differential_abundance: Differential abundance analysis

In umerijaz/microbiomeSeq: Microbial community analysis in an environmental context.

Description

This function finds the features that are significantly differentially abundant in the provided data, using DESeq implementation which models taxa abundance as a negative binomial distribution. See DESeq for more details. The significance of differentially abundant taxa is defined by log2 fold change and pvalue thresholds. These features are then assigned importance using random forest classifer. The measure of importance used in this implementation is mean decrease in accuracy.

Usage

differential_abundance(physeq, grouping_column, pvalue.threshold = 0.05,
  lfc.threshold = 0, filename = "NB_significant", output_norm = NULL)

Arguments

physeq	(Required). A link[phyloseq]phyloseq object containing merged information of abundance, taxonomic assignment, sample data including the measured variables and categorical information of the samples, and / or phylogenetic tree if available.
grouping_column	(Required). Character string specifying name of a categorical variable that is preffered for grouping the. information, this should be one of the components of grouping vector.
output_norm	(optional). A character string specifying method to be used for transforming abundance data to be used for plotting. note that, this normalisation occurs after DESeq analysis. Therefore, it is strictly for purposes of the output data and not for differential expression analysis.
pvalue.threshold.	Cut off p-value for significance of differentially abundance taxa, default is 0.05.
lfc.threshold.	Threshold for log2 fold change over which significance of differentially expressed taxa is considered.

Value

Returns a ggplot object. This can further be manipulated as preferred by user.

Returns a list of three items:

• SignfeaturesTable: A data.frame of taxa with raw and adjusted pvalues, basemean, log2 fold change and significance labels. computed by using raw p-values.

• importance: A data.frame of taxa mean decrease in accuracy as obtained by random forest classifier.

• plotdata: A data.frame of taxa and corresponding corrected p-values, importance rank organised in form accepted by ggplot.

Author(s)

Alfred Ssekagiri [e-mail], Umer Zeeshan Ijaz [e-mail]

References

http://userweb.eng.gla.ac.uk/umer.ijaz/, Umer Ijaz, 2015

Examples

data(pitlatrine)
physeq <- taxa_level(pitlatrine,"Phylum")
deseq_sig  <- differential_abundance(physeq, grouping_column = "Country")
#plot the significant features
plot_signif(deseq_sig$plotdata) #see function \link[microbiomeSeq]{plot_signif}

umerijaz/microbiomeSeq documentation built on May 30, 2019, 3:13 p.m.