# 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. {% content-ref url="/pages/ncvk6iQalI4IDdmAzizj" %} [Converting Data from Phyloseq into MicrobiomeStat](/setting-up-microbiomestat-installation-and-data-preparation/laying-the-foundation-creating-the-microbiomestat-data-object/navigating-data-from-phyloseq-into-microbiomestat.md) {% endcontent-ref %} 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: {% content-ref url="/pages/WK9NC4ukGZbAvQGuCKUR" %} [Creating the MicrobiomeStat Data Object](/setting-up-microbiomestat-installation-and-data-preparation/laying-the-foundation-creating-the-microbiomestat-data-object.md) {% endcontent-ref %} This section explains how to convert your data into the MicrobiomeStat format, enabling you to use the MicrobiomeStat toolkit for your research. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://www.microbiomestat.wiki/single-point-analysis/unraveling-cross-sectional-studies-with-microbiomestat.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.