Added DataFile objects to TASBE tutorials

Author: coverney

01_flow_cytometry/exercises.m

@@ -15,11 +15,11 @@
 % Examples of flow data (Fig1 to Fig4)
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 % pure scatter - often hard to interpret
-fcs_scatter([dosedata 'LacI-CAGop_C4_P3.fcs'],'PE-Tx-Red-YG-A','Pacific Blue-A',0,[0 0; 6 6],1); % Fig1
-fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','Pacific Blue-A',0,[0 0; 6 6],1); % Fig2
+fcs_scatter(DataFile('fcs', [dosedata 'LacI-CAGop_C4_P3.fcs']),'PE-Tx-Red-YG-A','Pacific Blue-A',0,[0 0; 6 6],1); % Fig1
+fcs_scatter(DataFile('fcs', [colordata '07-29-11_EYFP_P3.fcs']),'FITC-A','Pacific Blue-A',0,[0 0; 6 6],1); % Fig2
 % smoothed density plot omits details but often summarizes collective better
-data1 = fcs_scatter([dosedata 'LacI-CAGop_C4_P3.fcs'],'PE-Tx-Red-YG-A','Pacific Blue-A',1,[0 0; 6 6],1); % Fig3
-data2 = fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','Pacific Blue-A',1,[0 0; 6 6],1); % Fig4
+data1 = fcs_scatter(DataFile('fcs', [dosedata 'LacI-CAGop_C4_P3.fcs']),'PE-Tx-Red-YG-A','Pacific Blue-A',1,[0 0; 6 6],1); % Fig3
+data2 = fcs_scatter(DataFile('fcs', [colordata '07-29-11_EYFP_P3.fcs']),'FITC-A','Pacific Blue-A',1,[0 0; 6 6],1); % Fig4
 
 % Things to notice:
 % - look at the size of data1 and data2: there's a *LOT* of points in these samples
@@ -91,10 +91,10 @@
 
 CM = ColorModel('','',channels,colorfiles,{}); % simplified ColorModel, more features will be introduced in future tutorials
 
-filtered = read_filtered_au(CM,[colordata '07-29-11_EYFP_P3.fcs']); % applies any filters set in ColorModel
+filtered = read_filtered_au(CM,DataFile('fcs', [colordata '07-29-11_EYFP_P3.fcs'])); % applies any filters set in ColorModel
 
 CM = set_dequantization(CM,true); % dequantization adds noise to spread the data out more, especially useful at low levels
-[dequantized hdr] = read_filtered_au(CM,[dosedata 'LacI-CAGop_C4_P3.fcs']);
+[dequantized hdr] = read_filtered_au(CM,DataFile('fcs', [dosedata 'LacI-CAGop_C4_P3.fcs']));
 xc = dequantized(:,10); yc = dequantized(:,11);
 pos = xc>0 & yc>0;
 figure; smoothhist2D(log10([xc(pos) yc(pos)]),10,[200, 200],[],'image',[0 0; 6 6]); % Fig5

02_flow_compensation/exercises.m

@@ -16,21 +16,21 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 % Let's look at some single-color positive controls:
 % no significant spectral overlap
-fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','Pacific Blue-A',1,[0 0; 6 6],1); % Fig1
+fcs_scatter(DataFile('fcs', [colordata '07-29-11_EYFP_P3.fcs']),'FITC-A','Pacific Blue-A',1,[0 0; 6 6],1); % Fig1
 % minor spectral overlap
-fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','PE-TxRed YG-A',1,[0 0; 6 6],1); % Fig2
+fcs_scatter(DataFile('fcs', [colordata '07-29-11_EYFP_P3.fcs']),'FITC-A','PE-TxRed YG-A',1,[0 0; 6 6],1); % Fig2
 % significant spectral overlap
-fcs_scatter([colordata '07-29-11_mkate_P3.fcs'],'PE-TxRed YG-A','FITC-A',1,[0 0; 6 6],1); % Fig3
-fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','AmCyan-A',1,[0 0; 6 6],1); % Fig4
+fcs_scatter(DataFile('fcs', [colordata '07-29-11_mkate_P3.fcs']),'PE-TxRed YG-A','FITC-A',1,[0 0; 6 6],1); % Fig3
+fcs_scatter(DataFile('fcs', [colordata '07-29-11_EYFP_P3.fcs']),'FITC-A','AmCyan-A',1,[0 0; 6 6],1); % Fig4
 % massive spectral overlap
-fcs_scatter([colordata '07-29-11_EBFP2_P3.fcs'],'Pacific Blue-A','AmCyan-A',1,[0 0; 6 6],1); % Fig5
+fcs_scatter(DataFile('fcs', [colordata '07-29-11_EBFP2_P3.fcs']),'Pacific Blue-A','AmCyan-A',1,[0 0; 6 6],1); % Fig5
 
 % let's look at some of these without blending (plot 'density' set to 0):
-fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','PE-TxRed YG-A',0,[0 0; 6 6],1); % Fig6
-fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','AmCyan-A',0,[0 0; 6 6],1); % Fig7
-fcs_scatter([colordata '07-29-11_EBFP2_P3.fcs'],'Pacific Blue-A','AmCyan-A',0,[0 0; 6 6],1); % Fig8
+fcs_scatter(DataFile('fcs', [colordata '07-29-11_EYFP_P3.fcs']),'FITC-A','PE-TxRed YG-A',0,[0 0; 6 6],1); % Fig6
+fcs_scatter(DataFile('fcs', [colordata '07-29-11_EYFP_P3.fcs']),'FITC-A','AmCyan-A',0,[0 0; 6 6],1); % Fig7
+fcs_scatter(DataFile('fcs', [colordata '07-29-11_EBFP2_P3.fcs']),'Pacific Blue-A','AmCyan-A',0,[0 0; 6 6],1); % Fig8
 % notice that these are extremely tight compared to a two-color experiment:
-fcs_scatter([colordata '2012-03-12_EBFP2_EYFP_P3.fcs'],'Pacific Blue-A','FITC-A',0,[0 0; 6 6],1); % Fig9
+fcs_scatter(DataFile('fcs', [colordata '2012-03-12_EBFP2_EYFP_P3.fcs']),'Pacific Blue-A','FITC-A',0,[0 0; 6 6],1); % Fig9
 
 % What does autofluorescence look like?
 [raw hdr data] = fca_readfcs([colordata '07-29-11_blank_P3.fcs']);
@@ -42,8 +42,8 @@
 % of autofluorescence and instrument error.  There is not currently any elegant way of separating these.
 
 % fit to a gaussian model:
-mu = mean(data(:,10))
-sigma = std(data(:,10))
+mu = mean(data(:,10));
+sigma = std(data(:,10));
 
 % Notice that the fit to a gaussian is pretty good:
 range = -100:5:150;
@@ -115,7 +115,7 @@
 CM = set_ERF_channel_name(CM, 'FITC-A'); % We'll explain this in the next exercise
 
 % Now let's read some files...
-raw = read_filtered_au(CM,[dosedata 'LacI-CAGop_C3_P3.fcs']);
+raw = read_filtered_au(CM,DataFile('fcs', [dosedata 'LacI-CAGop_C3_P3.fcs']));
 % compensated = readfcs_compensated_au(CM,[dosedata 'LacI-CAGop_C3_P3.fcs'],0,1);
 % You should see an error: need to "resolve" the color model first! Comment
 % out above line of code and run again.
@@ -160,7 +160,7 @@
 % even when it can be compensated for.
 
 
-compensated = readfcs_compensated_au(CM,[dosedata 'LacI-CAGop_C3_P3.fcs'],0,1);
+compensated = readfcs_compensated_au(CM,DataFile('fcs', [dosedata 'LacI-CAGop_C3_P3.fcs']),0,1);
 % The last two arguments are:
 % 1) Whether to add autofluorescence back in after reading (generally not done)
 % 2) Whether to map all values <= 0 to 1 (which is zero on the log scale)

03_flow_MEFL/exercises.m

@@ -15,9 +15,9 @@
 % Calibration beads (Plots folder and Fig1 to Fig4):
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 % Let's look at an example of SpheroTech RCP-30-5A calibration beads:
-fcs_scatter([colordata '2012-03-12_Beads_P3.fcs'],'Pacific Blue-A','PE-Tx-Red-YG-A',1,[0 0; 6 6],1); % Fig1
+fcs_scatter(DataFile('fcs', [colordata '2012-03-12_Beads_P3.fcs']),'Pacific Blue-A','PE-Tx-Red-YG-A',1,[0 0; 6 6],1); % Fig1
 % and without blending (density is 0)...
-fcs_scatter([colordata '2012-03-12_Beads_P3.fcs'],'Pacific Blue-A','PE-Tx-Red-YG-A',0,[0 0; 6 6],1); % Fig2
+fcs_scatter(DataFile('fcs', [colordata '2012-03-12_Beads_P3.fcs']),'Pacific Blue-A','PE-Tx-Red-YG-A',0,[0 0; 6 6],1); % Fig2
 % Notice that there is a nice, nearly linear sequence of 5 peaks
 % the last one (bottom left) is pretty blurry, as it comes down into
 % autofluorescence (failed transfection)
@@ -31,8 +31,8 @@
 % down near the bottom instead.
 
 % Let's take a look at a different pair of channels:
-fcs_scatter([colordata '2012-03-12_Beads_P3.fcs'],'PE-Tx-Red-YG-A','FITC-A',1,[0 0; 6 6],1); % Fig3
-fcs_scatter([colordata '2012-03-12_Beads_P3.fcs'],'PE-Tx-Red-YG-A','FITC-A',0,[0 0; 6 6],1); % Fig4
+fcs_scatter(DataFile('fcs', [colordata '2012-03-12_Beads_P3.fcs']),'PE-Tx-Red-YG-A','FITC-A',1,[0 0; 6 6],1); % Fig3
+fcs_scatter(DataFile('fcs', [colordata '2012-03-12_Beads_P3.fcs']),'PE-Tx-Red-YG-A','FITC-A',0,[0 0; 6 6],1); % Fig4
 % Notice that the relationship of the peaks is not linear any more.
 % This is because the FITC peaks are much lower, and are blurring into autofluorescence
 % Thus, we need to calibrate using only peaks far from autofluorescence.
@@ -147,9 +147,9 @@
 
 % We recommend 3 colors as best practices:
 % Let's look at such a multi-color control file:
-fcs_scatter([colordata '2012-03-12_mkate_EBFP2_EYFP_P3.fcs'],'FITC-A','Pacific Blue-A',1,[0 0; 6 6],1); % Fig5
+fcs_scatter(DataFile('fcs', [colordata '2012-03-12_mkate_EBFP2_EYFP_P3.fcs']),'FITC-A','Pacific Blue-A',1,[0 0; 6 6],1); % Fig5
 % and without blending...
-fcs_scatter([colordata '2012-03-12_mkate_EBFP2_EYFP_P3.fcs'],'FITC-A','Pacific Blue-A',0,[0 0; 6 6],1); % Fig6
+fcs_scatter(DataFile('fcs', [colordata '2012-03-12_mkate_EBFP2_EYFP_P3.fcs']),'FITC-A','Pacific Blue-A',0,[0 0; 6 6],1); % Fig6
 % Notice that it's only nicely linear for the higher levels, 
 % and that it's much smearier than our compensation controls
 % The first is what set_translation_channel_min is for