WebArray: an online platform for microarray data analysis

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Abstract

Background

Many cutting-edge microarray analysis tools and algorithms, including commonly used limma and affy packages in Bioconductor, need sophisticated knowledge of mathematics, statistics and computer skills for implementation. Commercially available software can provide a user-friendly interface at considerable cost. To facilitate the use of these tools for microarray data analysis on an open platform we developed an online microarray data analysis platform, WebArray, for bench biologists to utilize these tools to explore data from single/dual color microarray experiments.

Results

The currently implemented functions were based on limma and affy package from Bioconductor, the spacings LOESS histogram (SPLOSH) method, PCA-assisted normalization method and genome mapping method. WebArray incorporates these packages and provides a user-friendly interface for accessing a wide range of key functions of limma and others, such as spot quality weight, background correction, graphical plotting, normalization, linear modeling, empirical bayes statistical analysis, false discovery rate (FDR) estimation, chromosomal mapping for genome comparison.

Conclusion

WebArray offers a convenient platform for bench biologists to access several cutting-edge microarray data analysis tools. The website is freely available at http://bioinformatics.skcc.org/webarray/. It runs on a Linux server with Apache and MySQL.

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Most cited references 4

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Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.

Jason Wong, Linda C. Li (2001)

Recent advances in cDNA and oligonucleotide DNA arrays have made it possible to measure the abundance of mRNA transcripts for many genes simultaneously. The analysis of such experiments is nontrivial because of large data size and many levels of variation introduced at different stages of the experiments. The analysis is further complicated by the large differences that may exist among different probes used to interrogate the same gene. However, an attractive feature of high-density oligonucleotide arrays such as those produced by photolithography and inkjet technology is the standardization of chip manufacturing and hybridization process. As a result, probe-specific biases, although significant, are highly reproducible and predictable, and their adverse effect can be reduced by proper modeling and analysis methods. Here, we propose a statistical model for the probe-level data, and develop model-based estimates for gene expression indexes. We also present model-based methods for identifying and handling cross-hybridizing probes and contaminating array regions. Applications of these results will be presented elsewhere.

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Survey of differentially methylated promoters in prostate cancer cell lines.

Michael McClelland, John Welsh, Q Yu … (2005)

DNA methylation and copy number in the genomes of three immortalized prostate epithelial and five cancer cell lines (LNCaP, PC3, PC3M, PC3M-Pro4, and PC3M-LN4) were compared using a microarray-based technique. Genomic DNA is cut with a methylation-sensitive enzyme HpaII, followed by linker ligation, polymerase chain reaction (PCR) amplification, labeling, and hybridization to an array of promoter sequences. Only those parts of the genomic DNA that have unmethylated restriction sites within a few hundred base pairs generate PCR products detectable on an array. Of 2732 promoter sequences on a test array, 504 (18.5%) showed differential hybridization between immortalized prostate epithelial and cancer cell lines. Among candidate hypermethylated genes in cancer-derived lines, there were eight (CD44, CDKN1A, ESR1, PLAU, RARB, SFN, TNFRSF6, and TSPY) previously observed in prostate cancer and 13 previously known methylation targets in other cancers (ARHI, bcl-2, BRCA1, CDKN2C, GADD45A, MTAP, PGR, SLC26A4, SPARC, SYK, TJP2, UCHL1, and WIT-1). The majority of genes that appear to be both differentially methylated and differentially regulated between prostate epithelial and cancer cell lines are novel methylation targets, including PAK6, RAD50, TLX3, PIR51, MAP2K5, INSR, FBN1, and GG2-1, representing a rich new source of candidate genes used to study the role of DNA methylation in prostate tumors.

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Improving false discovery rate estimation.

Stan Pounds, Cheng-Cheng Pao (2004)

Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR). However, rigorous control of the FDR at a preselected level is often impractical. Consequently, it has been suggested to use the q-value as an estimate of the proportion of false discoveries among a set of significant findings. However, such an interpretation of the q-value may be unwarranted considering that the q-value is based on an unstable estimator of the positive FDR (pFDR). Another method proposes estimating the FDR by modeling p-values as arising from a beta-uniform mixture (BUM) distribution. Unfortunately, the BUM approach is reliable only in settings where the assumed model accurately represents the actual distribution of p-values. A method called the spacings LOESS histogram (SPLOSH) is proposed for estimating the conditional FDR (cFDR), the expected proportion of false positives conditioned on having k 'significant' findings. SPLOSH is designed to be more stable than the q-value and applicable in a wider variety of settings than BUM. In a simulation study and data analysis example, SPLOSH exhibits the desired characteristics relative to the q-value and BUM. The Web site www.stjuderesearch.org/statistics/splosh.html has links to freely available S-plus code to implement the proposed procedure.

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Author and article information

Journal

Journal ID (nlm-ta): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2105

Publication date Collection: 2005

Publication date (Electronic): 21 December 2005

Volume: 6

Page: 306

Affiliations

[1 ]Genomic Core Facility, Sidney Kimmel Cancer Center, San Diego, CA 92121, USA

[2 ]Department of Cancer Genetics, Sidney Kimmel Cancer Center, San Diego, CA 92121, USA

Article

Publisher ID: 1471-2105-6-306

DOI: 10.1186/1471-2105-6-306

PMC ID: 1327694

PubMed ID: 16371165

SO-VID: 669a0d0f-d309-4801-b90e-3aa6376246d7

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

WebArray: an online platform for microarray data analysis

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Abstract

Background

Results

Conclusion

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Most cited references 4

Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.

Survey of differentially methylated promoters in prostate cancer cell lines.

Improving false discovery rate estimation.

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