From 115b43cf10410f0fb172902910d3e1e79cd3b13d Mon Sep 17 00:00:00 2001 From: cwieder Date: Mon, 9 Oct 2023 21:31:57 +0100 Subject: [PATCH] Deployed 8a72646 with MkDocs version: 1.4.2 --- gendocs_docs/index.html | 2 +- .../reference/pathintegrate/index.html | 81 ++++++++++++++++-- index.html | 1 - search/search_index.json | 2 +- sitemap.xml | 8 +- sitemap.xml.gz | Bin 199 -> 199 bytes 6 files changed, 80 insertions(+), 14 deletions(-) diff --git a/gendocs_docs/index.html b/gendocs_docs/index.html index ceaf423..72469cf 100644 --- a/gendocs_docs/index.html +++ b/gendocs_docs/index.html @@ -346,7 +346,7 @@

Installation

Tutorials and documentation

Please see our Quickstart guide on Google Colab

-

Full documentation and function reference for PathIntegrate can be found via our ReadTheDocs page

+

Full documentation and function reference for PathIntegrate can be found via our ReadTheDocs page

Citing PathIntegrate

If you use PathIntegrate in your research, please cite our paper: 

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration
diff --git a/gendocs_docs/reference/pathintegrate/index.html b/gendocs_docs/reference/pathintegrate/index.html
index 821aa39..c214b47 100644
--- a/gendocs_docs/reference/pathintegrate/index.html
+++ b/gendocs_docs/reference/pathintegrate/index.html
@@ -463,19 +463,56 @@ 
PathIntegrate

    min_coverage = 3
 )

+
+

PathIntegrate class for multi-omics pathway integration.

+

Args

+ +

Attributes

+

Methods:

.get_multi_omics_coverage

-

source +

source 

.get_multi_omics_coverage()

.MultiView

-

source +

source 

.MultiView(
    ncomp = 2
 )

+
+

Fits a PathIntegrate MultiView model using MBPLS.

+

Args

+ +

Returns

+

.SingleView

-

source +

source 

.SingleView(
    model = sklearn.linear_model.LogisticRegression, model_params = None
 )
@@ -492,25 +529,55 @@ 
.SingleView

 
  • object  : Fitted PathIntegrate SingleView model.
    • 
 
 
    .SingleViewCV
    
-
    source
+
    source
 
    .SingleViewCV(
    model = sklearn.linear_model.LogisticRegression, model_params = None,
    cv_params = None
 )
 
    
+
    
+
    Cross-validation for SingleView model.
    
+
    Args
    
+
      
+
    • model (object, optional) : SKlearn prediction model class. Defaults to sklearn.linear_model.LogisticRegression.
      • 
+
    • model_params (type, optional) : Model-specific hyperparameters. Defaults to None.
      • 
+
    • cv_params (dict, optional) : Cross-validation parameters. Defaults to None.
      • 
+
    
+
    Returns
    
+
      
+
    • object  : Cross-validation results.
      • 
+
    
 
    .SingleViewGridSearchCV
    
-
    source
+
    source
 
    .SingleViewGridSearchCV(
    param_grid, model = sklearn.linear_model.LogisticRegression,
    grid_search_params = None
 )
 
    
+
    
+
    Grid search cross-validation for SingleView model.
    
+
    Args
    
+
      
+
    • param_grid (dict) : Grid search parameters.
      • 
+
    • model (object, optional) : SKlearn prediction model class. Defaults to sklearn.linear_model.LogisticRegression.
      • 
+
    • grid_search_params (dict, optional) : Grid search parameters. Defaults to None.
      • 
+
    
+
    Returns
    
+
      
+
    • object  : GridSearchCV object.
      • 
+
    
 
    .MultiViewCV
    
-
    source
+
    source
 
    .MultiViewCV()
 
    
+
    
+
    Cross-validation for MultiView model.
    
+
    Returns
    
+
      
+
    • object  : Cross-validation results.
      • 
+
    
 
    .MultiViewGridSearchCV
    
-
    source
+
    source
 
    .MultiViewGridSearchCV()
 
    
 
diff --git a/index.html b/index.html
index 30bc428..96bcd23 100644
--- a/index.html
+++ b/index.html
@@ -408,7 +408,6 @@ 
    Installation

    Tutorials and documentation

    Please see our Quickstart guide on Google Colab

    -

    Full documentation and function reference for PathIntegrate can be found via our ReadTheDocs page

    Citing PathIntegrate

    If you use PathIntegrate in your research, please cite our paper: 

    PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration
diff --git a/search/search_index.json b/search/search_index.json
index f64ff59..060380f 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
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\ No newline at end of file
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Options are sspa.sspa_SVD, sspa.sspa_ssGSEA, sspa.sspa_KPCA, sspa.sspa_ssClustPA, sspa.sspa_zscore. min_coverage (int, optional) : Minimum number of molecules required in a pathway. Defaults to 3. Attributes omics_data (dict) : Dictionary of omics data. Keys are omics names, values are pandas DataFrames. omics_data_scaled (dict) : Dictionary of omics data scaled to mean 0 and unit variance. Keys are omics names, values are pandas DataFrames. metadata (pandas.Series) : Metadata for samples. Index is sample names, values are class labels. pathway_source (pandas.DataFrame) : Pathway source data. pathway_dict (dict) : Dictionary of pathways. Keys are pathway names, values are lists of molecules. sspa_scoring (object) : Scoring method for SSPA. min_coverage (int) : Minimum number of omics required to cover a pathway. sspa_method (object) : SSPA scoring method. sspa_scores_mv (dict) : Dictionary of SSPA scores for each omics data. Keys are omics names, values are pandas DataFrames. sspa_scores_sv (pandas.DataFrame) : SSPA scores for all omics data concatenated. coverage (dict) : Dictionary of pathway coverage. Keys are pathway names, values are number of omics covering the pathway. mv (object) : Fitted MultiView model. sv (object) : Fitted SingleView model. labels (pandas.Series) : Class labels for samples. Index is sample names, values are class labels. Methods: .get_multi_omics_coverage source . get_multi_omics_coverage () .MultiView source . MultiView ( ncomp = 2 ) Fits a PathIntegrate MultiView model using MBPLS. Args ncomp (int, optional) : Number of components. Defaults to 2. Returns object : Fitted PathIntegrate MultiView model. .SingleView source . SingleView ( model = sklearn . linear_model . LogisticRegression , model_params = None ) Fits a PathIntegrate SingleView model using an SKLearn-compatible predictive model. Args model (object, optional) : SKlearn prediction model class. Defaults to sklearn.linear_model.LogisticRegression. model_params ( type , optional) : Model-specific hyperparameters. Defaults to None. Returns object : Fitted PathIntegrate SingleView model. .SingleViewCV source . SingleViewCV ( model = sklearn . linear_model . LogisticRegression , model_params = None , cv_params = None ) Cross-validation for SingleView model. Args model (object, optional) : SKlearn prediction model class. Defaults to sklearn.linear_model.LogisticRegression. model_params ( type , optional) : Model-specific hyperparameters. Defaults to None. cv_params (dict, optional) : Cross-validation parameters. Defaults to None. Returns object : Cross-validation results. .SingleViewGridSearchCV source . SingleViewGridSearchCV ( param_grid , model = sklearn . linear_model . LogisticRegression , grid_search_params = None ) Grid search cross-validation for SingleView model. Args param_grid (dict) : Grid search parameters. model (object, optional) : SKlearn prediction model class. Defaults to sklearn.linear_model.LogisticRegression. grid_search_params (dict, optional) : Grid search parameters. Defaults to None. Returns object : GridSearchCV object. .MultiViewCV source . MultiViewCV () Cross-validation for MultiView model. Returns object : Cross-validation results. .MultiViewGridSearchCV source . MultiViewGridSearchCV ()","title":"Pathintegrate"},{"location":"gendocs_docs/reference/pathintegrate/#_1","text":"","title":""},{"location":"gendocs_docs/reference/pathintegrate/#pathintegrate","text":"source PathIntegrate ( omics_data : dict , metadata , pathway_source , sspa_scoring = sspa . sspa_SVD , min_coverage = 3 ) PathIntegrate class for multi-omics pathway integration. Args omics_data (dict) : Dictionary of omics data. Keys are omics names, values are pandas DataFrames containing omics data where rows contain samples and columns reprsent features. metadata (pandas.Series) : Metadata for samples. 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