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"),
norm = "none",
norm_para = list(),
p_adjust = c("none", "fdr", "bonferroni", "holm", "hochberg", "hommel", "BH", "BY"),
pvalue_cutoff = 0.05,
...
)a phyloseq::phyloseq object
character, the variable to set the group
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().
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).
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).
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.
arguments passed to specific normalization methods
method for multiple test correction, default none,
for more details see stats::p.adjust.
numeric, p value cutoff, default 0.05.
extra arguments passed to the corresponding differential analysis
functions, e.g. run_lefse().
a microbiomeMarker object.
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.