Update to TASBE 5

.travis.yml

@@ -0,0 +1,75 @@
+# vim ft=yaml
+# travis-ci.org definition for TASBEFlowAnalytics-Tutorial build (based on MOxUnit)
+# 
+# This pretends to be java because it needs GNU Octave which is not
+# available (as of April 2015)
+
+language: java
+
+services:
+  - xvfb
+cache:
+  - apt
+addons:
+  apt:
+    packages:
+      - ghostscript
+      - pstoedit
+env:
+ global:
+    - OCTAVE=octave
+ matrix:
+    - WITH_COVERAGE=
+    # Disable until ready to go there:
+    #- WITH_COVERAGE=true
+    #- RUN_DOC_TEST=true
+before_install:
+    # to prevent IPv6 being used for APT
+    - sudo bash -c "echo 'Acquire::ForceIPv4 \"true\";' > /etc/apt/apt.conf.d/99force-ipv4"
+    - travis_retry sudo apt-get -y -qq update
+    - travis_retry sudo apt-get install -y -qq software-properties-common python-software-properties
+    - travis_retry sudo apt-add-repository -y ppa:octave/stable
+    - travis_retry sudo apt-get -y -qq update
+    # get Octave 4,0
+    - travis_retry sudo apt-get -y -qq install octave liboctave-dev
+    # install Octave io package
+    # Forge is unreliable: trying to get directly instead:
+    # - octave --no-gui --quiet --eval "pkg install -forge io"
+    # - wget -O io-2.4.10.tar.gz https://downloads.sourceforge.net/octave/io-2.4.10.tar.gz?download --no-check-certificate
+    - octave --no-gui --quiet --eval "pkg install matlab_compat/io-2.4.10.tar.gz"
+    # install MOxUnit
+    - cd ..
+    - rm -rf MOxUnit
+    - git clone https://github.com/MOxUnit/MOxUnit.git
+    - make -C MOxUnit install
+    # install MOcov
+    - rm -rf MOcov
+    - git clone https://github.com/MOcov/MOcov.git
+    - make -C MOcov install
+    # retrieve MOdox
+    - rm -rf MOdox
+    - travis_retry git clone -v git://github.com/MOdox/MOdox.git
+    - make -C MOdox install
+    # retrieve TASBE for running tests
+    - rm -rf TASBEFlowAnalytics
+    - git clone --branch=develop  https://github.com/TASBE/TASBEFlowAnalytics 
+    - make -C TASBEFlowAnalytics install
+    # go back to original directory
+    - cd TASBEFlowAnalytics-Tutorial
+
+script:
+    - if [[  "$WITH_COVERAGE" == "true" ]]; then
+          TEST_ARGS=WITH_COVERAGE=true;
+          COVER_ARGS=COVER=`pwd`/TASBEFlowAnalytics-Tutorial;
+
+          if [[  "$TRAVIS" == "true"  ]]; then
+              OUTPUT_ARGS=COVER_JSON_FILE=`pwd`/coveralls.json;
+              AFTER_SCRIPT="curl --verbose -F json_file=@`pwd`/coveralls.json https://coveralls.io/api/v1/jobs";
+          fi;
+      elif [[  "$RUN_DOC_TEST" == "true" ]]; then
+          TEST_ARGS=RUN_DOC_TEST=true;
+      fi;
+
+    - echo Test arguments $TEST_ARGS $COVER_ARGS $OUTPUT_ARGS $RESULT_ARGS
+    - make test $TEST_ARGS $COVER_ARGS $OUTPUT_ARGS $RESULT_ARGS
+    - eval $AFTER_SCRIPT

01_flow_cytometry/exercises.m

@@ -3,32 +3,30 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 % example: addpath('~/Downloads/TASBEFlowAnalytics/');
-addpath('your-path-to-analytics');
+% addpath('../TASBEFlowAnalytics/'); % input your-path-to-analytics
 % turn off sanitized filename warnings:
 warning('off','TASBE:SanitizeName');
 
 colordata = '../example_controls/';
 dosedata = '../example_assay/';
 
 
-
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% Examples of flow data
+% Examples of flow data (Fig1 to Fig4)
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 % pure scatter - often hard to interpret
-fcs_scatter([dosedata 'LacI-CAGop_C4_C04_P3.fcs'],'PE-Tx-Red-YG-A','Pacific Blue-A',0,[0 0; 6 6],1);
-fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','Pacific Blue-A',0,[0 0; 6 6],1);
+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_C04_P3.fcs'],'PE-Tx-Red-YG-A','Pacific Blue-A',1,[0 0; 6 6],1);
-data2 = fcs_scatter([colordata '07-29-11_EYFP_P3.fcs'],'FITC-A','Pacific Blue-A',1,[0 0; 6 6],1);
+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
 % - because there is so much of it, pure scatter graphs are not sufficient for interpreting the data
 % - the axes are logarithmic, and variation is evenly distributed on the log scale
-% - the data runs up against the axes: there are values less than zero not shown
-%   less than zero values come from sensor error
-% - low values are quantized, but not round
+% - the data runs up against the axes: values less than zero (sensor error) are not shown
+% - low values are quantized, but not rounded
 % - the very highest values saturate, at around 10^5.5 in these files
 % - the populations of cells are complex, multimodal, and range widely in observed fluorescence
 
@@ -71,22 +69,33 @@
 % How high was this really?  We cannot know because it is saturated!
 
 
-
-
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Making a ColorModel (Fig5)
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 channels = {};
 
 channels{1} = Channel('Pacific Blue-A', 405, 450, 50);
+channels{1} = setPrintName(channels{1}, 'EBFP2');
+channels{1} = setLineSpec(channels{1}, 'b');
+colorfiles{1} = [colordata '2012-03-12_' 'ebfp2_P3.fcs'];
+
 channels{2} = Channel('PE-Texas Red-A', 561, 610, 20);
+channels{2} = setPrintName(channels{2}, 'mKate');
+channels{2} = setLineSpec(channels{2}, 'r');
+colorfiles{2} = [colordata '2012-03-12_' 'mkate_P3.fcs'];
+
 channels{3} = Channel('FITC-A', 488, 530, 30);
-CM = ColorModel('','',channels,{{},{},{}},{});
+channels{3} = setPrintName(channels{3}, 'EYFP'); % Name to print on charts
+channels{3} = setLineSpec(channels{3}, 'y'); % Color for lines, when needed
+colorfiles{3} = [colordata '2012-03-12_' 'EYFP_P3.fcs']; % If there is only one channel, the color file is optional
+
+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']);
-% Notice that 'filtered' is smaller than 'data': we've dropped the first 25 "units" of time
-% It should be in tenths of seconds, but in fact it's the uninterpreted timestep field
+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);
-[dequantized hdr] = read_filtered_au(CM,[dosedata 'LacI-CAGop_C4_C04_P3.fcs']);
+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,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]);
+figure; smoothhist2D(log10([xc(pos) yc(pos)]),10,[200, 200],[],'image',[0 0; 6 6]); % Fig5
 % if desired, quantization can be smoothed out by introduction of small noise