How to Analyze Microbiome Data from Human-Impacted Island Ecosystem

[saif_s (saif)]

hello
I'm a master's student working on an NSF-funded project investigating the impact of human visitation on island ecosystems. Our study involves three islands with varying levels of human visitation: high, medium, and low. The primary focus is on understanding the relationship between diet, influenced by visitor impact, and microbiome composition.

We've collected nearly 200 16S rRNA samples and a comprehensive dataset of physiological data (blood count, immune metrics, energy metrics, metabolome profiles, etc.) from multiple hosts on each island. Samples were collected annually (once every year for 3 consecutive years), but I'm unsure if the seasons were consistent across years.

My research questions are:

1. Does diet, influenced by varying levels of human visitation, correlate with changes in microbiome composition?
2. Which physiological factors (e.g., blood count, immune metrics) are most strongly associated with microbiome shifts?
3. How does microbiome composition change from year to year on each island?
4. Are there significant differences in microbiome composition between islands with varying visitor rates?

I'm considering the following approaches Logically

1. Within-year comparison: Analyze microbiome and physiological data for each island to identify relationships between variables and assess how microbiome composition varies across islands with different visitor rates.
2. Inter-year comparison: Compare the results from each year to understand the dynamics of microbiome composition and identify trends.

I need your help please

Experienced bioinformaticians, what is the most effective approach to analyze this complex dataset. I'm particularly interested in:

• Recommended statistical methods for analyzing microbiome data and correlating it with physiological factors.
• Strategies for handling time-series data (annual sampling) and accounting for potential seasonal effects.
• Best practices for visualizing and interpreting the results.

Any insights or suggestions would be greatly appreciated!

Additional Notes:

• Would it be beneficial to incorporate metadata about the specific diet of each host?
• Are there specific bioinformatics tools or software packages that would be particularly useful for this type of analysis?
• It's also worth mentioning that I haven't been exactly sure on which approach is generally more ideal. Would it be wiser to process all the data together y1vsy2vsy3 in qiime2 or is it better to process y1vsy2 and y2vsy3.
• Do senior bioinformaticians analyze the results of each individual year with all 3 islands per year and compare each year with the other?
• Finally, please recommend any papers, tutorials, videos that you think would help me accomplish my project successfully.

[jwdebelius (Justine Debelius)]

Hi @saif_s,

Welcome to the forum (and microbiome analysis in general).

First, let me say this sounds like both a cool project and that your question is complex. The kind of support you're asking for is on the scale of what I provide for my masters and PhD students and almost always results is authorship if the paper goes to publication. My recommendation(s) here would be to find local resources, including talking to your:

1. Masters advisor
2. Senior people in your lab group (e.g. postdocs, grad students, senior staff) who may have advice if your advisor is unavailable
3. A local microbiome biostatistican or omics biostatistican who might be able to help you scope out the project

If none of those people can help, there are also a lot of good people who consult.

From an overall QIIME 2 forum perspective, my comment here feeds into one of the forum wide policy of "do your own work":

@system

Your Work is Your Work

Technical challenges are real, software bugs happen, and the state of the discipline is constantly changing. This forum is here to help you navigate those (and other) challenges, but it is ultimately your responsibility to understand your data and your tools, perform your own analysis, and derive meaning from your data.

Selecting appropriate methods of analysis, choosing effective experimental parameters, and interpreting your results are fundamental parts of the research process. This type of work is generally associated with authorship, and researchers who share responsibility for these aspects of their work often do so through formal collaborations with co-authors. This remains true in computationally-oriented research. Posts flagrantly or insistently asking others to take responsibility for these parts of the research process will be treated as violations of this code of conduct. On the other hand, good-faith efforts to understand the domain, software, or analytical process, or to trouble-shoot technical problems, are highly valued as contributions here.

If you are a student working on an assignment or project, we are happy to provide resources, answer questions about how QIIME 2 works, or provide troubleshooting assistance for the software, as we do for all forum members. Good-faith efforts to learn or to troubleshoot technical problems are very welcome. Requests that others do your intellectual work for you will be treated as violations of this code of conduct. It is our goal to support learning, so we will not answer homework questions for you.

Some ways to take ownership of your work here on the forum:

• attempt to solve your issue independently first
• read and consider the full contents of error messages - what is it trying to tell you?
• search the forum for related topics (e.g. search your error message!)
• look for information in the docs - the tutorials are amazing
• try replacing "what is the answer" questions with "how" and "why" questions when appropriate

I encourage you to review the code of coduct and frequently asked questions as you move forward.

I'll also suggest that the QIIME 2 team has developed several tutorials, avalaible on our website, along with accompanying youtube materials. These dont specifically answer your questions, but might be a good way to get started.

Finally, I think your "additional notes" are interesting questions which might be better reframed as something more generalizable which could be discussed. I'll caveat with the fact that hte answer in the microbiome is often "It Depends " but that you're on a good start.

Best,
Justine