Differential analysis using limma-voom

Source: R/da_limma_voom.R

Differential expression analysis based on limma-voom.

run_limma_voom(
  ps,
  group,
  confounders = character(0),
  contrast = NULL,
  taxa_rank = "all",
  transform = c("identity", "log10", "log10p", "SquareRoot", "CubicRoot", "logit"),
  norm = "none",
  norm_para = list(),
  voom_span = 0.5,
  p_adjust = c("none", "fdr", "bonferroni", "holm", "hochberg", "hommel", "BH", "BY"),
  pvalue_cutoff = 0.05,
  ...
)

Arguments

ps

ps a phyloseq::phyloseq object.

group

character, the variable to set the group, must be one of the var of the sample metadata.

confounders

character vector, the confounding variables to be adjusted. default character(0), indicating no confounding variable.

contrast

this parameter only used for two groups comparison while there are multiple groups. For more please see the following details.

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.

  • "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.

norm_para

arguments passed to specific normalization methods. Most users will not need to pass any additional arguments here.

voom_span

width of the smoothing window used for the lowess mean-variance trend for limma::voom(). Expressed as a proportion between 0 and 1.

p_adjust

method for multiple test correction, default none, for more details see stats::p.adjust.

pvalue_cutoff

cutoff of p value, default 0.05.

...

extra arguments passed to limma::eBayes().

Value

a microbiomeMarker object.

Details

contrast must be a two length character or NULL (default). It is only required to set manually for two groups comparison when there are multiple groups. The order determines the direction of comparison, the first element is used to specify the reference group (control). This means that, the first element is the denominator for the fold change, and the second element is used as baseline (numerator for fold change). Otherwise, users do required to concern this parameter (set as default NULL), and if there are two groups, the first level of groups will set as the reference group; if there are multiple groups, it will perform an ANOVA-like testing to find markers which difference in any of the groups.

References

Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014). voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome biology, 15(2), 1-17.

Examples

data(enterotypes_arumugam)
mm <- run_limma_voom(
    enterotypes_arumugam,
    "Enterotype",
    contrast = c("Enterotype 3", "Enterotype 2"),
    pvalue_cutoff = 0.01,
    p_adjust = "none"
)
mm
#> microbiomeMarker-class inherited from phyloseq-class
#> normalization method:              [ none ]
#> microbiome marker identity method: [ limma_voom ]
#> marker_table() Marker Table:       [ 14 microbiome markers with 5 variables ]
#> otu_table()    OTU Table:          [ 244 taxa and  39 samples ]
#> sample_data()  Sample Data:        [ 39 samples by  9 sample variables ]
#> tax_table()    Taxonomy Table:     [ 244 taxa by 1 taxonomic ranks ]