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This method can be used to infer population structure by clustering samples into groups based upon ancestral groups [36].
For supervised clustering, samples from the two phenotypic groups (infiltrative/invasive and noninfiltrative/noninvasive according to histopathological criteria) were allocated to equivalent training and testing sets without preset criteria (Table 2).
Our study uses SSCC for clustering samples.
After clustering, samples were loaded on the Illumina GA-II machine.
The reduction to 500 PS also resulted in a better clustering of other 'distant clustering' samples.
Based on hierarchical clustering, samples from the larval, pupal, and adult stages were clearly separated.
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I examine the implications of clustered samples on inference.
Comparatively a high photodecomposition was noted from WO3nanowire cluster samples.
Fortunately, the cluster matching problem can be avoided by co-clustering samples 49, i.e., by clustering a concatenate consisting of either the entire.fcs data files for all samples, or of a random subset of events from each sample (to keep the total number of events within a reasonable range).
Unsupervised hierarchical clustering was used to cluster samples or genes.
Cluster sample study designs are cost effective, however cluster samples violate the simple random sample assumption of independence of observations.
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