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Previous cross-platform reproducibility studies have compared consistency of intensities as well as consistency of fold changes across different platforms using Pearson's correlation coefficient. In this study, we propose the use of measurement error models for estimating gene-specific correlations. Additionally, gene-specific reliability estimates are shown to be useful in prioritizing clones for sequence verification rather than selecting clones using a simple random sample. The proposed 'disattenuated' correlation may prove useful in a wide variety of studies when both X and Y are measured with error, such as in confirmation studies of microarray gene expression values, wherein more reliable laboratory assays such as real-time polymerase chain reaction are used.

The genome of Streptococcus sanguinis is a circular DNA molecule consisting of 2,388,435 bp and is 177 to 590 kb larger than the other 21 streptococcal genomes that have been sequenced. The G+C content of the S. sanguinis genome is 43.4%, which is considerably higher than the G+C contents of other streptococci. The genome encodes 2,274 predicted proteins, 61 tRNAs, and four rRNA operons. A 70-kb region encoding pathways for vitamin B 12 biosynthesis and degradation of ethanolamine and propanediol was apparently acquired by horizontal gene transfer. The gene complement suggests new hypotheses for the pathogenesis and virulence of S. sanguinis and differs from the gene complements of other pathogenic and nonpathogenic streptococci. In particular, S. sanguinis possesses a remarkable abundance of putative surface proteins, which may permit it to be a primary colonizer of the oral cavity and agent of streptococcal endocarditis and infection in neutropenic patients.

Background Recent research examining cross-platform correlation of gene expression intensities has yielded mixed results. In this study, we demonstrate use of a correction factor for estimating cross-platform correlations. Results In this paper, three technical replicate microarrays were hybridized to each of three platforms. The three platforms were then analyzed to assess both intra- and cross-platform reproducibility. We present various methods for examining intra-platform reproducibility. We also examine cross-platform reproducibility using Pearson's correlation. Additionally, we previously developed a correction factor for Pearson's correlation which is applicable when X and Y are measured with error. Herein we demonstrate that correcting for measurement error by estimating the "disattenuated" correlation substantially improves cross-platform correlations. Conclusion When estimating cross-platform correlation, it is essential to thoroughly evaluate intra-platform reproducibility as a first step. In addition, since measurement error is present in microarray gene expression data, methods to correct for attenuation are useful in decreasing the bias in cross-platform correlation estimates.