# Covariate Significance Testing User Tutorial Authors: Mahmoud Mostapha\, Jared Vicory\, Chao Huang\, Martin Styner\, Beatriz Paniagua Collaborators: ![](img/SlicerSALT-SPHARM-PDM-Tutorial_0.png) ![](img/SlicerSALT-SPHARM-PDM-Tutorial_1.png) ![](img/SlicerSALT-SPHARM-PDM-Tutorial_2.png) ## Covariate Significance Testing Description * This module provides the following functions: * Model Fitting & Hypothesis Testing * Visualize Statistical Results _Important note_: If you are downloading this module as part of the Slicer Extension mechanism then it will be named multivariate functional shape data analysis \(MFSDA\) Covariate Significance Testing provides an interface for MFSDA \(Multivariate Functional Shape Data Analysis\) method\, which is a Python based tool for statistical shape analysis\. A multivariate varying coefficient model is introduced to build the association between the multivariate shape measurements and demographic information and other clinical variables\. Statistical inference\, i\.e\.\, hypothesis testing\, is also included in this tool\, which can be used in investigating whether some covariates of interest are significantly associated with the shape information\. The hypothesis testing results are further used in clustering based analysis\, i\.e\.\, significant suregion detection\. The Covariate Significance Testing tool has of the following structure: ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_25.png) ## Covariate Significance Testing Installation on SlicerSALT Download the SlicerSALT packages for your respective operating system from the [SlicerSALT website](http://salt.slicer.org/) and install it\. Covariate Significance Testing will be ready to use as a module\. ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_3.png) ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_4.png) ## MFSDA Installation on 3D Slicer Download 3D Slicer packages for your respective operating system on the _[3D Slicer website](https://www.slicer.org/)_ and install it\. ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_6.png) In 3D Slicer\, open the Extension Manager ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_7.png) In the _Install Extension_ tab\, select _MFSDA_ under _Categories_ . Under MFSDA \, select the _Install_ button and restart Slicer when prompted ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_8.png) ## Covariate Significance Testing Module In SlicerSALT\, select _Covariate Significance Testing Module _ \(in Slicer\, select MFSDA \) from the _Modules_ drop\-down menu \( _Category:_ Shape Analysis\) or on the Search bar ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_9.png) ### Setting up Input Files For _Input CSV File_ _\,_ select the csv file which contains the location of input surface meshes \(\*\.vtk\) and corresponding covariates ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_10.png) In the CSV file the first column contains the location of input surface vtk files and each additional column represent a covariate to be tested\, which can be categorical or continuous. Below is an example of an input CSV file with one categorical covariate. ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_11.png) ### Input Surface Meshes Visualization Shape Population Viewer can be used to check the input surface meshes\, where the same CSV file as its input ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_12.png) Setting up Input Files ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_13.png) For _Input Spherical Template_ _\,_ select the vtk file which provides a common coordinates for the of input surface meshes ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_14.png) Below there is an example of an input spherical template file\, which can be an average surface or one of the input surfaces ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_15.png) ### Setting up Input Files For _Input Shape for p\-values_ _\,_ select the vtk file which will be used to visualize the fitted model parameters and the resulted p\-values ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_16.png) Below there is an example of an input shape for p\-values file\, which can be an average surface or one of the input surfaces ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_17.png) ### Setting up Output Directory ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_18.png) For _Output Files Directory_ _\,_ select the folder where the module will store the output files ### Running Covariate Significance Testing Module ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_19.png) Click on the _Run_ button\, to run the MFSDA\_run & MFSDA\_createShapes CLIs on the provided inputs files _SlicerSALT Error Log_ can also be used for debugging if the module was completed with errors\. ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_20.png) _SlicerSALT Error Log_ can also be used for debugging if the module was completed with errors\. ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_21.png) The outputs files for the Covariate Significance Testing Module are stored in the output folder specified by the user: and they include fitted model parameters, shape template with Scalars, hypothesis testing p\-values and the output files generated by running Covariate Significance Testing ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_22.png) ### Output Shape Template Visualization Shape Population Viewer can be used also to visualize the output shape template with fitted model parameters and p\-values as scalars ![](img/SlicerSALT-CovarianceSignificanceTesting-Tutorial_23.png) ## Acknowledgements \- Resources \- Questions