Comparing DNA integration site clusters with scan statistics.
BACKGROUND
Gene therapy with retroviral vectors can induce adverse effects when those vectors integrate in sensitive genomic regions. Retroviral vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of integration sites and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of false discovery rates providing statistical methods for comparing retroviral vectors.


RESULTS
A scan statistic for comparing two vectors using multiple window widths is proposed with software to detect clustering differentials and compute false discovery rates. Application to several sets of experimentally determined HIV integration sites demonstrates the software. Simulated datasets of various sizes and signal strengths are used to determine the power to discover clusters and evaluate a convenient lower bound. This provides a toolkit for planning evaluations of new gene therapy vectors.


METHODS
The geneRxCluster R package containing a simple tutorial and usage hints is available from http://www.bioconductor.org.
Division of Biostatistics and BioInformatics, Department of Family and Preventive Medicine, University of California at San Diego, La Jolla, CA 92093-0901 and Department of Microbiology, Perelman School of Medicine at the University of Pennsylvania, 425 Johnson Pavilion, Philadelphia, PA 19104-6076, USA.
Charles C
CC
Berry
Division of Biostatistics and BioInformatics, Department of Family and Preventive Medicine, University of California at San Diego, La Jolla, CA 92093-0901 and Department of Microbiology, Perelman School of Medicine at the University of Pennsylvania, 425 Johnson Pavilion, Philadelphia, PA 19104-6076, USA.
Karen E
KE
Ocwieja
Division of Biostatistics and BioInformatics, Department of Family and Preventive Medicine, University of California at San Diego, La Jolla, CA 92093-0901 and Department of Microbiology, Perelman School of Medicine at the University of Pennsylvania, 425 Johnson Pavilion, Philadelphia, PA 19104-6076, USA.
Nirav
N
Malani
Division of Biostatistics and BioInformatics, Department of Family and Preventive Medicine, University of California at San Diego, La Jolla, CA 92093-0901 and Department of Microbiology, Perelman School of Medicine at the University of Pennsylvania, 425 Johnson Pavilion, Philadelphia, PA 19104-6076, USA.
Frederic D
FD
Bushman
D000465:Algorithms; D001185:Artificial Intelligence; D004247:DNA / Q000737:chemistry; D003627:Data Interpretation, Statistical; D005822:Genetic Vectors; D023281:Genomics; D006678:HIV / Q000235:genetics*; D006801:Humans; D019169:Jurkat Cells; D012984:Software; D016662:Virus Integration
D004247:DNA