Microbiome data analysis presents intricate challenges, often demanding more nuanced approaches than initially anticipated. Consider the scenario of comparing different interaction methods in microbiome studies; the options are numerous, highlighting the complexity inherent in this field.
One key aspect to recognize is that certain statistical methods have specific limitations. For instance, ANCOM (Analysis of Compositions of Microbiomes) is not designed for single observations from a site. Furthermore, it’s crucial to understand that ANCOM analyzes features and their relation to weighted metrics, but it does not function as a test for distance matrices. This distinction is vital when choosing appropriate analytical tools for your specific research question.
Benchmarking analyses in microbiome research can become exceedingly complex. It’s beneficial to explore existing literature to gain insights from prior work in this area. Numerous studies have already delved into comparisons between different denoising and Amplicon Sequence Variant (ASV) picking platforms. These are essentially independent lines of inquiry, each requiring careful consideration. A pragmatic approach is often to select a method that is well-reasoned and consistently apply it throughout your analysis. For those seeking a deeper understanding of distance matrices, a comprehensive review by @yoshiki offers valuable perspectives and could address many related queries.
When examining correlations, Spearman correlation often proves more suitable for Mantel correlation statistics in microbiome data. This is because Spearman correlation makes fewer initial assumptions about the underlying data distribution. While distances can sometimes approximate a normal distribution asymptotically, it’s essential to employ a permutative test due to the inherent non-independence of distance data. Applying correlation directly to an Operational Taxonomic Unit (OTU) table is generally not recommended. A distance matrix, or even a distance calculated between replicates, typically yields more informative results than attempting to correlate directly from the OTU table.
For analyzing variations across sites or time points, PERMANOVA (Permutational Multivariate Analysis of Variance) can be a useful tool. You could apply PERMANOVA to assess site-specific differences and then, separately, examine changes over weeks. Alternatively, exploring dynamics within each site is possible, especially if you can integrate similar characteristics across various data aggregation tests, such as mixed models.
It’s important to acknowledge the inherent noise in microbiome data. While multiple measurements at each time point can introduce more data to manage, they are crucial for quantifying the noise. Microbiome data, by its nature, tends to be noisy, and this characteristic can be amplified in cross-sectional studies. However, repeated measurements offer a better grasp of sample-to-sample variation and noise. Even if the data distribution is noisy, having a distribution to compare is invaluable. Interestingly, this variation, whether representing true instability or just noise, might itself be a meaningful signal.
Considering these challenges, if resources permit, re-sequencing samples individually could be the most effective long-term solution, especially when dealing with complex datasets like guano samples. This approach can provide a more robust foundation for comparative analyses despite potentially not being the immediately desired answer.
