RCytoGPS (version 1.2.10)

CytobandData-class: The CytobandData Class

Description

A class to represent genome-wide data measured or summarized at cytoband-level resoluton.

Usage

CytobandData(data, info, genome = NULL)
# S4 method for CytobandData
summary(object, ...)

Value

The CytobandData conxtructor returns an object of the

CytobandData class. The summary method returns a table, which is the usual return value when the summary method is applied to a data frame (in this case, the DATA slot).

Arguments

data

A data frame that must contain at least one numeric column and may contain the five cytoband location columns. If the latter are missing, they must be supplied by the genome argument.

info

A data frame with Label and Description columns. If missing, it will be created from the column names in the data argument.

genome

A data frame containing cytoband locations. Often, simply uses the cytobandLocations object describing build 38 of the human genome.

object

An object of the CytobandData class.

...

Additional arguments to the summary method; ignored.

Slots

DATA:

A data frame containg five columns (Chromosome, loc.start, loc.end, Band, and Stain) defining chromosomal locations of cytobands, along with one or more columns of numerical data at cytoband resolution.

INFO:

A data frame with two columns (Label and Description) that describes the columns of the DATA slot.

Methods

summary:

signature(object = "RCytoGPS") Returns a summary of the DATA slot.

Author

Kevin R. Coombes , Dwayne G. Tally

Details

Conventional karyotyping describes chromosomal abnormalities (in a standardized text-based nomenclature) that are visible through a microsope. Karyotyping relies on a technique known as Giemsa staining, which creates a banding pattern along the chromosome of different shades of gray. The Loss-Gain-Fusion (LGF) model implemented at the http://cytogps.org web site converts text-based karyotypes into binary vectors, stored in JSON files. Our readLGF function parses the JSON files to create R data structures, including cytoband-resolution summaries of the frequencies of abnormalities. These summaries can be used to create CytobandData objects, which can be visualized using the barplot and image methods.

References

Abrams ZB, Tally DG, Coombes KR. RCytoGPS: An R Package for Analyzing and Visualizing Cytogenetic Data. In preparation.

Abrams ZB, Tally DG, Zhang L, Coombes CE, Payne PRO, Abruzzo LV, Coombes KR. Pattern recognition in lymphoid malignancies using CytoGPS and Mercator. Under review.

Examples

jsonDir <- system.file("Examples/JSONfiles", package = "RCytoGPS")
temp <- readLGF(folder = jsonDir)
cytoData <- data.frame(temp[["CL"]],temp[["frequency"]])
bandData <- CytobandData(cytoData)
class(bandData)
summary(bandData)