Table 1.
Comparison of the advantages and limitations of the six integrated R packages.
| R package | Function | Advantages | Limitations |
|---|---|---|---|
| Phyloseq | 1. Diversity analysis including alpha/beta diversity, community composition, and phylogenetic tree analysis 2. Network analysis |
1. Firstly utilize S4 class objects 2. Possess lots of analysis functions based on phyloseq objects 3. The network analysis process is simplified (Fig. S2E) 4. Ordinate analysis can be applied to arrange the order of samples and microbes on heatmap rows and columns (Fig. S2F) 5. Combine evolutionary trees with microbial abundance to display species richness (Fig. S2G) 6. Offer over 30 distance algorithms |
1. Introduction to phyloseq objects can be challenging for beginners 2. Statistical tests, including diversity tests and community/feature-level microbial difference analysis, are not well integrated into community analysis 3. Network analysis lacks test, attribute calculation |
| Microbiome | 1. Diversity analysis only including alpha/beta diversity, community composition | 1. The alpha diversity index is abundance 2. The t-SNE and CAP ordination algorithms 3. The stacked bar chart for community composition analysis can be sorted by specified microbial features (Fig. S3C) 4. Visualization of individual microbes (Fig. S3D) |
1. The t-SNE and CAP ordination analyses frequently encounter errors 2. The statistical tests, including diversity tests, community and feature-level differences tests is not ideal |
| Microbiome AnalystR |
1. Diversity analysis including alpha/beta diversity, community composition, and phylogenetic tree analysis 2. Difference analysis 3. Biomarker identification |
1. Various functions ranging from data-cleaning to visualization 2. Multiple algorithms to correct sequencing errors, leading more accurate evaluation of abundance 3. Machine learning can be utilized to extract feature variables (Fig. S4H) 4. Difference analysis using multiple methods, such as LEfSe or metagenomeSeq |
1. Difficulties in installing R packages with dependencies 2. Some functions may not work, including network analysis and difference analysis of relative abundance 3. Insufficient explanation of parameters and examples |
| Animalcules | 1. Diversity analysis 2. Difference analysis and biomarker identification |
1. SummarizedExperiment package supported 2. Interactively executed in R (Fig. S5A–J) 3. A 3D clustering plot can be generated |
1. Unable to save vector graphics and completed tables 2. Insufficient functionality |
| Microeco | 1. Diversity analysis 2. Difference analysis 3. Biomarker identification 4. Network, correlation analysis with other indicators 5. Functional prediction |
1. R6 class more expansibility than phyloseq objects 2. Simple function calling 3. Rich plots of diversity and difference analysis (Fig. S6A–H) 4. Unique correlation analysis of other indicators 5. Network analysis functionality (Fig. S6K) 6. FAPROTAX and FUNGuild function prediction |
1. New data structures increase the cost of learning time 2. So many functions and dependency caused frequent some malfunctioning |
| EasyAmplicon | 1. Diversity analysis 2. Provide script for preparing STAMP, LEfSe, PICRUSt 1&2, BugBase, FAPROTAX, iTOL 3. Provide slide tutorial for many analyses, such as QIIIME 2 |
1. It can be used in both command-line mode and interactive mode within RStudio 2. It offers multiple visualization styles, allowing for easy generation of publication-quality figures (Fig. S7) 3. Its open-source code facilitates reproducible analysis and allows for personalized modifications |
1. Need using the most popular tools, STAMP, LEfSe, PICRUSt 1&2, BugBase, FAPROTAX, and iTOL 2. Some functions need to be development |