-
Analytical and Bioanalytical Chemistry Oct 2009
Topics: Biosensing Techniques; Microarray Analysis; Microfluidics
PubMed: 19685237
DOI: 10.1007/s00216-009-3002-7 -
Applied and Environmental Microbiology Aug 2010PhyloTrac is an integrated desktop application for analysis of PhyloChip microarray data. PhyloTrac combined with PhyloChip provides turnkey and comprehensive...
PhyloTrac is an integrated desktop application for analysis of PhyloChip microarray data. PhyloTrac combined with PhyloChip provides turnkey and comprehensive identification and analysis of bacterial and archaeal communities in complex environmental samples. PhyloTrac is free for noncommercial organizations and is available for all major operating systems at http://www.phylotrac.org/.
Topics: Archaea; Bacteria; Computational Biology; Environmental Microbiology; Microarray Analysis; Oligonucleotide Array Sequence Analysis; Software
PubMed: 20581189
DOI: 10.1128/AEM.00303-10 -
PloS One Feb 2011The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray...
The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by "batch effects," the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.
Topics: Bayes Theorem; Case-Control Studies; Data Interpretation, Statistical; Gene Expression Profiling; Humans; Microarray Analysis; ROC Curve; Reference Standards; Research Design; Sample Size; Selection Bias; Validation Studies as Topic
PubMed: 21386892
DOI: 10.1371/journal.pone.0017238 -
Biochemistry and Molecular Biology... 2014This review provides a perspective on the initial development of microarray technologies by two independent groups in the late 1980s. (Review)
Review
This review provides a perspective on the initial development of microarray technologies by two independent groups in the late 1980s.
Topics: History, 20th Century; History, 21st Century; Humans; Microarray Analysis
PubMed: 24344052
DOI: 10.1002/bmb.20756 -
International Journal of Computational... 2010
Topics: Humans; MicroRNAs; Microarray Analysis; Systems Biology
PubMed: 21125771
DOI: No ID Found -
Journal of Computational Biology : a... May 2005Filtering is a common practice used to simplify the analysis of microarray data by removing from subsequent consideration probe sets believed to be unexpressed. The m/n... (Comparative Study)
Comparative Study Review
Filtering is a common practice used to simplify the analysis of microarray data by removing from subsequent consideration probe sets believed to be unexpressed. The m/n filter, which is widely used in the analysis of Affymetrix data, removes all probe sets having fewer than m present calls among a set of n chips. The m/n filter has been widely used without considering its statistical properties. The level and power of the m/n filter are derived. Two alternative filters, the pooled p-value filter and the error-minimizing pooled p-value filter are proposed. The pooled p-value filter combines information from the present-absent p-values into a single summary p-value which is subsequently compared to a selected significance threshold. We show that pooled p-value filter is the uniformly most powerful statistical test under a reasonable beta model and that it exhibits greater power than the m/n filter in all scenarios considered in a simulation study. The error-minimizing pooled p-value filter compares the summary p-value with a threshold determined to minimize a total-error criterion based on a partition of the distribution of all probes' summary p-values. The pooled p-value and error-minimizing pooled p-value filters clearly perform better than the m/n filter in a case-study analysis. The case-study analysis also demonstrates a proposed method for estimating the number of differentially expressed probe sets excluded by filtering and subsequent impact on the final analysis. The filter impact analysis shows that the use of even the best filter may hinder, rather than enhance, the ability to discover interesting probe sets or genes. S-plus and R routines to implement the pooled p-value and error-minimizing pooled p-value filters have been developed and are available from www.stjuderesearch.org/depts/biostats/index.html.
Topics: Computational Biology; Gene Expression Profiling; Humans; Microarray Analysis
PubMed: 15882143
DOI: 10.1089/cmb.2005.12.482 -
Critical Reviews in Food Science and... Aug 2010Microarray technology is a powerful tool for the global evaluation of gene expression profiles in tissues and for understanding many of the factors controlling the... (Review)
Review
Microarray technology is a powerful tool for the global evaluation of gene expression profiles in tissues and for understanding many of the factors controlling the regulation of gene transcription. This technique not only provides a considerable amount of information on markers and predictive factors that may potentially characterize a specific clinical picture, but also promises new applications for therapy. One of the most recent applications of microarrays concerns nutritional genomics. Nutritional genomics, known as nutrigenomics, aims to identify and understand mechanisms of molecular interaction between nutrients and/or other dietary bioactive compounds and the genome. Actually, many nutrigenomic studies utilize new approaches such as microarrays, genomics, and bioinformatics to understand how nutrients influence gene expression. The coupling of these new technologies with nutrigenomics promises to lead to improvements in diet and health. In fact, it may provide new information which can be used to ameliorate dietary regimens and to discover novel natural agents for the treatment of important diseases such as diabetes and cancer. This critical review gives an overview of the clinical relevance of a nutritional approach to several important diseases, and proposes the use of microarray for nutrigenomic studies.
Topics: Microarray Analysis; Nutrigenomics
PubMed: 20694930
DOI: 10.1080/10408390903044156 -
PloS One Jan 2011Recently emerged deep sequencing technologies offer new high-throughput methods to quantify gene expression, epigenetic modifications and DNA-protein binding. From a...
Recently emerged deep sequencing technologies offer new high-throughput methods to quantify gene expression, epigenetic modifications and DNA-protein binding. From a computational point of view, the data is very different from that produced by the already established microarray technology, providing a new perspective on the samples under study and complementing microarray gene expression data. Software offering the integrated analysis of data from different technologies is of growing importance as new data emerge in systems biology studies. Mayday is an extensible platform for visual data exploration and interactive analysis and provides many methods for dissecting complex transcriptome datasets. We present Mayday SeaSight, an extension that allows to integrate data from different platforms such as deep sequencing and microarrays. It offers methods for computing expression values from mapped reads and raw microarray data, background correction and normalization and linking microarray probes to genomic coordinates. It is now possible to use Mayday's wealth of methods to analyze sequencing data and to combine data from different technologies in one analysis.
Topics: Databases, Genetic; Gene Expression Profiling; High-Throughput Nucleotide Sequencing; Humans; Kidney; Liver; Male; Microarray Analysis; Oligonucleotide Array Sequence Analysis; Software
PubMed: 21305015
DOI: 10.1371/journal.pone.0016345 -
Computational Biology and Chemistry Oct 2005Microarrays are becoming a ubiquitous tool of research in life sciences. However, the working principles of microarray-based methodologies are often misunderstood or...
Microarrays are becoming a ubiquitous tool of research in life sciences. However, the working principles of microarray-based methodologies are often misunderstood or apparently ignored by the researchers who actually perform and interpret experiments. This in turn seems to lead to a common over-expectation regarding the explanatory and/or knowledge-generating power of microarray analyses. In this note we intend to explain basic principles of five (5) major groups of analytical techniques used in studies of microarray data and their interpretation: the principal component analysis (PCA), the independent component analysis (ICA), the t-test, the analysis of variance (ANOVA), and self organizing maps (SOM). We discuss answers to selected practical questions related to the analysis of microarray data. We also take a closer look at the experimental setup and the rules, which have to be observed in order to exploit microarrays efficiently. Finally, we discuss in detail the scope and limitations of microarray-based methods. We emphasize the fact that no amount of statistical analysis can compensate for (or replace) a well thought through experimental setup. We conclude that microarrays are indeed useful tools in life sciences but by no means should they be expected to generate complete answers to complex biological questions. We argue that even well posed questions, formulated within a microarray-specific terminology, cannot be completely answered with the use of microarray analyses alone.
Topics: Computational Biology; Data Interpretation, Statistical; Microarray Analysis; Multivariate Analysis; Principal Component Analysis; Research Design; Software
PubMed: 16219488
DOI: 10.1016/j.compbiolchem.2005.08.006 -
Clinical and Experimental Allergy :... Aug 2016During the last decades component-resolved diagnostics either as singleplex or multiplex measurements has been introduced into the field of clinical allergology,... (Review)
Review
During the last decades component-resolved diagnostics either as singleplex or multiplex measurements has been introduced into the field of clinical allergology, providing important information that cannot be obtained from extract-based tests. Here we review recent studies that demonstrate clinical applications of the multiplex microarray technique in the diagnosis and risk assessment of allergic patients, and its usefulness in studies of allergic diseases. The usefulness of ImmunoCAP ISAC has been validated in a wide spectrum of allergic diseases like asthma, allergic rhinoconjunctivitis, atopic dermatitis, eosinophilic esophagitis, food allergy and anaphylaxis. ISAC provides a broad picture of a patient's sensitization profile from a single test, and provides information on specific and cross-reactive sensitizations that facilitate diagnosis, risk assessment, and disease management. Furthermore, it can reveal unexpected sensitizations which may explain anaphylaxis previously categorized as idiopathic and also display for the moment clinically non-relevant sensitizations. ISAC can facilitate a better selection of relevant allergens for immunotherapy compared with extract testing. Microarray technique can visualize the allergic march and molecular spreading in the preclinical stages of allergic diseases, and may indicate that the likelihood of developing symptomatic allergy is associated with specific profiles of sensitization to allergen components. ISAC is shown to be a useful tool in routine allergy diagnostics due to its ability to improve risk assessment, to better select relevant allergens for immunotherapy as well as detecting unknown sensitization. Multiplex component testing is especially suitable for patients with complex symptomatology.
Topics: Allergens; Cross Reactions; Humans; Hypersensitivity; Immunotherapy; Microarray Analysis; Phenotype; Risk Assessment
PubMed: 27196983
DOI: 10.1111/cea.12761