin/parse_correlation_results.py")
@@ -77,7 +86,11 @@ process Clean_GWAS {
     publishDir "${params.output_folder}/harmonized_GWAS_files/", pattern: "*.txt", mode: 'copy'
     publishDir "${params.output_folder}", pattern: "harmonized_GWAS_1_file/*.txt", mode: 'copy'
     input:
+<<<<<<< HEAD
         path ref_panel from params.ref_panel_wg_channel
+=======
+        path ref_panel from params.ref_panel_WG 
+>>>>>>> 1e9f87b9950df3c765cf738e14fac767e4ecba8a
         path meta_chunk from meta_data
     output:
         path "harmonized_GWAS_1_file/*.txt" into cleaned_gwas_channel