In FrederickHuangLin/ANCOMBC: Microbiome differential abudance and correlation analyses with bias correction

knitr::opts_chunk$set(message = FALSE, warning = FALSE, comment = NA,
                      fig.width = 6.25, fig.height = 5)
library(ANCOMBC)
library(tidyverse)

1. Introduction

The data_sanity_check function performs essential validations on the input data to ensure its integrity before further processing. It verifies data types, confirms the structure of the input data, and checks for consistency between sample names in the metadata and the feature table, safeguarding against common data input errors.

2. Installation

Download package.

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("ANCOMBC")

Load the package.

library(ANCOMBC)

3. Examples

3.1 Import a `phyloseq` object

The HITChip Atlas dataset contains genus-level microbiota profiling with HITChip for 1006 western adults with no reported health complications, reported in [@lahti2014tipping]. The dataset is available via the microbiome R package [@lahti2017tools] in phyloseq [@mcmurdie2013phyloseq] format.

data(atlas1006, package = "microbiome")

atlas1006

List the taxonomic levels available for data aggregation.

phyloseq::rank_names(atlas1006)

List the variables available in the sample metadata.

colnames(microbiome::meta(atlas1006))

Data sanity and integrity check.

# With `group` variable
check_results = data_sanity_check(data = atlas1006,
                                  tax_level = "Family",
                                  fix_formula = "age + sex + bmi_group",
                                  group = "bmi_group",
                                  struc_zero = TRUE,
                                  global = TRUE,
                                  verbose = TRUE)

# Without `group` variable
check_results = data_sanity_check(data = atlas1006,
                                  tax_level = "Family",
                                  fix_formula = "age + sex + bmi_group",
                                  group = NULL,
                                  struc_zero = FALSE,
                                  global = FALSE,
                                  verbose = TRUE)

3.2 Import a `tse` object

tse = mia::makeTreeSummarizedExperimentFromPhyloseq(atlas1006)

List the taxonomic levels available for data aggregation.

mia::taxonomyRanks(tse)

List the variables available in the sample metadata.

colnames(SummarizedExperiment::colData(tse))

Data sanity and integrity check.

check_results = data_sanity_check(data = tse,
                                  assay_name = "counts",
                                  tax_level = "Family",
                                  fix_formula = "age + sex + bmi_group",
                                  group = "bmi_group",
                                  struc_zero = TRUE,
                                  global = TRUE,
                                  verbose = TRUE)

3.3 Import a `matrix` or `data.frame`

Both abundance data and sample metadata are required for this import method.

Note that aggregating taxa to higher taxonomic levels is not supported in this method. Ensure that the data is already aggregated to the desired taxonomic level before proceeding. If aggregation is needed, consider creating a phyloseq or tse object for importing.

abundance_data = microbiome::abundances(atlas1006)
meta_data = microbiome::meta(atlas1006)

Ensure that the rownames of the metadata correspond to the colnames of the abundance data.

all(rownames(meta_data) %in% colnames(abundance_data))

List the variables available in the sample metadata.

colnames(meta_data)

Data sanity and integrity check.

check_results = data_sanity_check(data = abundance_data,
                                  assay_name = "counts",
                                  tax_level = "Family",
                                  meta_data = meta_data,
                                  fix_formula = "age + sex + bmi_group",
                                  group = "bmi_group",
                                  struc_zero = TRUE,
                                  global = TRUE,
                                  verbose = TRUE)