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Bootstrapped ROC

bROC algorithm is used in the discovery of differentially expressed probes/genes in microarray and RNA-seq experiments.

bROC plugin deploys in CLC Main Workbench and CLC Genomics Workbench.

ROC (receiver operating characteristic) is a generally applicable, non-parametric procedure that provides insight into the discriminatory properties of data features for a binary classifier. However, the method is not efficient for gene expression experiments as they generally do not produce a sufficient number of samples. bROC overcomes that limitation by resampling (bootstrapping) the expression data to produce a large number of simulated measurements that preserve the statistical properties of the original data.

Thus, bROC can produce detailed curves of sensitivity (probability of true positive detection) vs. 1-specificity (probability of false positive detection) for all features of interest. CONF = 2 AUC - 1, where AUC is the area under ROC curve, is the primary statistics used for detection of regulated features (probes/genes).

Version 3 (August 2013) includes data normalization and graphical outputs.

Benefits

  • Works particularly well for experiments with a small number of experimental/biological replicates.
  • Includes data normalization (for RNA-seq, mainly).
  • When combined with RNA-seq Analysis (CLC bio), provides complete differential expression analysis workflow for RNA-seq data.
  • Graphical outputs facilitate interpretation of results.
  • With no user-selectable parameters, the algorithm is easy to use.
  • Non-parametric approach is applicable to all platforms producing expression data for a large number of features (transcripts, genes).

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