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However, in the context of gene expression, clustering methods suffer from three main drawbacks.
First, clustering methods only look at co-expression among all samples.
A third limitation of clustering methods is that they ignore the regulatory relationships between genes.
Hierarchical clustering methods construct a hierarchy of all the genes within the expression matrix.
Thalamuthu, A., Mukhopadhyay, I., Zheng, X. & Tseng, G. C. Evaluation and comparison of gene clustering methods in microarray analysis.
Clustering methods are compared using quality metrics.
Fig. 13 Comparison of clustering methods.
His publications include extensive work on clustering methods and applications in biostatistics.
All three tools use dimension reduction techniques and clustering methods.
Support-based clustering methods always undergo two phases.
Table 2 One way ANOVA test for multiple comparisons (I) Clustering methods (J) Clustering methods Mean difference (I-J) Std.
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