%% Cancer Detection % This example demonstrates using a neural network to detect cancer from % mass spectrometry data on protien profiles. % Copyright 2003-2008 The MathWorks, Inc. % $Revision: 1.1.8.7 $ $Date: 2008/06/20 08:04:07 $ %% 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. %% % You need the bioinformatics toolbox to run this demo. if isempty(ver('bioinfo')) errordlg('This demo requires the Bioinformatics Toolbox.'); return; end %% 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 demo 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. %% The Data % The data in this example is from the FDA-NCI Clinical Proteomics Program % Databank (http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp). % This example uses the high-resolution ovarian cancer data set that was % generated using the WCX2 protein array. % % This demonstration assumes that you downloaded, uncompressed, and % preprocessed the raw mass-spectrometry data from the FDA-NCI web site. % You can recreate the preprocessed data file OvarianCancerQAQCdataset.mat, % needed for this demo, by either running the script *msseqprocessing*, or, % by following the steps in the demo *biodistcompdemo* (Batch processing % through parallel computing). % % The preprocessing steps from the script and demo listed above are % intended to demonstrate a representative set of possible pre-processing % procedures. Using different steps or parameters may lead to different and % possibly improved results of this demonstration. load OvarianCancerQAQCdataset.mat whos %% % 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. [feat,stat] = rankfeatures(Y,grp,'CRITERION','ttest','NUMBER',100); %% 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. % % First, the data is separated into inputs and targets. The significant % features identified will act as the inputs to the neural network. The % targets for the neural network will be the logical indices of cancer % samples. Cancer samples will hence be identified with |1|'s and normal % samples will be identified with |0|'s. P = double(Y(feat, :)); % Inputs Cidx = strcmp('Cancer',grp); % Logical index vector for Cancer samples T = double(Cidx)'; % Targets %% % Since the neural network is initialized with random initial weights, the % results after training the network vary slightly every time the demo 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. rand('seed', 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. net = newff(P,T,5); % create neural network [net,tr] = train(net,P,T); % train network %% % The trained neural network can now be tested with the testing samples we % partitioned from the main dataset using |dividevec|. The testing data is % 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. testInputs = P(:,tr.testInd); testTargets = T(:,tr.testInd); out = round(sim(net,testInputs)); % Get response of trained network %% % The classification matrix provides a comprehensive picture of the % classifiers performance. The ideal classification matrix is the one in % which the sum of the diagonal is equal to the number of samples. diff = [testTargets - 2*out]; detections = length(find(diff==-1)); % cancer samples classified as cancerous false_positives = length(find(diff==1)); % cancer samples classified as normal true_positives = length(find(diff==0)); % normal samples classified as normal false_alarms = length(find(diff==-2)); % normal samples classified as cancerous Nt = size(testInputs,2); % Number of testing samples fprintf('Total testing samples: %d\n', Nt); %classification matrix cm = [detections false_positives; false_alarms true_positives] %% % It can also be understood in terms of percentages. The following matrix % provides the same information as above but in terms of percentages. cm_p = (cm ./ Nt) .* 100 % classification matrix in percentages %% fprintf('Percentage Correct classification : %f%%\n', 100*(cm(1,1)+cm(2,2))/Nt); fprintf('Percentage Incorrect classification : %f%%\n', 100*(cm(1,2)+cm(2,1))/Nt); %% % This demo 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. %% % ** displayEndOfDemoMessage(mfilename)