Merge branch 'pearson' into main (2D analyses mainly)

Author: MJWester1

.github/workflows/cat_src.yml

@@ -10,7 +10,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v4
 
     - name: Concatenate Files
       run: |

.github/workflows/ci.yml

@@ -22,7 +22,7 @@ jobs:
     # Steps represent a sequence of tasks that will be executed as part of the job
     steps:
       # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       # Sets up MATLAB on the GitHub Actions runner
       - name: Setup MATLAB

.github/workflows/draft-pdf.yml

@@ -1,20 +1,22 @@
-on: [push]
+on: 
+  push:
+    branches: [ main ]
 
 jobs:
   paper:
     runs-on: ubuntu-latest
     name: Paper Draft
     steps:
       - name: Checkout
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
       - name: Build draft PDF
         uses: openjournals/openjournals-draft-action@master
         with:
           journal: joss
           # This should be the path to the paper within your repo.
           paper-path: paper/paper.md
       - name: Upload
-        uses: actions/upload-artifact@v1
+        uses: actions/upload-artifact@v4
         with:
           name: paper
           # This is the output path where Pandoc will write the compiled

MATLAB/+smi_cluster/@PairAnalysis/PairAnalysis.m

@@ -31,12 +31,12 @@
 
    n_ROIs_ALL = defineROIs2(Files1, Files2, Pixel2nm, Color, ROI_sizes, ...
                             ResultsDir, RegistrationNeeded, RegistrationViaDC);
-   [results_pcc, resultsRC_pcc, results_c, results_cs, results_ls,   ...
-    results_o1, results_o2] =                                        ...
+   [results_pcc, resultsRC_pcc, results_ss, results_c, results_cs,          ...
+    results_ls, results_o1, results_o2] =                                   ...
       doAnalysis(n_ROIs, RoI, ROI_sizes, desc, particles, results_dir,      ...
                  options, PixelSize, HistBinSize, RmaxAxisLimit, E, minPts, ...
                  PlotNonOverlap, Color)
-   doBiStats(n_ROIs, RoI, desc, particles, results_dir, combined)
+   results_bi = doBiStats(n_ROIs, RoI, desc, particles, results_dir, combined)
    results_c = doClustering(n_ROIs, RoI, desc, results_dir, plotting, ...
                             PixelSize, E, minPts)
    results_cs = ...
@@ -47,7 +47,9 @@
                  plotting, HistBinSize, RmaxAxisLimit)
    results_o = doOverlap(n_ROIs, RoI, results_c, l12, desc, particles, ...
                          results_dir, PlotNonOverlap, Color, plotting)
-   overlayBaGoLROIs(pathnameB, filesB, MAPNfile, ROI_sizes, SRImageZoom);
+   results_ss = doSimpleStats(n_ROIs, RoI, PixelSize, desc, particles, ...
+                              results_dir)
+   overlayBaGoLROIs(pathnameB, filesB, MAPNfile, ROI_sizes, SRImageZoom)
    doPlot2(n_ROIs, RoI, desc, particles, results_dir, Color, plotting)
 
 % =============================================================================

MATLAB/+smi_cluster/@PairAnalysis/README.md

@@ -30,6 +30,8 @@ methods:
   Pair cross-correlation for each ROI and combined ROI
 - **[doPlot2](doPlot2.m)**:
   Plot 2D ROIs
+- **[doSimpleStats](doSimpleStats.m)**:
+  Pearson's correlation and Manders' split coefficients for each ROI
 - **[overlayBaGoLROIs](overlayBaGoLROIs.m)**:
   overlay label 1 onto label 2 Gaussian images produced from the input ROI file
   names into appropriately named files

MATLAB/+smi_cluster/@PairAnalysis/defineROIs2.m

@@ -101,16 +101,20 @@
       [n_ROIs, RoI, XYsize] = RT.getROI({SMD1, SMD2}, txt);
       n_ROIs_ALL = n_ROIs_ALL + n_ROIs;
 
-      % Redefine Pixel2nm for later use.
+      % Redefine Pixel2nm for later use.  This is done as original coordinates
+      % are in pixels, but are now converted into nm for use in the RoI
+      % structure, and uses such as clustering and related statistics.
+      % This is only really needed for H-SET clustering.  (2024/12/11)
       Pixel2nmSAVE = Pixel2nm;
-      Pixel2nm = 1;
+%     Pixel2nm = 1;
       saveas(gcf, fullfile(ResultsDir, sprintf('%s_ROIs.fig', desc)));
       print(fullfile(ResultsDir, sprintf('%s_ROIs.png', desc)), '-dpng');
       save(fullfile(ResultsDir, sprintf('%s_ROIs.mat', desc)), ...
            'Label1', 'Label2', 'Pixel2nm', 'XYsize', 'n_ROIs', 'RoI');
       close
       Pixel2nm = Pixel2nmSAVE;
       fid = fopen(fullfile(ResultsDir, sprintf('%s_ROIs.txt', desc)), 'w');
+      fprintf(fid, 'ROI #, [xmin, xmax, ymin, ymax]\n');
       for i = 1 : n_ROIs
          fprintf(fid, '%d %7.3f %7.3f %7.3f %7.3f\n', ...
                       i, RoI{i}.ROI ./ Pixel2nm);

MATLAB/+smi_cluster/@PairAnalysis/doAnalysis.m

@@ -1,5 +1,5 @@
-function [results_pcc, resultsRC_pcc, results_c, results_cs, results_ls, ...
-          results_o1, results_o2] =                                      ...
+function [results_pcc, resultsRC_pcc, results_ss, results_c, results_cs, ...
+          results_ls, results_o1, results_o2] =                          ...
    doAnalysis(n_ROIs, RoI, ROI_sizes, desc, particles, results_dir,      ...
               options, PixelSize, HistBinSize, RmaxAxisLimit, E, minPts, ...
               PlotNonOverlap, Color)
@@ -15,6 +15,8 @@
 %    options       string array of strings specifying analyses to be performed:
 %                      'combined'    the analysis is combined over all ROIs
 %                      'plotting'    plots are to be produced.
+%                      'SimpleStats' Pearson's correlation and Manders' split
+%                                    coefficients per ROI
 %                      'BiStats'     pairwise mutual distances and bivariate
 %                                    Ripley's statistics for each ROI
 %                      'Clustering'  clusters for each label per ROI
@@ -41,7 +43,7 @@
 %    desc_results.mat containing the various results_ cell arrays for one cell
 %    Various results containers from the called helper functions in case the
 %    user wants to have more details:
-%                       bivariate statistics
+%       results_bi      bivariate statistics
 %          Also, figures *_ROI*_L1/2_pairwiseCDF/PDF compared to a random dist.
 %                *_ROI*_L1,L2_pairwisePDF2/CDF2      2-label PDF/CDF
 %                *_ROI*_bivripley                    bivariate Ripley
@@ -108,6 +110,8 @@
       plotting = true;
    end
 
+   results_ss = [];
+   results_bi = [];
    results_pcc = [];
    resultsRC_pcc = [];
    results_c = [];
@@ -116,30 +120,42 @@
    results_o1 = [];
    results_o2 = [];
 
+   % Pearson's correlation and Manders' split coefficients per ROI.
+   if any(contains(options, "SimpleStats"))
+      results_ss = PA.doSimpleStats(n_ROIs, RoI, PixelSize, desc, particles, ...
+                                    results_dir);
+      fprintf("Done SimpleStats\n");
+   end
+
    % Pairwise mutual distances and bivariate Ripley's per ROI and the latter
    % also combined over all ROIs.
    if any(contains(options, "BiStats"))
-      PA.doBiStats(n_ROIs, RoI, desc, particles, results_dir, combined);
+      results_bi = PA.doBiStats(n_ROIs, RoI, desc, particles, results_dir, ...
+                                combined);
+      fprintf("Done BiStats\n");
    end
 
    % Clusters for each label per ROI.
    if any(contains(options, "Clustering"))
-      results_c  = PA.doClustering(n_ROIs, RoI, desc, results_dir, plotting,...
-                                PixelSize, E, minPts);
+      results_c = PA.doClustering(n_ROIs, RoI, desc, results_dir, plotting,...
+                                  PixelSize, E, minPts);
+      fprintf("Done Clustering\n");
    end
 
    % C2C nearest neighbor distances between label 1/label 2 clusters per ROI
    % and combined over all ROIs.
    if any(contains(options, "Clustering2"))
       results_cs = PA.doClusterSep2(n_ROIs, results_c, desc, particles, ...
                                     results_dir, plotting);
+      fprintf("Done Clustering2\n");
    end
 
    % Nearest neighbor distances between label 1/label 2 localizations per ROI
    % and combined over all ROIs.
    if any(contains(options, "LocSep2"))
       results_ls = PA.doLocSep2(n_ROIs, RoI, desc, particles, results_dir, ...
                                 plotting);
+      fprintf("Done LocSep2\n");
    end
 
    % Overlaps between label 1 clusters and label 2 localizations.
@@ -148,6 +164,7 @@
       results_o1 = PA.doOverlap(n_ROIs, RoI, results_c, l12, desc,      ...
                                 particles, results_dir, PlotNonOverlap, ...
                                 Color, plotting);
+      fprintf("Done Overlap1\n");
    end
 
    % Overlaps between label 2 clusters and label 1 localizations.
@@ -156,23 +173,27 @@
       results_o2 = PA.doOverlap(n_ROIs, RoI, results_c, l12, desc,      ...
                                 particles, results_dir, PlotNonOverlap, ...
                                 Color, plotting);
+      fprintf("Done Overlap2\n");
    end
 
    % Pair correlation per ROI and combined over all ROIs.
    if any(contains(options, "PairCorr"))
       [results_pcc, resultsRC_pcc] =                                        ...
          PA.doPairCorr(n_ROIs, RoI, ROI_sizes, desc, results_dir, combined, ...
                        plotting, HistBinSize, RmaxAxisLimit);
+      fprintf("Done PairCorr\n");
    end
 
    % 2D plot per ROI.
    if any(contains(options, "Plot2"))
       PA.doPlot2(n_ROIs, RoI, desc, particles, results_dir, Color, plotting);
+      fprintf("Done Plot2\n");
    end
 
    % Save results.
    save(fullfile(results_dir, sprintf('%s_results.mat', desc)), 'n_ROIs', ...
-        'RoI', 'results_pcc', 'resultsRC_pcc', 'results_c', 'results_cs', ...
-        'results_o1', 'results_o2');
+        'RoI', 'results_ss', 'results_c', 'results_cs',  'results_ls',    ...
+        'results_o1', 'results_o2', 'results_pcc', 'resultsRC_pcc');
+   fprintf("Done saving results\n");
 
 end

MATLAB/+smi_cluster/@PairAnalysis/doBiStats.m

@@ -1,4 +1,5 @@
-function doBiStats(n_ROIs, RoI, desc, particles, results_dir, combined)
+function results_bi = ...
+            doBiStats(n_ROIs, RoI, desc, particles, results_dir, combined)
 % Pairwise mutual distances and bivariate Ripley's statistics for each ROI.
 %
 % INPUTS:
@@ -17,11 +18,13 @@ function doBiStats(n_ROIs, RoI, desc, particles, results_dir, combined)
 % Created by
 %    Michael J. Wester (2022)
 
+   results_bi = [];
+
    SC = smi_cluster.StatisticsClustering();
    SC.ResultsDir = results_dir;
 
    if ~combined
-      results_birip = cell(n_ROIs, 1);
+      %results_birip = cell(n_ROIs, 1);
       for i = 1 : n_ROIs
          SC.BaseName = sprintf('%s_ROI%d', desc, i);
          % Compare the actual distribution of nearest neighbor distances to a

MATLAB/+smi_cluster/@PairAnalysis/doClustering.m

@@ -28,7 +28,10 @@
    %minPts = 3;  % minimum number of points in a cluster
 
    c = smi_cluster.Clustering();
-   c.PixelSize = PixelSize;
+   % PixelSize is only used for H-SET clustering, and has been set to nm units
+   % (Pixelsize = 1) earlier.
+%  c.PixelSize = PixelSize;
+   c.PixelSize = 1;
    c.Plotting = true;
    c.Alpha = 2;
    c.Valgorithm = 2;

MATLAB/+smi_cluster/@PairAnalysis/doPairCorr.m

@@ -35,7 +35,11 @@
    pc = smi_cluster.PairCorrelation();
 
    pc.ResultsDir = results_dir;    % results directory
-   pc.Fig_ext = 'png';             % figure extension
+   if combined
+      pc.Fig_ext = '';             % figure extension
+   else
+      pc.Fig_ext = 'png';          % figure extension
+   end
    pc.Rmax_axis = RmaxAxisLimit;   % sets plotting limit if > 0
    % Histogram bin size for pairwise correlation---this is the number of pixels
    % per bin over which correlation statistics are collected.
@@ -67,6 +71,7 @@
       fprintf('ROI combined\n\n');
       txt = sprintf('%s_RC', desc);
       pc.BaseName = txt;
+      pc.Fig_ext = 'png';   % figure extension
       resultsRC_pcc = ...
          pc.pair_correlation_ROIcombined(2, n_ROIs, ROIs_combined, 1)
    end