docs: updating tutorial for COMBINE25

docs/_static/config/beginner.yaml

@@ -26,8 +26,9 @@ algorithms:
       include: true
       run1:
         k: 1
-        # run2: # uncomment for step 3.2
-        #   k: [10, 100] # uncomment for step 3.2
+
+      # run2: # uncomment for step 3.2
+      #   k: [10, 100] # uncomment for step 3.2
 
 # Here we specify which pathways to run and other file location information.
 # Assume that if a dataset label does not change, the lists of associated input files do not change
@@ -45,7 +46,7 @@ reconstruction_settings:
 
   # Set where everything is saved
   locations:
-    reconstruction_dir: "output/basic"
+    reconstruction_dir: "output/beginner"
 
 analysis:
   # Create one summary per pathway file and a single summary table for all pathways for each dataset

docs/tutorial/advanced.rst

@@ -1,31 +1,182 @@
+###################################
 Advanced Capabilities and Features
-======================================
+###################################
 
-More like these are all the things we can do with this, but will not be showing
+Parameter tuning
+================
+Parameter tuning is the process of determining which parameter combinations should be explored for each algorithm for a given dataset.
+Parameter tuning focuses on defining and refining the parameter search space.
 
-- mention parameter tuning
-- say that parameters are not preset and need to be tuned for each dataset
+Each dataset has unique characteristics so there are no preset parameters combinations to use.
+Instead, we recommend tuning parameters individually for each new dataset.
+SPRAS provides a flexible framework for getting parameter grids for any algorithms for a given dataset.
 
-CHTC integration
+Grid Search
+------------
 
-Anything not included in the config file
+A grid search systematically checks different combinations of parameter values to see how each affects network reconstruction results.
 
-1. Global Workflow Control
+In SPRAS, users can define parameter grids for each algorithm directly in the configuration file.
+When executed, SPRAS automatically runs each algorithm across all parameter combinations and collects the resulting subnetworks.
 
-Sets options that apply to the entire workflow.
+SPRAS will also support parameter refinement using graph topological heuristics.
+These topological metrics help identify parameter regions that produce biologically plausible outputs networks.
+Based on these heuristics, SPRAS will generate new configuration files with refined parameter grids for each algorithm per dataset.
 
-- Examples: the container framework (docker, singularity, dsub) and where to pull container images from
+Users can further refine these grids by rerunning the updated configuration and adjusting the parameter ranges around the newly identified regions to find and fine-tune the most promising algorithm specific outputs for a given dataset.
 
-running spras with multiple parameter combinations with multiple algorithms on multiple Datasets
-- for the tutorial we are only doing one dataset
+.. note::
 
-4. Gold Standards
+    Some grid search features are still under development and will be added in future SPRAS releases.
 
-Defines the input files SPRAS will use to evaluate output subnetworks
+Parameter selection
+-------------------
 
-A gold standard dataset is comprised of: 
+Parameter selection refers to the process of determining which parameter combinations should be used for evaluation on a gold standard dataset.
 
-- a label: defines the name of the gold standard dataset
-- node_file or edge_file: a list of either node files or edge files. Only one or the other can exist in a single dataset. At the moment only one edge or one node file can exist in one dataset
-- data_dir: the path to where the input gold standard files live
-- dataset_labels: a list of dataset labels that link each gold standard links to one or more datasets via the dataset labels
+Parameter selection is handled in the evaluation code, which supports multiple parameter selection strategies.
+Once the grid space search is complete for each dataset, the user can enable evaluation (by setting evaluation ``include: true``) and it will run all of the parameter selection code.
+
+PCA-based parameter selection
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The PCA-based approach identifies a representative parameter setting for each pathway reconstruction algorithm on a given dataset.
+It selects the single parameter combination that best captures the central trend of an algorithm's reconstruction behavior.
+
+.. image:: ../_static/images/pca-kde.png
+   :alt: Principal component analysis visualization across pathway outputs with a kernel density estimate computed on top 
+   :width: 600
+   :align: center
+
+.. raw:: html
+
+   <div style="margin:20px 0;"></div>
+
+For each algorithm, all reconstructed subnetworks are projected into an algorithm-specific 2D PCA space based on the set of edges produced by the respective parameter combinations for that algorithm.
+This projection summarizes how the algorithm's outputs vary across different parameter combinations, allowing patterns in the outputs to be visualized in a lower-dimensional space.
+
+Within each PCA space, a kernel density estimate (KDE) is computed over the projected points to identify regions of high density.
+The output closest to the highest KDE peak is selected as the most representative parameter setting, as it corresponds to the region where the algorithm most consistently produces similar subnetworks.
+
+Ensemble network-based parameter selection
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+The ensemble-based approach combines results from all parameter settings for each pathway reconstruction algorithm on a given dataset.
+Instead of focusing on a single "best" parameter combination, it summarizes the algorithm's overall reconstruction behavior across parameters.
+
+All reconstructed subnetworks are merged into algorithm-specific ensemble networks, where each edge weight reflects how frequently that interaction appears across the outputs.
+Edges that occur more often are assigned higher weights, highlighting interactions that are most consistently recovered by the algorithm.
+
+These consensus networks help identify the core patterns and overall stability of an algorithm's output's without needing to choose a single parameter setting (no clear optimal parameter combination could exists).
+
+
+Ground truth-based evaluation without parameter selection
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The no parameter selection approach chooses all parameter combinations for each pathway reconstruction algorithm on a given dataset.
+This approach can be useful for idenitifying patterns in algorithm performance without favoring any specific parameter setting.
+
+Evaluation
+============
+
+In some cases, users may have a gold standard file that allows them to evaluate the quality of the reconstructed subnetworks generated by pathway reconstruction algorithms.
+
+However, gold standards may not exist for certain types of experimental data where validated ground truth interactions or molecules are unavailable or incomplete. 
+For example, in emerging research areas or poorly characterized biological systems, interactions may not yet be experimentally verified or fully known, making it difficult to define a reliable reference network for evaluation.
+
+Adding gold standard datasets and evaluation post analysis a configuration
+--------------------------------------------------------------------------
+
+In the configuration file, users can specify one or more gold standard datasets to evaluate the subnetworks reconstructed from each dataset.
+When gold standards are provided and evaluation is enabled (``include: true``), SPRAS will automatically compare the reconstructed subnetworks for a specific dataset against the corresponding gold standards.
+
+.. code-block:: yaml
+
+    gold_standards:
+        - 
+        label: gs1
+        node_files: ["gs_nodes0.txt", "gs_nodes1.txt"]
+        data_dir: "input"
+        dataset_labels: ["data0"]
+        - 
+        label: gs2
+        edge_files: ["gs_edges0.txt"]
+        data_dir: "input"
+        dataset_labels: ["data0", "data1"]
+
+    analysis:
+        evaluation:
+            include: true
+
+A gold standard dataset must include the following types of keys and files:
+
+- ``label``: a name that uniquely identifies a gold standard dataset throughout the SPRAS workflow and outputs.
+- ``node_file`` or ``edge_file``: A list of node or edge files. Only one of these can be defined per gold standard dataset.
+- ``data_dir``: The file path of the directory where the input gold standard dataset files are located.
+- ``dataset_labels``: a list of dataset labels indicating which datasets this gold standard dataset should be evaluated against.
+
+When evaluation is enabled, SPRAS will automatically run its built-in evaluation analysis on each defined dataset-gold standard pair.
+This evaluation computes metrics such as precision, recall, and precision-recall curves, depending on the parameter selection method used.
+
+For each pathway, evaluation can be run independently of any parameter selection method (the ground truth-based evaluation without parameter selection idea) to directly inspect precision and recall for each reconstructed network from a given dataset.
+
+.. image:: ../_static/images/pr-per-pathway-nodes.png
+   :alt: Precision and recall computed for each pathway and visualized on a scatter plot
+   :width: 600
+   :align: center
+
+.. raw:: html
+
+   <div style="margin:20px 0;"></div>
+
+Ensemble-based parameter selection generates precision-recall curves by thresholding on the frequency of edges across an ensemble of reconstructed networks for an algorithm for given dataset.
+
+.. image:: ../_static/images/pr-curve-ensemble-nodes-per-algorithm-nodes.png
+   :alt: Precision-recall curve computed for a single ensemble file / pathway and visualized as a curve
+   :width: 600
+   :align: center
+
+.. raw:: html
+
+   <div style="margin:20px 0;"></div>
+
+PCA-based parameter selection computes a precision and recall for a single reconstructed network selected using PCA from all reconstructed networks for an algorithm for given dataset.
+
+.. image:: ../_static/images/pr-pca-chosen-pathway-per-algorithm-nodes.png
+   :alt: Precision and recall computed for each pathway chosen by the PCA-selection method and visualized on a scatter plot
+   :width: 600
+   :align: center
+
+.. raw:: html
+
+   <div style="margin:20px 0;"></div>
+
+.. note:: 
+    Evaluation will only execute if ml has ``include: true``, because the PCA parameter selection step depends on the PCA ML analysis.
+
+.. note:: 
+    To see evaluation in action, run SPRAS using the config.yaml or egfr.yaml configuration files.
+
+HTCondor integration
+=====================
+
+Running SPRAS locally can become slow and resource intensive, especially when running many algorithms, parameter combinations, or datasets simultaneously.
+
+To address this, SPRAS supports an integration with `HTCondor <https://htcondor.org/>`__ (a high throughput computing system), allowing Snakemake jobs to be distributed in parallel and executed across available compute.
+
+See :doc:`Running with HTCondor <../htcondor>` for more information on SPRAS's integrations with HTConder.
+
+
+Ability to run with different container frameworks
+---------------------------------------------------
+
+CHTC uses Apptainer to run containerized software in secure, high-performance environments.
+
+SPRAS accommodates this by allowing users to specify which container framework to use globally within their workflow configuration.
+
+The global workflow control section in the configuration file allows a user to set which SPRAS supported container framework to use:
+
+.. code-block:: yaml
+
+    container_framework: docker
+
+The frameworks include Docker, Apptainer/Singularity, or dsub