Note that since there is only 1 instance, the data is simply stored a numeric vector.
Broadly speaking, humans have two copies of their genome. Occasionally however, a region of the genome is duplicated or destroyed; this is known as copy number variation. All humans have some degree of copy number variation; many cancers, however, display much more extreme variation. Comparative genomic hybridization (CGH) is one method for measuring the number of copies present at a genome-wide scale.
This data set consists of CGH data from glioblastoma tumors. In the data set, the data from two tumors (chromosome 7 in one patient, chromosome 13 in another) are spliced together in order to create a challenging data set for CNV detection consisting of both gains and losses over both short and large scales. It has become a popular benchmark data set for CNV studies.
The original data appear in:
Bredel M, Bredel C, Juric D, Harsh GR, Vogel H, Recht LD, and Sikic BI. (2005). High-Resolution Genome-Wide Mapping of Genetic Alterations in Human Glial Brain Tumors. Cancer Research, 65: 4088-4096.
The pseudo-chromosome data set was introduced by:
Tibshirani R, and Wang P. (2008). Spatial smoothing and hot spot detection for CGH data using the fused lasso. Biostatistics, 9: 18-29.
The data used to be available in the cghFLasso package, but this package appears to be no longer available.