Translocation detection from Hi-C data via scan statistics.
Translocation detection from Hi-C data via scan statistics. Biometrics. 2023; 79(2):1306
JGM, Humans, Chromosomes, Computer Simulation, Translocation, Genetic, Cluster Analysis, Cell Line
This work was partially supported by Faculty Research Excellence Program Award at UConn (to Y.Z.). A.C and D.M. are co-first authors.
Recent Hi-C technology enables more comprehensive chromosomal conformation research, including the detection of structural variations, especially translocations. In this paper, we formulate the interchromosomal translocation detection as a problem of scan clustering in a spatial point process. We then develop TranScan, a new translocation detection method through scan statistics with the control of false discovery. The simulation shows that TranScan is more powerful than an existing sophisticated scan clustering method, especially under strong signal situations. Evaluation of TranScan against current translocation detection methods on realistic breakpoint simulations generated from real data suggests better discriminative power under the receiver-operating characteristic curve. Power analysis also highlights TranScan's consistent outperformance when sequencing depth and heterozygosity rate is varied. Comparatively, Type I error rate is lowest when evaluated using a karyotypically normal cell line. Both the simulation and real data analysis indicate that TranScan has great potentials in interchromosomal translocation detection using Hi-C data.