Fig. 3: HATCHet identifies moderate amount of subclonal CNAs in prostate cancer patients.

a HATCHet identifies subclonal CNAs in 29 samples, while Battenberg identifies subclonal CNAs in all 49 samples. b In the 29 samples where both methods identify subclonal CNAs, HATCHet and Battenberg infer similar fractions of the genome with subclonal CNAs (dotted diagonal), while in the other 20 samples only Battenberg retrieves relatively high fractions of subclonal CNAs. c In sample A10-C of patient A10, both HATCHet and Battenberg identify reliable subclonal CNAs that correspond to sample-subclonal clusters (magenta) with clearly intermediate positions in the scaled BAF-RDR plot (each point corresponds to 50 kb genomic bin) between those of sample-clonal clusters (black clusters with corresponding copy-number states) with clonal CNAs (dashed black lines). d The sample-subclonal clusters in c correspond to large genomic regions (magenta) with values of RDR (for 50kb genomic bins) clearly distinct from the RDR values of regions from sample-clonal clusters (black). e In sample A10-A of patient A10, Battenberg identifies extensive clusters of 50kb genomic bins with subclonal CNAs (green). However, such clusters are not clearly distinguished in the scaled BAF-RDR plot from the sample-clonal clusters (black with corresponding copy-number states). HATCHet infers only clonal CNAs in this sample. f The sample-subclonal clusters in e correspond to large genomic regions (green) with values of RDR (for 50kb genomic bins) approximately equal to the RDR values of nearby regions from sample-clonal clusters (black).