Simulated average power rates with respect to the log fold change .

<p>Left: component-wise testing (dotted line), target testing with ‘ROAST’ (dashed line) and the combination approach (solid line), each in the case of an autoregressive covariance structure and non-overlapping target sets. Right: combination approach based on ‘globaltest’ (dotted line), ‘Wilcoxon’ (dashed line) and ‘Romer’ (solid line), each in the case of an unstructured covariance matrix and overlapping target sets.</p

Simulated for microRNA-selection based on combined target set and microRNA-wise testing.

<p>Simulated with respect to the type of gene set test and covariance matrix using the approach of combined target set and microRNA-wise testing. Results are presented for the simulation setting of overlapping and disjunct target sets. Presented numbers are the minimum, median and maximum simulated across the range of the log fold change (between 0 and 6). Rates larger than the pre-specified level of 0.05 are printed in bold.</p

Effect of overlapping target gene sets on the simulated .

<p>Effects are presented for component-wise testing (dotted line), target set-wise testing (dashed line) and the combination approach (solid line). While the competitive approaches such as the ‘Wilcoxon’-based gene set test (top) still maintained the pre-specified -level of 5 when target gene sets overlapped, the increased dramatically when employing the self-contained approaches such as the ‘globaltest’ procedure (bottom).</p

GO-Terms for Data Examples.

<p>The top-scoring Gene Ontology (GO) terms with lowest p-values (according to one-sided Fisher’s exact test) of miRNAs’ target sets from Neurogenesis (above) and HIV (below) data example.</p

Relation between simulated power rate curves.

<p>Relation between simulated power rate curves of microRNA-wise, target set-wise and combined testing. Although, power curves sometimes intersected, this table gives the general tendency of the relations between the three approaches. *Compare <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038365#pone-0038365-g003" target="_blank">Figure 3</a> left.</p

Flow chart of combining expression levels of miRNAs and their related target mRNAs.

<p>The links between microRNAs and their target mRNAs are taken from public databases. MicroRNAs and target sets are first analysed separately and obtained <i>p</i>-values are combined as final result.</p

Additional file 1 of Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images

Supplementary material 1

The Antiapoptotic Function of miR-96 in Prostate Cancer by Inhibition of FOXO1

<div><p>microRNAs (miRNAs) are small molecules that regulate gene expression posttranscriptionally. In a previous study, we identified miR-96 to be upregulated in prostate cancer specimens in comparison to normal adjacent tissue and to be an independent marker of biochemical relapse in a multivariate prediction model. Therefore, we investigated the functional role of miR-96 in prostate carcinogenesis. LNCaP and DU145 prostate cancer cells were transiently transfected with miR-96 precursors and phenotypic changes were analyzed. The miR-96 increased proliferation and impaired apoptosis induced by camptothecine in these cells. In silico target prediction analysis identified FOXO1 as potential pro-apoptotic miR-96 target. miR-96 was able to bind to both bindings sites in the FOXO1 3’ UTR in a luciferase reporter gene assay. Overexpression of miR-96 in LNCaP cells resulted in a reduced FOXO1 expression. Overexpression of FOXO1 induced a strong apoptotic phenotype that was partially rescued by coexpression of miR-96. RT-qPCR and immunohistochemistry of 69 prostate cancer specimens revealed a downregulation of FOXO1 and an inverse correlation of miR-96 and FOXO1 protein expression. In conclusion, we show that miR-96 can regulate apoptosis in prostate cancer, by inhibiting the FOXO1 transcription factor.</p> </div

Additional file 5 of Gene expression profiles in neurological tissues during West Nile virus infection: a critical meta-analysis

Result lists from GSEA analyses in group 2. The table displays result lists of the GSEA in group 2. Rows are representing those GO terms, for which at least two genes were available in each of the analysis variants. The GO-ID, as well as their specific name is given in the first column. The p-values (‘.p’) and adjusted p-values (‘.q’) are shown for each analysis variant (early, late and intermediate merging). The number of genes associated to the GO-term is presented in column ‘nPGenes’. The number of genes associated to the GO-term, which can be found in the data is presented in columns ‘early.nPGenes’ and ‘int.nPGenes’. (XLSX 947 kb