Merge pull request TASBE#10 from jakebeal/master

add bead channel selection option

@ColorModel/beads_to_mefl_model.m

@@ -224,8 +224,8 @@
     k_MEFL = 10^constrained_fit;
 else % 1 peak
     warning('TASBE:Beads','Only one bead peak found, assuming brightest');
-    fit_error = 0; first_peak = 8;
-    k_MEFL = PeakMEFLs(7)/peak_means;
+    fit_error = 0; first_peak = numel(PeakMEFLs);
+    k_MEFL = PeakMEFLs(end)/peak_means;
 end;
 
 % Optional plot

@ColorModel/set_bead_channel.m

@@ -0,0 +1,10 @@
+% Copyright (C) 2010-2017, Raytheon BBN Technologies and contributors listed 
+% in the AUTHORS file in TASBE analytics package distribution's top directory.
+%
+% This file is part of the TASBE analytics package, and is distributed
+% under the terms of the GNU General Public License, with a linking
+% exception, as described in the file LICENSE in the TASBE analytics
+% package distribution's top directory.
+
+function CM=set_bead_channel(CM, v)
+      CM.bead_channel=v;

@PlasmidExpressionModel/PlasmidExpressionModel.m

@@ -60,6 +60,12 @@
 
             %%%% GAUSSIAN MIXTURE MODEL METHOD
             which = cfp_data>drop_threshold;
+            if(sum(which)==0)
+                warning('Model:CFPDistributionSeparation','No data to model');
+                PEM=class(PEM,'PlasmidExpressionModel');
+                return;
+            end
+
             % Seeded centers: [8], [5 8], [5 6.5 8], [5 6 7 8], ...
             min_center =  log10(getMeanMEFL(CFP_af));
             if n_components == 1, 

compute_sample_statistics.m

@@ -60,10 +60,12 @@
     if numel(data),
         tmp_PEM = PlasmidExpressionModel(data(:,k),CFP_af,CFP_noise,getMEFLPerPlasmid(analysisParams),drop_threshold,n_components);
         dist = get_fp_dist(tmp_PEM);
-        sortable = zeros(n_components,3);
-        sortable(:,1) = dist.mu; sortable(:,2) = dist.Sigma(:); sortable(:,3) = dist.weight;
-        sortrows(sortable);
-        popcmeans(:,k) = sortable(:,1); popcstds(:,k) = sortable(:,2);  popcweights(:,k) = sortable(:,3);
+        if ~isempty(dist)
+            sortable = zeros(n_components,3);
+            sortable(:,1) = dist.mu; sortable(:,2) = dist.Sigma(:); sortable(:,3) = dist.weight;
+            sortrows(sortable);
+            popcmeans(:,k) = sortable(:,1); popcstds(:,k) = sortable(:,2);  popcweights(:,k) = sortable(:,3);
+        end
         if k==c_index, PEM = tmp_PEM; end;
     end
 end

fcs_scatter.m

@@ -30,11 +30,12 @@
 if linear(2), yv = yc; else yv = log10(yc(pos)); end;
 
 if nargin >= 4 && density
+    smoothing = 10;
     h = figure('PaperPosition',[1 1 5 5]);
     if ~visible, set(h,'visible','off'); end;
     if nargin < 5, range = []; end;
     if density >= 1, type = 'image'; else type = 'contour'; end
-    smoothhist2D([xv yv],10,[200, 200],[],type,range,largeoutliers);
+    smoothhist2D([xv yv],smoothing,[200, 200],[],type,range,largeoutliers);
 else
     h = figure('PaperPosition',[1 1 5 5]);
     if ~visible, set(h,'visible','off'); end;