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SMVar and RVM inflate the expected number of false-positives whereas Wilcoxon and the t-test tend to be conservative.
SMVar and RVM inflate the expected number of false-positives whereas Wilcoxon and the t-test tend to be conservative; ANOVA, SAM, limma and VarMixt show no deviation.
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In addition to showing the PPV resulting from values of prevalence, sensitivity and specificity, Box 2 also shows the expected number of lives saved by screening and the expected number of false positives for every 10,000 individuals screened.
For the cohort, the expected number of true positive test results = NPX, whilst the expected number of false positives = N 1- P)(1- Y).
Our results raise concerns about the ability of some methods to control the expected number of false positives at a desirable level.
Yet, for the five individuals there are few, if any, meaningful differences in gene expression (only one individual had more than the expected number of false positives, Fig. 3).
Only one individual had more than the expected number of false positives at the critical p-value: individual-00 had 7 (2%) significant genes at p-value 0.01 for 304 genes.
To reduce the complexity, and the expected number of false positives, we decided to evaluate only those genes with a high number of mismatches (irrespective of the samples cancer status).
Meinshausen and Bühlmann (2010) suggested a decision rule based on SSPs to control the expected number of false positives.
Hypergeometric E-value (expected number of false positives) was computed with the RSAT tool, compare-classes[ 19].
This found a threshold for p below which the expected number of false positives was less than 5%.
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