In ipb-halle/MetFamily: MetFamily: Discovering Regulated Metabolite Families in Untargeted Metabolomics Studies

library(MetFamily)

Introduction

Some text about the scenario, data and MTBLS297.

Cool paper [@Treutler16DiscoveringRegulatedMetabolite].

Cool R package: r Githubpkg("ipb-halle/MetFamily")

Loading the data

First, we load the data and summarise it

fileName <- system.file("extdata/showcase/Project_file_showcase_annotated.csv.gz", package = "MetFamily")

#qfeatures <- readMSDial()

# f <- system.file("extdata", "minimona.msp", package = "MsBackendMsp") ## Change to MetFamily example files
# spectra <- readMsp(f)

#convertQfeaturesSpectraToProjectFile <- function(qfeatures, 
#                                 spectra, 
#                                 parameterSet, 
#                                 progress = FALSE)


project <- readClusterDataFromProjectFile(file = fileName)

Filtering data

We can filter the data to remove low quality data points

# todo...

PCA

PCA on MS1 in Figure \@ref(fig:pca).

fileName <- system.file("extdata/testdata/filterObj.Rdata", package = "MetFamily")
load(fileName) 

pca <- calculatePCA(dataList=project, 
                         filterObj=filterObj, 
                         ms1AnalysisMethod="PCA (Principal Component Analysis)", 
                         scaling="None", 
                    logTransform=FALSE)

## Need to be global variables because they are not 
## passed to getPcaPerformanceIndicator() as parameters. 
pcaDimensionOne <<- 1
pcaDimensionTwo <<- 2

  resultObj <- calcPlotPCAscores(
    pcaObj = pca, 
    dataList = project, 
    filterObj = filterObj,
    pcaDimensionOne = pcaDimensionOne, 
    pcaDimensionTwo = pcaDimensionTwo, 
    showScoresLabels = FALSE, 
    xInterval = NULL, 
    yInterval = NULL
)

  resultObj <- calcPlotPCAloadings(
    pcaObj = pca, 
    dataList = project, 
    filter = filterObj, 
    pcaDimensionOne = pcaDimensionOne, 
    pcaDimensionTwo = pcaDimensionTwo, 
    selectionFragmentPcaLoadingSet = NULL,
    selectionAnalysisPcaLoadingSet = NULL,
    selectionSearchPcaLoadingSet   = NULL,
    xInterval = NULL, 
    yInterval = NULL,
    loadingsLabels = "None", 
    showLoadingsAbundance = FALSE, 
    showLoadingsFeaturesAnnotated   = TRUE,
    showLoadingsFeaturesUnannotated = TRUE,
    showLoadingsFeaturesSelected    = TRUE,
    showLoadingsFeaturesUnselected  = TRUE
  )

HCA

HCA on MS2 in Figure \@ref(fig:hca).

if (FALSE) {
p <- calcPlotDendrogram_plotly(dataList=project, 
                               filterObj=filterObj, 
                               clusterDataList=project, 
  distanceMeasure = "Jaccard", 
  showClusterLabels=FALSE, 
  hcaPrecursorLabels="m/z / RT", 
  selectionFragmentTreeNodeSet = NULL, 
  selectionAnalysisTreeNodeSet = NULL, 
  selectionSearchTreeNodeSet = NULL, 
  selectedSelection, 
  heatmapContent, 
  heatmapOrdering, 
  heatmapProportion)
}

fileName <- system.file("extdata/testdata/clusterDataList.Rdata", package = "MetFamily")
load(fileName) 
fileName <- system.file("extdata/testdata/hcaFilter.Rdata", package = "MetFamily")
load(fileName) 

returnObj <- calcPlotDendrogram(dataList=project, 
                                filter=filter, 
                                clusterDataList=clusterDataList, 
                                annoPresentAnnotationsList = annoPresentAnnotationsList ,
                                annoPresentColorsList = annoPresentColorsList,
                                distanceMeasure="Jaccard (intensity-weighted)", 
                                selectionFragmentTreeNodeSet = NULL, 
                                selectionAnalysisTreeNodeSet = NULL, 
                                selectionSearchTreeNodeSet = NULL, 
                                showClusterLabels = TRUE, 
                                hcaPrecursorLabels = "m/z / RT", 
                                xInterval = c(1,219))

References {.unnumbered}

Appendix {.unnumbered}

Session info

sessionInfo()

ipb-halle/MetFamily documentation built on Sept. 5, 2024, 12:01 a.m.