summarySelection {bayesCGH} | R Documentation |
When given a 'ClassifySegList' object, a vector of probes selected from this object, and a set of sets of arrays, this function summarises the probes into a set of regions and gives data on proportions of classification at each region.
summarySelection(seg, regions.clone.no, stats, group.arrays, group.names, probe.classes = list(c("Up", "Amplified"), c("Down", "Deleted"), c("Normal")), probe.class.name = c("Normal", "Gain", "Loss"), local.maximum = FALSE, cytobands = cytobands)
seg |
Object of class ClassifySegList . |
regions.clone.no |
Vector giving the probes to be summarised. |
stats |
Vector of some summary statistic (for all probes). |
group.arrays |
A list object of which each item should be a numeric vector giving the indices of the arrays in 'seg' belonging to a particular clinical set. |
group.names |
Names of the clinical sets defined by 'sel.groups'. |
probe.classes |
A list object of which each item is a character vector that describes the different groups of classifications of the aCGH data. Unlikely to change from default. |
probe.class.name |
A vector of names for the sets of probe classifications defined in 'probe.classes'. Again, unlikely to change from default. |
local.maximum |
If TRUE, then only those regions with locally maximal summary statistic will be reported. |
cytobands |
Location of cytobands on genome. Given in data attached to bayesCGH package. |
Probes from an aCGH array that are adjacent on the genome will often have exactly the same patterns of gain and loss across multiple arrays, and in the case of dense arrays, will come from the same gene. This function is intended to summarise the selected probes into contigious regions with identical characteristics.
List containing
summary |
Dataframe summarising the probes selected. |
genes |
Details of all genes associated with the selected probes. |
Thomas Hardcastle