Simple statistical analysis of metagenomic profiles

Perform simple statistical analysis of metagenomic profiles. This function is a wrapper of run_test_two_groups and run_test_multiple_groups.

run_simple_stat(
  ps,
  group,
  taxa_rank = "all",
  transform = c("identity", "log10", "log10p"),
  norm = "TSS",
  norm_para = list(),
  method = c("welch.test", "t.test", "white.test", "anova", "kruskal"),
  p_adjust = c("none", "fdr", "bonferroni", "holm", "hochberg", "hommel", "BH", "BY"),
  pvalue_cutoff = 0.05,
  diff_mean_cutoff = NULL,
  ratio_cutoff = NULL,
  eta_squared_cutoff = NULL,
  conf_level = 0.95,
  nperm = 1000,
  ...
)

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).

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, options include: "welch.test", "t.test" and "white.test" for two groups comparison, "anova"and "kruskal" for multiple groups comparison.

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

diff_mean_cutoff, ratio_cutoff

only used for two groups comparison, cutoff of different means and ratios, default NULL which means no effect size filter.

eta_squared_cutoff

only used for multiple groups comparison, numeric, cutoff of effect size (eta squared) default NULL which means no effect size filter.

conf_level

only used for two groups comparison, numeric, confidence level of interval.

nperm

integer, only used for two groups comparison, number of permutations for white non parametric t test estimation

...

only used for two groups comparison, extra arguments passed to t.test() or fisher.test().

Value

a microbiomeMarker object.

See also

run_test_two_groups(),run_test_multiple_groups()

Examples

data(enterotypes_arumugam)
ps <- phyloseq::subset_samples(
    enterotypes_arumugam,
    Enterotype %in% c("Enterotype 3", "Enterotype 2")
)
run_simple_stat(ps, group = "Enterotype")
#> microbiomeMarker-class inherited from phyloseq-class
#> normalization method:              [ TSS ]
#> microbiome marker identity method: [ welch.test ]
#> marker_table() Marker Table:       [ 16 microbiome markers with 5 variables ]
#> otu_table()    OTU Table:          [ 235 taxa and  24 samples ]
#> sample_data()  Sample Data:        [ 24 samples by  9 sample variables ]
#> tax_table()    Taxonomy Table:     [ 235 taxa by 1 taxonomic ranks ]