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%% Cancer Detection
% This example demonstrates using a neural network to detect cancer from
% mass spectrometry data on protein profiles.
% Copyright 2003-2014 The MathWorks, Inc.
%% Introduction
% Serum proteomic pattern diagnostics can be used to differentiate samples
% from patients with and without disease. Profile patterns are generated
% using surface-enhanced laser desorption and ionization (SELDI) protein
% mass spectrometry. This technology has the potential to improve clinical
% diagnostics tests for cancer pathologies.
%% The Problem: Cancer Detection
% The goal is to build a classifier that can distinguish between cancer and
% control patients from the mass spectrometry data.
%
% The methodology followed in this example is to select a reduced set of
% measurements or "features" that can be used to distinguish between cancer
% and control patients using a classifier.
%
% These features will be ion intensity levels at specific mass/charge
% values.
%% Formatting the Data
% The data in this example is from the FDA-NCI Clinical Proteomics Program
% Databank: http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp
%
% To recreate the data in *ovarian_dataset.mat* used in this example, download
% and uncompress the raw mass-spectrometry data from the FDA-NCI web site.
% Create the data file *OvarianCancerQAQCdataset.mat* by either
% running script *msseqprocessing* in Bioinformatics Toolbox (TM) or by
% following the steps in the example *biodistcompdemo* (Batch processing with
% parallel computing). The new file contains variables |Y|, |MZ| and |grp|.
%
% Each column in |Y| represents measurements taken from a patient. There
% are |216| columns in |Y| representing |216| patients, out of which |121|
% are ovarian cancer patients and |95| are normal patients.
%
% Each row in |Y| represents the ion intensity level at a specific
% mass-charge value indicated in |MZ|. There are |15000| mass-charge values
% in |MZ| and each row in |Y| represents the ion-intesity levels of the
% patients at that particular mass-charge value.
%
% The variable |grp| holds the index information as to which of these
% samples represent cancer patients and which ones represent normal
% patients.
%
% An extensive description of this data set and excellent introduction to
% this promising technology can be found in [1] and [2].
%% Ranking Key Features
% This is a typical classification problem in which the number of features
% is much larger than the number of observations, but in which no single
% feature achieves a correct classification, therefore we need to find a
% classifier which appropriately learns how to weight multiple features and
% at the same time produce a generalized mapping which is not over-fitted.
%
% A simple approach for finding significant features is to assume that each
% M/Z value is independent and compute a two-way t-test. *rankfeatures*
% returns an index to the most significant M/Z values, for instance 100
% indices ranked by the absolute value of the test statistic.
%
% To finish recreating the data from *ovarian_dataset.mat*, load the
% *OvarianCancerQAQCdataset.mat* and *rankfeatures* from Bioinformatics
% Toolbox to choose 100 highest ranked measurements as inputs |x|.
%
% ind = rankfeatures(Y,grp,'CRITERION','ttest','NUMBER',100);
% x = Y(ind,:);
%
% Define the targets |t| for the two classes as follows:
%
% t = double(strcmp('Cancer',grp));
% t = [t; 1-t];
%
% The preprocessing steps from the script and example listed above are
% intended to demonstrate a representative set of possible pre-processing
% and feature selection procedures. Using different steps or parameters
% may lead to different and possibly improved results of this example.
[x,t] = ovarian_dataset;
whos
%%
% Each column in |x| represents one of 216 different patients.
%
% Each row in |x| represents the ion intensity level at one of the 100
% specific mass-charge values for each patient.
%
% The variable |t| has 2 rows of 216 values each of which are either [1;0],
% indicating a cancer patient, or [0;1] for a normal patient.
%% Classification Using a Feed Forward Neural Network
% Now that you have identified some significant features, you can use this
% information to classify the cancer and normal samples.
%%
% Since the neural network is initialized with random initial weights, the
% results after training the network vary slightly every time the example is
% run. To avoid this randomness, the random seed is set to reproduce the
% same results every time. However this is not necessary for your own
% applications.
setdemorandstream(672880951)
%%
% A 1-hidden layer feed forward neural network with 5 hidden layer neurons
% is created and trained. The input and target samples are automatically
% divided into training, validation and test sets. The training set is
% used to teach the network. Training continues as long as the network
% continues improving on the validation set. The test set provides a
% completely independent measure of network accuracy.
%
% The input and output have sizes of 0 because the network has not yet
% been configured to match our input and target data. This will happen
% when the network is trained.
net = patternnet(5);
view(net)
%%
% Now the network is ready to be trained. The samples are automatically
% divided into training, validation and test sets. The training set is
% used to teach the network. Training continues as long as the network
% continues improving on the validation set. The test set provides a
% completely independent measure of network accuracy.
%
% The NN Training Tool shows the network being trained and the algorithms
% used to train it. It also displays the training state during training
% and the criteria which stopped training will be highlighted in green.
%
% The buttons at the bottom open useful plots which can be opened during
% and after training. Links next to the algorithm names and plot buttons
% open documentation on those subjects.
[net,tr] = train(net,x,t);
%%
% To see how the network's performance improved during training, either
% click the "Performance" button in the training tool, or call PLOTPERFORM.
%
% Performance is measured in terms of mean squared error, and shown in
% log scale. It rapidly decreased as the network was trained.
%
% Performance is shown for each of the training, validation and test sets.
% The version of the network that did best on the validation set is
% was after training.
plotperform(tr)
%%
% The trained neural network can now be tested with the testing samples we
% partitioned from the main dataset. The testing data was not used in
% training in any way and hence provides an "out-of-sample" dataset to
% test the network on. This will give us a sense of how well the network
% will do when tested with data from the real world.
%
% The network outputs will be in the range 0 to 1, so we threshold them
% to get 1's and 0's indicating cancer or normal patients respectively.
testX = x(:,tr.testInd);
testT = t(:,tr.testInd);
testY = net(testX);
testClasses = testY > 0.5
%%
% One measure of how well the neural network has fit the data is the
% confusion plot. Here the confusion matrix is plotted across all samples.
%
% The confusion matrix shows the percentages of correct and incorrect
% classifications. Correct classifications are the green squares on the
% matrices diagonal. Incorrect classifications form the red squares.
%
% If the network has learned to classify properly, the percentages in the
% red squares should be very small, indicating few misclassifications.
%
% If this is not the case then further training, or training a network
% with more hidden neurons, would be advisable.
plotconfusion(testT,testY)
%%
% Here are the overall percentages of correct and incorrect classification.
[c,cm] = confusion(testT,testY)
fprintf('Percentage Correct Classification : %f%%\n', 100*(1-c));
fprintf('Percentage Incorrect Classification : %f%%\n', 100*c);
%%
% Another measure of how well the neural network has fit data is the
% receiver operating characteristic plot. This shows how the false
% positive and true positive rates relate as the thresholding of outputs
% is varied from 0 to 1.
%
% The farther left and up the line is, the fewer false positives need to
% be accepted in order to get a high true positive rate. The best
% classifiers will have a line going from the bottom left corner, to the
% top left corner, to the top right corner, or close to that.
%
% Class 1 indicate cancer patiencts, class 2 normal patients.
plotroc(testT,testY)
%%
% This example illustrated how neural networks can be used as classifiers
% for cancer detection. One can also experiment using techniques like
% principal component analysis to reduce the dimensionality of the data
% to be used for building neural networks to improve classifier
% performance.
%
%% References
% [1] T.P. Conrads, et al., "High-resolution serum proteomic features for
% ovarian detection", Endocrine-Related Cancer, 11, 2004, pp. 163-178.
%%
% [2] E.F. Petricoin, et al., "Use of proteomic patterns in serum to
% identify ovarian cancer", Lancet, 359(9306), 2002, pp. 572-577.