run_ancombc: Differential analysis of compositions of microbiomes with...
In HuaZou/MicrobiomeAnalysis: Data analysis toolkits in metagenomics

run_ancombc

R Documentation

Differential analysis of compositions of microbiomes with bias correction (ANCOM-BC).

Description

Differential abundance analysis for microbial absolute abundance data. This function is a wrapper of ANCOMBC::ancombc().

Usage

run_ancombc(
  ps,
  group,
  confounders = character(0),
  contrast = NULL,
  taxa_rank = "all",
  transform = c("identity", "log10", "log10p", "SquareRoot", "CubicRoot", "logit"),
  norm = "none",
  norm_para = list(),
  p_adjust = c("none", "fdr", "bonferroni", "holm", "hochberg", "hommel", "BH", "BY"),
  prv_cut = 0.1,
  lib_cut = 0,
  struc_zero = FALSE,
  neg_lb = FALSE,
  tol = 1e-05,
  max_iter = 100,
  conserve = FALSE,
  pvalue_cutoff = 0.05
)

Arguments

`ps`	a `phyloseq::phyloseq` object, which consists of a feature table, a sample metadata and a taxonomy table.
`group`	the name of the group variable in metadata. Specifying `group` is required for detecting structural zeros and performing global test.
`confounders`	character vector, the confounding variables to be adjusted. default `character(0)`, indicating no confounding variable.
`contrast`	this parameter only used for two groups comparison while there are multiple groups. For more please see the following details.
`taxa_rank`	character to specify taxonomic rank to perform differential analysis on. Should be one of `phyloseq::rank_names(phyloseq)`, or "all" means to summarize the taxa by the top taxa ranks (`summarize_taxa(ps, level = rank_names(ps)[1])`), or "none" means perform differential analysis on the original taxa (`taxa_names(phyloseq)`, e.g., OTU or ASV).
`transform`	character, the methods used to transform the microbial abundance. See `transform_abundances()` for more details. The options include: "identity", return the original data without any transformation (default). "log10", the transformation is `log10(object)`, and if the data contains zeros the transformation is `log10(1 + object)`. "log10p", the transformation is `log10(1 + object)`. "SquareRoot", the transformation is `⁠Square Root⁠`. "CubicRoot", the transformation is `⁠Cubic Root⁠`. "logit", the transformation is `⁠Zero-inflated Logit Transformation⁠` (Does not work well for microbiome data).
`norm`	the methods used to normalize the microbial abundance data. See `normalize()` for more details. Options include: "none": do not normalize. "rarefy": random subsampling counts to the smallest library size in the data set. "TSS": total sum scaling, also referred to as "relative abundance", the abundances were normalized by dividing the corresponding sample library size. "TMM": trimmed mean of m-values. First, a sample is chosen as reference. The scaling factor is then derived using a weighted trimmed mean over the differences of the log-transformed gene-count fold-change between the sample and the reference. "RLE", relative log expression, RLE uses a pseudo-reference calculated using the geometric mean of the gene-specific abundances over all samples. The scaling factors are then calculated as the median of the gene counts ratios between the samples and the reference. "CSS": cumulative sum scaling, calculates scaling factors as the cumulative sum of gene abundances up to a data-derived threshold. "CLR": centered log-ratio normalization. "CPM": pre-sample normalization of the sum of the values to 1e+06.
`norm_para`	named `list`. other arguments passed to specific normalization methods. Most users will not need to pass any additional arguments here.
`p_adjust`	method to adjust p-values by. Default is "holm". Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". See `stats::p.adjust()` for more details.
`prv_cut`	a numerical fraction between 0 and 1. Taxa with proportion of zeroes greater than `prv_cut` will be excluded in the analysis. Default is 0.10.
`lib_cut`	a numerical threshold for filtering samples based on library sizes. Samples with library sizes less than `lib_cut` will be excluded in the analysis. Default is 0, i.e. do not filter any sample.
`struc_zero`	whether to detect structural zeros. Default is FALSE.
`neg_lb`	whether to classify a taxon as a structural zero in the corresponding study group using its asymptotic lower bound. Default is FALSE.
`tol`	the iteration convergence tolerance for the E-M algorithm. Default is 1e-05.
`max_iter`	the maximum number of iterations for the E-M algorithm. Default is 100.
`conserve`	whether to use a conservative variance estimate of the test statistic. It is recommended if the sample size is small and/or the number of differentially abundant taxa is believed to be large. Default is FALSE.
`pvalue_cutoff`	level of significance. Default is 0.05.

Details

contrast must be a two length character or NULL (default). It is only required to set manually for two groups comparison when there are multiple groups. The order determines the direction of comparison, the first element is used to specify the reference group (control). This means that, the first element is the denominator for the fold change, and the second element is used as baseline (numerator for fold change). Otherwise, users do required to concern this parameter (set as default NULL), and if there are two groups, the first level of groups will set as the reference group; if there are multiple groups, it will perform an ANOVA-like testing to find markers which difference in any of the groups.

Value

a microbiomeMarker object.

References

Lin, Huang, and Shyamal Das Peddada. "Analysis of compositions of microbiomes with bias correction." Nature communications 11.1 (2020): 1-11.

Examples

data(enterotypes_arumugam)
ps <- phyloseq::subset_samples(
    enterotypes_arumugam,
    Enterotype %in% c("Enterotype 3", "Enterotype 2")
)
run_ancombc(ps, group = "Enterotype")

HuaZou/MicrobiomeAnalysis documentation built on May 13, 2024, 11:10 a.m.