fix warning in docs

Co-authored-by: Elodie CHAPEAUBLANC <elodie.chapeaublanc@pasteur.fr>

doc/requirements.txt

@@ -2,3 +2,4 @@ sphinx
 sphinx-argparse
 sphinxcontrib.bibtex
 sphinx_rtd_theme
+sphinx-autobuild
\ No newline at end of file

doc/source/data_import.rst

@@ -11,7 +11,10 @@ JASS input data
 JASS data, from which all statistics can be computed, are stored in an HDF5 file.
 This file can be created with the procedure `create-inittable`. This procedure needs the following input files to complete:
 
-* **GWAS description** file that must contain the following columns and tab-separated:
+GWAS description
+~~~~~~~~~~~~~~~~
+
+This file that must contain the following columns and tab-separated:
 
 +-------------+----------+-------------------+--------------+--------------------+-----------------+-------------+--------------------+
 | Consortium  | Outcome  |       FullName    | Type         | Reference          | ReferenceLink   | dataLink    | internalDataLink   |
@@ -22,7 +25,10 @@ This file can be created with the procedure `create-inittable`. This procedure n
 The Consortium and outcome names must correspond to the name of the summary statistic files and covariance columns. The last four columns
 can be left blank if the user doesn't want to run JASS on a server.
 
-* **GWAS results files** in the tabular format by chromosome (tab separated) *all in the same folder* with the following columns with the same header:
+GWAS results files
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+GWAS results files in the tabular format by chromosome (tab separated) *all in the same folder* with the following columns with the same header:
 
 +----------+-------+------+-----+--------+
 | rsID     | pos   | A0   | A1  |  Z     |
@@ -33,7 +39,10 @@ can be left blank if the user doesn't want to run JASS on a server.
 The name of file *MUST* follow this pattern : "z_{CONSORTIUM}_{TRAIT}_chr{chromosome number}.txt".
 The consortium and the trait must be capitalized and must *NOT* contain _ .
 
-* OPTIONAL : a **covariance file** that corresponds to the covariance between traits under H0. This file is a tab-separated tabular file.
+Covariance file (OPTIONAL)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+A covariance file that corresponds to the covariance between traits under H0. This file is a tab-separated tabular file.
+
 We recommend that this covariance file to be computed using the `LDScore regression <https://github.com/bulik/ldsc/wiki/Heritability-and-Genetic-Correlation>`_
 However, this step can be fastidious and if not provided by the user, a matrix will be inferred from low signal zscore.
 
@@ -46,8 +55,12 @@ The traits names (columns and row names of the matrix) must correspond to the su
     GABRIEL_ASTHMA  0.085   0.025   0.0382  1.0134  -0.0104
     GEFOS_BMD-FOREARM   -0.0061     -0.0002     0.0048  -0.0104     1.0123
 
-* **Region file** of approximately independant LD regions to the BED file. For european ancestry and grch37/hg19, we suggest to use the regions as defined by :cite:`Berisa2015`, which is already available in `the data folder of the package <https://gitlab.pasteur.fr/statistical-genetics/jass/blob/master/data/fourier_ls-all.bed>`_.
-For grch38, we computed these regions for the five superpopulation available in 1000G using Big SNPR :cite:`10.1093/bioinformatics/btab519`. The corresponding files are stored at <https://gitlab.pasteur.fr/statistical-genetics/jass_suite_pipeline/-/tree/pipeline_ancestry/input_files>`_.
+Region file
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Region file of approximately independant LD regions to the BED file. For european ancestry and grch37/hg19, we suggest to use the regions as defined by :cite:`Berisa2015`, which is already available in `the data folder of the package <https://gitlab.pasteur.fr/statistical-genetics/jass/blob/master/data/fourier_ls-all.bed>`_.
+
+  For grch38, we computed these regions for the five superpopulation available in 1000G using Big SNPR :cite:`10.1093/bioinformatics/btab519`. The corresponding files are stored at <https://gitlab.pasteur.fr/statistical-genetics/jass_suite_pipeline/-/tree/pipeline_ancestry/input_files>`_.
 
 +----------+--------+--------+
 | chr      | start  |  stop  |
@@ -55,8 +68,8 @@ For grch38, we computed these regions for the five superpopulation available in
 | chr1     | 10583  | 1892607|
 +----------+--------+--------+
 
-For inferring approximately independant LD regions from your own panel we recommend using https://privefl.github.io/bigsnpr/ .
-See :cite:`10.1093/bioinformatics/btab519` on the matter
+  For inferring approximately independant LD regions from your own panel we recommend using https://privefl.github.io/bigsnpr/ .
+  See :cite:`10.1093/bioinformatics/btab519` on the matter.
 
 
 Init table generation
@@ -75,14 +88,14 @@ How to generate input data for JASS
 -----------------------------------
 
 Option 1 nextflow pipeline :
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Preprocessing steps for JASS (data harmonisation and imputation)have been gathered in one nextflow pipeline : `JASS pipeline Suite <https://gitlab.pasteur.fr/statistical-genetics/jass_suite_pipeline>`_.
 While this option might have stronger installation requirements, it ensure reproducibility by leveraging docker containers (fixed version of JASS and accompanying packages).
 It will also be much more efficient is you a large number of heterogeneous data to handle and a computing cluster available.
 
 
 Option 2 manually prepare input data:
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 To standardize the format of the input GWAS datasets, you can use the `JASS Pre-processing package <https://gitlab.pasteur.fr/statistical-genetics/JASS_Pre-processing>`_. The `JASS Pre-processing documentation <http://statistical-genetics.pages.pasteur.fr/JASS_Pre-processing/>`_ details the use of this tool.
 
 We think that the best way to compute such covariance from summary statistics is to use the LD-score regression (https://github.com/bulik/ldsc/wiki/Heritability-and-Genetic-Correlation). In the output of the LD-score genetic correlation use the intercept (intercept heritability of trait i for variance of trait i and intercept of the genetic covariance for the covariance between the two traits)::
@@ -123,6 +136,7 @@ Once, GWAS summary statistics are harmonized, they are integrated into
 one file by the using jass command line (see detail in command line usage)
 
 .. code-block:: console
+
    jass create-inittable --input-data-path "harmonized_GWAS_files/*.txt" --init-covariance-path $path1/Covariance_matrix_H0.csv --regions-map-path $path2/Region_file.bed --description-file-path $path3/Data_summary.csv --init-table-path $path4/init_table_EUR_not_imputed.hdf5
 
 .. bibliography:: reference.bib

doc/source/generating_joint_analysis.rst

@@ -17,10 +17,12 @@ Whatever the test used, the command will generate three output:
   - 'summaryTable': a double entry table summarizing the number of significant regions by test (univariate vs joint test)
 
 * A **.png Manhattan plot** of the joint test p-values:
+
 .. image:: ./_static/manhattan_glycemic_blood_asthma.png
 
 * A **.png Quadrant plot** which is a scatter plot of the minimum p-value by region of the joint test with respect to the minimum p-value by region of the univariate tests.
- This plot provides an easy way to see if your joint analysis detected association not previously reported in the litterature.
+  This plot provides an easy way to see if your joint analysis detected association not previously reported in the litterature.
+
 .. image:: ./_static/quadrant_glycemic_blood_asthma.png
 
 The Omnibus tests
@@ -61,4 +63,5 @@ Command Line example
 
 See command line usage for details
 .. code-block:: shell
+
   jass create-project-data --init-table-path init_table/init_table_EUR_not_imputed.hdf5 --phenotype z_MAGIC_GLUCOSE-TOLERANCE z_MAGIC_FAST-GLUCOSE z_MAGIC_FAST-INSULIN z_MAGIC_HBA1C --worktable-path ./work_glycemic.hdf5 --manhattan-plot-path ./manhattan_glycemic.png --quadrant-plot-path ./quadrant_glycemic.png

doc/source/install.rst

@@ -112,7 +112,7 @@ To run locally JASS as a web application, it is recommended to use the docker-co
 If using docker-compose is not an option, you need to launch two servers in two different processes, the `celery` task management server and the web server. The web server handles the HTTP requests, and sends all computation requests to the task management server.
 
 Launching with docker compose (recommended)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Once docker and docker-compose installed, just run
 
 .. code-block:: shell