minFragCart2Polar {MetCirc} | R Documentation

## Calculate the nearest feature in polar coordinates given cartesian coordinates

### Description

Calculates the nearest feature in polar coordinates given cartesian coordinates.

### Usage

    minFragCart2Polar(x, y, degreeOfFeatures)

### Arguments

`x` | cartesian x coordinate
`y` | cartesian y coordinate
`degreeOfFeatures` | 'list' of positions of features

### Details

'minFragCart2Polar' is employed to find the feature with the smallest distance from given cartesian coordinates.

### Value

'minFragCart2Polar' returns the index of the feature that has the smallest distance to the given coordinates. As 'minFragCart2Polar' is used in 'shinyCircos' for the track 1 only polar r coordinates between 0.8 and 1 will be used to find the feature with smallest distance.

### Author(s)

Thomas Naake, thomasnaake@googlemail.com

### Examples

    data("spectra", package = "MetCirc")
    similarityMat <- compare_Spectra(spectra_tissue[1:10],
        fun = normalizeddotproduct, binSize = 0.01)
    linkDf <- createLinkDf(similarityMatrix = similarityMat,
        spectra = spectra_tissue[1:10],
        condition = c("SPL", "LIM", "ANT", "STY"), lower = 0.5, upper = 1)
    ## cut link data.frame (here: only display links between groups)
    linkDf_cut <- cutLinkDf(linkDf, type = "inter")
    groupname <- c(as.character(linkDf_cut[, "spectrum1"]),
                    as.character(linkDf_cut[, "spectrum2"]))
    groupname <- unique(groupname)
    ## set circlize parameters
    circos.clear()
    circos.par(gap.degree = 0, cell.padding = c(0.0, 0, 0.0, 0),
        track.margin = c(0.0, 0))
    plotCircos(groupname, NULL, initialize = TRUE, featureNames = FALSE,
        groupName = FALSE, groupSector = FALSE, links = FALSE, highlight = FALSE)
    x <- 1
    y <- 0
    degreeFeatures <- lapply(groupname,
     function(x) mean(circlize:::get.sector.data(x)[c("start.degree", "end.degree")]))
    minFragCart2Polar(x, y, degreeOfFeatures = degreeFeatures)

[Package MetCirc version 1.24.0 Index]