dacomp.generate_example_dataset_multivariate_example: Generate an example dataset with a multivariate phenotype

In barakbri/dacomp: Non parametric differential abundance testing for microbiome counts data.

Description

Generate a simulated dataset, similar to dacomp.generate_example_dataset_continuous with the following difference: the generated dataset contains two phenotypes, instead of one. The change observed in a sample, is monotone increasing with the values of each measured covariate.

Usage

dacomp.generate_example_dataset_multivariate_example(
  n,
  m1 = 30,
  signal_strength_as_change_in_microbial_load = 0.1
)

Arguments

n	Number of samples.
m1	Number of differentially abundant taxa
signal_strength_as_change_in_microbial_load

Value

a list

• countsA counts matrix with n rows, and 1384 columns, rows represent samples,columns represent taxa.

• covariateThe measured phenotype, a matrix of size n X 2, rows in this matrix correspond to the rows of counts

• select_diff_abundantA vector containing the indices of taxa that are differentially abundant.

• taxonomyA table for the taxonomic affiliation of OTUs in the simulated dataset.

References

Brill, Barak, Amnon Amir, and Ruth Heller. 2019. Testing for Differential Abundance in Compositional Counts Data, with Application to Microbiome Studies. arXiv Preprint arXiv:1904.08937.

Kostic, Aleksandar D, Dirk Gevers, Chandra Sekhar Pedamallu, Monia Michaud, Fujiko Duke, Ashlee M Earl, Akinyemi I Ojesina, et al. 2012. Genomic Analysis Identifies Association of Fusobacterium with Colorectal Carcinoma. Genome Research 22 (2). Cold Spring Harbor Lab: 292–98.

McMurdie, Paul J, and Susan Holmes. 2013. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PloS One 8 (4). Public Library of Science: e61217.

Examples

data = dacomp.generate_example_dataset_multivariate_example(30)

barakbri/dacomp documentation built on June 17, 2021, 11:20 p.m.