fixed issue #1

impute_jass/doc/source/_autosummary/impute_jass.windows.rst

@@ -14,7 +14,6 @@ impute\_jass.windows
       format_result_df
       impg_like_imputation
       in_region
-      ld_region_centered_window_imputation
       parse_region_position
       prepare_zscore_for_imputation
       print_progression

impute_jass/doc/source/index.rst

@@ -21,10 +21,9 @@ The statistical model used to make the imputation is described in :cite:`Pasaniu
 The imputation execution time is optimized by precomputing Linkage desiquilibrium between SNPs.
 
 
-Dependancies
+Dependencies
 ============
-RAISS require plink version 1.9
-
+RAISS requires plink version 1.9 : `<https://www.cog-genomics.org/plink2>`_
 
 Installation
 ============
@@ -39,8 +38,8 @@ Precomputation of LD-correlation
 The imputation is based the Linkage desiquilibrium between SNPs.
 
 To save computation the LD is computed and saved perform the imputation is
-performed. To limit the number of SNP pairs, the LD is computed between SNPs in
-region of approximately LD-independant. For an european ancestry, you can use
+performed. To limit the number of SNP pairs, the LD is computed between pairs of
+SNPs in a approximately LD-independent regions. For an european ancestry, you can use
 the region defined by :cite:`Berisa2015` that are provided in the package data folder.
 
 To compute the LD you need to specify a reference panel splitted by chromosomes
@@ -70,7 +69,8 @@ all in the same folder with the following columns with the same header:
 | rs6548219| 30762 | A	  | G   | -1.133 |
 +----------+-------+------+-----+--------+
 
-This format can be obtain with the Processing package.
+This format can be obtain with the `JASS PreProcessing package <https://gitlab.pasteur.fr/statistical-genetics/JASS_Pre-processing>`.
+
 
 Launching imputation on one chromosome
 ======================================
@@ -81,14 +81,13 @@ If you have access to a cluster, an efficient way to use RAISS is to launch
 the imputation of each chromosome on a separate cluster node. The script
 launch_imputation_all_gwas.sh contain an example for SLURM scheduler.
 
-
 #TODO check command line interface
 
 Output
 ======
 
-The raiss package output imputed GWAS files in the tabular format:
-
+The raiss package outputs imputed GWAS files in the tabular format:
+#TODO suppress complementary columns
 +------------+---+--+----------------+-----+-----+----------------+------------------+---------+---------+
 |            |A0 |A1| Nsnp_to_impute |Var  |Z    |condition_number|correct_inversion |ld_score |   pos   |
 +============+===+==+================+=====+=====+================+==================+=========+=========+

impute_jass/impute_jass/stat_models.py

@@ -10,8 +10,8 @@ Fast and accurate imputation of summary statistics enhances evidence
 of functional enrichment, Bioinformatics, Volume 30, Issue 20, 15 October 2014,
 Pages 2906–2914
 
-
 """
+
 import numpy as np
 import scipy as sc
 import scipy.linalg

impute_jass/setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(name='impute_jass',
       version='0.1',
-      description='Preprocess GWAS summary statistic for JASS',
+      description='Imputation of summary statistics',
       url='http:https://gitlab.pasteur.fr/statistical-genetics/JASS_Pre-processing',
       author='Hanna Julienne',
       author_email='hanna.julienne@pasteur.fr',