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To resolve the first of these challenges, we have developed a method for significance clustering based on the parametric bootstrap.
Comparison with SAM [ 16], a popular method for significance analysis of microarrays, is also provided in this paper.
Enriched functions, such as common gene ontology, biological pathways, shared transcription factor or miRNA binding sites, were reported using the default g:SCS method for significance threshold determination.
For instance, Roxas and Li (2008) have demonstrated that the SAM method for significance analysis of microarrays (Tusher et al. 2001) can be effectively adapted to proteomics data for which, when compared to conventional t test, it provides richer information about protein differential expression profiles and better estimation of false discovery rates and miss rates.
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being significant (see methods for significance test).
In accord with these studies, we have observed significant gamma oscillations in the LFP for responses to the preferred orientation for 99% of all recording sites (P < 0.05, 2-tailed z-score relative to the spontaneous activity; see Material and Methods for significance criteria).
Hence, we performed SAMseq, a nonparametric method for estimating significance in RNA-seq data (Li and Tibshirani 2011) and discovered 3690 significant transcripts with FDR < 0.05.
To determine the number of statistically significant differentially expressed genes for hierarchical clustering, we performed SAMseq, a nonparametric method for estimating significance in RNA-seq data (Li and Tibshirani, 2011) and discovered 3690 significant transcripts with FDR < 0.05.
Analysis was done using the t-test method for determining significance of CRX expression across multiple datasets for normal and cancer cell lines.
The odds ratios for different association models were calculated with 95% confidence interval (CI) by multiple logistic regression with confounders determined by a backward conditional elimination method for a significance level below 0.05.
Comparisons were made using the Delong method for statistical significance of AUC [ 21].
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