Introduction

Cross-sectional microbiome studies, which randomly sample the microbiome from a study population at a single time point, have been widely used in microbiome research due to its simplicity and easy sample collection. Besides cross-sectional design, case-control study designs are also prevalent in microbiome research. Both designs will generate microbiome data with independence among samples and can be analyzed in a similar way by treating the microbiome measurement as the outcome. MicrobiomeStat provides full support of analyzing data from cross-sectional and case-control studies. We illustrate the use of MicrobiomeStat in analyzing these types of data using the peerj32 dataset, which was originated from a study that probed the relationship between human intestinal bacteria and lipid metabolism in response to a probiotic intervention.

Lahti L, Salonen A, Kekkonen RA, Salojärvi J, Jalanka-Tuovinen J, Palva A, Orešič M, de Vos WM. Associations between the human intestinal microbiota, Lactobacillus rhamnosus GG and serum lipids indicated by integrated analysis of high-throughput profiling data. PeerJ. 2013 Feb 26;1:e32. doi: 10.7717/peerj.32. PMID: 23638368; PMCID: PMC3628737.

Exploring the PeerJ32 Dataset

The peerj32 was originally in the phyloseq format. We have converted it to our MicrobiomeStat format using the mStat_convert_phyloseq_to_data_obj function. For an in-depth look at the conversion process, please refer to this section in our Gitbook.

Converting Data from Phyloseq into MicrobiomeStat

The major variable of interest in this study is the treatment group: LGG Probiotic or Placebo. Additionally, the dataset includes other variables such as the sex of participants, which can serve both as a stratifying factor in visualization and as a covariate in statistical testing.

Sample metadata overview from the peerj32 dataset. This table provides details about each sample, including the subject ID, cons, time point, sex of the participant, and the treatment group assignment (Placebo or LGG).

The dataset provides taxonomic classifications at three ranks: Phylum, Family, and Genus. The phylogenetic tree is not provided in this dataset.

Taxonomic annotations for the microbial features in the peerj32 dataset. Each row represents a microbial feature, classified at the Phylum, Family, and Genus levels.

We will use peerj32 dataset to showcase the utility of MicrobiomeStats in analyzing cross-sectional/case-control data. For users wishing to apply our tutorial to their own datasets, please see the following guide:

Creating the MicrobiomeStat Data Object

This section explains how to convert your data into the MicrobiomeStat format, enabling you to use the MicrobiomeStat toolkit for your research.