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Mar 07, 2024
A comprehensive analysis of microbiome data within a 16S sequencing report involves a diverse array of complex analytical techniques, algorithms, and statistical principles. This article, by evaluating their prevalence in published literature (16S or 16S + metabolomics), highlights seven essential components of analysis. These components are crucial for swiftly identifying key elements within extensive completion reports.
Based on species annotation results, the top 10 species with the highest relative abundance within each taxonomic level (Phylum, Class, Order, Family, Genus) are selected for individual samples or groups. These selections form bar charts depicting the cumulative relative abundance of these species. This visual representation allows for an immediate evaluation of species with relatively high abundance and their distribution across different taxonomic levels in each sample. The horizontal axis represents sample names (group names); the vertical axis represents relative abundance; "Others" represents the sum of the relative abundance of all other taxa outside of these 10 phyla in the figure.
Alpha diversity is employed to measure the diversity of microbial communities within samples. Various indices, including Observed species, Chao1, Ace, Shannon, Simpson, goods coverage, and PD whole tree, are used.
Box plots of alpha diversity indices are created to assess disparities between different groups. For example, observed_species and Shannon index can be used for inter-group difference analysis.
Beta diversity measures the similarity or dissimilarity in microbial community composition across distinct samples. Techniques such as Principal Component Analysis (PCA), Principal Coordinates Analysis (PCoA), and Non-Metric Multi-Dimensional Scaling (NMDS) are applied to examine and distinguish the composition of microbial communities among samples. In these plots, each point represents a sample, and points of the same color come from the same group. The closer the distance between two points, the more similar their species composition structure, indicating smaller compositional differences. The axes represent principal components, and percentages indicate the contribution of each component to sample variation.
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LefSe analysis (Linear Discriminant Analysis Effect Size) is utilized to identify biomarkers and significant variations between groups. This analysis focuses on both statistical significance and biological relevance, aiding in the identification of biomarkers that exhibit substantial differences between groups.
The bar chart in the LDA value distribution plot shows species with LDA Scores greater than the set value, which are biomarkers with statistically significant differences between groups. In the sub-branch diagram, differentially significant species (biomarkers) are colored according to their respective groups, helping to visualize the roles of microbiota taxa in different groups. The comprehensive analysis of microbiome data within a 16S sequencing report involves several advanced components, each shedding light on different aspects of microbial communities.
In the sub-branch diagram, the concentric circles radiating from the inside to the outside represent taxonomic levels from phylum to genus (or species). Each small circle at different taxonomic levels represents a classification, and the diameter of the small circle is proportional to its relative abundance. Coloring principle: Species with no significant differences are uniformly colored in yellow, while differentially significant species (biomarkers) are colored according to their respective groups. Red nodes represent microbiota taxa that play an important role in the red group, and green nodes represent microbiota taxa that play an important role in the green group.
Utilizes the Random Forest algorithm to determine the importance of variables in distinguishing between groups by combining multiple decision trees for predictions. Constructs ROC curves to assess the diagnostic value of specific species or groups.
The left chart measures the decrease in accuracy (MeanDecreaseAccuracy) in random forest predictions when a variable's value changes to a random number. The higher the value, the greater the variable importance. The right chart evaluates the impact of each variable on the heterogeneity of observations using the Gini index (MeanDecreaseGini), indicating variable importance.
Utilizes the best model selected via the Random Forest method to plot ROC curves, commonly used in medical research to evaluate diagnostic test efficiency. ROC curves are drawn to determine the diagnostic value of microorganisms or groups, with the area under the curve (AUC) calculated. The horizontal axis represents the false positive (Specificity) rate, and the vertical axis represents the true positive (Sensitivity) rate. The closer the ROC curve is to the upper-left corner, the higher the accuracy of the test. An AUC value of 1.0 indicates perfect differentiation between two groups with no prediction error. An AUC value between 1.0 and 0.5 indicates better diagnostic performance as AUC approaches 1. An AUC between 0.5 and 0.7 indicates low accuracy, between 0.7 and 0.9 indicates moderate accuracy, and above 0.9 indicates high accuracy. An AUC of 0.5 means the diagnostic method is ineffective and has no diagnostic value. AUC less than 0.5 does not reflect reality and is very rare in practice.
Conducts co-occurrence network analysis to identify relationships between microbial species within complex environments. Nodes represent different genera, node size indicates the average relative abundance, and node color represents the same phylum. Line thickness between nodes indicates species interaction strength, with colors signifying positive (red) or negative (blue) correlation.
Provides insights into the functional connections between changes in microbial community composition and diseases or phenotypes.
The horizontal axis represents functions, the vertical axis represents samples, and the grid shows relative abundance. Clustering applied to both functions and samples to identify relationships between functions related to diseases or phenotypes and inter-group differences in microbiota composition.
These components constitute crucial findings in 16S microbiome analysis, including Relative Abundance Bar Charts, Alpha Diversity, Beta Diversity, LefSe Analysis, Random Forest Analysis, Network Analysis, and Functional Clustering Heatmaps, offering in-depth insights into microbial communities. MetwareBio not only provides 16S sequencing and comprehensive Microbiome+Metabolome analysis but also offers customized data analysis to uncover the complexities of microbial interactions and functional connections. Reach out for more details.
