Description Usage Arguments Details Value References Examples
This function calculates the proportion of ambiguous clustering (PAC) for each value of k tested via consensus clustering.
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consensus_mats |
A list of consensus matrices, as created by a call to
|
pac_window |
Lower and upper bounds for the consensus index sub-interval over which to calculate the PAC. Must be on (0, 1). |
plot |
Return plot of PAC scores by k? |
Consensus clustering is a method for testing the stability of cluster
membership under resampling (Monti et al., 2003). Senbabaoglu et al. (2014)
demonstrated that traditional methods for estimating optimal cluster number
fail when probes are not independent, which they rarely are in omic data. The
authors propose a new measure, the proportion of ambiguous clustering (PAC),
which represents the increase in the empirical CDF curve for each potential
cluster number k over a user-defined sub-interval of the consensus
index generated by the consensus cluster algorithm. The default settings of
pac_window = c(0.1, 0.9)
are taken from the original PAC paper, and
generally lead to stable results.
A data frame with PAC scores for each value of k in
consensus_mats
. If plot = TRUE
, then PAC scores by k are
plotted.
Monti, S., Tamayo, P., Mesirov, J., & Golub, T. (2003). Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data. Machine Learning, 52: 91-118.
Senbabaoglu, Y., Michailidis, G. & Li, J.Z. (2014). Critical limitations of consensus clustering in class discovery. Scientific Reports, 4:6207.
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