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We observe that two curves match well when the sample size is small.
Generally, this approach works well when the sample size is sufficiently high and a flexible model is used.
Combinatorial mutual information works well when the sample sizes are small, and performs better than univariate significance testing.
The advantages of MIRA over RA are as follows: Combinatorial mutual information works well when the sample sizes are small, and performs better than univariate significance testing.
One limitation of our regression-based normalization is that it works well when the sample size of the experiment is fairly large, such as our example (n = 60) and the GEO datasets (n ≥ 12).
We did not use the full sample because the Hosmer-Lemeshow test has been shown to likely reject the null hypothesis of a good fit even for models that fit well when the sample size is greater than 25,000 due to increased statistical power [ 28].
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We believe that misclassification bias is unlikely to fully explain our observations as the same cohort segregated well when the CSF samples were applied to the antigen-specific ELISPOT test [28].
Analysis conditional on having proceeded to the second stage required adapted analysis methods, and a uniformly minimum variance conditional estimator (UMVCUE) can be used, which also performs well when the second stage sample size is slightly different from planned.
Compared to KM-RBF and KMRBF-BP, ILRBF-BP can adapt the training sample space well; when the number of training samples is changed, the number of RBF hidden neurons in ILRBF-BP is changed accordingly and can get a higher classifying accuracy.
Golub's weighted voting method (Golub et al, 1999) and the Compound Covariate Predictor of Radmacher et al (2002) are similar to diagonal linear discriminant analysis and tend to perform very well when the number of samples is small.
Then Robinson et al. [ 12] apply the TMM normalization factor TMM k (r ) to detect DE genes.The TMM normalization can normalize the samples well when the log-fold-changes are symmetry (Additional file 1: Figure S1).
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