Skin microbiota variation in Indian families

The present study involves seventy-two individuals from fifteen families in two geographical regions of Maharashtra, India. This study underscores the diversity and commonalities in skin microbiota composition within and between families.

Contents

• Usage
• Dependencies & Installation
• Generate TSE object
• Analysis scripts & visualization
• Authors
• Preprint
• License
• Cite the code

Usage

1. Use RStudio to reproduce the research work
2. Install the primary R packages
3. Create TSE object
4. Run the analysis scripts in RStudio and see the output

Dependencies and Installation

The primary R packages are mia and miaViz; For installation visit DOI: 10.18129/B9.bioc.mia and DOI: 10.18129/B9.bioc.miaViz

Generate TSE object

Create TSE object using metadata, otu table, and taxa table.

The data uses TreeSummarizedExperiment data container.

To generate TSE object in RStudio run the script using the following command

rmarkdown::render("fam_TSE.Rmd")

Analysis scripts and visualization

To start running the respective analysis first create the TSE object and then use the following command to reproduce the analysis and generate the output file as a .md document

rmarkdown::render("FILENAME.Rmd", output_format="md_document")

• diversity analysis | Figure 2: alpha diversity using Shannon index, beta diversity with PERMANOVA, PCoA and dbRDA for all co-factors.
• most prevalent microbiome | Figure 3: relative abundances of the most prevalent phyla and genera in families across geographical locations.
• Inter-generational analysis | Figure 4: within and between family comparison across three generations.
• Family-specific microbiome variation | Table 5: Differential abundance analysis with ancombc2 for family.
• supplementary | supplementary figure 1: Differential abundance analysis with ancombc2 for locations.
• supplementary | supplementary figure 2: Individual-wise relative abundances of most prevalent genera in the families across locations.
• supplementary | supplementary figure 3: Family-wise inter-generational analysis.

Authors

Renuka Potbhare, Ameeta Ravikumar, Eveliina Munukka, Richa Ashma and Leo Lahti

Preprint is available at

DOI: https://doi.org/10.1101/2023.12.09.570904

License

This research work is licensed under the Artistic 2.0 License. See LICENSE.txt for more information.

Cite the code