Special Issue "Advances in Data Analysis Methods and Tools"

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A special issue of Microarrays (ISSN 2076-3905).

Deadline for manuscript submissions: closed (31 August 2014)

Special Issue Editor

Guest Editor
Dr. Pawel Herzyk (Website)

Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK
Phone: +44 141 3303180
Interests: next generation sequencing; microarrays; genomics; transcriptomics; data analysis; bioinformatics; data integration

Special Issue Information

Dear Colleagues,

Ever since the first reports of successful application of DNA microarrays appeared in the second half of the 1990s, the technology has matured immensely and gained popularity amongst army of researchers. The density of features has increased dramatically, allowing for manufacturing of whole genome expression arrays or high density SNPs arrays, and was accompanied by massively improved reproducibility of results. This process has been paralleled by dramatic improvements in data analysis methods, which in case of expression arrays, led to confident identification of statistically significant gene/transcript expression changes in complex experiments as well as to demystifying underlying biological processes by linking the resulting gene lists to functional classes, gene networks and biological pathways.

Although recent next generation sequencing technology becomes increasingly popular for applications previously “reserved” for microarrays, e.g., transcriptome profiling, the problems associated with modelling data distribution, data normalisation and subsequent identification of differential expression, make sequencing data analysis challenging and to some extent ambiguous, particularly given small number of replicated samples. Consequently, microarray data analysis seems more robust and better supported, which is particularly important for complex multifactorial experiments.

In this issue we are inviting material about computational methods and tools related to various aspects of microarray data analysis, such as normalisation, statistical analysis, data analysis workflows or functional downstream analysis as applied to wide range of arrays.

Dr. Pawel Herzyk
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Microarrays is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Keywords

  • data analysis
  • differential expression
  • statistical significance
  • pathway analysis
  • gene networks
  • dimensionality reduction
  • clustering
  • copy numbers
  • SNP calling

Published Papers (2 papers)

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Research

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Open AccessArticle Assessing Agreement between miRNA Microarray Platforms
Microarrays 2014, 3(4), 302-321; doi:10.3390/microarrays3040302
Received: 3 July 2014 / Revised: 24 September 2014 / Accepted: 25 November 2014 / Published: 12 December 2014
PDF Full-text (1389 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Over the last few years, miRNA microarray platforms have provided great insights into the biological mechanisms underlying the onset and development of several diseases. However, only a few studies have evaluated the concordance between different microarray platforms using methods that took into [...] Read more.
Over the last few years, miRNA microarray platforms have provided great insights into the biological mechanisms underlying the onset and development of several diseases. However, only a few studies have evaluated the concordance between different microarray platforms using methods that took into account measurement error in the data. In this work, we propose the use of a modified version of the Bland–Altman plot to assess agreement between microarray platforms. To this aim, two samples, one renal tumor cell line and a pool of 20 different human normal tissues, were profiled using three different miRNA platforms (Affymetrix, Agilent, Illumina) on triplicate arrays. Intra-platform reliability was assessed by calculating pair-wise concordance correlation coefficients (CCC) between technical replicates and overall concordance correlation coefficient (OCCC) with bootstrap percentile confidence intervals, which revealed moderate-to-good repeatability of all platforms for both samples. Modified Bland–Altman analysis revealed good patterns of concordance for Agilent and Illumina, whereas Affymetrix showed poor-to-moderate agreement for both samples considered. The proposed method is useful to assess agreement between array platforms by modifying the original Bland–Altman plot to let it account for measurement error and bias correction and can be used to assess patterns of concordance between other kinds of arrays other than miRNA microarrays. Full article
(This article belongs to the Special Issue Advances in Data Analysis Methods and Tools)

Review

Jump to: Research

Open AccessReview The Transcriptomics to Proteomics of Hair Cell Regeneration: Looking for a Hair Cell in a Haystack
Microarrays 2013, 2(3), 186-207; doi:10.3390/microarrays2030186
Received: 27 May 2013 / Revised: 2 July 2013 / Accepted: 4 July 2013 / Published: 25 July 2013
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Abstract
Mature mammals exhibit very limited capacity for regeneration of auditory hair cells, while all non-mammalian vertebrates examined can regenerate them. In an effort to find therapeutic targets for deafness and balance disorders, scientists have examined gene expression patterns in auditory tissues under [...] Read more.
Mature mammals exhibit very limited capacity for regeneration of auditory hair cells, while all non-mammalian vertebrates examined can regenerate them. In an effort to find therapeutic targets for deafness and balance disorders, scientists have examined gene expression patterns in auditory tissues under different developmental and experimental conditions. Microarray technology has allowed the large-scale study of gene expression profiles (transcriptomics) at whole-genome levels, but since mRNA expression does not necessarily correlate with protein expression, other methods, such as microRNA analysis and proteomics, are needed to better understand the process of hair cell regeneration. These technologies and some of the results of them are discussed in this review. Although there is a considerable amount of variability found between studies owing to different species, tissues and treatments, there is some concordance between cellular pathways important for hair cell regeneration. Since gene expression and proteomics data is now commonly submitted to centralized online databases, meta-analyses of these data may provide a better picture of pathways that are common to the process of hair cell regeneration and lead to potential therapeutics. Indeed, some of the proteins found to be regulated in the inner ear of animal models (e.g., IGF-1) have now gone through human clinical trials. Full article
(This article belongs to the Special Issue Advances in Data Analysis Methods and Tools)

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