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16 | 16 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
17 | 17 | % Let's look at some single-color positive controls: |
18 | 18 | % no significant spectral overlap |
19 | | -fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','Pacific Blue-A',1,[0 0; 6 6],1); % Fig1 |
| 19 | +fcs_scatter(DataFile('fcs', [colordata '07-29-11_EYFP_P3.fcs']),'FITC-A','Pacific Blue-A',1,[0 0; 6 6],1); % Fig1 |
20 | 20 | % minor spectral overlap |
21 | | -fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','PE-TxRed YG-A',1,[0 0; 6 6],1); % Fig2 |
| 21 | +fcs_scatter(DataFile('fcs', [colordata '07-29-11_EYFP_P3.fcs']),'FITC-A','PE-TxRed YG-A',1,[0 0; 6 6],1); % Fig2 |
22 | 22 | % significant spectral overlap |
23 | | -fcs_scatter([colordata '07-29-11_mkate_P3.fcs'],'PE-TxRed YG-A','FITC-A',1,[0 0; 6 6],1); % Fig3 |
24 | | -fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','AmCyan-A',1,[0 0; 6 6],1); % Fig4 |
| 23 | +fcs_scatter(DataFile('fcs', [colordata '07-29-11_mkate_P3.fcs']),'PE-TxRed YG-A','FITC-A',1,[0 0; 6 6],1); % Fig3 |
| 24 | +fcs_scatter(DataFile('fcs', [colordata '07-29-11_EYFP_P3.fcs']),'FITC-A','AmCyan-A',1,[0 0; 6 6],1); % Fig4 |
25 | 25 | % massive spectral overlap |
26 | | -fcs_scatter([colordata '07-29-11_EBFP2_P3.fcs'],'Pacific Blue-A','AmCyan-A',1,[0 0; 6 6],1); % Fig5 |
| 26 | +fcs_scatter(DataFile('fcs', [colordata '07-29-11_EBFP2_P3.fcs']),'Pacific Blue-A','AmCyan-A',1,[0 0; 6 6],1); % Fig5 |
27 | 27 |
|
28 | 28 | % let's look at some of these without blending (plot 'density' set to 0): |
29 | | -fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','PE-TxRed YG-A',0,[0 0; 6 6],1); % Fig6 |
30 | | -fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','AmCyan-A',0,[0 0; 6 6],1); % Fig7 |
31 | | -fcs_scatter([colordata '07-29-11_EBFP2_P3.fcs'],'Pacific Blue-A','AmCyan-A',0,[0 0; 6 6],1); % Fig8 |
| 29 | +fcs_scatter(DataFile('fcs', [colordata '07-29-11_EYFP_P3.fcs']),'FITC-A','PE-TxRed YG-A',0,[0 0; 6 6],1); % Fig6 |
| 30 | +fcs_scatter(DataFile('fcs', [colordata '07-29-11_EYFP_P3.fcs']),'FITC-A','AmCyan-A',0,[0 0; 6 6],1); % Fig7 |
| 31 | +fcs_scatter(DataFile('fcs', [colordata '07-29-11_EBFP2_P3.fcs']),'Pacific Blue-A','AmCyan-A',0,[0 0; 6 6],1); % Fig8 |
32 | 32 | % notice that these are extremely tight compared to a two-color experiment: |
33 | | -fcs_scatter([colordata '2012-03-12_EBFP2_EYFP_P3.fcs'],'Pacific Blue-A','FITC-A',0,[0 0; 6 6],1); % Fig9 |
| 33 | +fcs_scatter(DataFile('fcs', [colordata '2012-03-12_EBFP2_EYFP_P3.fcs']),'Pacific Blue-A','FITC-A',0,[0 0; 6 6],1); % Fig9 |
34 | 34 |
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35 | 35 | % What does autofluorescence look like? |
36 | 36 | [raw hdr data] = fca_readfcs([colordata '07-29-11_blank_P3.fcs']); |
|
42 | 42 | % of autofluorescence and instrument error. There is not currently any elegant way of separating these. |
43 | 43 |
|
44 | 44 | % fit to a gaussian model: |
45 | | -mu = mean(data(:,10)) |
46 | | -sigma = std(data(:,10)) |
| 45 | +mu = mean(data(:,10)); |
| 46 | +sigma = std(data(:,10)); |
47 | 47 |
|
48 | 48 | % Notice that the fit to a gaussian is pretty good: |
49 | 49 | range = -100:5:150; |
|
115 | 115 | CM = set_ERF_channel_name(CM, 'FITC-A'); % We'll explain this in the next exercise |
116 | 116 |
|
117 | 117 | % Now let's read some files... |
118 | | -raw = read_filtered_au(CM,[dosedata 'LacI-CAGop_C3_P3.fcs']); |
| 118 | +raw = read_filtered_au(CM,DataFile('fcs', [dosedata 'LacI-CAGop_C3_P3.fcs'])); |
119 | 119 | % compensated = readfcs_compensated_au(CM,[dosedata 'LacI-CAGop_C3_P3.fcs'],0,1); |
120 | 120 | % You should see an error: need to "resolve" the color model first! Comment |
121 | 121 | % out above line of code and run again. |
|
160 | 160 | % even when it can be compensated for. |
161 | 161 |
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162 | 162 |
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163 | | -compensated = readfcs_compensated_au(CM,[dosedata 'LacI-CAGop_C3_P3.fcs'],0,1); |
| 163 | +compensated = readfcs_compensated_au(CM,DataFile('fcs', [dosedata 'LacI-CAGop_C3_P3.fcs']),0,1); |
164 | 164 | % The last two arguments are: |
165 | 165 | % 1) Whether to add autofluorescence back in after reading (generally not done) |
166 | 166 | % 2) Whether to map all values <= 0 to 1 (which is zero on the log scale) |
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