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New methods to analyse microarray data that partially lack a reference signal.

ABSTRACT: Microarray-based Comparative Genomic Hybridisation (CGH) has been used to assess genetic variability between bacterial strains. Crucial for interpretation of microarray data is the availability of a reference to compare signal intensities to reliably determine presence or divergence each DNA fragment. However, the production of a good reference becomes unfeasible when microarrays are based on pan-genomes.When only a single strain is used as a reference for a multistrain array, the accessory gene pool will be partially represented by reference DNA, although these genes represent the genomic repertoire that can explain differences in virulence, pathogenicity or transmissibility between strains. The lack of a reference makes interpretation of the data for these genes difficult and, if the test signal is low, they are often deleted from the analysis. We aimed to develop novel methods to determine the presence or divergence of genes in a Staphylococcus aureus multistrain PCR product microarray-based CGH approach for which reference DNA was not available for some probes.In this study we have developed 6 new methods to predict divergence and presence of all genes spotted on a multistrain Staphylococcus aureus DNA microarray, published previously, including those gene spots that lack reference signals. When considering specificity and PPV (i.e. the false-positive rate) as the most important criteria for evaluating these methods, the method that defined gene presence based on a signal at least twice as high as the background and higher than the reference signal (method 4) had the best test characteristics. For this method specificity was 100% and 82% for MRSA252 (compared to the GACK method) and all spots (compared to sequence data), respectively, and PPV were 100% and 76% for MRSA252 (compared to the GACK method) and all spots (compared to sequence data), respectively.A definition of gene presence based on signal at least twice as high as the background and higher than the reference signal (method 4) had the best test characteristics, allowing the analysis of 6-17% more of the genes not present in the reference strain. This method is recommended to analyse microarray data that partially lack a reference signal.

SUBMITTER: Carpaij N

PROVIDER: S-EPMC2779823 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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New methods to analyse microarray data that partially lack a reference signal.

Carpaij Neeltje N Fluit Ad C AC Lindsay Jodi A JA Bonten Marc Jm MJ Willems Rob Jl RJ

BMC genomics 20091113

<h4>Background</h4>Microarray-based Comparative Genomic Hybridisation (CGH) has been used to assess genetic variability between bacterial strains. Crucial for interpretation of microarray data is the availability of a reference to compare signal intensities to reliably determine presence or divergence each DNA fragment. However, the production of a good reference becomes unfeasible when microarrays are based on pan-genomes.When only a single strain is used as a reference for a multistrain array, ...[more]

PMID: 19912620

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Project description:ABSTRACT: BACKGROUND: Microarray-based Comparative Genomic Hybridisation (CGH) has been used to assess genetic variability between bacterial strains. Crucial for interpretation of microarray data is the availability of a reference to compare signal intensities to reliably determine presence or divergence each DNA fragment. However, the production of a good reference becomes unfeasible when microarrays are based on pan-genomes.When only a single strain is used as a reference for a multistrain array, the accessory gene pool will be partially represented by reference DNA, although these genes represent the genomic repertoire that can explain differences in virulence, pathogenicity or transmissibility between strains. The lack of a reference makes interpretation of the data for these genes difficult and, if the test signal is low, they are often deleted from the analysis. We aimed to develop novel methods to determine the presence or divergence of genes in a Staphylococcus aureus multistrain PCR product microarray-based CGH approach for which reference DNA was not available for some probes. RESULTS: In this study we have developed 6 new methods to predict divergence and presence of all genes spotted on a multistrain Staphylococcus aureus DNA microarray, published previously, including those gene spots that lack reference signals. When considering specificity and PPV (i.e. the false-positive rate) as the most important criteria for evaluating these methods, the method that defined gene presence based on a signal at least twice as high as the background and higher than the reference signal (method 4) had the best test characteristics. For this method specificity was 100% and 82% for MRSA252 (compared to the GACK method) and all spots (compared to sequence data), respectively, and PPV were 100% and 76% for MRSA252 (compared to the GACK method) and all spots (compared to sequence data), respectively. CONCLUSION: A definition of gene presence based on signal at least twice as high as the background and higher than the reference signal (method 4) had the best test characteristics, allowing the analysis of 6-17% more of the genes not present in the reference strain. This method is recommended to analyse microarray data that partially lack a reference signal. Data is also available from http://bugs.sgul.ac.uk/E-BUGS-85

Project description:BackgroundNowadays, microarray gene expression analysis is a widely used technology that scientists handle but whose final interpretation usually requires the participation of a specialist. The need for this participation is due to the requirement of some background in statistics that most users lack or have a very vague notion of. Moreover, programming skills could also be essential to analyse these data. An interactive, easy to use application seems therefore necessary to help researchers to extract full information from data and analyse them in a simple, powerful and confident way.ResultsPreP+07 is a standalone Windows XP application that presents a friendly interface for spot filtration, inter- and intra-slide normalization, duplicate resolution, dye-swapping, error removal and statistical analyses. Additionally, it contains two unique implementation of the procedures - double scan and Supervised Lowess-, a complete set of graphical representations - MA plot, RG plot, QQ plot, PP plot, PN plot - and can deal with many data formats, such as tabulated text, GenePix GPR and ArrayPRO. PreP+07 performance has been compared with the equivalent functions in Bioconductor using a tomato chip with 13056 spots. The number of differentially expressed genes considering p-values coming from the PreP+07 and Bioconductor Limma packages were statistically identical when the data set was only normalized; however, a slight variability was appreciated when the data was both normalized and scaled.ConclusionPreP+07 implementation provides a high degree of freedom in selecting and organizing a small set of widely used data processing protocols, and can handle many data formats. Its reliability has been proven so that a laboratory researcher can afford a statistical pre-processing of his/her microarray results and obtain a list of differentially expressed genes using PreP+07 without any programming skills. All of this gives support to scientists that have been using previous PreP releases since its first version in 2003.

Project description:Heart failure is one of the leading causes of death worldwide [1-4]. Current therapeutic strategies are inefficient and cannot cure this chronic and debilitating condition [5]. Ultimately, heart transplants are required for patient survival, but donor organs are scarce in availability and only prolong the life-span of patients for a limited time. Fibrosis is one of the main pathological features of heart failure [6,7], caused by inappropriate stimulation of fibroblasts and excessive extracellular matrix production. Therefore, an in-depth understanding of the cardiac fibroblast is essential to underpin effective therapeutic treatments for heart failure [5]. Fibroblasts in general have been an underappreciated cell type, regarded as relatively inert and providing only basic functionality; they are usually referred to as the 'biological glue' of all tissues in the body. However, more recent literature suggests that they actively participate in organ homeostasis and disease [7,8]. We have recently uncovered a unique molecular identity for fibroblasts isolated from the heart [9], expressing a set of cardiogenic transcription factors that have been previously associated with cardiomyocyte ontogenesis. This signature suggests that cardiac fibroblasts may be ideal for use in stem cell replacement therapies, as they may retain the memory of where they derive from embryologically. Our data also revealed that about 90% of fibroblasts from both tail and heart origins share a cell surface signature that has previously been described for mesenchymal stem cells (MSCs), raising the possibility that fibroblasts and MSCs may in fact be the same cell type. Thus, our findings carry profound implications for the field of regenerative medicine. Here, we describe detailed methodology and quality controls related to the gene expression profiling of cardiac fibroblasts, deposited at the Gene Expression Omnibus (GEO) under the accession number GSE50531. We also provide the R code to easily reproduce the data quantification and analysis processes.

Project description:BACKGROUND: Real-time reverse transcriptase quantitative polymerase chain reaction (real-time RT-qPCR) is the most accurate measure of gene expression in biological systems. The comparison of different samples requires the transformation of data through a process called normalisation. Reference or housekeeping genes are candidate genes which are selected on the basis of constitutive expression across samples, and allow the quantification of changes in gene expression. At present, no reference gene has been identified for any organism which is universally optimal for use across different tissue types or disease situations. We used microarray data to identify new reference genes generated from total RNA isolated from normal and osteoarthritic canine articular tissues (bone, ligament, cartilage, synovium and fat). RT-qPCR assays were designed and applied to each different articular tissue. Reference gene expression stability and ranking was compared using three different mathematical algorithms. RESULTS: Twelve new potential reference genes were identified from microarray data. One gene (mitochondrial ribosomal protein S7 [MRPS7]) was stably expressed in all five of the articular tissues evaluated. One gene HIRA interacting protein 5 isoform 2 [HIRP5]) was stably expressed in four of the tissues evaluated. A commonly used reference gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was not stably expressed in any of the tissues evaluated. Most consistent agreement between rank ordering of reference genes was observed between Bestkeeper(c) and geNorm, although each method tended to agree on the identity of the most stably expressed genes and the least stably expressed genes for each tissue. New reference genes identified using microarray data normalised in a conventional manner were more stable than those identified by microarray data normalised by using a real-time RT-qPCR methodology. CONCLUSION: Microarray data normalised by a conventional manner can be filtered using a simple stepwise procedure to identify new reference genes, some of which will demonstrate good measures of stability. Mitochondrial ribosomal protein S7 is a new reference gene worthy of investigation in other canine tissues and diseases. Different methods of reference gene stability assessment will generally agree on the most and least stably expressed genes, when co-regulation is not present.

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New methods to analyse microarray data that partially lack a reference signal.

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New methods to analyse microarray data that partially lack a reference signal.

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