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Aro_parametersTEMPLATE.m
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%% ============================================================
% Name: Aro_parameters.m
% Version: 2.5.1, 9 Feb 2015
% Author: Allison Wu, Scott Rifkin
% Command: loadParametersForAroSpotFindingSuite
% Description: The software is relatively parameter free, but does depend on a few.
% These have now been moved into this script file which should be
% placed on the MATLAB path - either with the other software
% files or (and perhaps better) in the data directory from
% where the software is run so that if changed they can be
% specifically linked to a set of data. They are listed
% below with their default values and under headings of
% which functions use them. Each file only reads in the
% ones it needs based on the value of callingFunction which
% is set in the calling function and is the function's name
%
% NestedDirectoryStructure
% tells all functions where to find things
% assumes that you are running it from the top directory
% images are in ImageData by dye
% Analysis in analysis folder
%
%
% NOTE: This is a template file. Copy this file into the top directory where
% the functions will be run from. Adjust anything that needs adjusting.
% Name it Aro_parameters.m
%%%%%%%%%%%%%%%%% directory structure %%%%%%%%%%%%%%%%%
% Is the directory file structure flat or nested? If flat, then everything is in the same directory (the file list gets big)
nestedOrFlatDirectoryStructure = 'nested'; % 'flat' is the other option
% What are the dyes that are being used? This should be a cell array. These will
% be the directory names where the images are and also the names of directories under spotStats, segStacks, wormGaussianFit, etc.
dyesUsed={'a594','cy5','tmr'};
isdapi='dapi'; %or '' if no dapi
if strcmp(nestedOrFlatDirectoryStructure,'nested')
topdir=pwd;
ImageDir='ImageData';
SegmentationMaskDir= 'SegmentationMasks';
if ~exist(SegmentationMaskDir,'dir')
mkdir(SegmentationMaskDir);
end;
AnalysisDir= 'Analysis1';
if ~exist(AnalysisDir,'dir')
mkdir(AnalysisDir);
end;
PlotDir=[AnalysisDir filesep 'Plots'];
if ~exist(PlotDir,'dir')
mkdir(PlotDir);
end;
WormGaussianFitDir=[AnalysisDir filesep 'WormGaussianFit'];
if ~exist(WormGaussianFitDir,'dir')
mkdir(WormGaussianFitDir);
end;
for i=1:length(dyesUsed)
d=[WormGaussianFitDir filesep dyesUsed{i}];
if ~exist(d,'dir')
mkdir(d);
end;
end;
SpotStatsDir=[AnalysisDir filesep 'SpotStats'];
if ~exist(SpotStatsDir,'dir')
mkdir(SpotStatsDir);
end;
for i=1:length(dyesUsed)
d=[SpotStatsDir filesep dyesUsed{i}];
if ~exist(d,'dir')
mkdir(d);
end;
end;
SegStacksDir=[AnalysisDir filesep 'SegStacks'];
if ~exist(SegStacksDir,'dir')
mkdir(SegStacksDir);
end;
for i=1:length(dyesUsed)
d=[SegStacksDir filesep dyesUsed{i}];
if ~exist(d,'dir')
mkdir(d);
end;
end;
if ~isempty(isdapi)
mkdir([SegStacksDir filesep isdapi]);
end;
TrainingSetDir=[AnalysisDir filesep 'TrainingSets'];
if ~exist(TrainingSetDir,'dir')
mkdir(TrainingSetDir);
end;
else
topdir=''; %want relative paths without extra cruff
ImageDir=topdir;
WormGaussianFitDir=topdir;
SpotStatsDir=topdir;
SegStacksDir=topdir;
SegmentationMaskDir=topdir;
TrainingSetDir=topdir;
PlotDir=topdir;
end;
%%%%%%%%%%%%%%%%%%%%% parameters %%%%%%%%%%%%%%
ST = dbstack(2); %Aro_parameters is called from the calling function by run(fullfile(pwd,'Aro_parameters.m'))
callingFunction=ST(1).name;
disp([callingFunction ': Reading Aro_parameters file in ' pwd '/']);
switch callingFunction
case 'trainRFClassifier'
% # of trees in the forest
ntrees=1000; % Should be relatively large
% Fraction of input data to sample with replacement from the input data for growing each new tree. Default value is 1.
% This is a parameter to pass into TreeBagger
FBoot=1; %(0 < Fboot <=1]
% Whether or not to do a pre-run to choose the best
% variables/statistics/features for building the tree
runVarFeatureSel=1; % 0 or 1
% TreeBagger determines which variables are important
% by: A numeric array of size 1-by-Nvars containing a measure of
% importance for each predictor variable (feature). For any
% variable, the measure is the increase in prediction error if the
% values of that variable are permuted across the out-of-bag
% observations. This measure is computed for every tree, then
% averaged over the entire ensemble and divided by the standard
% deviation over the entire ensemble.
% What percentile threshold should be used to determine which
% variables have predictive power (>) and which do not (<)
percentileThresholdForOOBPermutedVarDeltaError=25; %(0 < <=1]
%How many trees to use when looking for the optimum number of
%features to samples?
nTreeTry=500; % < ntrees
% Continue optimizing if the improvement is greater than improve
improve=0.01; % a small value
% How many more variables to try in each optimizing run?
stepFactor=1; %1 or more
%%%%%%%%
% What should the confidence interval width be?
intervalWidth=95; % (0 < < 100]
case 'classifySpots'
% What should the confidence interval width be?
intervalWidth=95;% (0 < < 100]
case 'evalFISHStacks'
% This function builds the list of regional maxima in an image
% stack to even consider
% it goes through the regional maxima which are sorted by
% background corrected intensity (by morphFilterSpotImage3D) and
% decides when the actual spots are long gone
%
%%%%%%%%%%
%What statistic should be used to evaluate whether to keep a local
%maximum in the running?
cutoffStat='scd'; %see calculateFISHStatistics for other options
% what is the minimum cutoff value for this statistic below which
% it really should not be considered?
cutoffStatisticValue=0.7; %depends on the statistic, usually (0 < <=1)
% How many recent local maxima should be considered?
stopN=30;
%What percentile of these have to be over the cutoff to keep going?
cutoffPercentile=70; % (0 < < 100]
% If the slice is bad, then lots of spots in a row will be crappy.
% This usually appears quickly and so there is no need to run
% through stopN spots before moving on...
badSliceStopN=5; %how many spots to consider if there get to be a string of bad ones. this would be less than stopN
% what is the cutoff value of the statistic to say that the spot
% definitely is bad
badSliceCutoffStatisticValue=0.3;
case 'makeSpotCountInterval'
%bootstrap repetitions for the interval
nBoots=1000; %should be a large number
case 'createSpotTrainingSet'
statsToUse = {'intensity';'totalHeight';'estimatedFloor';'scnmse';'scnrmse';'scr';'scd';'sce';...
'prctile_50';'prctile_60';'prctile_70';'prctile_80';'prctile_90';...
'fraction_center';'fraction_plusSign';'fraction_3box';'fraction_5star';'fraction_5box';'fraction_7star';'fraction_3ring';...
'raw_center';'raw_plusSign';'raw_3box';'raw_5star';'raw_5box';'raw_7star';'raw_3ring';'total_area';...
'sv1';'sv2';'sv3';'sv4';'sv5';...
'absDeltaPlusSign';'deltaPlusSign';'absPlusSignDelta';'plusSignPvalue';...
'absDeltaStarSign';'deltaStarSign';'absStarSignDelta';'starSignPvalue';...
'absDeltaCenterBox';'deltaCenterBox';'absCenterBoxDelta';'centerBoxPvalue';'ratioSigmaXY';...
'totalAreaRandPvalue';'cumSumPrctile90RP';'cumSumPrctile70RP';'cumSumPrctile50RP';'cumSumPrctile30RP';...
'cumSumPrctile90';'cumSumPrctile70';'cumSumPrctile50';'cumSumPrctile30'};
otherwise
end;