/
find_LMMEL_active_miRs.m
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find_LMMEL_active_miRs.m
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% This MATLAB script contains analyses for the manuscript:
% Systems analysis of the Ludwig Melbourne melanoma cell line panel
% identifies miR-29b as a driver of melanoma invasiveness.
% MC Andrews/J Cursons, DG Hurley, M Anaka, JS Cebon, A Behren,
% EJ Crampin
% Genome Biology, Submitted for review Feb 2016
% DOI: not-yet-assigned
%
% This script has been written to analyse data from the LM-MEL (primary
% melanoma) cell line panel, described in:
% The Ludwig institute for cancer research Melbourne melanoma cell line
% panel.
% Behren A, Anaka M, Lo PH, Vella LJ, Davis ID, Catimel J, Cardwell T,
% Gedye C, Hudson C, Stan R, Cebon J.
% Pigment Cell Melanoma Res 2013, 26:597-600.
% http://dx.doi.org/10.1111/pcmr.12097
%
% This script requires a number of functions, and utilises a number of
% freely-accessible databases, including:
%
% TargetScan:
% http://targetscan.org/
% For further reading, please refer to:
% Friedman et al (2009). Genome Research.
% http://dx.doi.org/10.1101/gr.082701.108
% Grimson et al (2007). Molecular Cell.
% http://dx.doi.org/10.1016/j.molcel.2007.06.017
% Lewis et al. (2005). Cell.
% http://dx.doi.org/10.1016/j.cell.2004.12.035
%
% DIANA-microT CDS:
% http://diana.imis.athena-innovation.gr/DianaTools/
% For further reading, please refer to:
% Paraskevopoulou et al (2013). Nucleic acids research.
% http://dx.doi.org/10.1093/nar/gkt393
% Reczko et al (2012). Bioinformatics.
% http://dx.doi.org/10.1093/bioinformatics/bts043
%
% miRTarBase:
% http://mirtarbase.mbc.nctu.edu.tw/
% For further reading, please refer to:
% Chou et al (2016). Nucleic acids research.
% http://dx.doi.org/10.1093/nar/gkv1258
%
% GeneOntology dB:
%
%
% Hoek group invasive/proliferative genes in melanoma:
% http://www.jurmo.ch/work_model.php
% For further reading, please refer to:
% Hoek et al (2006). Pigment Cell Res.
% http://dx.doi.org/10.1111/j.1600-0749.2006.00322.x
% Widmer et al (2012). Pigment Cell Melanoma Res.
% http://dx.doi.org/10.1111/j.1755-148X.2012.00986.x
%
% Ensembl Biomart
%
%
% We would like to convey our gratitude to the developers of these
% databases/tools.
%
% % % % % % % % % % % % % % % % % % % % % % % % %
%
% This script also has a number of dependencies:
% - MATLAB Statistics and Machine Learning Toolbox (for calc in
% calcStatAssoc/informationDynamics)
% - Java Information Dynamics Toolbox (JIDT) by JT Lizier
% http://dx.doi.org/10.3389/frobt.2014.00011
%
% Please refer to the function headers for specific details on the
% functions included with this script:
% loadLudwigData - extract LM-MEL panel data into structured arrays
% loadHoekLists - extract Hoek proliferative/invasive gene lists
% calcStatAssoc - calculate statistical associations between miR-mRNA pairs
% -> informationDynamics uses the JIDT described above
% -> MATLAB Statistics and Machine Learning Toolbox (for corr)
% loadmiRTarBase - extract the miRTarBase database v6.1
% loadDIANAmicroT - extract the DIANA microT CDS database
% loadTargetScanSummary - extract the TargetScan v7.0 database
% loadTargetScanMicRNAFams - extract miR families from TargetScan v7.0
% extractFullHumanGeneOntology - extract gene ontology database annotations
% matchHumanGOTermsToNums - identify GO terms of interest from input
% strings
% biomartEnsemblGeneToGeneOntology -
% createHeatMapArrays -
%
% % % % % % % % % % % % % % % % % % % % % % % % %
%
% For further details please contact:
%
% Dr Miles Andrews - Cancer Immunobiology Laboratory, Olivia Newton-John
% Cancer Research Institute, Australia
% miles.andrews@onjcri.org.au
%
% Dr Joe Cursons - Systems Biology Laboratory, University of Melbourne,
% Australia
% joseph.cursons@unimelb.edu.au
%
% Prof Edmund Crampin - Systems Biology Laboratory, University of
% Melbourne, Australia
% edmund.crampin@unimelb.edu.au
%
% Dr Andreas Behren - Cancer Immunobiology Laboratory, Olivia Newton-John
% Cancer Research Institute, Australia
% andreas.behren@onjcri.org.au
%
% This script was written by Joe Cursons - joseph.cursons@unimelb.edu.au
%
% Last Modified: 11/03/16
%
% % % % % % % % % % % % % % % % % % % % % % % % %
%% Perform directory structure pre-processing
% % % % % % % % % % % % % % % % % % % % % % % % %
%initiate the script by identifying the current directory path
stringCurrentDir = cd;
%add the path to any required functions
addpath(genpath('C:\wc\code\'));
%depending upon the OS, use a different folder separater (forward vs back
% slash)
if ispc,
strFoldSep = '\';
elseif isunix,
strFoldSep = '/';
else
%MATLAB on Windows can handle either forward or backslash (but MATLAB
% on *nix can't handle backslash)
disp(['warning: cannot determine the operating system, defaulting to ' ...
'forward slash for the folder path separator' ] );
strFoldSep = '/';
end
% % % % % % % % % % % % % % % % % % % % % % % % %
%% Specify analysis settings
% % % % % % % % % % % % % % % % % % % % % % % % %
%specify the percentile for the miTG-score threshold value for identifying
% 'high confidence' miRTarBase associations
numMITGScoreThreshPercentile = 70;
%equivalent to 0.6340
%specify the percentile for the context+ threshold value for identifying
% 'high confidence' TargetScan associations
numContPlusThreshPercentile = 85;
%equivalent to -0.2860
%specify the mutual information percentile for thresholding strong
% statistical associations
numMutInfoPercentileThresh = 90;
%specify the associations with miR-29b which are of particular interest
% (using the indices within the LM-MEL arrays), to label in Fig. 1B and
% plot for Fig. 2A
% miR mRNA
arrayMiR29bRels = { [ 478 18648 ]; %miR-29b-3p LAMC1
[ 478 37081 ]; %miR-29b-3p PPIC
[ 478 18690 ] }; %miR-29b-3p LASP1
%specify the associations with other miRs which are of particular interest
% (using the indices within the LM-MEL arrays) for Additional file 3
% miR mRNA
arrayOtherRelsOfInt = { [ 5 18965 ]; %let-7b-5p LIN28B
[ 504 39576 ]; %miR-30b-5p RUNX2
[ 504 40213 ]; %miR-30b-5p SERPINE1
[ 117 17326 ]; %miR-125b-5p IRF4
[ 396 43286 ]; %miR-211-5p TGFBR2
[ 478 5571 ]; %miR-29b-3p CDK6
[ 478 6439 ]; %miR-29b-3p COL4A1
[ 478 35992 ]; %miR-29b-3p PDGFC
[ 283 7223 ]; %miR-17-5p CYBRD1
[ 509 6594 ]; %miR-30d-5p CPE
[ 509 17536 ]; %miR-30d-5p JUN
[ 2574 12822 ]; %miR-98-5p HIC2
[ 244 34357 ]; %miR-146a-5p NRAS
[ 301 34386 ]; %miR-185-5p NRP1
[ 301 42200 ]; %miR-185-5p SRPX2
[ 354 31533 ]; %miR-199b-5p MBP
[ 396 1260 ]; %miR-211-5p ANXA1
[ 396 43085 ]; %miR-211-5p TCF4
[ 424 5889 ]; %miR-222-5p CHKA
[ 424 41829 ] }; %miR-222-5p SOX10
%for the Gene Ontology database analysis, use the following strings to
% match miR-targets to a functional annotation, relating to
% pigmentation or epithelial-mesenchymal plasticity
arrayInputPigmentGOTerms = { 'pigment'; 'melan' };
arrayInputEMPGOTerms = { 'epithel'; 'mesench'; 'EMT' };
%specify the minimum and maximum number of genes for GO categories which
% are retained within the analysis
numOntologyMinGenes = 5;
numOntologyMaxGenes = 500;
%control the processing of various databases used within the script and
% specify various output folders
structSettings = struct( 'InputFolder', 'C:\wc\2015_ludwig_melanoma\data\', ...
'IntermediateOutputFolder', 'C:\wc\2015_ludwig_melanoma\output\', ...
'OutputFolder', [ stringCurrentDir ], ...
'flagLoadStats', true, ...
'flagReCalcStats', false, ...
'flagSaveStats', false, ...
'mirTarBaseFolder', 'C:\db\miRTarBase_6p1\', ...
'processMirTarBase', false, ...
'DIANAmicroTFolder', 'C:\db\dianaMicroT_CDS\', ...
'processDIANAmicroT', false, ...
'TargetScanFolder', 'C:\db\targetscan_7p0\', ...
'processTargetScan', false );
% % % % % % % % % % % % % % % % % % % % % % % % %
%% Specify plotting/output settings
% % % % % % % % % % % % % % % % % % % % % % % % %
%specify the color (RGB) for the invasiveness clusters;
% [0 0 1] = blue = low invasiveness
% [1 0 0] = red = high invasiveness
arrayInvClusterColors = { [ 0 0 1 ];
[ 1 0 0 ] };
%specify the number of bins in either dimension (= MI or rP) for
% calculating statistical association density
numStatAssocSurfaceBins = 200;
%specify the step-size for calculating the percentile rank of the
% invasiveness-cluster distance
numInvSepDistPrctileStep = 0.1;
%specify the scaling for on screen display of figures
numFigScaleMult = 4;
%figure one layout settings
numFigOneHeight = 180; % mm
numFigOneWidth = 150; % mm
%calculate the corresponding position vector
arrayFigOnePosition = [ 50 -150 numFigOneWidth*numFigScaleMult numFigOneHeight*numFigScaleMult ];
%font size for the axis labels and annotation
numFigOneAxisLabelFontSize = 10;
numFigOneAnnotationFontSize = 8;
%specify the relative position for each subplot
arrayFigOneSubPlotPositions = { [ 0.15 0.60 0.500 0.33 ];
[ 0.15 0.06 0.605 0.35 ] };
%figure two layout settings
numFigTwoHeight = 255; % mm
numFigTwoWidth = 205; % mm
%calculate the corresponding position vector
arrayFigTwoPosition = [ 50 -150 numFigTwoWidth*numFigScaleMult numFigTwoHeight*numFigScaleMult ];
%specify the relative position for each subplot
% NB: we need to leave space for integrating the TCGA plots produced from
% python
arrayFigTwoSubPlotPos = { [ 0.070 0.90 0.08 0.06 ];
[ 0.235 0.90 0.08 0.06 ];
[ 0.400 0.90 0.08 0.06 ];
[ 0.645 0.90 0.08 0.06 ]; %MTB: miR-125b-5p vs IRF4
[ 0.895 0.90 0.08 0.06 ]; %MTB
[ 0.150 0.65 0.08 0.06 ]; %29b CDK6
[ 0.480 0.65 0.08 0.06 ]; %29b COL4A1
[ 0.810 0.65 0.08 0.06 ]; %29b PDGFC
[ 0.070 0.40 0.08 0.06 ];
[ 0.235 0.40 0.08 0.06 ];
[ 0.400 0.40 0.08 0.06 ];
[ 0.565 0.40 0.08 0.06 ];
[ 0.730 0.40 0.08 0.06 ];
[ 0.895 0.40 0.08 0.06 ];
[ 0.070 0.15 0.08 0.06 ];
[ 0.235 0.15 0.08 0.06 ];
[ 0.400 0.15 0.08 0.06 ];
[ 0.565 0.15 0.08 0.06 ];
[ 0.730 0.15 0.08 0.06 ];
[ 0.895 0.15 0.08 0.06 ] };
%figure three layout settings
numFigThreeHeight = 160; % mm
numFigThreeWidth = 160; % mm
%calculate the corresponding position vector
arrayFigThreePosition = [ 50 -150 numFigThreeWidth*numFigScaleMult numFigThreeHeight*numFigScaleMult ];
%font size for the axis labels and annotation
numFigThreeAxisLabelFontSize = 8;
numFigThreePanelLabelFontSize = 10;
numFigThreeMarkerFontSize = 6;
%specify the relative position for each subplot
arrayFig3P1SubPlotPos = { [ 0.15 0.54 0.605 0.16 ];
[ 0.85 0.71 0.020 0.10 ];
[ 0.10 0.15 0.20 0.20 ];
[ 0.42 0.15 0.20 0.20 ];
[ 0.74 0.15 0.20 0.20 ] };
% % % % % % % % % % % % % % % % % % % % % % % % %
%% Load the Ludwig Melanoma Dataset and Perform Statistical Analysis
% % % % % % % % % % % % % % % % % % % % % % % % %
%load in the quantitative miR/mRNA data, together with clustering
% information
[ structData, structClusters ] = loadLudwigData(structSettings);
numCellLines = size(structData(1).CellLine,1);
numMicRNAs = size(structData(1).Data,1);
numMessRNAs = size(structData(2).Data,1);
%extract miR name string length arrays for subsequent searching
arrayMicRNAStringLength = zeros(length(structData(1).Target),1,'uint8');
for iMicRNA = 1:length(structData(1).Target),
arrayMicRNAStringLength(iMicRNA) = length(structData(1).Target{iMicRNA});
end
%extract mRNA name string length arrays for subsequent searching
arrayMessRNAStringLength = zeros(length(structData(2).Target),1,'uint8');
for iMessRNA = 1:length(structData(2).Target),
arrayMessRNAStringLength(iMessRNA) = length(structData(2).Target{iMessRNA});
end
%extract cell line string lengths for matching between miRs and mRNAs
arrayMessRNACellLineStringLengths = zeros(numCellLines,1,'int16');
for iCellLine = 1:numCellLines,
arrayMessRNACellLineStringLengths(iCellLine) = length(structData(2).CellLine{iCellLine});
end
arrayMicRNACellLineStringLengths = zeros(numCellLines,1,'int16');
for iCellLine = 1:numCellLines,
arrayMicRNACellLineStringLengths(iCellLine) = length(structData(1).CellLine{iCellLine});
end
%extract the cell lines which are annotated with a low invasiveness
arrayLowInvCellLines = structClusters(2).groupMembers{1};
arrayLowInvCellLineStrLength = zeros(length(arrayLowInvCellLines),1,'uint8');
for iCellLine = 1:length(arrayLowInvCellLines),
arrayLowInvCellLineStrLength(iCellLine) = length(arrayLowInvCellLines{iCellLine});
end
%extract the cell lines which are annotated with a high invasiveness
arrayHighInvCellLines = structClusters(2).groupMembers{2};
arrayHighInvCellLineStrLength = zeros(length(arrayHighInvCellLines),1,'uint8');
for iCellLine = 1:length(arrayHighInvCellLines),
arrayHighInvCellLineStrLength(iCellLine) = length(arrayHighInvCellLines{iCellLine});
end
%extract invasive/proliferative gene lists as derived by Hoek et al (please
% see the script header for references)
[ structHoekLists ] = loadHoekLists( structSettings );
%calculate measures of statistical association between pairwise
% combinations of miR/mRNA
if structSettings.flagLoadStats,
load([structSettings.IntermediateOutputFolder 'StatAssoc.mat']);
elseif ((~structSettings.flagLoadStats) || (structSettings.flagReCalcStats)),
if structSettings.flagSaveStats,
[arrayMutInfo, arrayPearsCorr] = calcStatAssoc(structData);
save([structSettings.IntermediateOutputFolder 'StatAssoc.mat'], 'arrayMutInfo', 'arrayPearsCorr');
end
end
% % % % % % % % % % % % % % % % % % % % % % % % %
%% Load various databases to enrich the analysis
% % % % % % % % % % % % % % % % % % % % % % % % %
%etract mirTarBase
[ arrayMTBMicRNAIDs, arrayMTBMicRNANames, arrayMTBMessRNANames, arrayMTBMessRNAEntrezIDs, arrayMTBSpecies, arrayMTBExperiment ] = loadmiRTarBase(structSettings);
%examine the micro-RNA name lengths for later indexing
arrayMTBMicRNANameLengths = zeros(length(arrayMTBMicRNANames),1,'uint16');
for iMicRNA = 1:length(arrayMTBMicRNANames),
arrayMTBMicRNANameLengths(iMicRNA) = length(arrayMTBMicRNANames{iMicRNA});
end
%examine the mRNA name lengths for later indexing
arrayMTBMessRNANameLengths = zeros(length(arrayMTBMessRNANames),1,'uint16');
for iMessRNA = 1:length(arrayMTBMessRNANames),
arrayMTBMessRNANameLengths(iMessRNA) = length(arrayMTBMessRNANames{iMessRNA});
end
%extract DIANA-microT
[ arrayDMTMicRNANames, arrayDMTMessRNANames, arrayDMTMessRNAIDs, arrayDMTPredIntMITGScores, arrayDMTPredIntDetails ] = loadDIANAmicroT( structSettings );
%examine the micro-RNA name lengths for later indexing
arrayDMTMicRNANameLengths = zeros(length(arrayDMTMicRNANames),1,'uint16');
for iMicRNA = 1:length(arrayDMTMicRNANames),
arrayDMTMicRNANameLengths(iMicRNA) = length(arrayDMTMicRNANames{iMicRNA});
end
%examine the mRNA name lengths for later indexing
arrayDMTMessRNANameLengths = zeros(length(arrayDMTMessRNANames),1,'uint16');
for iMessRNA = 1:length(arrayDMTMessRNANames),
arrayDMTMessRNANameLengths(iMessRNA) = length(arrayDMTMessRNANames{iMessRNA});
end
%extract TargetScan
[ arrayTSMicRNANames, arrayTSMessRNANames, arrayTSMessRNARefSeq, arrayTSContPlus ] = loadTargetScanSummary( structSettings );
%examine the micro-RNA name lengths for later indexing
arrayTSMicRNANameLengths = zeros(length(arrayTSMicRNANames),1,'uint16');
for iMicRNA = 1:length(arrayTSMicRNANames),
arrayTSMicRNANameLengths(iMicRNA) = length(arrayTSMicRNANames{iMicRNA});
end
%examine the mRNA name lengths for later indexing
arrayTSMessRNANameLengths = zeros(length(arrayTSMessRNANames),1,'uint16');
for iMessRNA = 1:length(arrayTSMessRNANames),
arrayTSMessRNANameLengths(iMessRNA) = length(arrayTSMessRNANames{iMessRNA});
end
%extract the full miR family lists from TargetScan
[ arrayTSMicRNAFams, arrayTSMicRNAsByFam ] = loadTargetScanMicRNAFams( structSettings );
%extract the full set of Gene Ontology terms for target matching
structGOSettings = struct( 'performNewExtraction', true, ...
'dataFolder', 'C:\db\geneontology\');
[ arrayUniqueGONums, arrayUniqueGOGenes, arrayGOMembershipMatrix ] = extractFullHumanGeneOntology( structGOSettings );
%identify the GO terms associated with the unique set of GO numbers
structGOSettings = struct( 'performNewExtraction', true, ...
'dataFolder', 'C:\db\geneontology\');
arrayUniqueGOTerms = matchHumanGOTermsToNums(arrayUniqueGONums, structGOSettings);
%identify GO terms which contain the specified substrings
structGOMatchingFuncSettings = struct( 'biomartFolder', 'C:\db\biomart\', ...
'performNewExtraction', true );
%for pigmentation
[ ~, arrayPigGOTerm, arrayPigGONum ] = biomartEnsemblGeneToGeneOntology( arrayInputPigmentGOTerms, structGOMatchingFuncSettings );
arrayUniquePigGONums = unique(arrayPigGONum);
%and EMP
[ ~, arrayEMPGOTerm, arrayEMPGONum ] = biomartEnsemblGeneToGeneOntology( arrayInputEMPGOTerms, structGOMatchingFuncSettings );
arrayUniqueEMPGONums = unique(arrayEMPGONum);
% % % % % % % % % % % % % % % % % % % % % % % % %
%% Perform Data Pre-Processing
% % % % % % % % % % % % % % % % % % % % % % % % %
%the miR data contains a number of 'zero' entries (as it is from
% small-RNASeq) which need to be excluded
arrayNonZeroMicRNAObsIndices = find(structData(1).Data);
%before calculating summary statistics for miR data thresholding
numNonZeroMicRNATenPercentile = prctile(structData(1).Data(arrayNonZeroMicRNAObsIndices),10);
numNonZeroMicRNANinetyPercentile = prctile(structData(1).Data(arrayNonZeroMicRNAObsIndices),90);
%calculate summary statistics for mRNA data thresholding
numMessRNATenPercentile = prctile(structData(2).Data(:),10);
numMessRNATenPercentRange = 0.1*range(structData(2).Data(:));
%use the data derived thresholds to restrict the number of miRs being
% analysed
arrayMicRNAPassesAbundThresh = false(numMicRNAs,1);
arrayMicRNAPassesDynRangeThresh = false(numMicRNAs,1);
for iMicRNA = 1:numMicRNAs,
if sum(structData(1).Data(iMicRNA,:) > numNonZeroMicRNATenPercentile) > 0.25*numCellLines,
arrayMicRNAPassesAbundThresh(iMicRNA) = true;
end
if range(structData(1).Data(iMicRNA,:)) > numNonZeroMicRNANinetyPercentile,
arrayMicRNAPassesDynRangeThresh(iMicRNA) = true;
end
end
arrayMicRNAPassesTests = arrayMicRNAPassesDynRangeThresh & arrayMicRNAPassesAbundThresh;
%use the data derived thresholds to restrict the number of mRNAs being
% analysed
arrayMessRNAPassesAbundThresh = false(numMessRNAs,1);
arrayMessRNAPassesDynRangeThresh = false(numMessRNAs,1);
for iMessRNA = 1:numMessRNAs,
if sum(structData(2).Data(iMessRNA,:) > numMessRNATenPercentile) > 0.25*numCellLines,
arrayMessRNAPassesAbundThresh(iMessRNA) = true;
end
if range(structData(2).Data(iMessRNA,:)) > numMessRNATenPercentRange,
arrayMessRNAPassesDynRangeThresh(iMessRNA) = true;
end
end
arrayMessRNAPassesTests = arrayMessRNAPassesAbundThresh & arrayMessRNAPassesDynRangeThresh;
%use the 'good data' miR and mRNA list to restrict the number of pairwise
% associations being considered
arrayPairCompPassesTests = false(numMicRNAs, numMessRNAs);
for numMicRNA = find(arrayMicRNAPassesTests),
arrayPairCompPassesTests(numMicRNA, arrayMessRNAPassesTests) = true;
end
arrayPairCompsPassTestIndices = find(arrayPairCompPassesTests);
%use hist3 to bin the mutual information and Pearson's correlation values
% using a regular grid, for creating a surface
arrayStatAssocSurface = hist3(cat(2,arrayPearsCorr(arrayPairCompPassesTests), arrayMutInfo(arrayPairCompPassesTests)), [numStatAssocSurfaceBins numStatAssocSurfaceBins]);
%rescale/normalise the surface for plotting
numMaxSurfaceFreq = max(arrayStatAssocSurface(:));
arrayStatAssocSurfaceScaled = arrayStatAssocSurface/numMaxSurfaceFreq;
%from the subset of pairwise associations that we are interested in,
% calculate summary statistics for thresholding
numMutInfoThresh = prctile(arrayMutInfo(arrayPairCompsPassTestIndices), numMutInfoPercentileThresh);
numPearsCorrLowThresh = prctile(arrayPearsCorr(arrayPairCompsPassTestIndices), 2.5);
numPearsCorrUpThresh = prctile(arrayPearsCorr(arrayPairCompsPassTestIndices), 97.5);
%for the pairwise associations that we are interested in (to reduce the
% search domain..), identify those with support from the various databases
arrayPairCompIsDMTPred = false(numMicRNAs, numMessRNAs);
numMITGScoreThresh = double(prctile(arrayDMTPredIntMITGScores, numMITGScoreThreshPercentile))/1000;
arrayDMTAboveThreshIndices = find(arrayDMTPredIntMITGScores > uint16(numMITGScoreThresh*1000));
for iDMTRel = 1:length(arrayDMTAboveThreshIndices),
stringDMTMicRNA = arrayDMTMicRNANames{arrayDMTAboveThreshIndices(iDMTRel)};
stringDMTMessRNA = arrayDMTMessRNANames{arrayDMTAboveThreshIndices(iDMTRel)};
arrayMicRNAMatchIndices = find( strncmp(stringDMTMicRNA, structData(1).Target, length(stringDMTMicRNA)) & ...
(arrayMicRNAStringLength == length(stringDMTMicRNA) ) );
arrayMessRNAMatchIndices = find( strncmp(stringDMTMessRNA, structData(2).Target, length(stringDMTMessRNA)) & ...
(arrayMessRNAStringLength == length(stringDMTMessRNA) ) );
if ~isempty(arrayMicRNAMatchIndices) & ~isempty(arrayMessRNAMatchIndices),
if (length(arrayMicRNAMatchIndices) == 1) & (length(arrayMessRNAMatchIndices) == 1),
if arrayMicRNAPassesTests(arrayMicRNAMatchIndices) && arrayMessRNAPassesTests(arrayMessRNAMatchIndices),
arrayPairCompIsDMTPred(arrayMicRNAMatchIndices,arrayMessRNAMatchIndices) = true;
end
end
end
end
disp(['matching high-confidence TargetScan predicted relationships to the miR-mRNA relationship array..']);
arrayPairCompIsTSPred = false(numMicRNAs, numMessRNAs);
numContPlusThresh = double(prctile(arrayTSContPlus, numContPlusThreshPercentile))/-1000;
arrayTSAboveThreshIndices = find(arrayTSContPlus > uint16(-numContPlusThresh*1000));
numCnt = length(arrayTSAboveThreshIndices)/20;
for iTSRel = 1:length(arrayTSAboveThreshIndices),
if iTSRel >= numCnt,
numCnt = numCnt + length(arrayTSAboveThreshIndices)/20;
disp([num2str((numCnt/length(arrayTSAboveThreshIndices))*100) '% complete']);
end
stringTSMessRNA = arrayTSMessRNANames{arrayTSAboveThreshIndices(iTSRel)};
arrayMessRNAMatchIndices = find( strncmp(stringTSMessRNA, structData(2).Target, length(stringTSMessRNA)) & ...
(arrayMessRNAStringLength == length(stringTSMessRNA) ) );
%match the miR back to a family
stringTSMicRNA = arrayTSMicRNANames{arrayTSAboveThreshIndices(iTSRel)};
arrayFamHasMatch = false(length(arrayTSMicRNAFams),1);
for iFam = 1:length(arrayTSMicRNAFams),
if any(strncmp(stringTSMicRNA, arrayTSMicRNAsByFam{iFam}, length(stringTSMicRNA))),
arrayFamHasMatch(iFam) = true;
end
end
numFamIndex = find(arrayFamHasMatch);
if isempty(numFamIndex),
%disp(['warning: ' stringTSMessRNA ' cannot be matched to TargetScan families']);
else
if length(numFamIndex) == 1,
for iMemb = 1:length(arrayTSMicRNAsByFam{numFamIndex})
stringMicRNAFamMemb = arrayTSMicRNAsByFam{numFamIndex}{iMemb};
%search for the specified RNAs amongst the DIANA-microT results
arrayMicRNAMatchIndices = find( strncmp(stringMicRNAFamMemb, structData(1).Target, length(stringMicRNAFamMemb)) & ...
(arrayMicRNAStringLength == length(stringMicRNAFamMemb) ) );
if ~isempty(arrayMicRNAMatchIndices) && ~isempty(arrayMessRNAMatchIndices),
for iMicRNA = 1:length(arrayMicRNAMatchIndices),
for iMessRNA = 1:length(arrayMessRNAMatchIndices),
if arrayMicRNAPassesTests(arrayMicRNAMatchIndices(iMicRNA)) & arrayMessRNAPassesTests(arrayMessRNAMatchIndices(iMessRNA)),
arrayPairCompIsTSPred(arrayMicRNAMatchIndices(iMicRNA),arrayMessRNAMatchIndices(iMessRNA)) = true;
end
end
end
else
%disp(['warning: cannot find high scoring TargetScan relationship: ' stringTSMicRNA ' vs. ' stringTSMessRNA ', within the LM-MEL targets']);
end
end
else
%disp(['warning: ' stringTSMessRNA ' matched to multiple TargetScan families']);
end
end
end
arrayPairCompIsMTBPred = false(numMicRNAs, numMessRNAs);
for iMTBRel = 1:length(arrayMTBMicRNANames),
stringMTBMicRNA = arrayMTBMicRNANames{iMTBRel};
stringMTBMessRNA = arrayMTBMessRNANames{iMTBRel};
arrayMicRNAMatchIndices = find( strncmp(stringMTBMicRNA, structData(1).Target, length(stringMTBMicRNA)) & ...
(arrayMicRNAStringLength == length(stringMTBMicRNA) ) );
arrayMessRNAMatchIndices = find( strncmp(stringMTBMessRNA, structData(2).Target, length(stringMTBMessRNA)) & ...
(arrayMessRNAStringLength == length(stringMTBMessRNA) ) );
if ~isempty(arrayMicRNAMatchIndices) & ~isempty(arrayMessRNAMatchIndices),
if (length(arrayMicRNAMatchIndices) == 1) & (length(arrayMessRNAMatchIndices) == 1),
if arrayMicRNAPassesTests(arrayMicRNAMatchIndices) && arrayMessRNAPassesTests(arrayMessRNAMatchIndices),
stringExpType = arrayMTBExperiment{iMTBRel};
%check for 'strong' evidence supporting the association
numLucRepAssPos = strfind(stringExpType,'Luciferase reporter assay');
numQuantPCRPos = strfind(stringExpType,'qRT-PCR');
numWestBlotPos = strfind(stringExpType,'Western blot');
if ~isempty(numLucRepAssPos) | ~isempty(numWestBlotPos) | ~isempty(numQuantPCRPos),
arrayPairCompIsMTBPred(arrayMicRNAMatchIndices,arrayMessRNAMatchIndices) = true;
end
end
end
end
end
arrayPairCompIsTSandDMTPred = arrayPairCompIsTSPred & arrayPairCompIsDMTPred;
arrayPairCompIsOnlyTSPred = (arrayPairCompIsTSPred & ~arrayPairCompIsTSandDMTPred);
arrayPairCompIsOnlyDMTPred = (arrayPairCompIsDMTPred & ~arrayPairCompIsTSandDMTPred);
%identify GO terms with the specified number of component genes
arrayNumMembersPerGOTerm = sum(arrayGOMembershipMatrix,1);
arrayGOIndicesOfInterest = find( (arrayNumMembersPerGOTerm > numOntologyMinGenes) & ...
(arrayNumMembersPerGOTerm < numOntologyMaxGenes) );
%extract the length of this vector for looping/iterations
numGOsOfInterest = length(arrayGOIndicesOfInterest);
%identify GO terms with the appropriate number of genes which are also
% tagged
arrayOutputGOPigmentFlag = false(numGOsOfInterest,1);
arrayOutputGOEMPFlag = false(numGOsOfInterest,1);
for iGOTerm = 1:numGOsOfInterest,
%exract the actual index from GO terms with appropriate n, and match
% this back to the original GO number
numGOTerm = arrayUniqueGONums(arrayGOIndicesOfInterest(iGOTerm));
%compare the GO number to the 'pigmentation' list
if any(arrayUniquePigGONums == numGOTerm),
arrayOutputGOPigmentFlag(iGOTerm) = true;
end
%compare the GO number to the 'EMP' list
if any(arrayUniqueEMPGONums == numGOTerm),
arrayOutputGOEMPFlag(iGOTerm) = true;
end
end
%extract indices from the boolean arrays
arrayOutputGOPigmentIndex = find(arrayOutputGOPigmentFlag);
arrayOutputGOEMPIndex = find(arrayOutputGOEMPFlag);
%extract gene lists for over-representation analysis
arrayOutputGOPigmentTargets = zeros(size(arrayGOMembershipMatrix,1),1,'uint8');
for iPigGOTerm = 1:length(arrayOutputGOPigmentIndex),
arrayOutputGOPigmentTargets = arrayOutputGOPigmentTargets + uint8(arrayGOMembershipMatrix(:,arrayGOIndicesOfInterest(arrayOutputGOPigmentIndex(iPigGOTerm))));
end
arrayOutputGOPigmentTargetIndices = find(arrayOutputGOPigmentTargets > 0);
arrayLMMELMessRNAIsGOPigmentFlag = false(numMessRNAs,1);
for iMessRNA = 1:length(arrayOutputGOPigmentTargetIndices),
stringMessRNA = arrayUniqueGOGenes{arrayOutputGOPigmentTargetIndices(iMessRNA)};
arrayLMMELMessRNAMatch = find( strncmp(stringMessRNA,structData(2).Target,length(stringMessRNA)) & ...
(arrayMessRNAStringLength == length(stringMessRNA)) );
if ~isempty(arrayLMMELMessRNAMatch),
arrayLMMELMessRNAIsGOPigmentFlag(arrayLMMELMessRNAMatch) = true;
else
if strncmp(stringMessRNA, 'PMEL', 4) && (length(stringMessRNA) == 4),
disp([ 'hard-coded exception: ' stringMessRNA ' matched as ' ...
stringMessRNAToFind ' in the LM-MEL data' ]);
stringMessRNAToFind = 'SILV';
arrayLMMELMessRNAMatch = find( strncmp(stringMessRNAToFind,structData(2).Target,length(stringMessRNAToFind)) & ...
(arrayMessRNAStringLength == length(stringMessRNAToFind)) );
arrayLMMELMessRNAIsGOPigmentFlag(arrayLMMELMessRNAMatch) = true;
else
disp(['warning: ' stringMessRNA ' cannot be matched to the LM-MEL data']);
end
end
end
%extract gene lists for over-representation analysis
arrayOutputGOEMPTargets = zeros(size(arrayGOMembershipMatrix,1),1,'uint8');
for iPigGOTerm = 1:length(arrayOutputGOEMPIndex),
arrayOutputGOEMPTargets = arrayOutputGOEMPTargets + uint8(arrayGOMembershipMatrix(:,arrayGOIndicesOfInterest(arrayOutputGOEMPIndex(iPigGOTerm))));
end
arrayOutputGOEMPTargetIndices = find(arrayOutputGOEMPTargets > 0);
arrayLMMELMessRNAIsGOEMPFlag = false(numMessRNAs,1);
for iMessRNA = 1:length(arrayOutputGOEMPTargetIndices),
stringMessRNA = arrayUniqueGOGenes{arrayOutputGOEMPTargetIndices(iMessRNA)};
arrayLMMELMessRNAMatch = find( strncmp(stringMessRNA,structData(2).Target,length(stringMessRNA)) & ...
(arrayMessRNAStringLength == length(stringMessRNA)) );
if ~isempty(arrayLMMELMessRNAMatch),
arrayLMMELMessRNAIsGOEMPFlag(arrayLMMELMessRNAMatch) = true;
else
disp(['warning: ' stringMessRNA ' cannot be matched to the LM-MEL data']);
end
end
arrayLMMELMessRNAIsHoekProlif = false(numMessRNAs,1);
for iMessRNA = 1:length(structHoekLists(2).groupMembers{1}),
stringMessRNA = structHoekLists(2).groupMembers{1}{iMessRNA};
arrayLMMELMessRNAMatch = find( strncmp(stringMessRNA,structData(2).Target,length(stringMessRNA)) & ...
(arrayMessRNAStringLength == length(stringMessRNA)) );
if ~isempty(arrayLMMELMessRNAMatch),
arrayLMMELMessRNAIsHoekProlif(arrayLMMELMessRNAMatch) = true;
else
disp(['warning: ' stringMessRNA ' from the Hoek proliferative list' ...
' cannot be matched to the LM-MEL data']);
end
end
arrayLMMELMessRNAIsHoekInv = false(numMessRNAs,1);
for iMessRNA = 1:length(structHoekLists(2).groupMembers{2}),
stringMessRNA = structHoekLists(2).groupMembers{2}{iMessRNA};
arrayLMMELMessRNAMatch = find( strncmp(stringMessRNA,structData(2).Target,length(stringMessRNA)) & ...
(arrayMessRNAStringLength == length(stringMessRNA)) );
if ~isempty(arrayLMMELMessRNAMatch),
arrayLMMELMessRNAIsHoekInv(arrayLMMELMessRNAMatch) = true;
else
disp(['warning: ' stringMessRNA ' from the Hoek invasive list' ...
' cannot be matched to the LM-MEL data']);
end
end
%create some index arrays for the high and low invasiveness cell lines
arrayLowInvCellLineMessRNAIndex = zeros(length(arrayLowInvCellLines),1,'uint8');
arrayLowInvCellLineMicRNAIndex = zeros(length(arrayLowInvCellLines),1,'uint8');
for iLowInvLine = 1:length(arrayLowInvCellLines),
strCellLine = arrayLowInvCellLines{iLowInvLine};
arrayLowInvCellLineMicRNAIndex(iLowInvLine) = find( strncmp(strCellLine,structData(1).CellLine,length(strCellLine)) & ...
(arrayMicRNACellLineStringLengths == length(strCellLine)) );
arrayLowInvCellLineMessRNAIndex(iLowInvLine) = find( strncmp(strCellLine,structData(2).CellLine,length(strCellLine)) & ...
(arrayMessRNACellLineStringLengths == length(strCellLine)) );
end
arrayHighInvCellLineMessRNAIndex = zeros(length(arrayHighInvCellLines),1,'uint8');
arrayHighInvCellLineMicRNAIndex = zeros(length(arrayHighInvCellLines),1,'uint8');
for iHighInvLine = 1:length(arrayHighInvCellLines),
strCellLine = arrayHighInvCellLines{iHighInvLine};
arrayHighInvCellLineMicRNAIndex(iHighInvLine) = find( strncmp(strCellLine,structData(1).CellLine,length(strCellLine)) & ...
(arrayMicRNACellLineStringLengths == length(strCellLine)) );
arrayHighInvCellLineMessRNAIndex(iHighInvLine) = find( strncmp(strCellLine,structData(2).CellLine,length(strCellLine)) & ...
(arrayMessRNACellLineStringLengths == length(strCellLine)) );
end
%for all miR:mRNA associations calculate the distance between high- and
% low-invasiveness centroids, normalised against the 'full data hypotenuse'
% (to account for variations in the range of different mRNAs and miRs)
arrayPairCompInvasGroupDistScaled = zeros(numMicRNAs, numMessRNAs, 'double');
for iMicRNA = 1:numMicRNAs,
numMicRNARange = range(structData(1).Data(iMicRNA,:));
numMicRNARangeSq = numMicRNARange^2;
numLowInvMicRNAMean = mean(structData(1).Data(iMicRNA,arrayLowInvCellLineMicRNAIndex));
numHighInvMicRNAMean = mean(structData(1).Data(iMicRNA,arrayHighInvCellLineMicRNAIndex));
numMicRNADist = numHighInvMicRNAMean-numLowInvMicRNAMean;
for iMessRNA = 1:numMessRNAs,
numMessRNARange = range(structData(2).Data(iMessRNA,:));
numFullDataHyp = sqrt(numMicRNARangeSq + numMessRNARange^2);
numLowInvMessRNAMean = mean(structData(2).Data(iMessRNA,arrayLowInvCellLineMessRNAIndex));
numHighInvMessRNAMean = mean(structData(2).Data(iMessRNA,arrayHighInvCellLineMessRNAIndex));
numMessRNADist = numHighInvMessRNAMean - numLowInvMessRNAMean;
numDist = sqrt(numMicRNADist^2 + numMessRNADist^2);
arrayPairCompInvasGroupDistScaled(iMicRNA, iMessRNA) = numDist/numFullDataHyp;
end
end
%create an array of percentile values for later indexing
arrayInvSepDistPrctile = prctile(arrayPairCompInvasGroupDistScaled(arrayPairCompPassesTests),0:numInvSepDistPrctileStep:100);
% % % % % % % % % % % % % % % % % % % % % % % % %
%% Perform Data Analysis
% % % % % % % % % % % % % % % % % % % % % % % % %
%determine the total number of miRs indexed within the TargetScan family
% list
numMicRNAsForAnalysis = 0;
for iMicRNAFam = 1:length(arrayTSMicRNAFams),
numMicRNAsForAnalysis = numMicRNAsForAnalysis + length(arrayTSMicRNAsByFam{iMicRNAFam});
end
%create some output folders/arrays
arrayMicRNAFam = zeros(numMicRNAsForAnalysis, 1, 'uint16');
arrayMicRNAFamMemb = zeros(numMicRNAsForAnalysis, 1, 'uint16');
arrayLMMELIndex = zeros(numMicRNAsForAnalysis, 1, 'uint16');
arrayGoodObs = zeros(numMicRNAsForAnalysis, 1, 'uint16');
arrayPassStatTests = zeros(numMicRNAsForAnalysis, 1, 'uint16');
arrayTSTargetsAboveContPlusThresh = zeros(numMicRNAsForAnalysis, 1, 'uint16');
arrayDMTTargetsAboveMITGThresh = zeros(numMicRNAsForAnalysis, 1, 'uint16');
arrayTSTargetsAboveContPlusThreshAndPassStatTest = zeros(numMicRNAsForAnalysis, 1, 'uint16');
arrayDMTTargetsAboveMITGThreshAndPassStatTest = zeros(numMicRNAsForAnalysis, 1, 'uint16');
arrayNumGOPigTargets = zeros(numMicRNAsForAnalysis, 1, 'uint16');
arrayNumGOEMPTargets = zeros(numMicRNAsForAnalysis, 1, 'uint16');
numCounter = 1;
for iMicRNAFam = 1:length(arrayTSMicRNAFams),
for iMicRNAMemb = 1:length(arrayTSMicRNAsByFam{iMicRNAFam}),
stringMicRNA = arrayTSMicRNAsByFam{iMicRNAFam}{iMicRNAMemb};
numMatchInLMMELData = find( strncmp(stringMicRNA, structData(1).Target, length(stringMicRNA)) & ...
(arrayMicRNAStringLength == length(stringMicRNA)) );
if ~isempty(numMatchInLMMELData),
if length(numMatchInLMMELData) == 1,
arrayLMMELIndex(numCounter) = numMatchInLMMELData;
arrayMicRNAPassDataTestVector = arrayPairCompPassesTests(numMatchInLMMELData, :);
arrayMicRNATSVector = arrayPairCompIsTSPred(numMatchInLMMELData, :);
arrayMicRNADMTVector = arrayPairCompIsDMTPred(numMatchInLMMELData, :);
arrayMicRNAMutInfoVector = arrayMutInfo(numMatchInLMMELData, :);
arrayMicRNAPearsCorrVector = arrayPearsCorr(numMatchInLMMELData, :);
arrayMicRNAAssocPassThreshVector = ( arrayMicRNAMutInfoVector > numMutInfoThresh ) & ...
( arrayMicRNAPearsCorrVector < numPearsCorrLowThresh);
numGoodDataObs = sum(arrayMicRNAPassDataTestVector);
numPassStatTests = sum( arrayMicRNAAssocPassThreshVector & ...
arrayMicRNAPassDataTestVector );
numTSTargetsAboveContPlusThresh = sum(arrayMicRNATSVector);
numMTBTargetsAboveMITGThresh = sum(arrayMicRNADMTVector);
numTSTargetsAboveContPlusThreshAndPassStatTest = sum( arrayMicRNAAssocPassThreshVector & ...
arrayMicRNAPassDataTestVector & ...
arrayMicRNATSVector );
numMTBTargetsAboveMITGThreshAndPassStatTest = sum( arrayMicRNAAssocPassThreshVector & ...
arrayMicRNAPassDataTestVector & ...
arrayMicRNADMTVector );
numGOPigTargets = sum( arrayLMMELMessRNAIsGOPigmentFlag' & ...
(arrayMicRNATSVector | arrayMicRNADMTVector) );
numGOEMPTargets = sum( arrayLMMELMessRNAIsGOEMPFlag' & ...
(arrayMicRNATSVector | arrayMicRNADMTVector) );
arrayGoodObs(numCounter) = numGoodDataObs;
arrayPassStatTests(numCounter) = numPassStatTests;
arrayTSTargetsAboveContPlusThresh(numCounter) = numTSTargetsAboveContPlusThresh;
arrayDMTTargetsAboveMITGThresh(numCounter) = numMTBTargetsAboveMITGThresh;
arrayTSTargetsAboveContPlusThreshAndPassStatTest(numCounter) = numTSTargetsAboveContPlusThreshAndPassStatTest;
arrayDMTTargetsAboveMITGThreshAndPassStatTest(numCounter) = numMTBTargetsAboveMITGThreshAndPassStatTest;
arrayNumGOPigTargets(numCounter) = numGOPigTargets;
arrayNumGOEMPTargets(numCounter) = numGOEMPTargets;
else
disp(['warning: ' stringMicRNA ' from TargetScan matched to multiple miRs from the LM-MEL data']);
end
end
arrayMicRNAFam(numCounter) = iMicRNAFam;
arrayMicRNAFamMemb(numCounter) = iMicRNAMemb;
numCounter = numCounter+1;
end
end
%calculate
hist(arrayPairCompInvasGroupDistScaled(arrayPairCompPassesTests & arrayPassStats & (arrayPairCompIsTSPred | arrayPairCompIsDMTPred)),100);
%% a
arrayOutputMicRNAFlag = arrayGoodObs > 0;
arrayRelEnrichmentOfActiveTSTargets = double(arrayTSTargetsAboveContPlusThreshAndPassStatTest)./double(arrayTSTargetsAboveContPlusThresh);
arrayRelEnrichmentNaNValFlag = isnan(arrayRelEnrichmentOfActiveTSTargets);
arrayRelEnrichmentOfActiveTSTargets(arrayRelEnrichmentNaNValFlag) = 0;
[~,arrayRankByTSActiveEnrichIndex] = sort(arrayRelEnrichmentOfActiveTSTargets, 'descend');
arrayRelEnrichmentOfActiveDMTTargets = double(arrayDMTTargetsAboveMITGThreshAndPassStatTest)./double(arrayDMTTargetsAboveMITGThresh);
arrayRelEnrichmentOfGOPigTerms = double(arrayNumGOPigTargets)/double(sum(arrayLMMELMessRNAIsGOPigmentFlag));
arrayRelEnrichmentOfGOEMPTerms = double(arrayNumGOEMPTargets)/double(sum(arrayLMMELMessRNAIsGOEMPFlag));
% % % % % % % % % % % % % % % % % % % % % % % % %
%% Create the output figure - Figure 1
% % % % % % % % % % % % % % % % % % % % % % % % %
figOut = figure;
set(figOut, 'Position', arrayFigOnePosition);
set(figOut, 'PaperUnits', 'centimeters', 'PaperSize', [ numFigOneWidth/10, numFigOneHeight/10 ], 'PaperPosition', [ 0, 0, numFigOneWidth/10, numFigOneHeight/10 ] );
%specify the subplot axes for the Fig. 1A
subplot('Position', arrayFigOneSubPlotPositions{1});
%calculate alpha data for the surface mesh such that the low frequency data
% points are transparent
arrayAlphaData = log(arrayStatAssocSurfaceScaled+0.0001);
hold on;
%plot the mesh+contour plot
handCont = meshc(arrayStatAssocSurfaceScaled);
%control the surface transparency etc for display
handCont(1).FaceColor = 'interp';
handCont(1).EdgeColor = 'none';
handCont(1).FaceAlpha = 'interp';
handCont(1).AlphaDataMapping = 'scaled';
handCont(1).AlphaData = arrayAlphaData;
handCont(1).FaceLighting = 'phong';
handCont(1).AmbientStrength = 0.8;
%apply the x- and y- ticks and ticklabels
arraySurfacePearsCorrTicks = linspace(-1,1,5);
arraySurfaceMutInfoTicks = linspace(min(arrayMutInfo(arrayPairCompsPassTestIndices)),max(arrayMutInfo(arrayPairCompsPassTestIndices)),5);
set(gca, 'XTick', linspace(1,numStatAssocSurfaceBins,5), 'XTickLabels', sprintf('%03.2f\n',arraySurfaceMutInfoTicks));
set(gca, 'YTick', linspace(1,numStatAssocSurfaceBins,5), 'YTickLabels', sprintf('%03.2f\n',arraySurfacePearsCorrTicks));
set(gca, 'ZTick', []);
set(gca, 'FontSize', numFigOneAxisLabelFontSize);
%zlabel({'Relative';'frequency'}, 'FontSize', numFigOneAxisLabelFontSize);
xlabel('Mutual information', 'FontSize', numFigOneAxisLabelFontSize);
ylabel('Pearson''s correlation', 'FontSize', numFigOneAxisLabelFontSize);
%convert the mutual info and pearson correlation thresholds to 'binned'
% values
numMutInfoScaledVal = numStatAssocSurfaceBins*(numMutInfoThresh-min(arrayMutInfo(arrayPairCompsPassTestIndices)))/(max(arrayMutInfo(arrayPairCompsPassTestIndices))-min(arrayMutInfo(arrayPairCompsPassTestIndices)));
numPearsCorrUpThreshScaledVal = numStatAssocSurfaceBins*(numPearsCorrUpThresh-(-1))/2;
numPearsCorrLowThreshScaledVal = numStatAssocSurfaceBins*(numPearsCorrLowThresh-(-1))/2;
%draw in the threshold values for the mutual information and Pearson's
% correlation
line([0 numStatAssocSurfaceBins],[numPearsCorrUpThreshScaledVal numPearsCorrUpThreshScaledVal], 'Color', [0.6 0.6 0.6], 'LineStyle', '--', 'LineWidth', 3);
line([0 numStatAssocSurfaceBins], [numPearsCorrLowThreshScaledVal numPearsCorrLowThreshScaledVal], 'Color', [0.6 0.6 0.6], 'LineStyle', '--', 'LineWidth', 3);
line([numMutInfoScaledVal numMutInfoScaledVal], [0 numStatAssocSurfaceBins], 'Color', [0.6 0.6 0.6], 'LineStyle', '--', 'LineWidth', 3);
%create the annotation for the Pearson's correlation axis
numDoubleArrowXCent = arrayFigOneSubPlotPositions{1}(1) + arrayFigOneSubPlotPositions{1}(3) + 0.03;
numDoubleArrowYCent = arrayFigOneSubPlotPositions{1}(2) + arrayFigOneSubPlotPositions{1}(4)/2;
numDoubleArrowYLength = 0.13;
numDoubleArrowXDiff = 0.0;
numAssocDirLabel = numDoubleArrowXCent+0.05;
annotation('doublearrow', [numDoubleArrowXCent-numDoubleArrowXDiff, numDoubleArrowXCent+numDoubleArrowXDiff], [numDoubleArrowYCent-numDoubleArrowYLength, numDoubleArrowYCent+numDoubleArrowYLength]);
annotation( 'textbox', [numDoubleArrowXCent+0.02 numDoubleArrowYCent-0.05 0.1 0.1], 'String', {'Increasing';'strength';'of linear';'association'}, ...
'LineStyle', 'none', 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle', 'FontSize', numFigOneAnnotationFontSize);
annotation( 'textbox', [ numAssocDirLabel-numDoubleArrowXDiff numDoubleArrowYCent-0.17 0.1 0.1], 'String', {'Negative';'associations'}, ...
'LineStyle', 'none', 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle', 'FontSize', numFigOneAnnotationFontSize);
annotation( 'textbox', [numAssocDirLabel+numDoubleArrowXDiff numDoubleArrowYCent+0.07 0.1 0.1], 'String', {'Positive';'associations'}, ...
'LineStyle', 'none', 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle', 'FontSize', numFigOneAnnotationFontSize);
%create the annotation for the Mutual information axis
numArrowXStart = arrayFigOneSubPlotPositions{1}(1) + arrayFigOneSubPlotPositions{1}(3)*0.9;
numArrowYStart = arrayFigOneSubPlotPositions{1}(2) + arrayFigOneSubPlotPositions{1}(4)*1.02;
numArrowXDiff = -0.30;
numArrowYDiff = 0.0;
annotation( 'arrow', [numArrowXStart, numArrowXStart+numArrowXDiff], [numArrowYStart, numArrowYStart+numArrowYDiff] );
annotation( 'textbox', [numArrowXStart+numArrowXDiff numArrowYStart-0.03 0.30 0.10], 'String', {'Associations tend towards';'statistical independence'}, ...
'LineStyle', 'none', 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle', 'FontSize', numFigOneAnnotationFontSize);
%label the relative fraction of data points within each sextant
numFailMIPassPosPC = sum(sum((arrayPearsCorr(arrayPairCompPassesTests) >= numPearsCorrUpThresh) & (arrayMutInfo(arrayPairCompPassesTests) < numMutInfoThresh)));
numFailMIFailPC = sum(sum((arrayPearsCorr(arrayPairCompPassesTests) < numPearsCorrUpThresh) & (arrayPearsCorr(arrayPairCompPassesTests) > numPearsCorrLowThresh) & (arrayMutInfo(arrayPairCompPassesTests) < numMutInfoThresh)));
numFailMIPassNegPC = sum(sum((arrayPearsCorr(arrayPairCompPassesTests) <= numPearsCorrLowThresh) & (arrayMutInfo(arrayPairCompPassesTests) < numMutInfoThresh)));
numPassMIPassPosPC = sum(sum((arrayPearsCorr(arrayPairCompPassesTests) >= numPearsCorrUpThresh) & (arrayMutInfo(arrayPairCompPassesTests) >= numMutInfoThresh)));
numPassMIFailPC = sum(sum((arrayPearsCorr(arrayPairCompPassesTests) < numPearsCorrUpThresh) & (arrayPearsCorr(arrayPairCompPassesTests) > numPearsCorrLowThresh) & (arrayMutInfo(arrayPairCompPassesTests) >= numMutInfoThresh)));
numPassMIPassNegPC = sum(sum((arrayPearsCorr(arrayPairCompPassesTests) <= numPearsCorrLowThresh) & (arrayMutInfo(arrayPairCompPassesTests) >= numMutInfoThresh)));
numTotalAssoc = sum(sum((arrayPairCompPassesTests)));
%top left
numXPos = arrayFigOneSubPlotPositions{1}(1) + arrayFigOneSubPlotPositions{1}(3)*0.1;
numYPos = arrayFigOneSubPlotPositions{1}(2) + arrayFigOneSubPlotPositions{1}(4)*0.90;
annotation( 'textbox', [ numXPos numYPos 0.01 0.01], 'String', [num2str(100*numFailMIPassPosPC/numTotalAssoc, '%03.2f') '%'], ...
'LineStyle', 'none', 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle', 'FontSize', numFigOneAnnotationFontSize, ...
'Color', 'r');
%mid left
numXPos = arrayFigOneSubPlotPositions{1}(1) + arrayFigOneSubPlotPositions{1}(3)*0.1;
numYPos = arrayFigOneSubPlotPositions{1}(2) + arrayFigOneSubPlotPositions{1}(4)*0.48;
annotation( 'textbox', [ numXPos numYPos 0.01 0.01], 'String', [num2str(100*numFailMIFailPC/numTotalAssoc, '%03.2f') '%'], ...
'LineStyle', 'none', 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle', 'FontSize', numFigOneAnnotationFontSize, ...
'Color', 'r');
%bottom left
numXPos = arrayFigOneSubPlotPositions{1}(1) + arrayFigOneSubPlotPositions{1}(3)*0.1;
numYPos = arrayFigOneSubPlotPositions{1}(2) + arrayFigOneSubPlotPositions{1}(4)*0.05;
annotation( 'textbox', [ numXPos numYPos 0.01 0.01], 'String', [num2str(100*numFailMIPassNegPC/numTotalAssoc, '%03.2f') '%'], ...
'LineStyle', 'none', 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle', 'FontSize', numFigOneAnnotationFontSize, ...
'Color', 'r');
%top right
numXPos = arrayFigOneSubPlotPositions{1}(1) + arrayFigOneSubPlotPositions{1}(3)*0.90;
numYPos = arrayFigOneSubPlotPositions{1}(2) + arrayFigOneSubPlotPositions{1}(4)*0.90;
annotation( 'textbox', [ numXPos numYPos 0.01 0.01], 'String', [num2str(100*numPassMIPassPosPC/numTotalAssoc, '%03.2f') '%'], ...
'LineStyle', 'none', 'HorizontalAlignment', 'center', 'VerticalAlignment', 'middle', 'FontSize', numFigOneAnnotationFontSize, ...