Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data

Background 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. Results 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 \u3e 2 groups. Conclusions The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies

Persistence of travel and leisure sector equity indices

Volatility persistence of travel and leisure sector equity indices and of some of its components is analyzed, and tests of whether persistence has changed over time are performed. Given the typical leading indicator behavior of financial variables, understanding and characterizing the properties of these indices may help shed light on the behavior of the tourism sector and of its resilience to crises. For the purpose of analysis, our sample is split into three subsamples according to the World tourism cycle: (i) from January 1996 to December 2002; (ii) from January 2003 to August 2007; and (iii) from September 2007 to July 2014. Results suggest the existence of long-memory dynamics driving series volatility, and that shocks to volatility tend to be more persistent during periods of turmoil and affect regions differently.info:eu-repo/semantics/publishedVersio