diff --git a/docs/source/behavior_table.rst b/docs/source/behavior_table.rst index 50a459ce..86b31f8d 100644 --- a/docs/source/behavior_table.rst +++ b/docs/source/behavior_table.rst @@ -17,16 +17,17 @@ Setting the Behavior ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Only one field can be set as the Behavior to test against. This is done by highlighting (clicking on) -the desired column and pressing the green button (marked "Set Behavior"). When clicked away from or another -column is clicked, the column should stay green. *Note: If a column is selected as a behavior or covariate(s), -it must have a nuermical value. No blanks or NaNs* +the desired column and pressing the green button (marked ``Set Behavior``). When clicked away from or another +column is clicked, the column should stay green. + +**Note**: If a column is selected as a behavior or covariate(s), it must have a nuermical value. No blanks or NaNs Setting Covariate(s) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Setting covariates is the same as setting the Behavior except that more than column may be selected. Covariates can also be unselected. These actions are done by highlighting a column and pressing either of the two pink buttons -(marked: "Add Covariate" and "Remove Covariate"). These columns should be pink afterwards. +(marked: ``Add Covariate`` and ``Remove Covariate``). These columns should be pink afterwards. Setting Permutation Groups ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -38,4 +39,4 @@ To do this a column should be added to the behavior file. This column should be per group. For example, one group would be 0, all of the subjects in this group should have a 0 in this column. The next group would be 1, each subject in this group should have a 1. Continue this for the number of groups desired. -This column can then be selected by pushing the blue buttons (marked: "Add Permutation Group Level" and "Remove Permutation Group Level"). \ No newline at end of file +This column can then be selected by pushing the blue buttons (marked: ``Add Permutation Group Level`` and ``Remove Permutation Group Level``). \ No newline at end of file diff --git a/docs/source/edge_level_tests.rst b/docs/source/edge_level_tests.rst index 922891a9..fa44e588 100644 --- a/docs/source/edge_level_tests.rst +++ b/docs/source/edge_level_tests.rst @@ -5,13 +5,13 @@ Methods ------------------------- The non-permuted method calculates the correlation of each Region Of Interest (ROI) to all other -ROIs via the given test. These results are stored as a correlation coefficient, ``coeff``, a p-value, ``prob``, -and a thresholded p-value, ``prob_sig``. The permuted method is identical except the variables have a ``_perm`` suffix. +ROIs via the given test. These results are stored as a correlation coefficient, ``coeff``, a *p*-value, ``prob``, +and a thresholded *p*-value, ``prob_sig``. The permuted method is identical except the variables have a ``_perm`` suffix. Common Inputs -------------------------- -:P: Edge-level p-value threshold +:P: Edge-level *p*-value threshold :Network Atlas: :doc:`Network Atlas ` :Functional Connectivity: Initial coorelation matrix (r-values or Fisher z-transformed r-values) of size N\ :sub:`ROIs`\ x N\ :sub:`ROIs`\ x N\ :sub:`scans`\ :Behavior: MATLAB table (``.mat``) or tab seperated text file (``.txt``) @@ -30,28 +30,29 @@ Common Inputs After loading this file, the table should display in the GUI. The user may mark one column as 'Behavior' for the score of interest. Other columns may be marked as 'Covariates' which are partialed prior to running statistics. - (Note: Network Level Analysis cannot handle missing values for behavior or covariates. If there are ``NaNs`` or missing values, do not select this columns) + + **Note**: Network Level Analysis cannot handle missing values for behavior or covariates. If there are ``NaNs`` or missing values in a column, using this column will result in errors Provided Tests -------------------------------- * **Pearson's r** - * MATLAB `corr ` function with ``type``, ``Pearson`` -* **Spearman's** :math:`\rho`\ + * MATLAB `corr _` function with ``type``, ``Pearson`` +* **Spearman's** :math:`\rho` - * MATLAB `corr ` function with ``type``, ``Spearman`` -* **Spearman's** :math:`\rho`\ **estimator** + * MATLAB `corr _` function with ``type``, ``Spearman`` +* **Spearman's** :math:`\rho` **estimator** * Faster approximation of the Spearman's rho function at the cost of slightly less accurate result. - * Based on developer testing, rho values may differ by :math:`10^{-4}` and p-values by :math:`10^{-5}`. - * This error is passed on to the network-level tests, and can cause p-value difference by :math:`10^{-4}` + * Based on developer testing, rho values may differ by :math:`10^{-4}` and *p*-values by :math:`10^{-5}`. + * This error is passed on to the network-level tests, and can cause *p*-value difference by :math:`10^{-4}` * These differences were found with 10,000 permutations. Less permutations results in higher error in a less evenly distributed fashion. * This is recommended for exploratory research with the Spearman's rho function for publications -* **Kendall's** :math:`\tau`\ **-b** +* **Kendall's** :math:`\tau` **-b** - * Implements Kendall's :math:`\tau`\ -b using C code in a MATLAB MEX file (``+mex/+src/kendallTauB.c``) - * Faster implementation that stardard MATLAB code providing identical :math:`\tau`\ and p-values. + * Implements Kendall's :math:`\tau` -b using C code in a MATLAB MEX file (``+mex/+src/kendallTauB.c``) + * Faster implementation that stardard MATLAB code providing identical :math:`\tau` and *p*-values. * Run-time difference from *O*\ (*n*\ :sup:`2`) to *O*\ (*n* log *n*) * This is done with a red-black tree. * **Welch's t-test** @@ -68,12 +69,12 @@ Provided Tests * Allows loading of observed and permuted edge-level data the user has pre-calculated outside the NLA. * Four ``.mat`` files needed as inputs - * p-values should be thresholded + * *p*-values should be thresholded - :Observed p: ``.mat`` file containing N\ :sub:`ROI_pairs`\ x 1 matrix of logical values, the observed, thresholded edge-level p-values. + :Observed p: ``.mat`` file containing N\ :sub:`ROI_pairs`\ x 1 matrix of logical values, the observed, thresholded edge-level *p*-values. N\ :sub:`ROI_pairs`\ are the lower triangle values of a N\ :sub:`ROIs`\ x N\ :sub:`ROIs`\ matrix. :Observed coeff: ``.mat`` file containing N\ :sub:`ROI_pairs`\ x 1 matrix of observed edge-level coefficients. - :Permuted p: ``.mat`` file containing N\ :sub:`ROI_pairs`\ x N\ :sub:`permutations`\ of logical values. Observed, thresholded, permuted p-values. + :Permuted p: ``.mat`` file containing N\ :sub:`ROI_pairs`\ x N\ :sub:`permutations`\ of logical values. Observed, thresholded, permuted *p*-values. :Permuted coeff: ``.mat`` file containing N\ :sub:`ROI_pairs`\ x N\ :sub:`permutations`\ of permuted edge-level coefficients. Creating additional edge-level tests @@ -82,6 +83,7 @@ Creating additional edge-level tests To create an edge-level test, a test class must be added to the codebase. Refer to current tests in ``+nla/+edge/+test`` for examples. Guidelines are listed below * **Test objects** + All test objects must inherit from ``nla.edge.test.Base`` and be in the ``+nla/+edge/+test`` directory. There are also a few methods and properties that must be included @@ -122,6 +124,7 @@ To create an edge-level test, a test class must be added to the codebase. Refer fulfilled. These values are all stored in the ``input_struct`` object. * **Result object** + A result object must be defined for the test edge-level results. If no custom data fields are needed, then the object in ``+nla/+edge/+test/Base.m`` may be used and this step can be skipped. diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst index 19c7bf7a..93ecf886 100644 --- a/docs/source/getting_started.rst +++ b/docs/source/getting_started.rst @@ -4,41 +4,42 @@ Getting Started Running with example data -------------------------------------------------- -First, open the NLA software (as described in :doc:`setup`). Select 'Pearson's r' as the edge-level +First, open the NLA software (as described in :doc:`setup`). Select ``Pearson's r`` as the edge-level test from the edge-level test dropdown. -Click 'Select' to choose a network atlas, navigating to the ``support_files`` folder withing your -'NetworkLevelAnalysis' installation and selecting ``Wheelock_2020_CerebralCortex_15nets_288ROI_on_MNI.mat``. +Click ``Select`` to choose a network atlas, navigating to the ``support_files`` folder withing your +NetworkLevelAnalysis installation and selecting ``Wheelock_2020_CerebralCortex_15nets_288ROI_on_MNI.mat``. This file is used to parcellate the data. Then, select the functional connectivity, located in the ``examples/fc_and_behavior`` folder under the name ``sample_func_conn.mat``. Click 'Yes' to Fisher z-transform the data. Take a moment to visualize the functional -connectivity (FC) average by clicking 'View'. Note that the FC appears to match the parcellation, (effects +connectivity (FC) average by clicking ``View``. Note that the FC appears to match the parcellation, (effects generally line up with network boundaries) - this can be a useful diagnostic tool if you are having issues with parcellations not matching data. Finally, load the behavior ``sample_behavior.mat`` from the ``examples/fc_and_behavior`` folder (The 'file type' drop-down -will need to be changed from 'Text' to 'MATLAB table' in the file browser). Set the behavioral variable to 'Flanker_AgeAdj' by -clicking on that column in the table and then the 'Set Behavior' button. +will need to be changed from ``Text`` to ``MATLAB table`` in the file browser). Set the behavioral variable to 'Flanker_AgeAdj' by +clicking on that column in the table and then the ``Set Behavior`` button. Having finished our edge-level inputs, we now move over to the network-level panel on the right side. Select all the tests by clicking the top one, and then shift+clicking the bottom one. -_running_network_tests: -Run the tests using the 'Run' button on the bottom-right. The number of permutations can be changed with the input field -to the left of the 'Run' button. After pushing the 'Run' button, a result window will open. The edge-level test will be run -and the results can be visualized by pressing 'View' in the upper-left of the result window. To run the network-level tests, -push the 'Run' button in the results window. This will take longer, a progress window will show up displaying the progress. -To visualize the results, expand the lists in the reloaded (automatically) panel, and highlight a test. Press the 'View figures' +.. _running_network_tests: + +Run the tests using the ``Run`` button on the bottom-right. The number of permutations can be changed with the input field +to the left of the ``Run`` button. After pushing the ``Run`` button, a result window will open. The edge-level test will be run +and the results can be visualized by pressing ``View`` in the upper-left of the result window. To run the network-level tests, +push the ``Run`` button in the results window. This will take longer, a progress window will show up displaying the progress. +To visualize the results, expand the lists in the reloaded (automatically) panel, and highlight a test. Press the ``View figures`` button. Other visualization options, such as chord plots and convergence maps, can also be shown. The results can be saved using the -'File' menu in the top-left. These results can be loaded into MATLAB or opened in the NLA main window also using the 'File' menu on that +``File`` menu in the top-left. These results can be loaded into MATLAB or opened in the NLA main window also using the ``File`` menu on that window. Running with example pre-calculated data ---------------------------------------------------------- Similarly to the previous example, open the NLA window and load the ``Wheelock_2020_CerebralCortex_15nets_288ROI_on_MNI.mat`` parcellation. This -time, select the 'Precalculated data' edge-level test. Load the four input matrices in the ``examples/precalculated`` folder. +time, select the ``Precalculated data`` edge-level test. Load the four input matrices in the ``examples/precalculated`` folder. * Observed coefficients: ``SIM_obs_coeff.mat`` * Observed, thresholded p-values: ``SIM_obs_p.mat`` @@ -46,6 +47,6 @@ time, select the 'Precalculated data' edge-level test. Load the four input matri * Permuted, thresholded p-values: ``SIM_perm_p.mat`` Set the lower and upper coefficient bounds to the range of the coefficients. For this case, the range is [-2, 2]. These bounds can be checked -with the 'View' button for the edge-level results button. In the bottom right corner, set the ``perm_count`` to the desired amount of +with the ``View`` button for the edge-level results button. In the bottom right corner, set the ``perm_count`` to the desired amount of permutations. The example data provided has a maximums of 600 permutations. Run the tests using the procedure described in the :ref:`previous section `. \ No newline at end of file diff --git a/docs/source/methodology.rst b/docs/source/methodology.rst index 89823ee6..80ba99dd 100644 --- a/docs/source/methodology.rst +++ b/docs/source/methodology.rst @@ -17,14 +17,14 @@ General Linear Model / Edge-wise Statistical Model Selection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ NLA also requires the user to specify the desired statistical model for testing associations between -behavioral data and edge-wise�or ROI-pair connectivity�connectome data. The analysis pipeline within +behavioral data and edge-wise or ROI-pair connectivity connectome data. The analysis pipeline within the NLA toolbox offers both parametric and non-parametric correlation. Connectivity Matrices ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Other software packages are used to create the connectivity matrices that are provided as input into the -NLA toolbox. One useful option for mapping functional connectivity matrices is CONN - MATLAB-based +NLA toolbox. One useful option for mapping functional connectivity matrices is `CONN _` - a MATLAB-based software with the ability to compute, display, and analyze functional connectivity in fMRI. The NLA Method @@ -40,9 +40,9 @@ Both p-value and test-statistic binarization are offered in the current NLA pipe supported the incorporation of a proportional edge density threshold, given that uneven edge density thresholds have been shown to unfairly bias results :cite:p:`vandenHeuvelM`. For enrichment statistic calculation, NLA offers a number of statistical tests. Prior research has relied on -chi-square and Fisher's Exact test, as well as a Kolmogorov-Smirnov (KS) test and non-parametric tests +chi-squared and Fisher's Exact tests. As well as a Kolmogorov-Smirnov (KS) test and non-parametric tests based on ranks, which compare the distribution of test values within a region to other regions :cite:p:`WheelockM:2018,RudolphM,MoothaV,ZahnJ`. In -addition, KS alternatives such as averaging or minmax have also shown promise in connectome +addition, KS alternatives such as averaging or min-max have also shown promise in connectome applications :cite:p:`ChenJ,NewtonM,YaariG,EfronB`. NLA then conducts data-driven permutation testing to establish significance. In the NLA toolbox, network diff --git a/docs/source/network_level_tests.rst b/docs/source/network_level_tests.rst index 007d485b..4b87f5c8 100644 --- a/docs/source/network_level_tests.rst +++ b/docs/source/network_level_tests.rst @@ -9,7 +9,7 @@ the given statistical test. The full connectome method ranks the non-permuted (observed) significance of each network against the significance of the same network calculated over many permutations using the same test. -I DON"T KNOW HOW TO EXPLAIN THE PROBABILITY BEING CALCULATED - Jim +**I DON"T KNOW HOW TO EXPLAIN THE PROBABILITY BEING CALCULATED** - Jim The within network-pair method measures how significant each network is compared to all permutations of only the selected network. @@ -25,10 +25,10 @@ Provided Tests * **Hypergeomtric** - * MATLAB's `hypercdf ` used to find the probablity + * MATLAB's `hypercdf _` used to find the probablity * **Chi-squred** - * Runs a :math:`\chi`\ :sup:`2` test. + * Runs a :math:`\chi` :sup:`2` test. .. math:: @@ -36,16 +36,18 @@ Provided Tests * **Kolmogorov-Smirnov** - * MATLAB `kstest2 `_ function. + * MATLAB `kstest2 _` function. * **Wilcoxon rank-sum test** - * MATLAB `ranksum `_ function. + * MATLAB `ranksum _` function. + + * MATLAB's `kstest2 _` - * MATLAB's `kstest2 ` * **Wilcoxon** - * MATLAB's `ranksum ` + * MATLAB's `ranksum _` + * **Welch's t-test** * Implements an optomized Welch's t-test comparing the functional connectivity of two groups. @@ -56,7 +58,7 @@ Provided Tests * **Student's t-test** - * MATLAB `ttest2 `_ function. + * MATLAB `ttest2 _` function. Creating additional network-level tests ----------------------------------------------------- @@ -64,6 +66,7 @@ Creating additional network-level tests To create a network-level test, a test class must be added to the codebase. Refer to the current tests in ``+nla/+net/+test`` * **Test objects** + All test objects must inherit from ``nla.net.test.Base`` and be in the ``+nla/+net/+test`` directory. There are also properties and methods that must be included. @@ -83,7 +86,8 @@ To create a network-level test, a test class must be added to the codebase. Refe :statistics: All statistics that will be generated by the test. No special characters :ranking_statistic: The statistic used for ranking and calculating p-values. Note: if there is a single sample version of the statisticin addition to a two sample statistic, the GUI will automatically add "single_sample\_" during rankings for non-permuted and within network pair ranking. - * ``run`` + * A ``run`` method + :: result = run(obj, test_options, edge_test_results, network_atlas, permutations) diff --git a/docs/source/overview.rst b/docs/source/overview.rst index 9a4779fa..1347978a 100644 --- a/docs/source/overview.rst +++ b/docs/source/overview.rst @@ -60,13 +60,13 @@ differences during fetal brain development, and autism in adults :cite:p:`Eggebr Edge-level Statistic ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -First, correlations are calculated between behavioral scores and Fisher z-transformed functional +First, correlations are calculated between behavioral scores and Fisher Z-transformed functional connectivity correlation measures for each pair of ROI. For behavioral scores that are normally -distributed, Pearson r correlations are used to calculate the associations. Non-parametric Spearman rank +distributed, Pearson correlations are used to calculate the associations. Non-parametric Spearman rank correlations are used to assess the relationship between functional connectivity and behavioral scores -that are not normally distributed. Other tests of correlation such as Kendall's tau and 2-sample Welch�s t +that are not normally distributed. Other tests of correlation such as Kendall's Tau and 2-sample Welch's *t* can also be used. Network pairs are then tested for enrichment of strong correlation values, defined as -only those values that remain after being nominally thresholded. An uncorrected p-threshold (e.g., 0.05 or +only those values that remain after being nominally thresholded. An uncorrected *p*-threshold (e.g. 0.05 or 0.01) is applied and the remaining correlations are binarized. Network Level Statistics @@ -75,24 +75,29 @@ Network Level Statistics After the edge-level statistic matrix has been calculated, it is given as input to a variety of network-level tests. First, it is input directly to the tests, and the resulting statistic is called the "non-permuted network level statistic" (for every given network-level test). Then, permuted edge-level statistics are calculated via -the same method as described previously, but with the behavioral scores permuted across subjects. The -network-level test is performed on this as well, and the significance of permuted network-level statistics -ranked against the non-permuted, to calculate the permuted experiment-wide p-value (an empirical p- +the same method as described in the previous section, but with the behavioral scores permuted across subjects. The +network-level test is performed with the permuted behavioral scores, also. The significance of permuted network-level statistics +ranked against the non-permuted, to calculate the permuted experiment-wide *p*-value (an empirical *p*- value produced from this ranking). Additionally, "single-sample within-net-pair" statistics are calculated for each test, which, rather than comparing a given network to the connectome over a number of permutations (as in the permuted network-level test), performs a single-sample test on the network alone, which is then ranked against permutations of said network similarly to the permuted network-level test. -A number of statistic tests are utilized at the network level. The 1-degree-of-freedom :math:\chi^{2} test is used to -compare the observed number of strong (thresholded and binarized) brain-behavior correlations within -one pair of functional networks to the number of strong brain-behavior correlations that would be -expected if strong correlations were uniformly distributed across all possible network pairs. A large -resulting test statistic can indicate that the number of strong correlations within a specific network pair is -enriched. The hypergeometric test aims to assess the likelihood of observing a given number of strong -correlations within a pair of networks, given (1) the total number of strong correlations observed over the -entire connectome and (2) the total number of possible hits for that network pair (i.e., the total number or -ROI-pairs within a given network pair). Other tests such as Kolmogorov-Smirnov, Wilcoxon rank-sum, -Welch's t can be used, as well as Cohen's d to measure effect sizes. +A number of statistic tests are utilized at the network level. + #. The 1-degree-of-freedom :math:`\chi^{2}` test is used to + compare the observed number of strong (thresholded and binarized) brain-behavior correlations within + one pair of functional networks to the number of strong brain-behavior correlations that would be + expected if strong correlations were uniformly distributed across all possible network pairs. A large + resulting test statistic can indicate that the number of strong correlations within a specific network pair is + enriched. + #. The hypergeometric test aims to assess the likelihood of observing a given number of strong + correlations within a pair of networks, given: + #. The total number of strong correlations observed over the + entire connectome + #. The total number of possible hits for that network pair (i.e., the total number or + ROI-pairs within a given network pair). + #. Other tests such as Kolmogorov-Smirnov, Wilcoxon rank-sum, + Welch's *t* can be used, as well as Cohen's *d* to measure effect sizes. As described, significance for all statistical tests is determined using permutation testing. Behavioral labels are randomly permuted and correlated with the connectome data (typically 10k times) to create null brain-behavior correlation matrices. Tests are calculated on these permuted brain-behavior @@ -136,7 +141,7 @@ Graph Theoretical Toolboxes Graph Theoretical Toolboxes are another comparable approach to NLA, offering an analysis methodology to quantify network characteristics such as integration, segregation, resilience, and relative contribution of individual network nodes to overall information flow within the network :cite:p:`RubinovM`. Various other toolboxes -have been created to address network thresholding, graph metric calculation, and graph visualization� +have been created to address network thresholding, graph metric calculation, and graph visualization - such as GRETNA, GEPHI, and BrainNet Viewer. Additional methodologies aim to determine network topology differences by leveraging generalized estimating equations and generalized linear and nonlinear mixed models :cite:p:`BahramiM,GinestetC,SimpsonS`. Each of these tools has helped to advance the application of graph theory approaches diff --git a/docs/source/preface.rst b/docs/source/preface.rst index 081d6108..57504b1f 100644 --- a/docs/source/preface.rst +++ b/docs/source/preface.rst @@ -3,8 +3,8 @@ Preface This is the reference manual for the Network Level Analysis (NLA) Toolbox. NLA is an extensible MATLAB- based software package for the analysis of behavioral associations with brain connectivity data. NLA -utilizes a model-based statistical approach known variously as 'pathway analysis', 'over-representation -analysis', or 'enrichment analysis', which was first used to describe behavioral or clinical associations in +utilizes a model-based statistical approach known variously as "pathway analysis", "over-representation +analysis", or "enrichment analysis", which was first used to describe behavioral or clinical associations in genome-wide association studies :cite:p:`RivalsI,KhatriP,BackesC,SubramanianA`. Enrichment is a model-based data reduction approach to elucidate statistically significant network- diff --git a/docs/source/setup.rst b/docs/source/setup.rst index 5aeda87f..8d364126 100644 --- a/docs/source/setup.rst +++ b/docs/source/setup.rst @@ -6,16 +6,18 @@ Add NLA Folders to MATLAB Path In order to for any NLA functions to work, MATLAB must be able to find them on the path. To do this, in the MATLAB file explorer, navigate to where you have downloaded or cloned the NetworkLevelAnalysis -folder to. Right click the folder, hover over 'Add to Path' in the context menu, and click the 'Selected -Folders and Subfolders' option. +folder to. Right click the folder, hover over ``Add to Path`` in the context menu, and click the ``Selected +Folders and Subfolders`` option. + **NOTE**: If you only add the base 'NetworkLevelAnalysis' folder to the path the code will not work, you must -pick the 'Selected Folders and Subfolders' option +pick the ``Selected Folders and Subfolders`` option Running the GUI ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ To open the GUI, navigate to the root directory of the NetworkLevelAnalysis package in MATLAB and run the command ``NLA_GUI`` via the MATLAB command line. + **Note**: Running the GUI through an X11-based remote connection (eg: MobaXTerm or similar) can be very laggy in some cases. It is strongly recommended to use the GUI through a more modern remote protocol such as VNC instead. @@ -23,9 +25,10 @@ such as VNC instead. Running as a Pipeline Script ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -To run NLA via a script instead, open the file main_pipeline.m (located in the root directory of the +To run NLA via a script instead, open the file ``main_pipeline.m`` (located in the root directory of the NetworkLevelAnalysis package) in MATLAB, and proceed through the stages of the pipeline. There is also -a pipeline for precalculated data located in precalculated_pipeline.m +a pipeline for precalculated data located in ``precalculated_pipeline.m`` + **Note**: The pipeline scripts are more complex and easy-to-mess-up than the GUI, and should only be used if you have a good reason to do so. diff --git a/docs/source/using_the_gui.rst b/docs/source/using_the_gui.rst index 3981e025..efe537f3 100644 --- a/docs/source/using_the_gui.rst +++ b/docs/source/using_the_gui.rst @@ -11,18 +11,19 @@ Or, in the file browser section of the MATLAB window, right click on ``NLA_GUI.m Main windows of Network Level Analysis program -1. Edge-level test dropdown selector (See :doc:`Edge-level Statistical Tests <../edge_level_tests>`) -2. Edge-level test pane +#. Edge-level test dropdown selector + (See :doc:`Edge-level Statistical Tests <../edge_level_tests>`) +#. Edge-level test pane This pane will list all of the options and inputs needed for each test that's currently selected. Usually there are selectors for functional connectivity, network atlas, and behavior files. There may also be other options depending on the test. If "Precalculated data" is selected, there will be selectors for data instead. (See: :ref:`Precalculated data loader `) -3. Behavior table +#. Behavior table This will display the table when the behavior file is loaded. The table is used to select the behvaior to test, co-variates used (optional), and permutation groupings (optional). (See: :doc:`Behavior Table <../behavior_table>`) -4. Network-level test pane +#. Network-level test pane Selection of network-level test(s). One can be selected, or multiple with Ctrl/Shift + left click. See :doc:`Network-level tests` -5. Run options +#. Run options Checkboxes to select test method(s). If within network pair is selected, full connectome will also be selected. Permutation count is how many permutations to run. More permutations will take more time, but will produce more precise results. Run will run the edge level test and open the results window. @@ -31,7 +32,7 @@ Or, in the file browser section of the MATLAB window, right click on ``NLA_GUI.m Loading Results ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -If previous data was saved (See :ref:`saving results `) there is an option to load it here. Click the ``File`` menu in the upper left-hand corner and select "Load Previous Results." +If previous data was saved (See :ref:`saving results `) there is an option to load it here. Click the ``File`` menu in the upper left-hand corner and select ``Load Previous Results``. Depending on the size of the saved data, this could take a bit of time. Results Window