A note on between-group PCA

In the context of binary classification with continuous predictors, we proove two properties concerning the connections between Partial Least Squares (PLS) dimension reduction and between-group PCA, and between linear discriminant analysis and between-group PCA. Such methods are of great interest for the analysis of high-dimensional data with continuous predictors, such as microarray gene expression data

Comparing Prediction Accuracy for Supervised Techniques in Gene Expression Data

Classification is one of the most important tasks for different application such as text categorization, tone recognition, image classification, micro-array gene expression, proteins structure predictions, data classification etc. Microarray based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. The classification of different tumor types is of great significance in cancer diagnosis and drug innovation. One challenging area in the studies of gene expression data is the classification of different types of tumors into correct classes. Diagonal discriminant analysis, regularized discriminant analysis, support vector machines and k-nearest neighbor have been suggested as among the best methods for small sample size situations. The methods are applied to datasets from four recently published cancer gene expression studies. Four publicly available microarray data sets are Leukemia, Lymphoma, SRBCT & Prostate. The performance of the classification technique has been evaluated according to the percentage of misclassification through hold-out cross validation

Model-based Methods of Classification: Using the mclust Software in Chemometrics

Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a model for the data that allows for overlapping clusters, producing a probabilistic clustering that quantifies the uncertainty of observations belonging to components of the mixture. The resulting clustering model can also be used for some other important problems in multivariate analysis, including density estimation and discriminant analysis. Examples of the use of model-based clustering and classification techniques in chemometric studies include multivariate image analysis, magnetic resonance imaging, microarray image segmentation, statistical process control, and food authenticity. We review model-based clustering and related methods for density estimation and discriminant analysis, and show how the R package mclust can be applied in each instance.

Comparing Prediction Accuracy for Machine Learning and Other Classical Approaches in Gene Expression Data

Microarray based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. The classification of different tumor types is of great significance in cancer diagnosis and drug innovation. Using a large number of genes to classify samples based on a small number of microarrays remains a difficult problem. Feature selection techniques can be used to extract the marker genes which influence the classification accuracy effectively by eliminating the unwanted noisy and redundant genes. Quite a number of methods have been proposed in recent years with promising results. But there are still a lot of issues which need to be addressed and understood. Diagonal discriminant analysis, regularized discriminant analysis, support vector machines and k-nearest neighbor have been suggested as among the best methods for small sample size situations. In this paper, we have compared the performance of different discrimination methods for the classification of tumors based on gene expression data. The methods are applied to datasets from four recently published cancer gene expression studies. The performance of the classification technique has been evaluated for varying number of selected features in terms of misclassification rate  using hold-out cross validation. Our study shows that KNN, RDA and SVM with linear kernel methods have lower misclassification rate than the other algorithms. Keywords: microarray, gene expression, KNN, DLDA, RDA, SV

Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested

Microarray-based cancer prediction using single genes

<p>Abstract</p> <p>Background</p> <p>Although numerous methods of using microarray data analysis for cancer classification have been proposed, most utilize many genes to achieve accurate classification. This can hamper interpretability of the models and ease of translation to other assay platforms. We explored the use of single genes to construct classification models. We first identified the genes with the most powerful univariate class discrimination ability and then constructed simple classification rules for class prediction using the single genes.</p> <p>Results</p> <p>We applied our model development algorithm to eleven cancer gene expression datasets and compared classification accuracy to that for standard methods including Diagonal Linear Discriminant Analysis, <it>k</it>-Nearest Neighbor, Support Vector Machine and Random Forest. The single gene classifiers provided classification accuracy comparable to or better than those obtained by existing methods in most cases. We analyzed the factors that determined when simple single gene classification is effective and when more complex modeling is warranted.</p> <p>Conclusions</p> <p>For most of the datasets examined, the single-gene classification methods appear to work as well as more standard methods, suggesting that simple models could perform well in microarray-based cancer prediction.</p

Model-based Methods of Classification: Using the mclust Software in Chemometrics

Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a model for the data that allows for overlapping clusters, producing a probabilistic clustering that quantifies the uncertainty of observations belonging to components of the mixture. The resulting clustering model can also be used for some other important problems in multivariate analysis, including density estimation and discriminant analysis. Examples of the use of model-based clustering and classification techniques in chemometric studies include multivariate image analysis, magnetic resonance imaging, microarray image segmentation, statistical process control, and food authenticity. We review model-based clustering and related methods for density estimation and discriminant analysis, and show how the R package mclust can be applied in each instance

Optimal classifier selection and negative bias in error rate estimation: An empirical study on high-dimensional prediction

In biometric practice, researchers often apply a large number of different methods in a "trial-and-error" strategy to get as much as possible out of their data and, due to publication pressure or pressure from the consulting customer, present only the most favorable results. This strategy may induce a substantial optimistic bias in prediction error estimation, which is quantitatively assessed in the present manuscript. The focus of our work is on class prediction based on high-dimensional data (e.g. microarray data), since such analyses are particularly exposed to this kind of bias. In our study we consider a total of 124 variants of classifiers (possibly including variable selection or tuning steps) within a cross-validation evaluation scheme. The classifiers are applied to original and modified real microarray data sets, some of which are obtained by randomly permuting the class labels to mimic non-informative predictors while preserving their correlation structure. We then assess the minimal misclassification rate over the different variants of classifiers in order to quantify the bias arising when the optimal classifier is selected a posteriori in a data-driven manner. The bias resulting from the parameter tuning (including gene selection parameters as a special case) and the bias resulting from the choice of the classification method are examined both separately and jointly. We conclude that the strategy to present only the optimal result is not acceptable, and suggest alternative approaches for properly reporting classification accuracy

Extreme Value Distribution Based Gene Selection Criteria for Discriminant Microarray Data Analysis Using Logistic Regression

One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, $\hat{L}(D|M)$, and the expected maximum likelihood of the model given an ensemble of surrogate data with randomly permuted label, $\hat{L}(D_0|M)$. Typically, the computational burden for obtaining $\hat{L}(D_0|M)$ is immense, often exceeding the limits of computing available resources by orders of magnitude. Here, we propose an approach that circumvents such heavy computations by mapping the simulation problem to an extreme-value problem. We present the derivation of an asymptotic distribution of the extreme-value as well as its mean, median, and variance. Using this distribution, we propose two gene selection criteria, and we apply them to two microarray datasets and three classification tasks for illustration.Comment: to be published in Journal of Computational Biology (2004