Normalized Stochasticity Ratio in community assembly (Latest version 3.0.6)

Daliang Ning

• Downloaded 7935 times from 2019.6.15 to 2021.1.10.
• Recommendation: iCAMP (assessment of different community assembly processes)

• 2021.1.8 NST v3.0.6 updated on CRAN; fixed some bugs and updated github link and references.
• 2020.9.20 set up GitHub repository for NST package. Different versions and an example are uploaded.
• 2020.9.16 NST v3.0.3 is published on CRAN. Phylogentic NST is added.
• 2019.8.20 The paper about NST framework development is published on PNAS (Ning et al 2019 PNAS).
• 2019.6.15 NST v2.0.3 is published on CRAN.

The following indexes can be find in the output of function tNST or pNST.

• tNST: Taxonomic Normalized Stochasticity Ratio (Ning et al 2019 PNAS)
• pNST: Phylogenetic Normalized Stochasticity Ratio (Ning et al 2020 Nat Commun)
• MST: Modified Stochasticity Ratio (a special form of Normalized Stochasticity Ratio) (Liang et al 2019 bioRxiv)
• ST: Stochasticity Ratio (not normalized) (Zhou et al 2014 PNAS)
• SES/betaNTI/betaNRI: Standard Effect Size based on null model analysis of taxonomic (e.g. Kraft et al 2011 Science) or phylogenetic beta diversity (e.g. betaNRI, betaNTI; Webb & Kembel 2008 Bioinformatics; Stegen et al 2012 ISME J).
• RC: Modified Raup-Crick index based on unweighted (RCjaccard; Chase et al 2011 Ecosphere) or weighted taxonomic beta diversity (RCbray; Stegen et al 2013 ISME J).
• Taxonomic beta diversity indexes: "manhattan", "euclidean", "canberra", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup" , "binomial", "chao", "cao", "mahalanobis", "mGower", "mEuclidean", "mManhattan", "chao.jaccard", "chao.sorensen", using function vegdist and designdist from package vegan and function for chao.jaccard and chao.sorensen from package fossil.
• Phylogenetic beta diversity: beta mean nearest taxon distance (betaMNTD; Webb & Kembel 2008 Bioinformatics).
• Null model algorithms: various null model algorithms including many different ways to contrain richness, occurrance frequency, and abundance (Gotelli 2000 Ecology; Webb & Kembel 2008 Bioinformatics; Stegen et al 2013 ISME J); plus 'taxa shuffle' (Kembel 2009 Ecol Lett), which is specific to phylogenetic metrics.

• Operating systems: Windows, or Mac, or Linux, any versions which can run R (version >= 3.5).
• Dependencis: R (version >=3.5; https://www.r-project.org/), R packages: vegan,parallel,permute,ape,bigmemory,iCAMP.
• NST current version 3.0.4 has been tested on the current development version of R (4.1.0, 2020-8-18 r79041), R 4.0.2, and R 3.5.3.
• Any required non-standard hardware: No. However, if you calculate pNST for a large dataset (e.g. >20,000 taxa), a server with enough CPU threads (e.g. >=20) is preferred to finish the calculation in reasonable time.

• Downlaod and install R (https://www.r-project.org/).
• Install NST.
  • Install published NST: Open R, use function "install.packages" as below.

  install.packages("NST")
  

  • Install from source file:
    • Download an NST version from this repository NST/RPackage/AllVersions.
    • Open R, install or update following packages: vegan,parallel,permute,ape,bigmemory,iCAMP.

    install.packages(c("vegan", "permute", "ape", "bigmemory", "iCAMP"))
    

    • In R, click Packages/install package from local file, then select the file. For windows, select the .zip file. For Mac/Linux, select the .gz file. Alternatively, in Linux sytem, if you open R in a terminal, use following command to install from the .gz file (revise "/Path/to/the/folder" to the real path of the .gz file on your computer, revise "xxx" to the version number of NST):

    install.packages(pkgs="/Path/to/the/folder/NST_xxx.tar.gz", repos = NULL, type="source")
    

• The whole installation typically takes several minutes. Usually, <5 min for R installation, <1 min for the NST package, <5 min for installation of other packages.

• Before analyze your own data with NST, you may go through a simple example dataset in the folder /Examples/SimpleOTU.
• When analyzing your own data, check the format of the example data files (community.csv, tree.nwk, treatment.csv) in the folder "SimpleOTU". Revise your data files to the same format.
• Change the folder paths and file names in the "NST.example.r" to your data as indicated.
• Change parameter settings (e.g. nworker, the thread number for parallel computing) according to your need. You may check the help document of each function for detailed explanation.
• Run the codes and check the output files in the output folder you've specified. You may check the help documents in the R package for details.

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