Statistical analysis of an RNA titration series evaluates microarray precision and sensitivity on a whole-array basis

BACKGROUND: Concerns are often raised about the accuracy of microarray technologies and the degree of cross-platform agreement, but there are yet no methods which can unambiguously evaluate precision and sensitivity for these technologies on a whole-array basis. RESULTS: A methodology is described for evaluating the precision and sensitivity of whole-genome gene expression technologies such as microarrays. The method consists of an easy-to-construct titration series of RNA samples and an associated statistical analysis using non-linear regression. The method evaluates the precision and responsiveness of each microarray platform on a whole-array basis, i.e., using all the probes, without the need to match probes across platforms. An experiment is conducted to assess and compare four widely used microarray platforms. All four platforms are shown to have satisfactory precision but the commercial platforms are superior for resolving differential expression for genes at lower expression levels. The effective precision of the two-color platforms is improved by allowing for probe-specific dye-effects in the statistical model. The methodology is used to compare three data extraction algorithms for the Affymetrix platforms, demonstrating poor performance for the commonly used proprietary algorithm relative to the other algorithms. For probes which can be matched across platforms, the cross-platform variability is decomposed into within-platform and between-platform components, showing that platform disagreement is almost entirely systematic rather than due to measurement variability. CONCLUSION: The results demonstrate good precision and sensitivity for all the platforms, but highlight the need for improved probe annotation. They quantify the extent to which cross-platform measures can be expected to be less accurate than within-platform comparisons for predicting disease progression or outcome

Empirical array quality weights in the analysis of microarray data

BACKGROUND: Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or "filtered" from further analysis to avoid misleading results. RESULTS: In this article, a graduated approach to array quality is considered based on empirical reproducibility of the gene expression measures from replicate arrays. Weights are assigned to each microarray by fitting a heteroscedastic linear model with shared array variance terms. A novel gene-by-gene update algorithm is used to efficiently estimate the array variances. The inverse variances are used as weights in the linear model analysis to identify differentially expressed genes. The method successfully assigns lower weights to less reproducible arrays from different experiments. Down-weighting the observations from suspect arrays increases the power to detect differential expression. In smaller experiments, this approach outperforms the usual method of filtering the data. The method is available in the limma software package which is implemented in the R software environment. CONCLUSION: This method complements existing normalisation and spot quality procedures, and allows poorer quality arrays, which would otherwise be discarded, to be included in an analysis. It is applicable to microarray data from experiments with some level of replication

Terminal osteoblast differentiation, mediated by runx2 and p27KIP1, is disrupted in osteosarcoma

The molecular basis for the inverse relationship between differentiation and tumorigenesis is unknown. The function of runx2, a master regulator of osteoblast differentiation belonging to the runt family of tumor suppressor genes, is consistently disrupted in osteosarcoma cell lines. Ectopic expression of runx2 induces p27KIP1, thereby inhibiting the activity of S-phase cyclin complexes and leading to the dephosphorylation of the retinoblastoma tumor suppressor protein (pRb) and a G1 cell cycle arrest. Runx2 physically interacts with the hypophosphorylated form of pRb, a known coactivator of runx2, thereby completing a feed-forward loop in which progressive cell cycle exit promotes increased expression of the osteoblast phenotype. Loss of p27KIP1 perturbs transient and terminal cell cycle exit in osteoblasts. Consistent with the incompatibility of malignant transformation and permanent cell cycle exit, loss of p27KIP1 expression correlates with dedifferentiation in high-grade human osteosarcomas. Physiologic coupling of osteoblast differentiation to cell cycle withdrawal is mediated through runx2 and p27KIP1, and these processes are disrupted in osteosarcoma

Additional file 1: Table S1. of Consistent gene expression profiles in MexTAg transgenic mouse and wild type mouse asbestos-induced mesothelioma

PCR primers. (DOCX 23 kb