Mercurial > repos > immuneml > immuneml_tools

changeset 6:2d3dd9ff7e84 draft

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"planemo upload commit 74f2bd15d2b7723c8e5a22d743913706dc7d8333-dirty"
author immuneml
date Tue, 27 Jul 2021 09:30:50 +0000
parents 48569213d91c
children 45ca02982e1f
files README.md README.rst immuneml_create_dataset.xml immuneml_simulate_dataset.xml immuneml_simulate_events.xml immuneml_train_ml_model.xml immuneml_train_recept.xml immuneml_train_repert.xml immuneml_yaml.xml
diffstat 9 files changed, 25 insertions(+), 26 deletions(-) [+]
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--- a/README.md	Fri Jul 02 11:04:53 2021 +0000
+++ b/README.md	Tue Jul 27 09:30:50 2021 +0000
@@ -21,4 +21,5 @@
 The line has to be inside `<registration>` along with the other datatypes.
 
 ### The immuneML package
-Conda installation of immuneML typically takes several minutes. There is general information about Galaxy's dependency resolution here: https://docs.galaxyproject.org/en/release_20.05/admin/conda_faq.html
+Conda installation of immuneML typically takes several minutes. There is general information about Galaxy dependency resolution here: https://docs.galaxyproject.org/en/release_20.05/admin/conda_faq.html 
+
--- a/README.rst	Fri Jul 02 11:04:53 2021 +0000
+++ b/README.rst	Tue Jul 27 09:30:50 2021 +0000
@@ -1,9 +1,7 @@
-Dependencies
+The tools rely on immuneML version 2.0.1 or newer.
+
+Installation
 ============
-The tools rely on immuneML version 2.0.1 or newer. 
-
-Installation: datatype
-======================
 You will need to define a new datatype ``immuneml_receptors``, which is done as follows:
 
 1. In your ``galaxy.yml`` look up the name of your ``datatypes_config_file``. If the name is not yet defined, set
@@ -16,6 +14,6 @@
 ``<datatype extension="immuneml_receptors" type="galaxy.datatypes.text:Html" subclass="True"/>``
 The line has to be inside ``<registration>`` along with the other datatypes.
 
-Installation: The immuneML package
-==================================
-Conda installation of immuneML typically takes several minutes. There is general information about Galaxy's dependency resolution here: https://docs.galaxyproject.org/en/release_20.05/admin/conda_faq.html
+The immuneML package
+====================
+Conda installation of immuneML typically takes several minutes. There is general information about Galaxy dependency resolution here: https://docs.galaxyproject.org/en/release_20.05/admin/conda_faq.html 
--- a/immuneml_create_dataset.xml	Fri Jul 02 11:04:53 2021 +0000
+++ b/immuneml_create_dataset.xml	Tue Jul 27 09:30:50 2021 +0000
@@ -150,7 +150,7 @@
 
         Before creating a dataset, the relevant data files must first be uploaded to the Galaxy interface. This can be done either
         by uploading files from your local computer (use the 'Upload file' tool under the 'Get local data' menu), or by fetching
-        remote data from the iReceptor Plus Gateway or VDJdb (see `How to import remote AIRR datasets in Galaxy <https://docs.immuneml.uio.no/galaxy/galaxy_import_remote_data.html>`_).
+        remote data from the iReceptor Plus Gateway or VDJdb (see `How to import remote AIRR datasets in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_import_remote_data.html>`_).
 
         The imported immuneML dataset is stored in a Galaxy collection, which will appear as a history item on the right side of the screen,
         and can later be selected as input to other tools.
@@ -159,7 +159,7 @@
         on default settings for importing datasets. The advanced interface gives full control over import settings through a YAML
         specification. In most cases, the simplified interface will suffice.
 
-        For the exhaustive documentation of this tool and more information about immuneML datasets, see the tutorial `How to make an immuneML dataset in Galaxy <https://docs.immuneml.uio.no/galaxy/galaxy_dataset.html>`_.
+        For the exhaustive documentation of this tool and more information about immuneML datasets, see the tutorial `How to make an immuneML dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_dataset.html>`_.
 
         **Tool output**
 
--- a/immuneml_simulate_dataset.xml	Fri Jul 02 11:04:53 2021 +0000
+++ b/immuneml_simulate_dataset.xml	Tue Jul 27 09:30:50 2021 +0000
@@ -41,7 +41,7 @@
         Note that since these labels are randomly assigned, they do not bear any meaning and it is not possible to train a ML model with high classification accuracy on this data.
         Meaningful labels can be added using the `Simulate immune events into existing repertoire/receptor dataset <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_simulation>`_ Galaxy tool.
 
-        For the exhaustive documentation of this tool and an example YAML specification, see the tutorial `How to simulate an AIRR dataset in Galaxy <https://docs.immuneml.uio.no/galaxy/galaxy_simulate_dataset.html>`_.
+        For the exhaustive documentation of this tool and an example YAML specification, see the tutorial `How to simulate an AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_simulate_dataset.html>`_.
 
         **Tool output**
 
--- a/immuneml_simulate_events.xml	Fri Jul 02 11:04:53 2021 +0000
+++ b/immuneml_simulate_events.xml	Tue Jul 27 09:30:50 2021 +0000
@@ -39,7 +39,7 @@
         If you instead want to simulate a synthetic dataset from scratch, start with the
         tool `Simulate a synthetic immune receptor or repertoire dataset <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_simulate_dataset>`_.
 
-        For the exhaustive documentation of this tool and an example YAML specification, see the tutorial `How to simulate immune events into an existing AIRR dataset in Galaxy <https://docs.immuneml.uio.no/galaxy/galaxy_simulate_signals.html>`_.
+        For the exhaustive documentation of this tool and an example YAML specification, see the tutorial `How to simulate immune events into an existing AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_simulate_signals.html>`_.
 
         **Tool output**
 
--- a/immuneml_train_ml_model.xml	Fri Jul 02 11:04:53 2021 +0000
+++ b/immuneml_train_ml_model.xml	Tue Jul 27 09:30:50 2021 +0000
@@ -49,7 +49,7 @@
       see `Train immune receptor classifiers (easy interface) <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_classifiers>`_ and
       `Train immune repertoire classifiers (easy interface) <https://galaxy.immuneml.uio.no/root?tool_id=novice_immuneml_interface>`_.
 
-      For more details on how to train ML models in Galaxy, see `the documentation <https://docs.immuneml.uio.no/galaxy/galaxy_train_ml_models.html>`_.
+      For more details on how to train ML models in Galaxy, see `the documentation <https://docs.immuneml.uio.no/latest/galaxy/galaxy_train_ml_models.html>`_.
 
       **Tool output**
 
@@ -63,7 +63,7 @@
         Furthermore, the folder contains the complete YAML specification file for the immuneML run, the HTML output and a log file.
 
       - optimal_ml_settings.zip: a .zip file containing the raw files for the optimal trained ML settings (ML model, encoding, and
-        optionally preprocessing steps). This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/galaxy/galaxy_apply_ml_models.html>`_.
+        optionally preprocessing steps). This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_apply_ml_models.html>`_.
 
     ]]>
 
--- a/immuneml_train_recept.xml	Fri Jul 02 11:04:53 2021 +0000
+++ b/immuneml_train_recept.xml	Tue Jul 27 09:30:50 2021 +0000
@@ -98,7 +98,7 @@
         Alternatively, if you want to predict a property per immune repertoire, such as disease status, check out the
         `Train immune repertoire classifiers (simplified interface) <https://galaxy.immuneml.uio.no/root?tool_id=novice_immuneml_interface>`_ tool instead.
 
-        The full documentation can be found `here <https://docs.immuneml.uio.no/galaxy/galaxy_simple_receptors.html>`_.
+        The full documentation can be found `here <https://docs.immuneml.uio.no/latest/galaxy/galaxy_simple_receptors.html>`_.
 
         **Basic terminology**
 
@@ -112,7 +112,7 @@
         determines how we should represent our data to the ML model. This representation is called **encoding**. In this tool, the encoding is automatically chosen based on
         the user's assumptions about the dataset.
 
-        .. image:: https://docs.immuneml.uio.no/_images/receptor_classification_overview.png
+        .. image:: https://docs.immuneml.uio.no/latest/_images/receptor_classification_overview.png
             :height: 500
 
         |
@@ -137,7 +137,7 @@
         A graphical representation of how a CDR3 sequence can be divided into k-mers, and how these k-mers can relate to specific positions in a 3D immune receptor
         (here: antibody) is shown in this figure:
 
-        .. image:: https://docs.immuneml.uio.no/_images/3mer_to_3d.png
+        .. image:: https://docs.immuneml.uio.no/latest/_images/3mer_to_3d.png
             :height: 250
 
         |
@@ -187,7 +187,7 @@
           Furthermore, the folder contains the complete YAML specification file for the immuneML run, the HTML output and a log file.
 
         - optimal_ml_settings.zip: a .zip file containing the raw files for the optimal trained ML settings (ML model, encoding).
-          This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/galaxy/galaxy_apply_ml_models.html>`_.
+          This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_apply_ml_models.html>`_.
 
         - receptor_classification.yaml: the YAML specification file that was used by immuneML internally to run the analysis. This file can be
           downloaded, altered, and run again by immuneML using the  `Train machine learning models <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.
--- a/immuneml_train_repert.xml	Fri Jul 02 11:04:53 2021 +0000
+++ b/immuneml_train_repert.xml	Tue Jul 27 09:30:50 2021 +0000
@@ -117,7 +117,7 @@
         Alternatively, if you want to predict a property per immune receptor, such as antigen specificity, check out the
         `Train immune receptor classifiers (simplified interface) <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_classifiers>`_ tool instead.
 
-        The full documentation can be found `here <https://docs.immuneml.uio.no/galaxy/galaxy_simple_repertoires.html>`_.
+        The full documentation can be found `here <https://docs.immuneml.uio.no/latest/galaxy/galaxy_simple_repertoires.html>`_.
 
         **Basic terminology**
 
@@ -125,7 +125,7 @@
         One could thus have a label named ‘CMV_status’ with possible classes ‘positive’ and ‘negative’. The labels and classes must be present in the metadata
         file, in columns where the header and values correspond to the label and classes respectively.
 
-        .. image:: https://docs.immuneml.uio.no/_images/metadata_repertoire_classification.png
+        .. image:: https://docs.immuneml.uio.no/latest/_images/metadata_repertoire_classification.png
             :height: 150
 
         |
@@ -137,7 +137,7 @@
         the user's assumptions about the dataset.
 
 
-        .. image:: https://docs.immuneml.uio.no/_images/repertoire_classification_overview.png
+        .. image:: https://docs.immuneml.uio.no/latest/_images/repertoire_classification_overview.png
             :height: 500
 
         |
@@ -166,7 +166,7 @@
         A graphical representation of how a CDR3 sequence can be divided into k-mers, and how these k-mers can relate to specific positions in a 3D immune receptor
         (here: antibody) is shown in this figure:
 
-        .. image:: https://docs.immuneml.uio.no/_images/3mer_to_3d.png
+        .. image:: https://docs.immuneml.uio.no/latest/_images/3mer_to_3d.png
             :height: 250
 
         |
@@ -216,7 +216,7 @@
           Furthermore, the folder contains the complete YAML specification file for the immuneML run, the HTML output and a log file.
 
         - optimal_ml_settings.zip: a .zip file containing the raw files for the optimal trained ML settings (ML model, encoding).
-          This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/galaxy/galaxy_apply_ml_models.html>`_.
+          This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_apply_ml_models.html>`_.
 
         - repertoire_classification.yaml: the YAML specification file that was used by immuneML internally to run the analysis. This file can be
           downloaded, altered, and run again by immuneML using the  `Train machine learning models <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.
--- a/immuneml_yaml.xml	Fri Jul 02 11:04:53 2021 +0000
+++ b/immuneml_yaml.xml	Tue Jul 27 09:30:50 2021 +0000
@@ -51,10 +51,10 @@
         `applying <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_apply_ml_model>`_ ML models instead of this tool.
         These other tools are able to export the relevant output files to Galaxy history elements.
 
-        However, when you want to run the `ExploratoryAnalysis <https://docs.immuneml.uio.no/specification.html#exploratoryanalysis>`_ instruction,
+        However, when you want to run the `ExploratoryAnalysis <https://docs.immuneml.uio.no/latest/specification.html#exploratoryanalysis>`_ instruction,
         or other analyses that do not have a corresponding Galaxy tool, this generic tool can be used.
 
-        For the exhaustive documentation of this tool and an example YAML specification for exploratory analysis, see the tutorial `How to run any AIRR ML analysis in Galaxy <https://docs.immuneml.uio.no/galaxy/galaxy_general_yaml.html>`_.
+        For the exhaustive documentation of this tool and an example YAML specification for exploratory analysis, see the tutorial `How to run any AIRR ML analysis in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_general_yaml.html>`_.
 
 
         **Tool output**