Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery

Walsh, Christopher J.; Hu, Pingzhao; Batt, Jane; Santos, Claudia C. dos

doi:10.3390/microarrays4030389

Cited by 101 publications

(96 citation statements)

References 74 publications

(144 reference statements)

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“…Despite such advancements and success in utilizing microarrays and RNA-Seq technologies in the quest for unraveling the mechanisms of axolotl limb regeneration, a strong and popular methodology that has the potential for enhancing our current knowledge on limb regeneration is missing in the axolotl literature. Integrative data analysis (IDA) is a key methodology that is applied across many scientific disciplines and aims to derive scientific consensus on a particular research question [38][39][40]. Although the concept of IDA has recently been expanded to refer to experiments aiming to integrate information from several layers of "omics" information (aka multi-omics) [41], the utilized IDA in this study refers to the process of combining information from different platforms across independent studies [40].…”

Section: Introductionmentioning

confidence: 99%

“…Integrative data analysis (IDA) is a key methodology that is applied across many scientific disciplines and aims to derive scientific consensus on a particular research question [38][39][40]. Although the concept of IDA has recently been expanded to refer to experiments aiming to integrate information from several layers of "omics" information (aka multi-omics) [41], the utilized IDA in this study refers to the process of combining information from different platforms across independent studies [40]. The latter IDA concept is commonly used in biomedical sciences to detect differentially expressed (DE) genes for having a better gene signature for basic science and clinical applications [40,42,43].…”

Section: Introductionmentioning

confidence: 99%

“…Although the concept of IDA has recently been expanded to refer to experiments aiming to integrate information from several layers of "omics" information (aka multi-omics) [41], the utilized IDA in this study refers to the process of combining information from different platforms across independent studies [40]. The latter IDA concept is commonly used in biomedical sciences to detect differentially expressed (DE) genes for having a better gene signature for basic science and clinical applications [40,42,43]. As microarray and RNA-Seq data are increasingly becoming publicly available, IDA can benefit the axolotl regeneration field by improving candidate gene selection and establishing biomarkers for different stages of limb regeneration.…”

Section: Introductionmentioning

confidence: 99%

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Integrative Analysis of Axolotl Gene Expression Data From Regenerative and Wound Healing Limb Tissues

Sibai

Parlayan

Tuğlu

et al. 2019

Preprint

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Axolotl (Ambystoma mexicanum) is a urodele amphibian endowed with remarkable regenerative capacities manifested in scarless wound healing and full restoration of amputated limbs. Several regenerative cues of the axolotl limb were successfully unraveled due to the advent of highthroughput technologies and their employment in tackling research questions on several OMICS levels. The field of regenerative biology and medicine has therefore utilized the axolotl as a major and powerful experimental model. Studies which have previously unraveled differentially expressed (DE) genes en masse in different phases of the axolotl limb regeneration have primarily used microarrays and RNA-Seq technologies. However, as different labs are conducting such experiments, sufficient consistency may be lacking due to statistical limitations arising from limited number of sample replicates as well as possible differences in study designs.This study, therefore, aims to bridge such gaps by performing an integrative analysis of publicly available microarray and RNA-Seq data from axolotl limb samples having comparable study designs. Three biological groups were conceived for the analysis; homeostatic tissues (control group), from amputation/injury timepoint up to around 50 hours post amputation (wound healing group), and from 50 hours to 28 days post amputation/injury (regenerative group). Integrative analysis was separately carried out on the selected microarray and RNA-Seq data from axolotl limb samples using the "merging" method. Differential expression analysis was separately implemented on the processed data from both technologies using the R/Bioconductor "limma" package. A total of 1254 genes (adjusted P < 0.01) were found DE in regenerative samples compared to the control, out of which 351 showed magnitudes of Log Fold Changes (LogFC) > 1 and were identified as the top DE genes from data of both technologies. Downstream analyses illustrated consistent correlations of the logFCs of DE genes distributed among the biological comparisons, within and between both technologies. Gene ontology annotations demonstrated concordance with the literature on the biological process involved in the axolotl limb regeneration. qPCR analysis validated the observed gene expression level differences between regenerative and control samples for a set of five genes. Future studies may benefit from the utilized concept and approach for enhanced statistical power and robust discovery of biomarkers of regeneration.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrative Analysis of Axolotl Gene Expression Data From Regenerative and Wound Healing Limb Tissues

Sibai

Parlayan

Tuğlu

et al. 2019

Preprint

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“…A drawback of this method is that it is hard to control the distribution of data after normalization. A recent survey of Affymetrix microarray data normalization software can be found in [20]. Our approach dealing with distributions opens the way to normalising data generated from different platforms or chip-sets.…”

Section: Introductionmentioning

confidence: 99%

DBNorm: normalizing high-density oligonucleotide microarray data based on distributions

et al. 2017

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BackgroundData from patients with rare diseases is often produced using different platforms and probe sets because patients are widely distributed in space and time. Aggregating such data requires a method of normalization that makes patient records comparable.ResultsThis paper proposed DBNorm, implemented as an R package, is an algorithm that normalizes arbitrarily distributed data to a common, comparable form. Specifically, DBNorm merges data distributions by fitting functions to each of them, and using the probability of each element drawn from the fitted distribution to merge it into a global distribution. DBNorm contains state-of-the-art fitting functions including Polynomial, Fourier and Gaussian distributions, and also allows users to define their own fitting functions if required.ConclusionsThe performance of DBNorm is compared with z-score, average difference, quantile normalization and ComBat on a set of datasets, including several that are publically available. The performance of these normalization methods are compared using statistics, visualization, and classification when class labels are known based on a number of self-generated and public microarray datasets. The experimental results show that DBNorm achieves better normalization results than conventional methods. Finally, the approach has the potential to be applicable outside bioinformatics analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1912-5) contains supplementary material, which is available to authorized users.

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“…Pathway analysis is the common term for these methods [12,76]. We used sub-network enrichment analysis (SNEA) method [77] for pathway analysis of AGA hair follicle-related public microarray data.…”

Section: Pathway Analysis Of Microarray Experimental Data In Agamentioning

confidence: 99%

Androgenic Alopecia: Cross-Talk Between Cell Signal Transduction Pathways

Nesterova¹,

Yuryev²

2017

Hair and Scalp Disorders

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Signaling pathways that control coordination of hair follicle cells genetic program are the convenient model for explaining the hierarchy of intercellular interactions governing cyclic growth of hair. This chapter describes models of molecular signaling pathways specific to dermal papilla cells from balding human scalp in hair follicle cycle. These models include already published data, as well as information inferred from pathway analysis of microarray data and protein-protein interaction database. Interplay of androgenic-alopecia-related signaling pathways FGF, TGFB, BMP, and WNT, as well as cyclin-dependent kinases signaling, is shown.

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Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery

Cited by 101 publications

References 74 publications

Integrative Analysis of Axolotl Gene Expression Data From Regenerative and Wound Healing Limb Tissues

Integrative Analysis of Axolotl Gene Expression Data From Regenerative and Wound Healing Limb Tissues

DBNorm: normalizing high-density oligonucleotide microarray data based on distributions

Androgenic Alopecia: Cross-Talk Between Cell Signal Transduction Pathways

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