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Metabolite clustering analysis for core experiments

Source: R/MetaboliteClusteringAnalysis.R
mca_core.Rd

Performs metabolite clustering analysis and computes clusters based on regulatory rules between intracellular and culture media metabolomics in core experiments.

Usage

mca_core(
  data_intra,
  data_core,
  metadata_info_intra = c(ValueCol = "Log2FC", StatCol = "p.adj", cutoff_stat = 0.05,
    ValueCutoff = 1),
  metadata_info_core = c(DirectionCol = "core", ValueCol = "Log2(Distance)", StatCol =
    "p.adj", cutoff_stat = 0.05, ValueCutoff = 1),
  feature = "Metabolite",
  save_table = "csv",
  method_background = "Intra&core",
  path = NULL
)

Arguments

data_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.

data_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

metadata_info_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_data_intra,StatCol=ColumnName_data_intra, cutoff_stat= NumericValue, ValueCutoff=NumericValue) Default=c(ValueCol="Log2FC",StatCol="p.adj", cutoff_stat= 0.05, ValueCutoff=1)

metadata_info_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_data_core,ValueCol=ColumnName_data_core,StatCol=ColumnName_data_core, cutoff_stat= NumericValue, ValueCutoff=NumericValue)Default=c(DirectionCol="core", ValueCol="Log2(Distance)",StatCol="p.adj", cutoff_stat= 0.05, ValueCutoff=1)

feature

Optional: Column name of Column including the Metabolite identifiers. This MUST BE THE SAME in each of your Input files. Default="Metabolite"

save_table

Optional: File types for the analysis results are: "csv", "xlsx", "txt" default: "csv"

method_background

Optional: Background method `Intra|core, Intra&core, core, Intra or * Default="Intra&core"

path

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

data(intracell_dma)

# Create mock CoRe DMA results with required columns
core_dma <- data.frame(
    Metabolite = intracell_dma$Metabolite[1:50],
    `Log2(Distance)` = runif(50, -2, 2),
    p.adj = runif(50, 0, 0.1),
    core = sample(c("Consumption", "Release"), 50, replace = TRUE),
    check.names = FALSE
)

Res <- mca_core(
    data_intra = as.data.frame(intracell_dma),
    data_core = core_dma,
    save_table = NULL
)