+ Site Statistics
References:
54,258,434
Abstracts:
29,560,870
PMIDs:
28,072,757
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data



Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data



Bmc Bioinformatics 14: 214



The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool to be analyzed as a single data set (as in a melting pot of data). Specific issues relating to data heterogeneity across microarray studies, such as differences within and between labs or differences among experimental conditions, could lead to equivocal results in a melting pot approach. We applied statistical theory to determine the specific effect of different means and heteroskedasticity across 19 groups of microarray data on the sign and magnitude of gene-to-gene Pearson correlation coefficients obtained from the pool of 19 groups. We quantified the biases of the pooled coefficients and compared them to the biases of correlations estimated by an effect-size model. Mean differences across the 19 groups were the main factor determining the magnitude and sign of the pooled coefficients, which showed largest values of bias as they approached ±1. Only heteroskedasticity across the pool of 19 groups resulted in less efficient estimations of correlations than did a classical meta-analysis approach of combining correlation coefficients. These results were corroborated by simulation studies involving either mean differences or heteroskedasticity across a pool of N > 2 groups. The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 052269755

Download citation: RISBibTeXText

PMID: 23822712

DOI: 10.1186/1471-2105-14-214


Related references

DNA microarray Pooling of RNA versus pooling of gene expression data in the analysis of human prostate cancer specimens. Proceedings of the American Association for Cancer Research Annual Meeting 44: 699-700, July, 2003

Microarray Я US: a user-friendly graphical interface to Bioconductor tools that enables accurate microarray data analysis and expedites comprehensive functional analysis of microarray results. Bmc Research Notes 5: 282, 2013

Meta-analysis of QTc interval pooling data from heterogeneous trials. Pharmaceutical Statistics 1(1): 17-23, 2002

Integrated analysis of the heterogeneous microarray data. Bmc Bioinformatics 12 Suppl 5: S3, 2011

Application of reliability coefficients in cDNA microarray data analysis. Statistics in Medicine 25(6): 1051-1066, 2005

Texture analysis of heterogeneous data; a farewell to F-coefficients. Journal of Structural Geology 22(11-12): 1565-1568, 2000

A Microsoft FORTRAN 77 Program for Pooling Subgroup Correlation Coefficients. Educational and Psychological Measurement 57(5): 876-878, 1997

ArrayNorm: comprehensive normalization and analysis of microarray data. Bioinformatics 20(12): 1971-1973, 2004

Permutation-based adjustments for the significance of partial regression coefficients in microarray data analysis. Genetic Epidemiology 32(1): 1-8, 2007

Response to the pooling of heterogeneous data. Fertility & Sterility 80(3): 680-681, September, 2003

A comprehensive comparison of different clustering methods for reliability analysis of microarray data. Journal of Medical Signals and Sensors 3(1): 22-30, 2013

GSE: a comprehensive database system for the representation, retrieval, and analysis of microarray data. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 2008: 539-550, 2008

CARMAweb: comprehensive R- and bioconductor-based web service for microarray data analysis. Nucleic Acids Research 34(Web Server Issue): W498-W503, 2006

"Uneasy science"--the pooling of heterogeneous data. Fertility and Sterility 77(6): 1308-9; Author Reply 1309-11, 2002

Integration and analysis of heterogeneous microarray data sources for supporting drug target identification in atherosclerosis. BMC Systems Biology 1(1 Supplement): 0-0, 2007