Will try to detect off-targets and low quality alignments (outliers). It
tries k-means clustering on normalized number of events per read and read
alignment score. If there are 3 clusters (decided based on silhouette
criterion) cluster with high event count and low alignment score will be
marked for filtering. When there is less than 1000
aln it will filter nothing.
(data.frame) Should contain events from alignments in GRanges style with columns eg. seqnames, width, start, end, score.
(logical vector) where TRUE indicates events that are potential off-targets or low quality alignments.
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