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On integrating multi-experiment microarray data.

ABSTRACT: With the extensive use of microarray technology as a potential prognostic and diagnostic tool, the comparison and reproducibility of results obtained from the use of different platforms is of interest. The integration of those datasets can yield more informative results corresponding to numerous datasets and microarray platforms. We developed a novel integration technique for microarray gene-expression data derived by different studies for the purpose of a two-way Bayesian partition modelling which estimates co-expression profiles under subsets of genes and between biological samples or experimental conditions. The suggested methodology transforms disparate gene-expression data on a common probability scale to obtain inter-study-validated gene signatures. We evaluated the performance of our model using artificial data. Finally, we applied our model to six publicly available cancer gene-expression datasets and compared our results with well-known integrative microarray data methods. Our study shows that the suggested framework can relieve the limited sample size problem while reporting high accuracies by integrating multi-experiment data.

SUBMITTER: Tsiliki G

PROVIDER: S-EPMC3996576 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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On integrating multi-experiment microarray data.

Tsiliki Georgia G Vlachakis Dimitrios D Kossida Sophia S

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 20140421 2016

With the extensive use of microarray technology as a potential prognostic and diagnostic tool, the comparison and reproducibility of results obtained from the use of different platforms is of interest. The integration of those datasets can yield more informative results corresponding to numerous datasets and microarray platforms. We developed a novel integration technique for microarray gene-expression data derived by different studies for the purpose of a two-way Bayesian partition modelling wh ...[more]

PMID: 24751870

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Project description:MotivationThe statistical analysis of single-cell data is a challenge in cell biological studies. Tailored statistical models and computational methods are required to resolve the subpopulation structure, i.e. to correctly identify and characterize subpopulations. These approaches also support the unraveling of sources of cell-to-cell variability. Finite mixture models have shown promise, but the available approaches are ill suited to the simultaneous consideration of data from multiple experimental conditions and to censored data. The prevalence and relevance of single-cell data and the lack of suitable computational analytics make automated methods, that are able to deal with the requirements posed by these data, necessary.ResultsWe present MEMO, a flexible mixture modeling framework that enables the simultaneous, automated analysis of censored and uncensored data acquired under multiple experimental conditions. MEMO is based on maximum-likelihood inference and allows for testing competing hypotheses. MEMO can be applied to a variety of different single-cell data types. We demonstrate the advantages of MEMO by analyzing right and interval censored single-cell microscopy data. Our results show that an examination of censoring and the simultaneous consideration of different experimental conditions are necessary to reveal biologically meaningful subpopulation structures. MEMO allows for a stringent analysis of single-cell data and enables researchers to avoid misinterpretation of censored data. Therefore, MEMO is a valuable asset for all fields that infer the characteristics of populations by looking at single individuals such as cell biology and medicine.Availability and implementationMEMO is implemented in MATLAB and freely available via github (https://github.com/MEMO-toolbox/MEMO).Contactseva-maria.geissen@ist.uni-stuttgart.de or nicole.radde@ist.uni-stuttgart.deSupplementary informationSupplementary data are available at Bioinformatics online.

Project description:Droughts are the most expensive natural disasters on the planet. As a result of climate change and human activities, the incidence and impact of drought have grown in China. Timely and effective monitoring of drought is crucial for water resource management, drought mitigation, and national food security. In this study, we constructed a comprehensive drought index (YCDI) suitable for the Yellow River Basin using principal component analysis and the entropy weight-AHP method, which integrated a standardized precipitation evapotranspiration index (SPEI), self-calibrating Palmer drought severity index (scPDSI), vegetation condition index (VCI), and standardized water storage index (SWSI). SWSI is calculated by the terrestrial water storage anomaly (TWSA), which can more comprehensively reflect the impact of surface water resources on drought (as compared with soil moisture-based indexes). The study results showed that: (1) compared with single drought index, YCDI has stronger ability to monitor drought process. In terms of time scale and drought degree, the monitoring results based on YCDI were similar with data presented in the China Flood and Drought Bulletin and Meteorological Drought Yearbook, reaching ~87% and ~69%, respectively. The correlation between drought intensity and crop harvest area was 0.56. (2) By the combined analysis of the Mann-Kendall test and Moving T test, it was found that the abrupt change of YCDI index at the time of 2009, mainly due to the precipitation in 2009 reached the lowest value in the past 30 years in northern China and extreme high temperature weather. (3) The YCDI of Henan and Shandong provinces in the middle and lower reaches of the basin decreased more significantly, with the maximum value reaching 0.097/yr, while the index in the upper reaches showed an increasing trend with the maximum rate of 0.096/yr. (4) The frequency of mild drought, moderate drought, severe drought and extreme drought in the Yellow River basin during the study period was 15.84%, 12.52%, 4.03% and 0.97%, respectively. Among them, the highest frequency of droughts occurred in Ningxia, Inner Mongolia and central Shaanxi provinces. Drought causation in the Yellow River basin is more influenced by human activities than climate change in the middle and lower reaches, while climate change is the main factor in the upper reaches. Overall, YCDI is a reliable indicator for monitoring the spatial and temporal evolution of drought in the Yellow River basin, and it can be used for monitoring soil moisture changes and vegetation dynamics, which can provide scientific guidance for regional drought governance.

Dataset Information

On integrating multi-experiment microarray data.

Publications

On integrating multi-experiment microarray data.

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