Find makers (differentially expressed metagenomic features)

Source: R/da_all.R

run_marker is a wrapper of all differential analysis functions.

run_marker(
  ps,
  group,
  da_method = c("lefse", "simple_t", "simple_welch", "simple_white", "simple_kruskal",
    "simple_anova", "edger", "deseq2", "metagenomeseq", "ancom", "ancombc", "aldex",
    "limma_voom", "sl_lr", "sl_rf", "sl_svm"),
  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"),
  pvalue_cutoff = 0.05,
  ...
)

Arguments

ps

a phyloseq::phyloseq object

group

character, the variable to set the group

da_method

character to specify the differential analysis method. The options include:

  • "lefse", linear discriminant analysis (LDA) effect size (LEfSe) method, for more details see run_lefse().

  • "simple_t", "simple_welch", "simple_white", "simple_kruskal", and "simple_anova", simple statistic methods; "simple_t", "simple_welch" and "simple_white" for two groups comparison; "simple_kruskal", and "simple_anova" for multiple groups comparison. For more details see run_simple_stat().

  • "edger", see run_edger().

  • "deseq2", see run_deseq2().

  • "metagenomeseq", differential expression analysis based on the Zero-inflated Log-Normal mixture model or Zero-inflated Gaussian mixture model using metagenomeSeq, see run_metagenomeseq().

  • "ancom", see run_ancom().

  • "ancombc", differential analysis of compositions of microbiomes with bias correction, see run_ancombc().

  • "aldex", see run_aldex().

  • "limma_voom", see run_limma_voom().

  • "sl_lr", "sl_rf", and "sl_svm", there supervised leaning (SL) methods: logistic regression (lr), random forest (rf), or support vector machine (svm). For more details see run_sl().

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

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.

...

extra arguments passed to the corresponding differential analysis functions, e.g. run_lefse().

Value

a microbiomeMarker object.

Details

This function is only a wrapper of all differential analysis functions, We recommend to use the corresponding function, since it has a better default arguments setting.