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hierarchical-meta-storms
A C++ software contains two core components: i) a dissimilarity algorithm that comprehensively calculates the distances among microbiome functional profiles by considering their multi-level metabolic pathway hierarchy among functional gene families, and ii) a PCoA implementation optimized by multi-thread parallel computing to rapidly parse out the beta-diversity pattern for thousands of samples.
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Hierarchical Meta-Storms (HMS) comprehensively calculates the dissimilarities of microbiome functional profiles by considering multi-level metabolic pathway hierarchy. It contains two core components: i) a dissimilarity algorithm that comprehensively calculates the distances among microbiome functional profiles by considering their multi-level metabolic pathway hierarchy among functional gene families, and ii) a PCoA implementation optimized by multi-thread parallel computing to rapidly parse out the beta-diversity pattern for thousands of samples. It takes the microbiome functional profiles of KO and their relative abundance as input, and computes and outputs their pairwise distance matrix and then the principle coordinates of PCoA.
In addition, we also encapsulate the C++ source codes as an R package (Github: hrms) using RcppArmadillo framework and submitted to CRAN, making the kernel functions of calculating the microbiome distances and principle coordinates could be callable by R interpreter in both R terminal and R scripts.
System Requirement and dependency
Hardware Requirements
Hierarchical Meta-Storms only requires a standard computer with sufficient RAM to support the operations defined by a user. For typical users, this would be a computer with about 2 GB of RAM. For optimal performance, we recommend a computer with the following specs:
RAM: 8+ GB
CPU: 4+ cores, 3.3+ GHz/core
Software Requirements
OpenMP library is the C/C++ parallel computing library. Most Linux releases have OpenMP already been installed in the system. In Mac OS X, to install the compiler that supports OpenMP, we recommend using the Homebrew package manager:
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The output file “samples.func.dist” is the pairwise distance matrix.
b. Implement the PCoA
MS-get-pc -d samples.func.dist -o samples.pcoa.pc
The output file “samples.pcoa.pc” is the coordinates results of PCoA.
Example dataset
Here we provide a demo dataset (Synthetic Dataset 1) with functional KO profiles of 30 artificial microbiomes in “example” folder. In this package, “dataset1.ko.abd” is the relative abundance on KOs-level, and “dataset1.meta” is the group information of the samples.
Then the output file “dataset1.dist” is the pairwise distance of the 30 samples and "dataset1.pcoa.pc" is the coordinates results of PCoA based on the distance matrix.
This demo run should take less than 1 minute on a recommended computer.
Tools in this package
a. MS-comp-func
It calculates the hierarchical meta-storms distance matrix among microbiome functional profiles. Run:
MS-comp-func -h
for detailed parameters.
b. MS-get-pc
It calculates the PCoA based the distance matrix. Run:
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