run_test_multiple_groups: Statistical test for multiple groups

In HuaZou/MicrobiomeAnalysis: Data analysis toolkits in metagenomics

Statistical test for multiple groups

Description

Differential expression analysis for multiple groups.

Usage

run_test_multiple_groups(
  ps,
  group,
  taxa_rank = "all",
  transform = c("identity", "log10", "log10p", "SquareRoot", "CubicRoot", "logit"),
  norm = "TSS",
  norm_para = list(),
  method = c("anova", "kruskal"),
  p_adjust = c("none", "fdr", "bonferroni", "holm", "hochberg", "hommel", "BH", "BY"),
  pvalue_cutoff = 0.05,
  effect_size_cutoff = NULL
)

Arguments

ps	a phyloseq::phyloseq object
group	character, the variable to set the group
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	arguments passed to specific normalization methods
method	test method, must be one of "anova" or "kruskal"
p_adjust	method for multiple test correction, default none, for more details see stats::p.adjust.
pvalue_cutoff	numeric, p value cutoff, default 0.05.
effect_size_cutoff	numeric, cutoff of effect size default NULL which means no effect size filter. The eta squared is used to measure the effect size for anova/kruskal test.

Value

a microbiomeMarker object.

See Also

run_posthoc_test(),run_test_two_groups(),run_simple_stat()

Examples

data(enterotypes_arumugam)
ps <- phyloseq::subset_samples(
    enterotypes_arumugam,
    Enterotype %in% c("Enterotype 3", "Enterotype 2", "Enterotype 1")
)
mm_anova <- run_test_multiple_groups(
    ps,
    group = "Enterotype",
    method = "anova"
)

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