Finds the cutoff point for the barcode count filtering based on the barcode count distribution.
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A logical value, if
The one dimension kmeans clustering is applied for identify the "true barcode" based on read count. The the algorithm detail is: 1. Remove the barcodes with count below the median of counts. 2. Transform the count by log2(x+1). 3. Apply the 1 dimension clustering to the logarized count, with the cluster number of 2 and weights of the logarized count. 4. Choose the minimum count value in the cluster with higher count as cutoff point.
For more info about 1 dimension kmeans used here please refer to
Ckmeans.1d.dp, which has been used here.
vector of the cutoff point.
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