function [ctrs1, ctrs2, F, hAxes] = dscatter2(X,Y, varargin)
% DSCATTER2 creates a scatter plot coloured by density.
%
% This is a modifyed version of the original dscatter function:
% http://www.mathworks.com/matlabcentral/fileexchange/8430-flow-cytometry-data-reader-and-visualization/content/dscatter.m
% The modifyed version returns the smoothed scatter data (ctrs1, ctrs2, F)
%
% DSCATTER(X,Y) creates a scatterplot of X and Y at the locations
% specified by the vectors X and Y (which must be the same size), colored
% by the density of the points.
%
% DSCATTER(...,'MARKER',M) allows you to set the marker for the
% scatter plot. Default is 's', square.
%
% DSCATTER(...,'MSIZE',MS) allows you to set the marker size for the
% scatter plot. Default is 10.
%
% DSCATTER(...,'FILLED',false) sets the markers in the scatter plot to be
% outline. The default is to use filled markers.
%
% DSCATTER(...,'PLOTTYPE',TYPE) allows you to create other ways of
% plotting the scatter data. Options are "surf','mesh' and 'contour'.
% These create surf, mesh and contour plots colored by density of the
% scatter data.
%
% DSCATTER(...,'BINS',[NX,NY]) allows you to set the number of bins used
% for the 2D histogram used to estimate the density. The default is to
% use the number of unique values in X and Y up to a maximum of 200.
%
% % DSCATTER(...,'SMOOTHING',LAMBDA) allows you to set the smoothing factor
% used by the density estimator. The default value is 20 which roughly
% means that the smoothing is over 20 bins around a given point.
%
% DSCATTER(...,'LOGY',true) uses a log scale for the yaxis.
%
% Examples:
%
% [data, params] = fcsread('SampleFACS');
% dscatter(data(:,1),10.^(data(:,2)/256),'log',1)
% % Add contours
% hold on
% dscatter(data(:,1),10.^(data(:,2)/256),'log',1,'plottype','contour')
% hold off
% xlabel(params(1).LongName); ylabel(params(2).LongName);
%
% See also FCSREAD, SCATTER.
% Copyright 2003-2004 The MathWorks, Inc.
% $Revision: $ $Date: $
% Reference:
% Paul H. C. Eilers and Jelle J. Goeman
% Enhancing scatterplots with smoothed densities
% Bioinformatics, Mar 2004; 20: 623 - 628.
lambda = [];
nbins = [];
plottype = 'scatter';
contourFlag = false;
msize = 10;
marker = 's';
logy = false;
filled = true;
if nargin > 2
if rem(nargin,2) == 1
error('Bioinfo:IncorrectNumberOfArguments',...
'Incorrect number of arguments to %s.',mfilename);
end
okargs = {'smoothing','bins','plottype','logy','marker','msize','filled'};
for j=1:2:nargin-2
pname = varargin{j};
pval = varargin{j+1};
k = strmatch(lower(pname), okargs); %#ok
if isempty(k)
error('Bioinfo:UnknownParameterName',...
'Unknown parameter name: %s.',pname);
elseif length(k)>1
error('Bioinfo:AmbiguousParameterName',...
'Ambiguous parameter name: %s.',pname);
else
switch(k)
case 1 % smoothing factor
if isnumeric(pval)
lambda = pval;
else
error('Bioinfo:InvalidScoringMatrix','Invalid smoothing parameter.');
end
case 2
if isscalar(pval)
nbins = [ pval pval];
else
nbins = pval;
end
case 3
plottype = pval;
case 4
logy = pval;
Y = log10(Y);
case 5
contourFlag = pval;
case 6
marker = pval;
case 7
msize = pval;
case 8
filled = pval;
end
end
end
end
minx = min(X,[],1);
maxx = max(X,[],1);
miny = min(Y,[],1);
maxy = max(Y,[],1);
if isempty(nbins)
nbins = [min(numel(unique(X)),200) ,min(numel(unique(Y)),200) ];
end
if isempty(lambda)
lambda = 20;
end
edges1 = linspace(minx, maxx, nbins(1)+1);
ctrs1 = edges1(1:end-1) + .5*diff(edges1);
edges1 = [-Inf edges1(2:end-1) Inf];
edges2 = linspace(miny, maxy, nbins(2)+1);
ctrs2 = edges2(1:end-1) + .5*diff(edges2);
edges2 = [-Inf edges2(2:end-1) Inf];
[n,p] = size(X);
bin = zeros(n,2);
% Reverse the columns to put the first column of X along the horizontal
% axis, the second along the vertical.
[dum,bin(:,2)] = histc(X,edges1);
[dum,bin(:,1)] = histc(Y,edges2);
H = accumarray(bin,1,nbins([2 1])) ./ n;
G = smooth1D(H,nbins(2)/lambda);
F = smooth1D(G',nbins(1)/lambda)';
% = filter2D(H,lambda);
if logy
ctrs2 = 10.^ctrs2;
Y = 10.^Y;
end
okTypes = {'surf','mesh','contour','image','scatter'};
k = strmatch(lower(plottype), okTypes); %#ok
if isempty(k)
error('dscatter:UnknownPlotType',...
'Unknown plot type: %s.',plottype);
elseif length(k)>1
error('dscatter:AmbiguousPlotType',...
'Ambiguous plot type: %s.',plottype);
else
switch(k)
case 1 %'surf'
% start
F = F./max(F(:));
ind = sub2ind(size(F),bin(:,1),bin(:,2));
col = F(ind);
col2=col;
%end
h = surf(ctrs1,ctrs2,F,'edgealpha',0);
case 2 % 'mesh'
h = mesh(ctrs1,ctrs2,F);
case 3 %'contour'
[dummy, h] =contour(ctrs1,ctrs2,F);
% [dummy, h] =contour(ctrs1,ctrs2,F,...
% 'LineWidth',2,'LevelStep',0.00015,'Parent',axes1);
case 4 %'image'
nc = 256;
F = F./max(F(:));
colormap(repmat(linspace(1,0,nc)',1,3));
h =image(ctrs1,ctrs2,floor(nc.*F) + 1);
case 5 %'scatter'
F = F./max(F(:));
ind = sub2ind(size(F),bin(:,1),bin(:,2));
col = F(ind);
% col2=col;
if filled
h = scatter(X,Y,msize,col,marker,'filled');
else
h = scatter(X,Y,msize,col,marker);
end
end
end
if logy
set(gca,'yscale','log');
end
if nargout > 0
hAxes = get(h,'parent');
end
%%%% This method is quicker for symmetric data.
% function Z = filter2D(Y,bw)
% z = -1:(1/bw):1;
% k = .75 * (1 - z.^2);
% k = k ./ sum(k);
% Z = filter2(k'*k,Y);
function Z = smooth1D(Y,lambda)
[m,n] = size(Y);
E = eye(m);
D1 = diff(E,1);
D2 = diff(D1,1);
P = lambda.^2 .* D2'*D2 + 2.*lambda .* D1'*D1;
Z = (E + P) \ Y;