Paired Sample 16S Sequencing Data from the MiPair Study — subset

The subset_pairs.obj dataset contains paired sample 16S sequencing data from the MiPair study. This data set is designed for design-based comparative analysis with paired microbiome data, focusing on the effects of antibiotic treatment on mouse gut microbiome.

Usage

data(subset_pairs.obj)

Format

A MicrobiomeStat Data Object containing the following data matrices as described in detail by Jang H, Koh H, Gu W, Kang B. (2022):

feature.tab

A matrix of microbial abundances, where rows represent taxa and columns represent samples.

meta.dat

A data frame with 80 rows and 3 variables:

Antibiotic: Factor with 2 levels: "Baseline" and "Week 2", indicating the time point of sample collection relative to antibiotic treatment.
MouseID: Factor with unique identifiers for each mouse, ranging from 1 to 224.
Sex: Factor with 2 levels: "M" (male) and "F" (female), indicating the sex of each mouse.

feature.ann

A data frame containing taxonomic annotations for the microbial features.

tree

A phylogenetic tree object representing the evolutionary relationships among the microbial taxa.

The dataset includes samples from 80 mice, with paired measurements taken at baseline and 2 weeks after antibiotic treatment. There are 40 unique mice, each sampled at two time points.

Source

Data source: https://github.com/YJ7599/MiPairGit

References

Jang H, Koh H, Gu W, Kang B. (2022) Integrative web cloud computing and analytics using MiPair for design-based comparative analysis with paired microbiome data. Scientific Reports 12(20465).

Author

Jang H, Koh H, Gu W, Kang B

Examples

data(subset_pairs.obj)
# View the first few rows of the metadata
head(subset_pairs.obj$meta.dat)
#>         Antibiotic MouseID Sex
#> JP29F3W   Baseline      29   M
#> JP25F3W   Baseline      25   F
#> JP38F3W   Baseline      38   F
#> JP14F3W   Baseline      14   M
#> JP16F3W   Baseline      16   M
#> JP2F3W    Baseline       2   M

# Check the dimensions of the feature table
dim(subset_pairs.obj$feature.tab)
#> [1] 257  86

# Summarize the sex distribution
table(subset_pairs.obj$meta.dat$Sex)
#> 
#>  F  M 
#> 44 42 

# Count the number of samples at each time point
table(subset_pairs.obj$meta.dat$Antibiotic)
#> 
#> Baseline   Week 2 
#>       43       43