Bullard, J. H., Purdom, E., Hansen, K. D. & Dudoit, S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments.
We are actively involved in evaluation of statistical methods for multiple types of genetic sequence data to provide high confidence in the results of our ongoing analyses.
Metcalfe [ 3] demonstrated the need to randomly generate these kind of data for the evaluation of statistical methods.
Notably, an evaluation of statistical methods for normalization in mRNA-Seq experiments stressed (ref. 2) the requirement for further research in the development of statistical and computational methods dedicated to the processing of mRNA-Seq data.
The Genetic Analysis Workshops are research meetings held every 2 years; they are devoted to the evaluation and comparison of statistical methods for mapping, identifying, and characterizing genetic factors involved in complex diseases.
An evaluation of the sensitivity and specificity of statistical methods used in BioSense has been published [ 19]; this includes a comparison with methods used in EARS but not with those used in other systems.
Comparisons and evaluations of the statistical methods currently included in CASE can be found in, for example, [ 14] and [ 15].
Statistical Papers provides a forum for the presentation and critical assessment of statistical methods.
The parallelism and the randomness of the algorithm bring additional issues to the application of statistical methods for the evaluation of performance, such as the choice of performance measures, the data distribution of these measures, and the variability brought by parallel executions.
The use of statistical methods for the evaluation of the results showed a large and significant difference in aromatic acid concentrations received using different sampling techniques and sample preparations.
More specifically, it can be useful for tuning software tools parameters, but also testing new algorithms for label-free quantitative analysis, or for evaluation of downstream statistical methods.
