μ-CS: An extension of the TM4 platform to manage Affymetrix binary data

<p>Abstract</p> <p>Background</p> <p>A main goal in understanding cell mechanisms is to explain the relationship among genes and related molecular processes through the combined use of technological platforms and bioinformatics analysis. High throughput platforms, such as microarrays, enable the investigation of the whole genome in a single experiment. There exist different kind of microarray platforms, that produce different types of binary data (images and raw data). Moreover, also considering a single vendor, different chips are available. The analysis of microarray data requires an initial preprocessing phase (i.e. normalization and summarization) of raw data that makes them suitable for use on existing platforms, such as the TIGR M4 Suite. Nevertheless, the annotations of data with additional information such as gene function, is needed to perform more powerful analysis. Raw data preprocessing and annotation is often performed in a manual and error prone way. Moreover, many available preprocessing tools do not support annotation. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of microarray data are needed.</p> <p>Results</p> <p>The paper presents <it>μ</it>-CS (Microarray Cel file Summarizer), a cross-platform tool for the automatic normalization, summarization and annotation of Affymetrix binary data. <it>μ</it>-CS is based on a client-server architecture. The <it>μ</it>-CS client is provided both as a plug-in of the TIGR M4 platform and as a Java standalone tool and enables users to read, preprocess and analyse binary microarray data, avoiding the manual invocation of external tools (e.g. the Affymetrix Power Tools), the manual loading of preprocessing libraries, and the management of intermediate files. The <it>μ</it>-CS server automatically updates the references to the summarization and annotation libraries that are provided to the <it>μ</it>-CS client before the preprocessing. The <it>μ</it>-CS server is based on the web services technology and can be easily extended to support more microarray vendors (e.g. Illumina).</p> <p>Conclusions</p> <p>Thus <it>μ</it>-CS users can directly manage binary data without worrying about locating and invoking the proper preprocessing tools and chip-specific libraries. Moreover, users of the <it>μ</it>-CS plugin for TM4 can manage Affymetrix binary files without using external tools, such as APT (Affymetrix Power Tools) and related libraries. Consequently, <it>μ</it>-CS offers four main advantages: (i) it avoids to waste time for searching the correct libraries, (ii) it reduces possible errors in the preprocessing and further analysis phases, e.g. due to the incorrect choice of parameters or the use of old libraries, (iii) it implements the annotation of preprocessed data, and finally, (iv) it may enhance the quality of further analysis since it provides the most updated annotation libraries. The <it>μ</it>-CS client is freely available as a plugin of the TM4 platform as well as a standalone application at the project web site (<url>http://bioingegneria.unicz.it/M-CS</url>).</p

Improving Gene Selection in Microarray Data Analysis Using Fuzzy Patterns Inside a CBR System

In recent years, machine learning and data mining fields have found a successful application area in the field of DNA microarray technology. Gene expression profiles are composed of thousands of genes at the same time, representing complex relationships between them. One of the well-known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments or cases. In this context, the ability to identify an accurate gene selection strategy is crucial to reduce the generalization error (false positives) of state-of-the-art classification algorithms. This paper presents a reduction algorithm based on the notion of fuzzy gene expression, where similar (co-expressed) genes belonging to different patients are selected in order to construct a supervised prototype-based retrieval model. This technique is employed to implement the retrieval step in our new gene-CBR system. The proposed method is illustrated with the analysis of microarray data belonging to bone marrow cases from 43 adult patients with cancer plus a group of three cases corresponding to healthy persons

Markowitz Minimum Variance Portfolio Optimization using New Machine Learning Methods

The use of improved covariance matrix estimators as an alternative to the sample covariance is considered an important approach for enhancing portfolio optimization. In this thesis, we propose the use of sparse inverse covariance estimation for Markowitz minimum variance portfolio optimization, using existing methodology known as Graphical Lasso [16], which is an algorithm used to estimate the inverse covariance matrix from observations from a multivariate Gaussian distribution. We begin by benchmarking Graphical Lasso, showing the importance of regularization to control sparsity. Experimental results show that Graphical Lasso has a tendency to overestimate the diagonal elements of the estimated inverse covariance matrix as the regularization increases. To remedy this, we introduce a new method of setting the optimal regularization which shows performance that is at least as good as the original method by [16]. Next, we show the application of Graphical Lasso in a bioinformatics gene microarray tissue classification problem where we have a large number of genes relative to the number of samples. We perform dimensionality reduction by estimating graphical Gaussian models using Graphical Lasso, and using gene group average expression levels as opposed to individual expression levels to classify samples. We compare classification performance with the sample covariance, and show that the sample covariance performs better. Finally, we use Graphical Lasso in combination with validation techniques that optimize portfolio criteria (risk, return etc.) and Gaussian likelihood to generate new portfolio strategies to be used for portfolio optimization with and without short selling constraints. We compare performance on synthetic and real stock market data with existing covariance estimators in literature, and show that the newly developed portfolio strategies perform well, although performance of all methods depend on the ratio between the estimation period and number of stocks, and on the presence or absence of short selling constraints