choose_k-method: Choice of the final classification of peaks

Description Usage Arguments Details Value Author(s) See Also Examples

Description

Selection of the final classification of the peaks, given the desired number of clusters and the presence or absence of the alignment procedure chosen. This choice is usually driven by the graph returned by cluster_peak.

Usage

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## S4 method for signature 'GRanges'
choose_k(object, k = NULL, shift.peak = NULL, cleaning = TRUE)

Arguments

object

GRanges object. It must contain the metadata columns associated to the chosen classification. Further details are provided in shift.peak.

k

integer. Number of chosen clusters.

shift.peak

logical. If TRUE, the clustering with alignment is chosen, if FALSE, the classification without alignment is selected. If shift.peak = TRUE, object must contain the metadata columns labels_shift, coef_shift, dist_shift; if shift.peak = FALSE it must contain the columns labels_NOshift and dist_NOshift.

cleaning

logical. If TRUE, all metadata columns generated by FunChIP on the GRanges object are removed, and one new column containing the classification result is added: cluster. If FALSE, the metadata columns generated by FunChIP are kept. Default is TRUE. See Value for further details on the metadata column added.

Details

The choice of the optimal number of clusters and the presence of alignment can be guided by the graph plotted in the cluster_peak method. In particular, for the optimal number of clusters k the distance significantly decreases with respect to the lower values of k, and negligibly increases with respect to higher values of k (elbow in the line). The introduction of the alignment leads to better clustering of the data if if the global distance is significantly lowered. registration.

Value

if cleaning = FALSE, the GRanges object with a new metadata column:

If cleaning = TRUE, all the metadata columns added through all the analysis are removed and object is returned with just the metadata column cluster

Author(s)

Alice Parodi, Marco J. Morelli, Laura M. Sangalli, Piercesare Secchi, Simone Vantini

See Also

cluster_peak

Examples

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# load the data
data(peaks)

# k = 3 clusters with the alignment
# with integer shifts are chosen

peaks.classified <- choose_k(peaks.data.cluster, k = 3, 
shift.peak = TRUE, cleaning = FALSE)

FunChIP documentation built on Nov. 17, 2017, 9:59 a.m.