Introduction
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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.
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.
The dataset provides taxonomic classifications at three ranks: Phylum, Family, and Genus. The phylogenetic tree is not provided in this dataset.
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:
This section explains how to convert your data into the MicrobiomeStat format, enabling you to use the MicrobiomeStat toolkit for your research.
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).peerj32
dataset. Each row represents a microbial feature, classified at the Phylum, Family, and Genus levels.