replayPlotAdd: Plot plotCircos or highlight

In tnaake/MetCirc: Navigating mass spectral similarity in high-resolution MS/MS metabolomics data metabolomics data

Plot plotCircos or highlight

Description

replayPlotAdd plots additional plots on a plot, either plots plotCircos or highlight.

Usage

replayPlotAdd(
  orderMatch = "mz",
  onCircle = FALSE,
  linkDf,
  mz_match,
  rt_match,
  clust_match,
  ind,
  indMz,
  indRT,
  indCluster
)

Arguments

orderMatch	character(1), either "mz", "retentionTime", or "clustering"
onCircle	logical, are coordinates on circle. If FALSE and no features are selected (length(ind) == 0), then filled plots are replayed, otherwise highlighted plots are replayed.
linkDf	data.frame that contains information of linked features for given thresholds
mz_match	character, ordered vector according to m/z
rt_match	character, ordered vector according to retention time
clust_match	character, ordered vector according to clustering
ind	numeric, indices of clicked features
indMz	numeric, indices of clicked features for "mz" ordering
indRT	numeric, indices of clicked features for "retentionTime" ordering
indCluster	numeric, indices of clicked features for "clustering" ordering

Details

Helper function for shinyCircos.

Value

Depending on onCircle and indMz either returns plotCircos or highlight

Author(s)

Thomas Naake, [e-mail]

Examples

data("spectra", package = "MetCirc")
similarityMat <- Spectra::compareSpectra(sps_tissue[1:10],
    FUN = MsCoreUtils::ndotproduct, ppm = 10, m = 0.5, n = 2)
rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name[1:10]
  
## order according to m/z 
mz_match <- MetCirc:::typeMatch_link0(similarityMatrix = similarityMat, 
    sps = sps_tissue, type = "mz", 
    condition = c("SPL", "LIM", "ANT", "STY"))
linkDf <- mz_match[["link0df"]]
mz_match <- mz_match[["type_match"]]

## order according to retention time 
rt_match <- MetCirc:::typeMatch_link0(similarityMatrix = similarityMat, 
    sps = sps_tissue, type = "retentionTime", 
    condition = c("SPL", "LIM", "ANT", "STY"))
rt_match <- rt_match[["type_match"]]

## order according to clustering
clust_match <- MetCirc:::typeMatch_link0(similarityMatrix = similarityMat, 
    sps = sps_tissue, type = "clustering", 
    condition = c("SPL", "LIM", "ANT", "STY"))
clust_match <- clust_match[["type_match"]]
circos.initialize(mz_match,##, levels  =  mz_match),
    xlim = matrix(rep(c(0,1), length(mz_match)), ncol = 2, byrow = TRUE))
#circos.trackPlotRegion(factor(mz_match, levels = mz_match), ylim = c(0,1))  
MetCirc:::replayPlotAdd(orderMatch = "mz", onCircle = FALSE, linkDf = linkDf, 
    mz_match = mz_match, rt_match = rt_match, clust_match = clust_match, 
    ind = 1, indMz = NULL, indRT = NULL, indCluster = NULL)

tnaake/MetCirc documentation built on April 23, 2023, 8:56 a.m.