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Combining the output from the parametric and non-parametric array analyses resulted in a collection of 1,552 differentially expressed genes (limma n = 903, RankProd n = 649) that broadly mirrored previous findings from the datasets in question but were necessarily more inclusive.
Analysis of the datasets in question revealed that total daily activity was not significantly different in either the telemetry or the RW.
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The modified cost matrix defined in Section 3.4 is agnostic to the size of the dataset in question.
The sequence termination and additional filtering required will both depend on the characteristics of the dataset in question.
The time required for these corrections very much depends on the size and features of the dataset in question.
We note that for the dataset in question, the initial R-squared value is close to 0.38, and upon completion of the algorithm R-squared is approximately 0.75.
Plots designated as outliers are distinctive in their composition and insufficiently replicated across the dataset in question to allow a cluster to be defined (De Cáceres et al. 2010); in our example these plots are too dissimilar to be assigned to any associations or alliances defined using the national New Zealand dataset (Wiser and De Cáceres 2013).
An algorithm whose scores fall in that area would be both highly reproducible and highly accurate, and none of the methods we consider here reach that level of performance, irrespective of the dataset in question.
The AU test estimates the probability that a given tree is the true tree according to which a dataset in question was generated.
Third, we used both PAM and hierarchical clustering, rather than K-means clustering, which may have different performance characteristics depending on the nature of the dataset in question.
However, if the dataset in question violates such assumptions or is very large, as would be the case for NGS datasets, these approaches tend to suffer from long run times.
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