A statistical framework for integrating two microarray data sets in differential expression analysis

<p>Abstract</p> <p>Background</p> <p>Different microarray data sets can be collected for studying the same or similar diseases. We expect to achieve a more efficient analysis of differential expression if an efficient statistical method can be developed for integrating different microarray data sets. Although many statistical methods have been proposed for data integration, the genome-wide concordance of different data sets has not been well considered in the analysis.</p> <p>Results</p> <p>Before considering data integration, it is necessary to evaluate the genome-wide concordance so that misleading results can be avoided. Based on the test results, different subsequent actions are suggested. The evaluation of genome-wide concordance and the data integration can be achieved based on the normal distribution based mixture models.</p> <p>Conclusion</p> <p>The results from our simulation study suggest that misleading results can be generated if the genome-wide concordance issue is not appropriately considered. Our method provides a rigorous parametric solution. The results also show that our method is robust to certain model misspecification and is practically useful for the integrative analysis of differential expression.</p

Parallel multiplicity and error discovery rate (EDR) in microarray experiments

<p>Abstract</p> <p>Background</p> <p>In microarray gene expression profiling experiments, differentially expressed genes (DEGs) are detected from among tens of thousands of genes on an array using statistical tests. It is important to control the number of false positives or errors that are present in the resultant DEG list. To date, more than 20 different multiple test methods have been reported that compute overall Type I error rates in microarray experiments. However, these methods share the following dilemma: they have low power in cases where only a small number of DEGs exist among a large number of total genes on the array.</p> <p>Results</p> <p>This study contrasts parallel multiplicity of objectively related tests against the traditional simultaneousness of subjectively related tests and proposes a new assessment called the Error Discovery Rate (EDR) for evaluating multiple test comparisons in microarray experiments. Parallel multiple tests use only the negative genes that parallel the positive genes to control the error rate; while simultaneous multiple tests use the total unchanged gene number for error estimates. Here, we demonstrate that the EDR method exhibits improved performance over other methods in specificity and sensitivity in testing expression data sets with sequence digital expression confirmation, in examining simulation data, as well as for three experimental data sets that vary in the proportion of DEGs. The EDR method overcomes a common problem of previous multiple test procedures, namely that the Type I error rate detection power is low when the total gene number used is large but the DEG number is small.</p> <p>Conclusions</p> <p>Microarrays are extensively used to address many research questions. However, there is potential to improve the sensitivity and specificity of microarray data analysis by developing improved multiple test comparisons. This study proposes a new view of multiplicity in microarray experiments and the EDR provides an alternative multiple test method for Type I error control in microarray experiments.</p

Gene Expression Profiling in the Type 1 Diabetes Rat Diaphragm

BACKGROUND:Respiratory muscle contractile performance is impaired by diabetes, mechanisms of which included altered carbohydrate and lipid metabolism, oxidative stress and changes in membrane electrophysiology. The present study examined to what extent these cellular perturbations involve changes in gene expression. METHODOLOGY/PRINCIPAL FINDINGS:Diaphragm muscle from streptozotocin-diabetic rats was analyzed with Affymetrix gene expression arrays. Diaphragm from diabetic rats had 105 genes with at least +/-2-fold significantly changed expression (55 increased, 50 decreased), and these were assigned to gene ontology groups based on over-representation analysis using DAVID software. There was increased expression of genes involved in palmitoyl-CoA hydrolase activity (a component of lipid metabolism) (P = 0.037, n = 2 genes, fold change 4.2 to 27.5) and reduced expression of genes related to carbohydrate metabolism (P = 0.000061, n = 8 genes, fold change -2.0 to -8.5). Other gene ontology groups among upregulated genes were protein ubiquitination (P = 0.0053, n = 4, fold change 2.2 to 3.4), oxidoreductase activity (P = 0.024, n = 8, fold change 2.1 to 6.0), and morphogenesis (P = 0.012, n = 10, fold change 2.1 to 4.3). Other downregulated gene groups were extracellular region (including extracellular matrix and collagen) (P = 0.00032, n = 13, fold change -2.2 to -3.7) and organogenesis (P = 0.032, n = 7, fold change -2.1 to -3.7). Real-time PCR confirmed the directionality of changes in gene expression for 30 of 31 genes tested. CONCLUSIONS/SIGNIFICANCE:These data indicate that in diaphragm muscle type 1 diabetes increases expression of genes involved in lipid energetics, oxidative stress and protein ubiquitination, decreases expression of genes involved in carbohydrate metabolism, and has little effect on expression of ion channel genes. Reciprocal changes in expression of genes involved in carbohydrate and lipid metabolism may change the availability of energetic substrates and thereby directly modulate fatigue resistance, an important issue for a muscle like the diaphragm which needs to contract without rest for the entire lifetime of the organism

I Keep my Problems to Myself: Negative Social Network Orientation, Social Resources, and Health-Related Quality of Life in Cancer Survivors

BACKGROUND: Cancer survivors treated with hematopoietic stem cell transplant rely on their social network for successful recovery. However, some survivors have negative attitudes about using social resources (negative social network orientation) that are critical for their recovery. PURPOSE: We examined the association between survivors’ social network orientation and health-related quality of life (HRQoL) and whether it was mediated by social resources (network size, perceived support, and negative and positive support-related social exchanges). METHODS: In a longitudinal study, 255 survivors completed validated measures of social network orientation, HRQoL, and social resources. Hypotheses were tested using path analysis. RESULTS: More negative social network orientation predicted worse HRQoL (p < .001). This association was partially mediated by lower perceived support and more negative social exchanges. CONCLUSIONS: Survivors with negative social network orientation may have poorer HRQoL in part due to deficits in several key social resources. Findings highlight a subgroup at risk for poor transplant outcomes and can guide intervention development