Package 'MetCirc' reference manual

Title:	Navigating mass spectral similarity in high-resolution MS/MS metabolomics data metabolomics data
Description:	MetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data. MetCirc uses the Spectra object infrastructure defined in the package Spectra that stores MS/MS spectra. MetCirc offers functionality to calculate similarity between precursors based on the normalised dot product, neutral losses or user-defined functions and visualise similarities in a circular layout. Within the interactive framework the user can annotate MS/MS features based on their similarity to (known) related MS/MS features.
Authors:	Thomas Naake <[email protected]>, Johannes Rainer <[email protected]> and Emmanuel Gaquerel <[email protected]>
Maintainer:	Thomas Naake <[email protected]>
License:	GPL (>= 3)
Version:	1.37.0
Built:	2024-12-09 06:16:27 UTC
Source:	https://github.com/bioc/MetCirc

Calculate polar coordinates from cartesian coordinates

Description

cart2Polar calculates polar coordinates from cartesian coordinates.

Usage

cart2Polar(x, y)
cart2Polar(x, y)

Arguments

`x`	`numeric` cartesian x coordinate
`y`	`numeric` cartesian y coordinate

Details

cart2Polar is employed to translate cartesian coordinates into polar coordinates especially in interactive shiny applications when using hovering and clicking features.

Value

cart2Polar returns a list of colar coordinates r and theta

Author(s)

Thomas Naake, [email protected]

Examples

x <- 1; y <- 1
cart2Polar(x, y)

x <- 1; y <- 1
cart2Polar(x, y)

Plot a legend for circos plot

Description

circosLegend plots a legend for circos plot using group names.

Usage

circosLegend(groupname, highlight = TRUE, colour = NULL, cex = 1)
circosLegend(groupname, highlight = TRUE, colour = NULL, cex = 1)

Arguments

`groupname`	`character` vector containing "group" and "name" to display that is a unique identifier of the features, "group" and "name" have to be separated by `"_"` where "group" is the first and "name" is the last element
`highlight`	`logical`, should colours be adjusted to highlight settings?
`colour`	`NULL` or `character`, `colour` defines the colours which are used for plotting, if `NULL` default colours are used
`cex`	`numeric`, parameter that controls size of the legend in the plot

Details

Internal use in shinyCircos or outside of shinyCircos to reproduce figures.

Value

The function will open a new plot and display colours together with labels.

Author(s)

Thomas Naake, [email protected]

Examples

data("spectra", package = "MetCirc")

## create similarity matrix
similarityMat <- Spectra::compareSpectra(sps_tissue[1:10],
    FUN = MsCoreUtils::ndotproduct, ppm = 20, m = 0.5, n = 2)
rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name[1:10]

linkDf <- createLinkDf(similarityMatrix = similarityMat,
    sps = sps_tissue[1:10],
    condition = c("SPL", "LIM", "ANT", "STY"), lower = 0.01, 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)

## plot legend
circosLegend(groupname, highlight = TRUE, colour = NULL, cex = 1)

data("spectra", package = "MetCirc")

## create similarity matrix
similarityMat <- Spectra::compareSpectra(sps_tissue[1:10],
    FUN = MsCoreUtils::ndotproduct, ppm = 20, m = 0.5, n = 2)
rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name[1:10]

linkDf <- createLinkDf(similarityMatrix = similarityMat,
    sps = sps_tissue[1:10],
    condition = c("SPL", "LIM", "ANT", "STY"), lower = 0.01, 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)

## plot legend
circosLegend(groupname, highlight = TRUE, colour = NULL, cex = 1)

Example data for 'MetCirc': 'compartmentTissue'

Description

The 'data.frame' 'compartmentTissue' is used in the subsection 'Preparing the tissue data set for analysis' in the vignette of 'MetCirc'. In 'compartmentTissue', information on the organ-localisation of each MS/MS spectrum is stored.

Format

'data.frame'

Value

'data.frame'

Author(s)

Thomas Naake, [email protected]

Source

internal

Example data for 'MetCirc': convertExampleDF

Description

'convertExampleDF' is a 'data.frame' which comprises information on a specific metabolite per row stating the average retention time, average m/z, the name of the metabolite, the adduct ion name and the spectrum reference file name. The function 'allocatePrecursor2mz' uses 'data.frame's of the kind of 'sd01\_outputXCMS' and 'sd02\_deconvoluted' to create a 'data.frame' of the kind of 'convertExampleDF'. Allocation of precursor ions to candidate m/z values is based on minimal distance of m/z and deviance of retention time based on an objective function. See '?allocatePrecursor2mz' for further information.

Format

'data.frame'

Value

'data.frame'

Author(s)

Thomas Naake, [email protected]

Source

internal

Convert MSP data frame into object of class `Spectra`

Description

Convert msp data frame into object of class [Spectra()]

Usage

convertMsp2Spectra(msp)
convertMsp2Spectra(msp)

Arguments

msp

data.frame that mimicks the .msp file format, see Details for further information.

Details

msp is a data frame of a .msp file, a typical data file for MS/MS libraries. The data frame has two columns and contains in the first column the entries "NAME:", "PRECURSORMZ:" (or "EXACTMASS:"), "RETENTIONTIME:", Num Peaks:" and information on fragments and peak areas/intensities and will extract the respective information in the second column.

Value

convertMsp2Spectra returns an object of class 'Spectra'

Author(s)

Thomas Naake, [email protected]

Examples

data("convertMsp2Spectra", package = "MetCirc")
convertMsp2Spectra(msp = msp2spectra)

data("convertMsp2Spectra", package = "MetCirc")
convertMsp2Spectra(msp = msp2spectra)

Create a link matrix

Description

Create a link matrix which links every feature in similarity matrix with another.

Usage

createLink0df(similarityMatrix, sps, condition)
createLink0df(similarityMatrix, sps, condition)

Arguments

`similarityMatrix`	`matrix`, a similarity matrix that contains the NDP similarity measure between all precursors in the data set
`sps`	`Spectra` object
`condition`	`character`, which conditions should be included?

Details

createLink0df creates a matrix from a similarity matrix which includes all connections between features in the similarity matrix, but exclude links which have a similarity of exactly 0.

Value

createLink0df returns a 'matrix' that gives per each row information on linked features

Author(s)

Thomas Naake, [email protected]

Examples

data("spectra", package = "MetCirc")
data("similarityMat", package = "MetCirc")
link0df <- createLink0df(similarityMatrix = similarityMat,
    sps = sps_tissue, condition = c("SPL", "LIM", "ANT", "STY"))

data("spectra", package = "MetCirc")
data("similarityMat", package = "MetCirc")
link0df <- createLink0df(similarityMatrix = similarityMat,
    sps = sps_tissue, condition = c("SPL", "LIM", "ANT", "STY"))

Create a data frame which contains features to link (indices)

Description

Create a data frame which contains features to link (indices).

Usage

createLinkDf(similarityMatrix, sps, condition, lower, upper)
createLinkDf(similarityMatrix, sps, condition, lower, upper)

Arguments

`similarityMatrix`	`matrix`, a similarity matrix that contains the similarity measure between all precursors in the data set
`sps`	`Spectra` object containing spectral data corresponding to features in `similarityMatrix`
`condition`	`character` `vector` containing the conditions/samples for which a `linkDf` is created
`lower`	`numeric(1)`, threshold value for similarity values, linked features below this value will not be included
`upper`	`numeric(1)`, threshold value for similarity values, linked features above this value will not be included

Details

lower and upper are numerical values and truncate spectra based on their similarity. The function createLinkDf is a wrapper for the functions createLink0df and thresholdLinkDf.

Value

createLinkDf returns a data.frame that gives per each row information on linked features

Author(s)

Thomas Naake, [email protected]

Examples

data("spectra", package = "MetCirc")
data("similarityMat", package = "MetCirc")
link0df <- createLink0df(similarityMatrix = similarityMat,
    sps = sps_tissue, condition = c("SPL", "LIM", "ANT", "STY"))
createLinkDf(similarityMatrix = similarityMat, sps = sps_tissue,
    condition = c("SPL", "LIM", "ANT", "STY"), lower = 0.5, upper = 1)

data("spectra", package = "MetCirc")
data("similarityMat", package = "MetCirc")
link0df <- createLink0df(similarityMatrix = similarityMat,
    sps = sps_tissue, condition = c("SPL", "LIM", "ANT", "STY"))
createLinkDf(similarityMatrix = similarityMat, sps = sps_tissue,
    condition = c("SPL", "LIM", "ANT", "STY"), lower = 0.5, upper = 1)

Create a cut data frame with information on links

Description

Create a cut link data frame

Usage

cutLinkDf(linkDf, type = c("all", "inter", "intra"))
cutLinkDf(linkDf, type = c("all", "inter", "intra"))

Arguments

`linkDf`	`data.frame`, that gives per each row information on linked features
`type`	`character`, one of "all", "inter" or "intra"

Details

This function is used to truncate features from linkDf. If type = "all", linkDf will not be changed; if type = "inter" the returned linkDf will only contain entries of links which are between groups and not inside groups; contrary to that, if type = "intra" the returned linkDf will only contain entries of links which are inside groups and not between groups.

Value

cutLinkDf returns a data.frame that gives per each row information on linked features

Author(s)

Thomas Naake, [email protected]

Examples

data("spectra", package = "MetCirc")
data("similarityMat", package = "MetCirc")
linkDf <- createLinkDf(similarityMatrix = similarityMat,
    sps = sps_tissue, condition = c("SPL", "LIM", "ANT", "STY"),
    lower = 0.75, upper = 1)
cutLinkDf(linkDf = linkDf, type = "all")

data("spectra", package = "MetCirc")
data("similarityMat", package = "MetCirc")
linkDf <- createLinkDf(similarityMatrix = similarityMat,
    sps = sps_tissue, condition = c("SPL", "LIM", "ANT", "STY"),
    lower = 0.75, upper = 1)
cutLinkDf(linkDf = linkDf, type = "all")

Get indices in `linkDf` of feature

Description

Gets indices in linkDf of feature

Usage

getLinkDfIndices(groupnameselected, linkDf)
getLinkDfIndices(groupnameselected, linkDf)

Arguments

`groupnameselected`	`character` vector with `groupname` of selected feature, vector containing "group" and "name" to display, that is a unique identifier of the features, "group" and "name" have to be separated by `"_"` where "group" is the first and "name" is the last element
`linkDf`	`data.frame`, in each row there is information about features to be connected

Details

Internal use for function highlight

Value

getLinkDfIndices returns indices concerning linkDf to which groupnameselected connects

Author(s)

Thomas Naake, [email protected]

Examples

## Not run: getLinkDfIndices(groupnameselected, linkMatrix)

## Not run: getLinkDfIndices(groupnameselected, linkMatrix)

Add links and highlight sectors

Description

A function to add links and highlight sectors to an initialised and plotted circlize plot with one track.

Usage

highlight(
  groupname,
  ind,
  linkDf,
  colour = NULL,
  transparency = 0.4,
  links = TRUE
)
highlight(
  groupname,
  ind,
  linkDf,
  colour = NULL,
  transparency = 0.4,
  links = TRUE
)

Arguments

`groupname`	`character` vector containing "group" and "name" to display that is a unique identifier of the features, "group" and "name" have to be separated by `"_"` where "group" is the first and "name" is the last element
`ind`	`numeric`, indices which will be highlighted
`linkDf`	`data.frame`, in each row there is information about features to be connected
`colour`	`NULL` or `character`, `colour` defines the colours which are used for plotting, if 'NULL' default colours are used
`transparency`	`numeric`, defines the transparency of the colours
`links`	`logical`, should links of unselected features be plotted

Details

Internal use for shinyCircos or outside of shinyCircos to reproduce the figure.

Value

The function will update an existing plot by highlighting a specified sector and connected links.

Author(s)

Thomas Naake, [email protected]

Examples

data("spectra", package = "MetCirc")

## create similarity matrix
similarityMat <- Spectra::compareSpectra(sps_tissue[1:10],
    FUN = MsCoreUtils::ndotproduct, ppm = 20, m = 0.5, n = 2)
rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name[1:10]

## order similarityMat according to retentionTime and update rownames
simM <- orderSimilarityMatrix(similarityMat, sps = sps_tissue[1:10],
    type = "retentionTime")

## create link matrix
linkDf <- createLinkDf(similarityMatrix = simM, sps = sps_tissue,
    condition = c("SPL", "LIM", "ANT", "STY"), lower = 0.01, upper = 1)

## cut link matrix (here: only display links between groups)
linkDf_cut <- cutLinkDf(linkDf, type = "inter")

## 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))
groupname <- c(as.character(linkDf_cut[, "spectrum1"]),
    as.character(linkDf_cut[, "spectrum2"]))
groupname <- unique(groupname)

## here: set indSelected arbitrarily
indSelected <- c(2,3)

## actual plotting
plotCircos(groupname, linkDf_cut, initialize = TRUE,
    featureNames = TRUE, cexFeatureNames = 0.2, groupSector = TRUE,
    groupName = FALSE, links = FALSE, highlight = TRUE)

## highlight
highlight(groupname = groupname, ind = indSelected, linkDf = linkDf_cut,
    colour = NULL, transparency = 0.4, links = TRUE)

data("spectra", package = "MetCirc")

## create similarity matrix
similarityMat <- Spectra::compareSpectra(sps_tissue[1:10],
    FUN = MsCoreUtils::ndotproduct, ppm = 20, m = 0.5, n = 2)
rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name[1:10]

## order similarityMat according to retentionTime and update rownames
simM <- orderSimilarityMatrix(similarityMat, sps = sps_tissue[1:10],
    type = "retentionTime")

## create link matrix
linkDf <- createLinkDf(similarityMatrix = simM, sps = sps_tissue,
    condition = c("SPL", "LIM", "ANT", "STY"), lower = 0.01, upper = 1)

## cut link matrix (here: only display links between groups)
linkDf_cut <- cutLinkDf(linkDf, type = "inter")

## 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))
groupname <- c(as.character(linkDf_cut[, "spectrum1"]),
    as.character(linkDf_cut[, "spectrum2"]))
groupname <- unique(groupname)

## here: set indSelected arbitrarily
indSelected <- c(2,3)

## actual plotting
plotCircos(groupname, linkDf_cut, initialize = TRUE,
    featureNames = TRUE, cexFeatureNames = 0.2, groupSector = TRUE,
    groupName = FALSE, links = FALSE, highlight = TRUE)

## highlight
highlight(groupname = groupname, ind = indSelected, linkDf = linkDf_cut,
    colour = NULL, transparency = 0.4, links = TRUE)

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)
minFragCart2Polar(x, y, degreeOfFeatures)

Arguments

`x`	`numeric`, cartesian x coordinate
`y`	`numeric`, 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, [email protected]

Examples

library("MsCoreUtils")
data("spectra", package = "MetCirc")

## create similarity matrix
similarityMat <- Spectra::compareSpectra(sps_tissue[1:10],
    FUN = MsCoreUtils::ndotproduct, ppm = 20, m = 0.5, n = 2)
rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name[1:10]

linkDf <- createLinkDf(similarityMatrix = similarityMat,
    sps = sps_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)

library("MsCoreUtils")
data("spectra", package = "MetCirc")

## create similarity matrix
similarityMat <- Spectra::compareSpectra(sps_tissue[1:10],
    FUN = MsCoreUtils::ndotproduct, ppm = 20, m = 0.5, n = 2)
rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name[1:10]

linkDf <- createLinkDf(similarityMatrix = similarityMat,
    sps = sps_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)

Example data for 'MetCirc': 'msp2spectra'

Description

'convertMsp2Spectra' contains the object 'msp2spectra' that is a data frame in .MSP format, a typical format for MS/MS library building. Each entry consists of the metabolite name (NAME), the precursor mz (PRECURSORMZ), the retention time (RETENTIONTIME), number of peaks (Num Peaks), together with fragments and their intensity values. In the example used in the function 'convertMsp2Spectra' the 'data.frame' 'msp2spectra' is used to construct an object of class 'MSpectra'.

Format

'data.frame'

Value

'data.frame'

Author(s)

Thomas Naake, [email protected]

Source

http://prime.psc.riken.jp/Metabolomics_Software/MS-DIAL/, truncated .MSP file of GNPS MS/MS Negative (contains 22 entries): http://prime.psc.riken.jp/Metabolomics_Software/MS-DIAL/MSMS-GNPS-Curated-Neg.msp

Calculate similarity based on neutral losses

Description

Calculate similarity based on neutral losses (NLS)

Usage

neutralloss(x, y, m = 0.5, n = 2, na.rm = TRUE, ...)
neutralloss(x, y, m = 0.5, n = 2, na.rm = TRUE, ...)

Arguments

`x`	`Spectra` object from `Spectra` containing intensity and m/z values, first MS/MS spectrum
`y`	`Spectra` object from `Spectra` containing intensity and m/z values, second MS/MS spectrum
`m`	`numeric(1)`, exponent to calculate peak intensity-based weights
`n`	`numeric(1)`, exponent to calculate m/z-based weights
`na.rm`	`logical(1)`, if `NA` values should be removed
`...`	further arguments

Details

Similarities of spectra based on neutral losses are calculated according to the following formula:

$NLS = \frac{\sum(W_{S1, i} \cdot W_{S2, i}) ^ 2}{ \sum(W_{S1, i} ^ 2) \cdot \sum(W_{S2, i} ^ 2) }$

with $W = [ peak intensity] ^{m} \cdot [ NL ]^n$ and $NL = | m/z - precursor m/z |$ . For further information see Li et al. (2015): Navigating natural variation in herbivory-induced secondary metabolism in coyote tobacco populations using MS/MS structural analysis. PNAS, E4147–E4155.

In here, the precursor m/z is taken by the m/z feature with the highest intensity.

neutralloss returns a numeric value ranging between 0 and 1, where 0 indicates no similarity between the two MS/MS features, while 1 indicates that the MS/MS features are identical. Prior to calculating

$W_{S1}$

$W_{S2}$

, all intensity values are divided by the maximum intensity value.

Value

neutralloss returns a numeric similarity coefficient between 0 and 1

Author(s)

Thomas Naake, [email protected]

Examples

data("spectra", package = "MetCirc")
Spectra::compareSpectra(sps_tissue[1:10], FUN = neutralloss, m = 0.5, n = 2)

data("spectra", package = "MetCirc")
Spectra::compareSpectra(sps_tissue[1:10], FUN = neutralloss, m = 0.5, n = 2)

Order columns and rows of a similarity matrix according to m/z, retention time and clustering

Description

Internal function for shiny application. May also be used outside of shiny to reconstruct figures.

Usage

orderSimilarityMatrix(
  similarityMatrix,
  sps,
  type = c("retentionTime", "mz", "clustering"),
  group = FALSE
)
orderSimilarityMatrix(
  similarityMatrix,
  sps,
  type = c("retentionTime", "mz", "clustering"),
  group = FALSE
)

Arguments

`similarityMatrix`	`matrix`, `similarityMatrix` contains pair-wise similarity coefficients which give information about the similarity between precursors
`sps`	`Spectra` object containing spectra that are compared in `similarityMatrix`
`type`	`character(1)`, one of `"retentionTime"`, `"mz"`, or `"clustering"`
`group`	`logical(1)`, if `TRUE` `group` separated by `"_"` will be cleaved from `rownames`/`colnames` of `similarityMatrix` and matched against names of `sps` (`sps$name`), if `FALSE` `rownames`/`colnames` of `similarityMatrix` are taken as are and matched against names of `sps` (`sps$name`)

Details

orderSimilarityMatrix takes a similarity matrix, Spectra object (sps, containing information on m/z and retention time), and a character vector as arguments. It will then reorder rows and columns of the similarityMatrix object such, that it orders rows and columns of similarityMatrix according to m/z, retention time or clustering in each group.

orderSimilarityMatrix is employed in the shinyCircos function to create similarityMatrix objects which will allow to switch between different types of ordering in between groups (sectors) in the circos plot. It may be used as well externally, to reproduce plots outside of the reactive environment (see vignette for a workflow).

Value

matrix, orderSimilarityMatrix returns a similarity matrix with ordered rownames according to the character vector type

Author(s)

Thomas Naake, [email protected]

Examples

data("spectra", package = "MetCirc")
similarityMat <- Spectra::compareSpectra(sps_tissue[1:10], 
    FUN = MsCoreUtils::ndotproduct, ppm = 10)
rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name[1:10]

## order according to retention time 
orderSimilarityMatrix(similarityMatrix = similarityMat, 
    sps = sps_tissue, type = "retentionTime", group = FALSE)
    
data("spectra", package = "MetCirc")
similarityMat <- Spectra::compareSpectra(sps_tissue[1:10], 
    FUN = MsCoreUtils::ndotproduct, ppm = 10)
rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name[1:10]

## order according to retention time 
orderSimilarityMatrix(similarityMatrix = similarityMat, 
    sps = sps_tissue, type = "retentionTime", group = FALSE)

Circular plot to visualize similarity

Description

Circular plot to visualize similarity.

Usage

plotCircos(
  groupname,
  linkDf,
  initialize = c(TRUE, FALSE),
  featureNames = c(TRUE, FALSE),
  cexFeatureNames = 0.3,
  groupSector = c(TRUE, FALSE),
  groupName = c(TRUE, FALSE),
  links = c(TRUE, FALSE),
  highlight = c(TRUE, FALSE),
  colour = NULL,
  transparency = 0.2
)
plotCircos(
  groupname,
  linkDf,
  initialize = c(TRUE, FALSE),
  featureNames = c(TRUE, FALSE),
  cexFeatureNames = 0.3,
  groupSector = c(TRUE, FALSE),
  groupName = c(TRUE, FALSE),
  links = c(TRUE, FALSE),
  highlight = c(TRUE, FALSE),
  colour = NULL,
  transparency = 0.2
)

Arguments

`groupname`	`character` vector containing `"group"` and `"name"` to display that is a unique identifier of the features, `"group"` and `"name"` have to be separated by `"_"` where `"group"` is the first and `"name"` is the last element
`linkDf`	`data.frame` containing linked features in each row, has five columns (`group1`, `spectrum1`, `group2`, `spectrum2`, `similarity`)
`initialize`	`logical`, should plot be initialized?
`featureNames`	`logical`, should feature names be displayed?
`cexFeatureNames`	`numeric` size of feature names
`groupSector`	`logical`, should groups be displayed with background colours?
`groupName`	`logical`, should group names (e.g. compartment names or individual names) be displayed?
`links`	`logical`, should links be plotted?
`highlight`	`logical`, `highlight` is set to `TRUE` by default
`colour`	`NULL` or `character`, `colour` defines the colours which are used for plotting, if `NULL` default colours are used
`transparency`	`numeric`, defines the transparency of the colours

Details

Internal use for shinyCircos or used outside of shinyCircos to reproduce figure

Value

The function will initialize a circlize plot and/or will plot features of a circlize plot.

Author(s)

Thomas Naake, [email protected]

Examples

library("MsCoreUtils")
data("spectra", package = "MetCirc")

## create similarity matrix
similarityMat <- Spectra::compareSpectra(sps_tissue[1:10],
    FUN = MsCoreUtils::ndotproduct, ppm = 20, m = 0.5, n = 2)
rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name[1:10]

## order similarityMat according to retentionTime
simM <- orderSimilarityMatrix(similarityMat, sps = sps_tissue[1:10],
            type = "retentionTime")
            
## create link data.frame
linkDf <- createLinkDf(similarityMatrix = simM, sps = sps_tissue,
     condition = c("SPL", "LIM", "ANT", "STY"), lower = 0.01, upper = 1)
## cut link data.frame (here: only display links between groups)
linkDf_cut <- cutLinkDf(linkDf, type = "inter")

## set circlize paramters
circos.clear()
circos.par(gap.degree = 0, cell.padding = c(0.0, 0, 0.0, 0),
         track.margin = c(0.0, 0))
groupname <- c(as.character(linkDf_cut[, "spectrum1"]),
                as.character(linkDf_cut[, "spectrum2"]))
groupname <- unique(groupname)

## actual plotting
plotCircos(groupname, linkDf_cut, initialize = TRUE,
    featureNames = TRUE, cexFeatureNames = 0.3, groupSector = TRUE,
    groupName = FALSE, links = FALSE, highlight = FALSE, colour = NULL,
    transparency = 0.2)

library("MsCoreUtils")
data("spectra", package = "MetCirc")

## create similarity matrix
similarityMat <- Spectra::compareSpectra(sps_tissue[1:10],
    FUN = MsCoreUtils::ndotproduct, ppm = 20, m = 0.5, n = 2)
rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name[1:10]

## order similarityMat according to retentionTime
simM <- orderSimilarityMatrix(similarityMat, sps = sps_tissue[1:10],
            type = "retentionTime")
            
## create link data.frame
linkDf <- createLinkDf(similarityMatrix = simM, sps = sps_tissue,
     condition = c("SPL", "LIM", "ANT", "STY"), lower = 0.01, upper = 1)
## cut link data.frame (here: only display links between groups)
linkDf_cut <- cutLinkDf(linkDf, type = "inter")

## set circlize paramters
circos.clear()
circos.par(gap.degree = 0, cell.padding = c(0.0, 0, 0.0, 0),
         track.margin = c(0.0, 0))
groupname <- c(as.character(linkDf_cut[, "spectrum1"]),
                as.character(linkDf_cut[, "spectrum2"]))
groupname <- unique(groupname)

## actual plotting
plotCircos(groupname, linkDf_cut, initialize = TRUE,
    featureNames = TRUE, cexFeatureNames = 0.3, groupSector = TRUE,
    groupName = FALSE, links = FALSE, highlight = FALSE, colour = NULL,
    transparency = 0.2)

Plot pair-wise spectra

Description

plotSpectra plots a spectra of a subject and query spectra. plotSpectra uses ggplot plotting functionality.

Usage

plotSpectra(sps, subject, query)
plotSpectra(sps, subject, query)

Arguments

`sps`	`Spectra` object
`subject`	`character`, name of spectra that is aligned against, `character` with preceding sample name
`query`	`character`, name of spectra that is aligned to `subject`, `character` with preceding sample name

Details

Internally, all intensities are normalized to 100%.

Value

ggplot2 plot

Author(s)

Thomas Naake, [email protected]

Examples

data("spectra", package = "MetCirc")
plotSpectra(sps = sps_tissue, subject = "SPL_1", query = "SPL_2")

data("spectra", package = "MetCirc")
plotSpectra(sps = sps_tissue, subject = "SPL_1", query = "SPL_2")

Display information on connected features of selected features

Description

Displays information on connected features of selected features.

Usage

printInformationSelect(
  select,
  sps = NULL,
  linkDfInd,
  linkDf,
  similarityMatrix,
  roundDigits = 2
)
printInformationSelect(
  select,
  sps = NULL,
  linkDfInd,
  linkDf,
  similarityMatrix,
  roundDigits = 2
)

Arguments

`select`	`character`, obtained from `groupname`, `character` of selected feature
`sps`	`Spectra` object containing spectra that are compared in `similarityMatrix`
`linkDfInd`	`numeric` indices of selected features
`linkDf`	`data.frame` that contains information of linked features for given thresholds
`similarityMatrix`	`matrix` that is used to get information on the degree of similarity, `similarityMatrix` is an ordered version of a similarity matrix, see `?orderSimilarityMatrix`
`roundDigits`	`numeric(1)`, how many digits should be displayed?

Details

printInformationSelect is for internal use.

Value

character that is in HTML format

Author(s)

Thomas Naake, [email protected]

Examples

data("spectra", package = "MetCirc")
sps_tissue@metadata$names <- rep("Unknown", 259)
sps_tissue@metadata$information <- rep("Unknown", 259)
sps_tissue@metadata$classes <- rep("Unknown", 259)
sps_tissue@metadata$adduct <- rep("Unknown", 259)
similarityMat <- Spectra::compareSpectra(sps_tissue[1:10],
    FUN = MsCoreUtils::ndotproduct, ppm = 20, m = 0.5, n = 2)
rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name[1:10]
linkDf <- createLinkDf(similarityMatrix = similarityMat,
    sps = sps_tissue[1:10], 
    condition = c("SPL", "LIM", "ANT", "STY"), lower = 0.01, 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)

## arbitrarily select a feature
ind <- 2
linkDfInds <- getLinkDfIndices(groupname[ind], linkDf_cut)
MetCirc:::printInformationSelect(select = groupname[ind], 
    sps = sps_tissue[1:10], linkDfInd = linkDfInds, 
    linkDf = linkDf_cut, similarityMatrix = similarityMat)

data("spectra", package = "MetCirc")
sps_tissue@metadata$names <- rep("Unknown", 259)
sps_tissue@metadata$information <- rep("Unknown", 259)
sps_tissue@metadata$classes <- rep("Unknown", 259)
sps_tissue@metadata$adduct <- rep("Unknown", 259)
similarityMat <- Spectra::compareSpectra(sps_tissue[1:10],
    FUN = MsCoreUtils::ndotproduct, ppm = 20, m = 0.5, n = 2)
rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name[1:10]
linkDf <- createLinkDf(similarityMatrix = similarityMat,
    sps = sps_tissue[1:10], 
    condition = c("SPL", "LIM", "ANT", "STY"), lower = 0.01, 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)

## arbitrarily select a feature
ind <- 2
linkDfInds <- getLinkDfIndices(groupname[ind], linkDf_cut)
MetCirc:::printInformationSelect(select = groupname[ind], 
    sps = sps_tissue[1:10], linkDfInd = linkDfInds, 
    linkDf = linkDf_cut, similarityMatrix = similarityMat)

Record a plot of filled features and the degree of features

Description

recordPlotFill_degreeFeatures records a plot of filled features and returns the degree of features.

Usage

recordPlotFill_degreeFeatures(type_match, ...)
recordPlotFill_degreeFeatures(type_match, ...)

Arguments

`type_match`	`character`, ordered vector according to type
`...`	further arguments passed to `plotCircos`

Details

Helper function for shinyCircos.

Value

list of length 2, entry plotFill is of recordedplot and entry degreeFeatures is a list of vectors of numeric(1)

Author(s)

Thomas Naake, [email protected]

Examples

type_match <- c("a_1", "a_2", "a_3", "b_1", "b_2", "b_3", "c_1", "c_2")
MetCirc:::recordPlotFill_degreeFeatures(type_match)

type_match <- c("a_1", "a_2", "a_3", "b_1", "b_2", "b_3", "c_1", "c_2")
MetCirc:::recordPlotFill_degreeFeatures(type_match)

Return a `recordedplot` of `plotCircos` plot with `highlight = TRUE`

Description

recordPlotHighlight returns a recordedplot object of plotCircos with highlight = TRUE

Usage

recordPlotHighlight(type_match, ...)
recordPlotHighlight(type_match, ...)

Arguments

`type_match`	`character`, ordered vector according to type
`...`	further arguments passed to `plotCircos`

Details

Helper function for shinyCircos.

Value

recordedplot

Author(s)

Thomas Naake, [email protected]

Examples

type_match <- c("a_1", "a_2", "a_3", "b_1", "b_2", "b_3", "c_1", "c_2")
MetCirc:::recordPlotHighlight(type_match)

type_match <- c("a_1", "a_2", "a_3", "b_1", "b_2", "b_3", "c_1", "c_2")
MetCirc:::recordPlotHighlight(type_match)

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
)
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, [email protected]

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)

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)

Wrapper for `replayPlot`

Description

replayPlotOrder will call replayPlot from grDevices with a recordedplot object based on orderMatch.

Usage

replayPlotOrder(orderMatch = "mz", onCircle = FALSE, plot_l, ind)
replayPlotOrder(orderMatch = "mz", onCircle = FALSE, plot_l, ind)

Arguments

`orderMatch`	`character`, 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.
`plot_l`	`list` with plots
`ind`	`numeric`, indices of clicked features

Details

Helper function for shinyCircos.

Value

replayedplot

Author(s)

Thomas Naake, [email protected]

Examples

type_match <- c("a_1", "a_2", "a_3", "b_1", "b_2", "b_3", "c_1", "c_2")
plotCircos(type_match, NULL, initialize = TRUE, featureNames = TRUE,
    groupSector = TRUE, groupName = FALSE, links = FALSE,
    highlight = TRUE)
p <- recordPlot()
plot.new()
plot_l <- list(highlightMz = p)
MetCirc:::replayPlotOrder(orderMatch = "mz", onCircle = TRUE,
    plot_l = plot_l, ind = NULL)

type_match <- c("a_1", "a_2", "a_3", "b_1", "b_2", "b_3", "c_1", "c_2")
plotCircos(type_match, NULL, initialize = TRUE, featureNames = TRUE,
    groupSector = TRUE, groupName = FALSE, links = FALSE,
    highlight = TRUE)
p <- recordPlot()
plot.new()
plot_l <- list(highlightMz = p)
MetCirc:::replayPlotOrder(orderMatch = "mz", onCircle = TRUE,
    plot_l = plot_l, ind = NULL)

Example data for 'MetCirc': 'sd01_outputXCMS'

Description

'sd01_outputXCMS' is the output file from the package 'XCMS' using the data from Li et al. (2015). See Li et al. (2015) for further details.

Format

'data.frame'

Value

'data.frame'

Author(s)

Thomas Naake, [email protected]

Source

Li et al. (2015)

Example data for 'MetCirc': sd02_deconvoluted

Description

'sd02_deconvoluted' contains MS/MS data from Li et al. (2015). It is a 'data.frame' which hosts m/z values, retention time, intensity and the respective precursor m/z values. 'sd02_deconvoluted' originates from Li et al. (2015). See Li et al. (2015) for further information.

Format

'data.frame'

Value

'data.frame'

Author(s)

Thomas Naake, [email protected]

Source

Li et al. (2015)

Select variable based on condition

Description

select returns mz, rt or clust depending on condition.

Usage

select(condition, mz, rt, clust)
select(condition, mz, rt, clust)

Arguments

`condition`	`character(1)`, either `"mz"`, `"retentionTime"`, or `"clustering"`
`mz`	object to return if `condition == "mz"`
`rt`	object to return if `condition == "retentionTime"`
`clust`	object to return if `condition == "clustering"`

Details

Helper function for shinyCircos, replayPlotOrder and replayPlotAdd.

Value

mz, rt or clust depending on condition

Author(s)

Thomas Naake [email protected]

Examples

mz <- 1
rt <- 2
clust <- 3
MetCirc:::select(condition = "mz", mz = mz, rt = rt, clust = clust)

mz <- 1
rt <- 2
clust <- 3
MetCirc:::select(condition = "mz", mz = mz, rt = rt, clust = clust)

Interactive visualisation of similarity and navigation of MS/MS features

Description

Visualise the similarity of MS/MS features in a reactive context. See Details the vignette for further descriptions on how to use shinyCircos.

Usage

shinyCircos(similarityMatrix, sps, condition, ...)
shinyCircos(similarityMatrix, sps, condition, ...)

Arguments

`similarityMatrix`	`matrix`, `similarityMatrix` contains pair-wise similarity coefficients which give information about the similarity between MS/MS features
`sps`	`Spectra`, `sps` will be used to display information about the selected feature and will store information of annotation
`condition`	`character` vector, specifies which conditions/samples are displayed
`...`	further arguments passed to `shinyCircos`, e.g. `cexFeatureNames` to pass to `plotCircos` to set font size in `plotCircos` of feature names

Details

The function is based on the shiny and circlize package. The user can choose interactively thresholds, type of links (between or within groups), display information about MS/MS features, permanently select MS/MS features and export selected precursors. The Spectra object stores annotation information about the MS/MS features. Names of features within the similarityMatrix have to be found as entries in Spectra. sps$name are used as identifiers and colnames/rownames from similarityMatrix are cleaved by the group identifier (separated by "_"). Annotation information is taken from spectra from the columns "names", "information", "classes" and "adduct" in the slot metadata of spectra. After exiting the application, the annotation will be written to the respective columns in the slot metadata. If one or several of these columns is already present in metadata, the column(s) will be used as the source of annotation information.

Value

character, shinyCircos returns a character vector with the permanently selected precursors and an object with the MSpectra object containing the annotation.

Author(s)

Thomas Naake, [email protected]

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]
## Not run: 
shinyCircos(similarityMatrix = similarityMat,
    sps = sps_tissue, condition = c("SPL", "LIM", "ANT", "STY"))

## End(Not run)

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]
## Not run: 
shinyCircos(similarityMatrix = similarityMat,
    sps = sps_tissue, condition = c("SPL", "LIM", "ANT", "STY"))

## End(Not run)

Example data for 'MetCirc': 'similarityMat'

Description

'similarityMat' is a 'matrix' containing the pair-wise similarity scores derived from the 'idMSMStissueproject' data set. See the vignette for a workflow to reproduce the object 'similarityMat'.

Format

'matrix'

Value

'matrix'

Author(s)

Thomas Naake, [email protected]

Source

data("spectra", package = "MetCirc") similarityMat <- Spectra::compareSpectra(sps_tissue, fun = ndotproduct, ppm = 10) rownames(similarityMat) <- colnames(similarityMat) <- sps_tissue$name save(similarityMat, file = "similarityMat.RData", compress = "xz")

Get MS/MS spectra that are present in condition

Description

spectraCondition returns the names of Spectra that are present in condition, corresponding to the slot metadata.

Usage

spectraCondition(sps, condition)
spectraCondition(sps, condition)

Arguments

`sps`	`Spectra` object of `Spectra` package
`condition`	`character`, vector with conditions found as columns in the metadata slot

Details

Helper function in createLink0df and shinyCircos.

Value

list, named list with character vector as entries that contains the names of the MS/MS entries in spectra that are present in the conditon (tissues, stress conditions, time points, etc.)

Author(s)

Thomas Naake [email protected]

Examples

data("spectra", package = "MetCirc")
MetCirc:::spectraCondition(sps = sps_tissue,
    condition = c("SPL", "LIM", "ANT", "STY"))
data("spectra", package = "MetCirc")
MetCirc:::spectraCondition(sps = sps_tissue,
    condition = c("SPL", "LIM", "ANT", "STY"))

Example data for 'MetCirc': 'sps_tissue'

Description

'sps_tissue' is a 'Spectra' object derived from the 'idMSMStissueproject' data set. See the vignette for a workflow to reproduce the object 'spectra'.

Format

'matrix'

Value

'matrix'

Author(s)

Thomas Naake, [email protected]

Source

data("idMSMStissueproject", package = "MetCirc") ## get all MS/MS spectra tissue <- tissue[tissue[, "id"] id_uniq <- unique(tissue[, "id"])

## obtain precursor m/z from id_uniq prec_mz <- lapply(strsplit(as.character(id_uniq), split = "_"), "[", 1) |> unlist() |> as.numeric()

## obtain m/z from fragments per precursor m/z mz_l <- lapply(id_uniq, function(id_i) tissue[tissue[, "id"] == id_i, "mz"])

## obtain corresponding intensity values int_l <- lapply(id_uniq, function(id_i) tissue[tissue[, "id"] == id_i, "intensity"])

## order mz and intensity values int_l <- lapply(seq_along(int_l), function(i) int_l[[i]][order(mz_l[[i]])]) mz_l <- lapply(seq_along(mz_l), function(i) sort(mz_l[[i]]))

## obtain retention time by averaging all retention time values rt <- lapply(id_uniq, function(id_i) tissue[tissue[, "id"] == id_i, "rt"]) |> lapply(mean) |> unlist()

## create list of Spectra objects and concatenate sps_l <- lapply(seq_len(length(mz_l)), function(i) spd <- S4Vectors::DataFrame( name = as.character(i), rtime = rt[i], msLevel = 2L, precursorMz = prec_mz[i]) spd$mz <- list(mz_l[[i]]) spd$intensity <- list(int_l[[i]]) Spectra::Spectra(spd)) sps_tissue <- Reduce(c, sps_l)

## combine list of spectrum2 objects to MSpectra object, ## use SPL, LIM, ANT, STY for further analysis sps_tissue@metadata <- data.frame( compartmentTissue[, c("SPL", "LIM", "ANT", "STY")])

save(sps_tissue, file = "spectra.RData", compress = "xz")

Threshold a data frame containing information on links

Description

Threshold a link data frame based on lower and upper similarity values. The function will return the links that lie within the defined bounds.

Usage

thresholdLinkDf(link0df, lower = 0.75, upper = 1)
thresholdLinkDf(link0df, lower = 0.75, upper = 1)

Arguments

`link0df`	`data.frame`, a link data frame that gives per each row information on linked features
`lower`	`numeric`, threshold value for similarity values, below this value linked features will not be returned
`upper`	`numeric`, threshold value for similarity values, above this value linked features will not be returned

Details

lower and upper are numerical values and truncate mass spectra based on their similarity values.

Value

thresholdLinkDf returns a data.frame that gives per each row information on linked features which are linked within certain thresholds.

Author(s)

Thomas Naake, [email protected]

Examples

data("spectra", package = "MetCirc")
data("similarityMat", package = "MetCirc")
link0df <- createLink0df(similarityMatrix = similarityMat,
    sps_tissue, condition = c("SPL", "LIM", "ANT", "STY"))
thresholdLinkDf(link0df = link0df, lower = 0.5, upper = 1)

data("spectra", package = "MetCirc")
data("similarityMat", package = "MetCirc")
link0df <- createLink0df(similarityMatrix = similarityMat,
    sps_tissue, condition = c("SPL", "LIM", "ANT", "STY"))
thresholdLinkDf(link0df = link0df, lower = 0.5, upper = 1)

Example data for 'MetCirc': 'tissue'

Description

The 'data.frame' 'tissue' is used in the subsection 'Preparing the tissue data set for analysis' in the vignette of 'MetCirc'. MS/MS data are merged across floral organs in this 'data.frame'.

Format

'data.frame'

Value

'data.frame'

Author(s)

Thomas Naake, [email protected]

Source

internal

Get typeMatch and link0 data frame

Description

typeMatch_link0 returns a list with accessors "link0df" and "type_match"

Usage

typeMatch_link0(similarityMatrix, sps, type, condition)
typeMatch_link0(similarityMatrix, sps, type, condition)

Arguments

`similarityMatrix`	`matrix` with pairwise similarity values
`sps`	`Spectra` object
`type`	`character(1)`, either `"mz"`, `"retentionTime"`, `"clustering"`
`condition`	`character`

Details

Helper function for shinyCircos.

Value

list of length 2, entry link0df is a data.frame and entry type_match is a character vector

Author(s)

Thomas Naake, [email protected]

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 retention time 
MetCirc:::typeMatch_link0(similarityMatrix = similarityMat, 
    sps = sps_tissue, type = "mz", 
    condition = c("SPL", "LIM", "ANT", "STY"))

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 retention time 
MetCirc:::typeMatch_link0(similarityMatrix = similarityMat, 
    sps = sps_tissue, type = "mz", 
    condition = c("SPL", "LIM", "ANT", "STY"))

Package 'MetCirc'

Help Index

Calculate polar coordinates from cartesian coordinates

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Plot a legend for circos plot

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Example data for 'MetCirc': 'compartmentTissue'

Description

Format

Value

Author(s)

Source

Example data for 'MetCirc': convertExampleDF

Description

Format

Value

Author(s)

Source

Convert MSP data frame into object of class Spectra

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Create a link matrix

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Create a data frame which contains features to link (indices)

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Create a cut data frame with information on links

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Get indices in linkDf of feature

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Add links and highlight sectors

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Calculate the nearest feature in polar coordinates given cartesian coordinates

Description

Convert MSP data frame into object of class `Spectra`

Get indices in `linkDf` of feature

Return a `recordedplot` of `plotCircos` plot with `highlight = TRUE`