rdn/mt_plot_array.m at master · mhulsman/rdn · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
%MT_PLOT_ARRAY - Plots image of array difference versus median array
%
%  MT_PLOT_ARRAY(PROBES,ARRAYNR,VARARGIN)
%
% INPUT
%   PROBES			Probe structure
%   ARRAYNR         Number of array to visualize
%   VARARGIN        'range'     : Followed by colormap range (default: [-1 1])
%                   'no_image'  : Leave out image correction
%                   'absolute'  : Show absolute values instead of difference w.r.t. median
%                   'image_effects' : Plot image effects estimated by mt_cor_image (implies 'absolute')
%                   'batch_effects' : Take median over each row in the array for all arrays. Show
%                                     the results of all arrays as columns in the image.
%                                     With this etting, arraynr has no effect.
%
%
% DESCRIPTION
% Plots (PM) probe value (differences) on their array location to allow inspection for
% spatial array effects. If an array also contains mismatch probes (which are located
% next to their corresponding PM probes), then mismatch probe positions are filled with
% the PM value.
%
% SEE ALSO
% MT_COR_IMAGE, MT_PLOT_SEQ_EFFECTS, MT_PLOT_AMP_EFFECTS, MT_PLOT_GENE
%
% (c) Marc Hulsman, 2009
% Information & Communication Theory Group
% Faculty of Electrical Engineering, Mathematics and Computer Science
% Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands

function mt_plot_array(probes,arraynr,varargin)

   for i = 1:length(varargin)
      if(isstr(varargin{i}))
         switch(varargin{i})
            case 'range',
               i = i + 1;
               c_range = varargin{i};
            case 'no_image',
               no_image = 1;
            case 'absolute',
               absolute = 1;
            case 'image_effects',
                image_effects = 1;
                absolute = 1;
            case 'batch_effects',
                batch_effects = 1;
         end;
      end;
   end;
   if(~exist('image_effects'))
        if(exist('no_image'))
            data = mt_real_signal(probes,'no_image');
        else
            data = mt_real_signal(probes);
        end;
   else
        data = probes.image_factors;
   end;

   [narrays,nprobe] = size(probes.pm);


   if(~exist('absolute'))
       med_data = median(data);
       array_pm = data - repmat(med_data,narrays,1);
   else
       array_pm = data;
   end;
   if(isfield(probes,'array_ind'))
        array_ind = probes.array_ind(i);
   else
        array_ind = 1;
   end;

   if(exist('batch_effects'))
       map = [];
       fprintf(1,' Determining batch effects');
       for a = 1:length(arraynr)
           i = arraynr(a);
           if(mod(a,50) == 0)
               fprintf(1,'.');
           end;
           if(isfield(probes,'array_ind'))
                   array_ind = probes.array_ind(i);
           else
                   array_ind = 1;
           end;
           cmap = mt_map_to_array(probes,array_pm(i,:),array_ind);
           map = [map median(cmap')'];
       end;
       fprintf(1,'\n');
   else
       if(length(arraynr) == 1)
            array_pm = array_pm(arraynr,:);
            if(isfield(probes,'array_ind'))
                   array_ind = probes.array_ind(arraynr);
            else
                   array_ind = 1;
            end;
            map = mt_map_to_array(probes,array_pm,array_ind);
       else
            xmap = zeros(length(arraynr),probes.nrows,probes.ncols);
            fprintf(1,' Averaging arrays');
            for a = 1:length(arraynr)
                i = arraynr(a);
                if(mod(a,10) == 0)
                    fprintf(1,'.');
                end;
                if(isfield(probes,'array_ind'))
                    array_ind = probes.array_ind(i);
                else
                    array_ind = 1;
                end;
                xmap(a,:,:) = mt_map_to_array(probes,array_pm(i,:),array_ind);
            end;
            map = squeeze(mean(xmap,1));
            fprintf(1,'\n');
       end;
   end;
   imagesc(map);

   if(~exist('c_range'))
    if(exist('image_effects') || exist('batch_effects'))
        c_range = [-0.5 0.5];
    else
        if(exist('absolute'))
            c_range = [min(map(:)) max(map(:))]
        else
            c_range = [-1 1];
        end;
    end;
   end;

   set(gca,'CLim',c_range);

   colorbar
   if(length(arraynr) == 1)
       title(sprintf('Array image %d:%s',arraynr,probes.array_filenames{arraynr}))
   else
      if(exist('batch_effects'))
        title('Arrays median row effect image')
      else
        title('Arrays average image')
      end;
   end;