Identification of embryonic pancreatic genes using <i>Xenopus</i> DNA microarrays

Hayata, Tadayoshi; Blitz, Ira L.; Iwata, Nahoko; Cho, Ken W.Y.

doi:10.1002/dvdy.21868

Cited by 5 publications

(1 citation statement)

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“…We have selected the datasets under the GEO accession numbers GSE18057 [ 23 ], GSE10124 [ 24 ], GSE12736 [ 25 ], and GSE9386 [ 26 ] whose clustering analysis have been previously provided by the relevant references. The four datasets were derived from the species Oryza sativa (Asian rice), Xenopus laevis (African clawed frog), Homo sapiens (human), and Zea mays (maize), respectively, and their respective numbers of samples are 36, 6, 16, and 24.…”

Section: Methodsmentioning

confidence: 99%

UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Abu‐Jamous

Roberts

et al. 2015

BMC Bioinformatics

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BackgroundCollective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets.ResultsHere, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn.ConclusionsThe UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0614-0) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Abu‐Jamous

Roberts

et al. 2015

BMC Bioinformatics

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Expression Data Analysis for the Identification of Potential Biomarker of Pregnancy Associated Breast Cancer

Thanmalagan

Naorem

Venkatesan

2016

Pathol. Oncol. Res.

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Breast cancer affects every 1 of 3000 pregnant women or in the first post-partum year is referred as Pregnancy Associated Breast Cancer (PABC) in mid 30s. Even-though rare disease, classified under hormone receptor negative status which metastasis quickly to other parts by extra cellular matrix degradation. Hence it is important to find an optimal treatment option for a PABC patient. Also additional care should be taken to choose the drug; in order to avoid fetal malformation and post-partum stage side-effects. The adaptation of target based therapy in the clinical practice may help to substitute the mastectomy treatment. Recent studies suggested that certain altered Post Translational Modifications (PTMs) may be an indicative of breast cancer progression; an attempt is made to consider the over represented PTM as a parameter for gene selection. The public dataset of PABC from GEO were examined to select Differentially Expressed Genes (DEG). The corresponding PTMs for DEG were collected and association between them was found using data mining technique. Usually clustering algorithm has been applied for the study of gene expression with drawback of clustering of gene products based on specified features. But association rule mining method overcome this shortcoming and determines the useful and in depth relationships. From the association, genes were selected to study the interactions and pathways. These studies emphasis that the genes KLF12, FEN1 MUC1 and SP110, can be chosen as target, which control cancer development, without any harm to pregnancy as well as fetal developmental process.

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Chronic sublethal exposure to silver nanoparticles disrupts thyroid hormone signaling during Xenopus laevis metamorphosis

et al. 2015

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Identification of embryonic pancreatic genes using Xenopus DNA microarrays

Cited by 5 publications

References 100 publications

UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Expression Data Analysis for the Identification of Potential Biomarker of Pregnancy Associated Breast Cancer

Chronic sublethal exposure to silver nanoparticles disrupts thyroid hormone signaling during Xenopus laevis metamorphosis

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