run_ancom: Perform differential analysis using ANCOM

In HuaZou/MicrobiomeAnalysis: Data analysis toolkits in metagenomics

Perform differential analysis using ANCOM

Description

Perform significant test by comparing the pairwise log ratios between all features.

Usage

run_ancom(
  ps,
  group,
  confounders = character(0),
  taxa_rank = "all",
  transform = c("identity", "log10", "log10p", "SquareRoot", "CubicRoot", "logit"),
  norm = "TSS",
  norm_para = list(),
  p_adjust = c("none", "fdr", "bonferroni", "holm", "hochberg", "hommel", "BH", "BY"),
  pvalue_cutoff = 0.05,
  W_cutoff = 0.75
)

Arguments

ps	a phyloseq-class object.
group	character, the variable to set the group.
confounders	character vector, the confounding variables to be adjusted. default character(0), indicating no confounding variable.
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. "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 for multiple test correction, default none, for more details see stats::p.adjust.
pvalue_cutoff	significance level for each of the statistical tests, default 0.05.
W_cutoff	lower bound for the proportion for the W-statistic, default 0.7.

Details

In an experiment with only two treatments, this tests the following hypothesis for feature i:

H_{0i}: E(log(\mu_i^1)) = E(log(\mu_i^2))

where \mu_i^1 and \mu_i^2 are the mean abundances for feature i in the two groups.

The developers of this method recommend the following significance tests if there are 2 groups, use non-parametric Wilcoxon rank sum test stats::wilcox.test(). If there are more than 2 groups, use nonparametric stats::kruskal.test() or one-way ANOVA stats::aov().

Value

a microbiomeMarker object, in which the slot of marker_table contains four variables:

• feature, significantly different features.

• enrich_group, the class of the differential features enriched.

• effect_size, differential means for two groups, or F statistic for more than two groups.

• W, the W-statistic, number of features that a single feature is tested to be significantly different against.

Author(s)

Huang Lin, Yang Cao

References

Mandal et al. "Analysis of composition of microbiomes: a novel method for studying microbial composition", Microbial Ecology in Health & Disease, (2015), 26.

Examples

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

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