Package 'mbX'

Clean and Process Microbiome Data

Description

Processes microbiome and metadata files (e.g., 16S rRNA sequencing data) to produce an analysis-ready dataset. Supports CSV, TXT, and 'Excel' file formats. This function validates file formats, reads the data, and merges the datasets by the common column 'SampleID'. If a 'Taxonomy' column exists, the data are filtered to include only rows matching the provided taxonomic level.

Usage

ezclean(microbiome_data, metadata, level = "d")

Arguments

microbiome_data	A string specifying the path to the microbiome data file.
metadata	A string specifying the path to the metadata file.
level	A string indicating the taxonomic level for filtering the data (e.g., "genus").

Value

A data frame containing the cleaned and merged dataset.

Examples

## Not run: 
  mb  <- system.file("extdata", "microbiome.csv", package = "mbX")
  md  <- system.file("extdata", "metadata.csv",   package = "mbX")
  if (nzchar(mb) && nzchar(md)) {
    cleaned_data <- ezclean(mb, md, "g")
    head(cleaned_data)
  } else {
    message("Sample data files not found.")
  }

## End(Not run)

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Statistical Analysis and Visualization of Microbiome Data

Description

Performs Kruskal_Wallis tests, post_hoc Dunn comparisons, Compact Letter Display (CLD) summaries, and generates boxplots annotated with CLD letters for taxa abundances grouped by a chosen metadata variable.

Usage

ezstat(microbiome_data, metadata, level, selected_metadata)

Arguments

microbiome_data	Character; path to the microbiome abundance table (CSV, TSV, XLS, or XLSX).
metadata	Character; path to the sample metadata file (CSV, TXT, XLS, or XLSX).
level	Character; taxonomic rank to aggregate at (e.g. "genus", "g").
selected_metadata	Character; name of the categorical metadata column to group by.

Details

This function first calls ezclean to produce a cleaned, merged table of sample IDs, metadata, and taxa abundances at the requested taxonomic level. It then:

1. Runs Kruskal_Wallis tests on each taxon and writes results with FDR_correction.

2. Performs Dunns pairwise post_hoc tests (BH_adjusted) for taxa with KW p less than or equal to 0.05.

3. Computes CLD letters for significantly different groups and writes a summary Excel.

4. Generates high-resolution (900 dpi) boxplots annotated with CLD letters.

Value

Invisibly returns the data.frame of cleaned sample_taxa abundances used for all analyses.

Examples

## Not run: 
  mb  <- system.file("extdata", "microbiome.csv", package = "mbX")
  md  <- system.file("extdata", "metadata.csv",   package = "mbX")
  if (nzchar(mb) && nzchar(md)) {
    ezstat(mb, md, "genus", "Group")
  }

## End(Not run)

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Visualize Microbiome Data

Description

Generates publication-ready visualizations for microbiome data. This function first processes the microbiome and metadata files using ezclean(), then creates a bar plot using ggplot2. Supported file formats are CSV, TXT, and 'Excel'. Note: Only one of the parameters top_taxa or threshold should be provided.

Usage

ezviz(
  microbiome_data,
  metadata,
  level,
  selected_metadata,
  top_taxa = NULL,
  threshold = NULL,
  flip = FALSE
)

Arguments

microbiome_data	A string specifying the path to the microbiome data file.
metadata	A string specifying the path to the metadata file.
level	A string indicating the taxonomic level for filtering the data (e.g., "genus").
selected_metadata	A string specifying the metadata column used for grouping.
top_taxa	An optional numeric value indicating the number of top taxa to keep. Use this OR threshold, but not both.
threshold	An optional numeric value indicating the minimum threshold value; taxa below this threshold will be grouped into an "Other" category.
flip	Logical. If 'TRUE', the order of the stacks is reversed.

Value

A ggplot object containing the visualization.

Examples

mb  <- system.file("extdata", "microbiome.csv", package = "mbX")
md  <- system.file("extdata", "metadata.csv",   package = "mbX")
plot_obj <- ezviz(
  microbiome_data = mb,
  metadata        = md,
  level           = "genus",
  selected_metadata    = "sample_type",
  top_taxa        = 20,
  flip            = FALSE
)
print(plot_obj)

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