How to draw and interpret the Partial Mantel's test?
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- Опубликовано: 6 фев 2025
- #howtodraw #interpretation #mantel's #asifmolbio
In this video, I demonstrate how to interpret the results of the Mantel test, which assesses the correlation between two distance matrices. I also explain how to draw and visualize the Mantel test results, highlighting their significance in understanding relationships between genetic and environmental data.
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Check out my R script for performing Mantel’s Test with examples on GitHub: Mantel's Test. github.com/asifmolbio/Mantel-s-test Feel free to explore and ask any questions!
Hi Dr. Asif, you are simply awesome!
Just have a quick question: could you please tell me how can I generate an OTU table from 16S sequencing data?
To generate an OTU table from 16S sequencing data, you typically start by performing quality control and trimming of raw sequences to remove low-quality reads and adapters. Next, the sequences are denoised (using tools like DADA2 or USEARCH) to remove sequencing errors and generate amplicon sequence variants (ASVs), or alternatively, clustered into operational taxonomic units (OTUs) at a defined similarity threshold (usually 97%). After clustering or denoising, a sequence table is created, which lists the abundance of each OTU/ASV across your samples. Then, taxonomic assignment is performed by comparing your representative sequences against a reference database (e.g., Silva, Greengenes), which annotates the OTUs with their taxonomic classifications. The resulting OTU table can be exported for further analysis and visualization using tools like QIIME2, R (phyloseq), or other microbiome analysis software.
@@asifmolbio Thank you very much for the detailed information. It seems I have already processed the data using the DADA2 tool and generated the ASV table. I am wondering if I can reach out to you via email or any other way.
Hello dr.asif . Thank you for making this topic so easy and understand-able for me … you really explain very well. ❤ Waoo China Waoo
Thanks for your kind words!
Hello Dr. Asif, Thank you for creating such useful videos-they have been incredibly helpful. I recently received my data from a company in three formats: a mydata file, a taxonomic file, and an OTU table file. I am now looking to analyze this data and create figures from it. Could you please suggest the best approach or share any guidance on how to proceed?
To analyze your data and create figures, here’s a general approach:
1. Data Preparation:
• Understand the content of the files: The OTU table contains the abundance of microbial taxa across samples, the taxonomic file links OTUs to their taxonomic classifications, and the mydata file holds metadata like sample information or experimental conditions.
2. Data Exploration:
• Summarize the data to check distributions of OTUs across samples. This will give you a basic idea of the microbial composition in your samples.
3. Diversity Analysis:
• Alpha diversity measures diversity within each sample (e.g., richness, evenness).
• Beta diversity compares diversity between samples (e.g., how different samples are from one another based on their microbial composition).
4. Visualization:
• Use bar plots to show the relative abundance of taxa across samples.
• Use PCA or NMDS plots to visualize how samples group based on their microbial communities.
• A heatmap can show patterns of OTU abundance across samples.
5. Statistical Testing:
• Perform statistical tests to identify which microbial taxa differ significantly between conditions (e.g., disease vs. control). This could be through methods like ANOVA or PERMANOVA.
6. Interpretation:
• Analyze your visualizations to find trends, such as which taxa are enriched or depleted in different conditions, and relate these findings to the disease or condition you’re studying.
This workflow allows you to analyze the data, visualize microbial patterns, and identify meaningful differences. If needed, you can use additional tools for more complex analysis or pathway predictions.
Here is one video
How to interpret results of Microbiome analysis | alpha beta diversity
ruclips.net/video/BNZl8NmSLmQ/видео.html
@@asifmolbio thank you sooo much dr asif for your wonderfull response and helpfull information, thanks alot once again
Hello Dr. Asif. First of all, thank you for helping to understand the Partial Mantel Test. A couple weeks ago, I was studying specifically this test, however I have still some doubts. My question is the following, can you explain to me, with your words, the issues about Mantel's r?, because I don't understand yet this issue. I got the ideas about Pearson's r and Mantel's p, but I can't see the statistical meaning of Mantel's t yet. Thank you for everything.
Hello Manuel,
Thank you for your question, and I’m happy to help you clarify things further!
Mantel’s r (the Mantel correlation coefficient) is quite similar to Pearson’s r in that it measures the strength and direction of the linear relationship between two distance matrices. Essentially, it quantifies how similar the distances between pairs of objects are across two different datasets or variables. If r is close to +1 or -1, it indicates a strong relationship, while a value near 0 indicates no significant relationship.
Now, regarding Mantel's t, it's a test statistic used in the Mantel test, which is essentially a permutation test to assess the significance of the observed Mantel correlation. While r tells you about the strength of the relationship, t is the value that you compare against a distribution of values generated by randomly permuting the data. The larger the t value, the more likely your observed r is not due to random chance.
To summarize:
Mantel’s r: Correlation coefficient, like Pearson's r, showing how related two distance matrices are.
Mantel’s t: The test statistic that is used in permutation testing to determine the statistical significance of r.
If you have further questions or need additional clarification, feel free to ask!
Best regards,
Dr. Asif
Mantel's test is used to assess the correlation between two distance matrices, allowing researchers to determine whether there is a significant relationship between the distances (or dissimilarities) between objects in two different datasets. It is commonly used when you have data in the form of distance or dissimilarity matrices, which could represent, for example, genetic distances, ecological distances, or spatial distances between samples.
Here are some key reasons why Mantel's test is used:
Comparing Dissimilarities: Mantel's test allows you to compare the pairwise dissimilarities or distances between objects across two different datasets. For example, in ecology, you might want to test if geographic distances between sampling sites correlate with differences in species composition.
Testing Non-Linear Relationships: While Pearson's correlation is used for linear relationships, Mantel’s test can be used for non-linear relationships as long as the data are represented in distance matrices.
Handling Complex Data: Mantel’s test is particularly useful when working with complex datasets where you don’t have a direct correspondence between variables. For example, it’s widely used in fields like genomics, ecology, and evolutionary biology to explore relationships between genetic, environmental, and geographical data.
Permutation-Based: The test is based on permutations, which allows you to assess the statistical significance of the observed correlation without making assumptions about the underlying distributions of the data.
In summary, Mantel's test is valuable when you want to assess the correlation between two distance matrices, especially when the data is complex or non-linear, and it’s used widely in ecological, genomic, and evolutionary studies.