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It is one of the simplest clustering techniques and it is commonly used in medical imaging and biometrics. The K-means clustering algorithm typically uses the Euclidean properties of the vector space. After the initial partitioning of the vector space into K parts, the algorithm calculates the center points in each subspace and adjusts the partition so that each vector is assigned to the cluster the center of which is the closest.

Microarray Image and Data Analysis: Theory and Practice - CRC Press Book

This is repeated iteratively until either the partitioning stabilizes or the given number of iterations is exceeded [ 17 ]. A self-organizing map SOM is a neural networkbased non-hierarchal clustering approach. SOMs work in a manner similar to K-means clustering [ 18 ]. The commonly used and freely available programs for clustering analysis are illustrated in see Table 2. Classification is also known as class prediction, discriminant analysis, or supervised learning. Given a set of pre-classified examples, for example, different types of cancer classes such as AML and ALL a classifier will a find a rule that will allow to assign new samples to one of the above classes [ 19 ].

For classification task, one must have sufficient sample numbers to allow an algorithm to be trained known as training test and then to have it tested on an independent set of samples known as test set. Using normalized gene expression data as input vectors, classification rules can be built.

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There are a wide range of algorithms that can be used for classification, including k Nearest Neighbors kNN , Artificial Neural Networks, weighted voting and support vector machines SVM. The promising application of classification is in clinical diagnostics to find disease types and sub types. The general data mining and machine learning application tools are used for classification tasks are illustrated in the Table 3 see Table 3.

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Classification, clustering and identification of differential genes can be considered as basic microarray data analysis tasks with gene expression profiles alone. However, Gene expression profiles can be linked to other external resources to make new discoveries and knowledge. Some of the common applications that addressed with gene expression data with other biomedical information are discussed below. The identification of functional elements such as transcription-factor binding sites TFBS on a whole-genome level is the next challenge for genome sciences and gene-regulation studies.

Transcription factors act as critical molecular switches in the gene expression profiling. Transcription factors play a prominent role in transcription regulation; identifying and characterizing their binding sites is central to annotating genomic regulatory regions and understanding gene-regulatory networks [ 23 ]. Various groups have exploited this problem and discovered putative binding sites in the promoter regions of genes that are co-expressed [ 24 ].

Some of common tools for transcription factor binding site prediction and underlying algorithm are illustrated in Table 4 see Table 4. Protein-protein interactions PPI are useful tools for investigating the cellular functions of genes. It is a core of the entire interactomics system of any living cell.

PPI improves our understanding of diseases and can provide the basis for new therapeutic approaches [ 25 ].

Statistical Methods for Microarray Data Analysis Methods and Protocols Methods in Molecular Biology

Combining coexpressed as well as interacting genes in the same cluster several meaningful predictions related to gene functions, evolutionary prelateships and pathways can be made [ 31 ]. Obviously, the next promising method for analyzing microarray data is pathway analysis as it involves the cascade of network interactions.

Analyzing the microarray data in a pathway perspective could lead to a higher level of understanding of the system [ 32 ]. This integrates the normalized array data and their annotations, such as metabolic pathways and gene ontology and functional classifications.

Metabolic pathway analysis can identify more subtle changes in expression than the gene lists that result from univariate statistical analysis [ 33 ]. There are several web based tools and academic softwares are available to predict protein interactions and pathways from microarray data and are tabulated in Table 5 see Table 5.

Gene Set Enrichment Analysis GSEA is a computational method that determines whether a set of genes shows statistically significant and concordant differences between two biological states. The gene sets are defined based on prior biological knowledge, e. The goal of GSEA is to determine whether members of a gene set tend to occur toward the top or bottom of the list, in which case the gene set is correlated with the phenotypic class distinction [ 34 ].


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The freely available software packages for gene enrichment are illustrated in Table 6 see Table 6. DNA Microarray is a revolutionary technology and microarray experiments produce considerably more data than other techniques.


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Integrating gene expression data with other biomedical resources will provide new mechanistic or biological hypotheses. However, innovative statistical techniques and computing software are essential for the successful analysis of microarray data. This review shows the current bioinformatics tools and the promising applications for analyzing data from microarray experiments. The various data analysis perspectives and softwares mentioned in the paper will help the biological expertise as a good foundation for computational analysis of microarray data.

National Center for Biotechnology Information , U. Journal List Bioinformation v. Author information Article notes Copyright and License information Disclaimer. Received Feb 2; Accepted Feb 3. This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited. This article has been cited by other articles in PMC. Associated Data Supplementary Materials Data 1.

Microarray Data Analysis

Abstract Microarrays are one of the latest breakthroughs in experimental molecular biology that allow monitoring the expression levels of tens of thousands of genes simultaneously. Keywords: Microarrays, Gene expression, Microarray data analysis, Bioinformatics tools. Background Microarray is one such technology which enables the researchers to investigate and address issues which were once thought to be non traceable by facilitating the simultaneous measurement of the expression levels of thousands of genes [ 1 , 2 ]. Open in a separate window.

Figure 1. Microarray Data Analysis Microarray data sets are commonly very large, and analytical precision is influenced by a number of variables. Identification of Differentially Expressed Genes Differentially expressed genes are the genes whose expression levels are significantly different between two groups of experiments [ 7 ]. Cluster Analysis Clustering is the most popular method currently used in the first step of gene expression data matrix analysis.

Classification Classification is also known as class prediction, discriminant analysis, or supervised learning. Knowledge Discovery with Microarray Data Classification, clustering and identification of differential genes can be considered as basic microarray data analysis tasks with gene expression profiles alone.

Identification of transcription factor binding sites The identification of functional elements such as transcription-factor binding sites TFBS on a whole-genome level is the next challenge for genome sciences and gene-regulation studies. Protein-protein interaction network and pathway analysis Protein-protein interactions PPI are useful tools for investigating the cellular functions of genes. Gene set enrichment analysis Gene Set Enrichment Analysis GSEA is a computational method that determines whether a set of genes shows statistically significant and concordant differences between two biological states.

Conclusion DNA Microarray is a revolutionary technology and microarray experiments produce considerably more data than other techniques. Supplementary material Data 1: Click here to view. We removed those tools from further consideration. We also view systems that are supposed to work together as one system. We successsfully installed seven systems. The process of installation we followed is described in Supplementary Data, Section S3. The reasons for failed installations were manifold, such as nonavailability of code although stated otherwise in the publication , dependence on heavily outdated libraries or unresolvable installation errors.

Detailed information on tools that could not be installed can be found in Supplementary Data, Section S4. Over 70 different tools for management and analysis of microarray data have been developed by many groups around the world. We performed a literature-based analysis of all tools we could find references for and evaluated each tool with respect to a list of 55 predefined criteria. The complete list of all considered systems together with their evaluation can be found in the Supplementary Table S2.

Overall, we found the individual capabilities of the tools differing to a vast degree. Many tools have a strong focus on some specific functionality and support other tasks only marginally. Of the 78 systems, 29 focus strictly on data analysis, while 17 only address data storage Figure 1. While most of the tools focus on one experimental technology only, i. Tools for managing TMA are strictly separated from the others, which means that we could not find a single tool that was able to handle TMA and any other kind of microarray data together.

Venn diagrams showing capabilities of tools with respect to functionality focus. The majority of tools addresses data analysis, with approximately one-half of them having a dedicated storage system attached.

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Robust gene selection methods using weighting schemes for microarray data analysis

Only 17 systems are storage only. A majority of tools concentrates on one technology only, and currently no tool can handle TMA and expression arrays. From the 78 tools subjected to the literature-based evaluation, we selected 22 for closer inspection Table 2. Of these, in four cases, two tools are supposed to work together closely, so we considered them as one system. In this section, we describe each of these systems in detail. Information on the tools we could not install are given in Supplementary Data, Section S4.

Tools in bold were installed successfully. The table has less than 22 lines as in four cases, a storage-oriented and an analysis-oriented system are packaged together so tightly that we considered them as one system. Detailed reasons for failed installations can be found in Supplementary Data, Section S4.

Supplementary Table S1 summarizes the system requirements of the successfully installed tools. The list encompasses web-based systems as well as stand-alone applications. The source code is publicly available for all of them.