more details in the doc index

jass_preprocessing/doc/source/index.rst

@@ -41,25 +41,55 @@ execute the following lines:
   cd JASS_Pre-processing/
   pip3 install ./jass_preprocessing/
 
-
-
-
 Preprocessing example
 =====================
 
-The file : "/JASS_Pre-processing/main_preprocessing.py" gives an example on how to use
-this package.
+The file : "/JASS_Pre-processing/main_preprocessing.py" gives a complete example on
+how to use this package.
 
 
 Input
 ======
 
-* A reference panel (1000 genome format)
+* A reference panel (1000 genome format). The user is expected to provide a reference panel in tsv format with the following columns in that order, without header:
+
++-----+-----+------------+-----+-----+---------+
+| chr | pos |   snp_id   | ref | alt |   MAF   |
++=====+=====+============+=====+=====+=========+
+|  1  |13116| rs62635286 |  T  |  G  |0.0970447|
++-----+-----+------------+-----+-----+---------+
+|  1  |13118| rs200579949|  A  |  G  |0.0970447|
++-----+-----+------------+-----+-----+---------+
+|  1  |14604| rs541940975|  A  |  G  | 0.147564|
++-----+-----+------------+-----+-----+---------+
+|  1  |14930| rs75454623 |  A  |  G  | 0.482228|
++-----+-----+------------+-----+-----+---------+
+
 * Folder containing all raw gwas data (all chromosomes in one file)
 * a list containing the name of GWAS file to the string format.
-* A descriptor csv files that will described each GWAS summary statistic files
+* A descriptor csv files that will described each GWAS summary statistic files:
+
+  * a header
+  * 1 line per study
+  * the fields are:
+
+
++-------------------------------------------+------------------------------------------------------------+
+|                     category              |                         field name                         |
++===========================================+============================================================+
+|             path to the data              |                            filename                        |
++-------------------------------------------+------------------------------------------------------------+
+|            study info fields              | consortia,outcome,fullName,type,Nsample,Ncase,Ncontrol,Nsnp|
++-------------------------------------------+------------------------------------------------------------+
+|    names of the header in the GWAS file   |      snpid,a1,a2,freq,pval,n,z,OR,se,code,imp,ncas,ncont   |
++-------------------------------------------+------------------------------------------------------------+
+
+.. |               I don't know                |                          altNcas,altNcont|
+
+
 * it must contain the following columns:
 
+Hard coded path (l.20-29 of JASS_Pre-processing/main_preprocessing.py)
 
 Indices and tables
 ==================

main_preprocessing.py

@@ -16,6 +16,24 @@ import pandas as pd
 import seaborn as sns
 import time
 
+
+#Hard coded path (l.20-29 of JASS_Pre-processing/main_preprocessing.py)
+
+#| variable name | description | current default value|
+#|---------------|-------------|----------------------|
+#| netPath | Main project folder, must end by "/" | /mnt/atlas/ |
+#| GWAS_labels* | Path to the file describing the format of the individual GWASs files | netPath+'PCMA/1._DATA/RAW.GWAS/GWAS_labels.csv' |
+#| GWAS_path* | Path to the folder containing the GWASs summ stat files, must end by "/" | netPath+'PCMA/1._DATA/RAW.GWAS/'|
+#| diagnostic_folder | folder for histograms of sample size distribution among SNPs | /mnt/atlas/PCMA/1._DATA/sample_size_distribution/ |
+#| ldscore_format | data formated to use LDscore, 1 file per study | /mnt/atlas/PCMA/1._DATA/ldscore_data/ |
+#| REF_filename* | file containing the reference panel for imputation | netPath+'PCMA/0._REF/1KGENOME/summary_genome_Filter_part2.out' |
+#| pathOUT | **unused in main_preprocessing.py**  | netPath+'PCMA/1._DATA/RAW.summary/'|
+#| ImpG_output_Folder | main ouput folder | netPath+ 'PCMA/1._DATA/preprocessing_test/' |
+
+#+ Hard coded variable: perSS = 0.7: the proportion of the 90th percentile of the sample size used to filter the SNPs
+
+
+
 perSS = 0.7
 netPath = "/mnt/atlas/"
 GWAS_labels = netPath+'PCMA/1._DATA/RAW.GWAS/GWAS_labels.csv'