This script performs metabolite clustering analysis and computes clusters of metabolites based on regulatory rules between Intracellular and culture media metabolomics (CoRe experiment).

Usage

MCA_CoRe(
  InputData_Intra,
  InputData_CoRe,
  SettingsInfo_Intra = c(ValueCol = "Log2FC", StatCol = "p.adj", StatCutoff = 0.05,
    ValueCutoff = 1),
  SettingsInfo_CoRe = c(DirectionCol = "CoRe", ValueCol = "Log2(Distance)", StatCol =
    "p.adj", StatCutoff = 0.05, ValueCutoff = 1),
  FeatureID = "Metabolite",
  SaveAs_Table = "csv",
  BackgroundMethod = "Intra&CoRe",
  FolderPath = NULL
)

Arguments

InputData_Intra: DF for your data (results from e.g. DMA) containing metabolites in rows with corresponding Log2FC and stat (p-value, p.adjusted) value columns.
InputData_CoRe: DF for your data (results from e.g. DMA) containing metabolites in rows with corresponding Log2FC and stat (p-value, p.adjusted) value columns. Here we additionally require
SettingsInfo_Intra: Optional: Pass ColumnNames and Cutoffs for the intracellular metabolomics including the value column (e.g. Log2FC, Log2Diff, t.val, etc) and the stats column (e.g. p.adj, p.val). This must include: c(ValueCol=ColumnName_InputData_Intra,StatCol=ColumnName_InputData_Intra, StatCutoff= NumericValue, ValueCutoff=NumericValue) Default=c(ValueCol="Log2FC",StatCol="p.adj", StatCutoff= 0.05, ValueCutoff=1)
SettingsInfo_CoRe: Optional: Pass ColumnNames and Cutoffs for the consumption-release metabolomics including the direction column, the value column (e.g. Log2Diff, t.val, etc) and the stats column (e.g. p.adj, p.val). This must include: c(DirectionCol= ColumnName_InputData_CoRe,ValueCol=ColumnName_InputData_CoRe,StatCol=ColumnName_InputData_CoRe, StatCutoff= NumericValue, ValueCutoff=NumericValue)Default=c(DirectionCol="CoRe", ValueCol="Log2(Distance)",StatCol="p.adj", StatCutoff= 0.05, ValueCutoff=1)
FeatureID: Optional: Column name of Column including the Metabolite identifiers. This MUST BE THE SAME in each of your Input files. Default="Metabolite"
SaveAs_Table: Optional: File types for the analysis results are: "csv", "xlsx", "txt" default: "csv"
BackgroundMethod: Optional: Background method `Intra|CoRe, Intra&CoRe, CoRe, Intra or * Default="Intra&CoRe"
FolderPath: Optional: Path to the folder the results should be saved at. default: NULL

Value

List of two DFs: 1. Summary of the cluster count and 2. the detailed information of each metabolites in the clusters.

Examples


Media <- MetaProViz::ToyData("CultureMedia_Raw")
ResM <- MetaProViz::PreProcessing(InputData = Media[-c(40:45) ,-c(1:3)],
                                  SettingsFile_Sample = Media[-c(40:45) ,c(1:3)] ,
                                  SettingsInfo = c(Conditions = "Conditions", Biological_Replicates = "Biological_Replicates", CoRe_norm_factor = "GrowthFactor", CoRe_media = "blank"),
                                  CoRe=TRUE)
#> For Consumption Release experiment we are using the method from Jain M.  REF: Jain et. al, (2012), Science 336(6084):1040-4, doi: 10.1126/science.1218595.
#> FeatureFiltering: Here we apply the modified 80%-filtering rule that takes the class information (Column `Conditions`) into account, which additionally reduces the effect of missing values (REF: Yang et. al., (2015), doi: 10.3389/fmolb.2015.00004). Filtering value selected: 0.8
#> 3 metabolites where removed: N-acetylaspartylglutamate, hypotaurine, S-(2-succinyl)cysteine
#> Missing Value Imputation: Missing value imputation is performed, as a complementary approach to address the missing value problem, where the missing values are imputing using the `half minimum value`. REF: Wei et. al., (2018), Reports, 8, 663, doi:https://doi.org/10.1038/s41598-017-19120-0
#> NA values were found in Control_media samples for metabolites. For metabolites including NAs MVI is performed unless all samples of a metabolite are NA.
#> Metabolites with high NA load (>20%) in Control_media samples are: dihydroorotate.
#> Metabolites with only NAs (=100%) in Control_media samples are: hydroxyphenylpyruvate. Those NAs are set zero as we consider them true zeros
#> Total Ion Count (TIC) normalization: Total Ion Count (TIC) normalization is used to reduce the variation from non-biological sources, while maintaining the biological variation. REF: Wulff et. al., (2018), Advances in Bioscience and Biotechnology, 9, 339-351, doi:https://doi.org/10.4236/abb.2018.98022
#> Error in ggplot2::autoplot(stats::prcomp(as.matrix(InputData), scale. = as.logical(Scaling)),     data = InputPCA, colour = Param_Col, fill = Param_Col, shape = Param_Sha,     size = 3, alpha = 0.8, label = T, label.size = 2.5, label.repel = TRUE,     loadings = as.logical(ShowLoadings), loadings.label = as.logical(ShowLoadings),     loadings.label.vjust = 1.2, loadings.label.size = 2.5, loadings.colour = "grey10",     loadings.label.colour = "grey10"): Objects of class <prcomp> are not supported by autoplot.
#> ℹ Have you loaded the required package?

MediaDMA <- MetaProViz::DMA(InputData=ResM[["DF"]][["Preprocessing_output"]][ ,-c(1:4)],
                            SettingsFile_Sample=ResM[["DF"]][["Preprocessing_output"]][ , c(1:4)],
                            SettingsInfo = c(Conditions = "Conditions", Numerator = NULL, Denominator  = "HK2"),
                            StatPval ="aov",
                            CoRe=TRUE)
#> Error: object 'ResM' not found

IntraDMA <- MetaProViz::ToyData(Data="IntraCells_DMA")

Res <- MetaProViz::MCA_CoRe(InputData_Intra = IntraDMA%>%tibble::rownames_to_column("Metabolite"),
                            InputData_CoRe = MediaDMA[["DMA"]][["786-M1A_vs_HK2"]])
#> Error: object 'MediaDMA' not found