Analyses
Last updated
Last updated
Enterotype analysis categorizes individuals into distinct microbiome types based on their microbial composition. It helps in understanding common patterns in microbiome profiles, potentially associated with health or disease.
Co-occurrence analysis identifies how different microbes tend to appear together or separately in microbiome datasets. It reveals potential microbial interactions or ecological relationships within the microbial community.
Alpha diversity measures the diversity of microbes within a single sample, indicating how many different species are present and how evenly they are distributed. High alpha diversity suggests a rich and balanced microbiome.
Beta diversity assesses differences in microbiome composition between multiple samples, revealing the dissimilarity or similarity of microbial communities across datasets. It is valuable for comparing microbiomes from various sources.
Differential abundance analysis (DAA) identifies potential biomarkers by comparing the abundance of microbes between groups, such as healthy and IBD. DAA tools use factors like fold change and p-values. Different tools may yield varying results. This engine uses seven DAA tools (ANCOM-BC, ALDEx2, DESeq2, LEfSe, MaAsLin2, SIAMCAT, and LinDA) to find candidate biomarkers and compare results to identify consensus biomarkers.
Simply, DAA helps find microbes that are more common in one group (e.g., IBD patients) compared to another (e.g., healthy individuals). We used seven tools to find these special microbes and compare their results to identify the most important ones.
Spearman co-occurrence analysis focuses on correlations between microbes, specifically using the Spearman rank correlation coefficient. It is helpful for detecting non-linear relationships in how microbes co-occur.
Group composition analysis examines the overall makeup of microbial groups or taxa in a dataset, highlighting the relative abundance of different categories, providing insights into the dominant microbial types.
Microbiome quality control includes various procedures to ensure the accuracy and reliability of microbiome data, such as data filtering, removing errors, and addressing biases.
Pie chart composition visually represents the distribution of microbial groups in a dataset using pie charts. It is a simple way to understand the relative proportions of different microbial categories.
Summary statistics provide key statistical measures like means, medians, and standard deviations, offering a concise overview of central tendencies and variability in microbiome data.
Statistical matching employs methods to compare and match microbiome datasets, helping to identify common elements and disparities, facilitating cross-dataset comparisons and integration.
