This folder contains R scripts for microbial community assembly analyses, including NST calculation, neutral model analysis, C-score variance analysis and co-occurrence network analysis.
input includes a) abundance table of microbial entities (e.g., OTUs, ASVs), each row is a sample, each column is an OTU. b) a one-column matrix indicating the group of each sample (only used in NST calculation)
Note: a minimum number of 20 to 25 samples is suggested for the analyses; Time-series data or local scale samples are recommended for the analyses, samples of large spatial scales may lead to strong bias.
This script uses a developed R package NST (Reference doi:10.1073/pnas.1904623116) to calculate NST (normalized stochasticity ratio), which range from 0 to 100% and provides quantitative assessment of ecological stochasticity based on null model. A larger NST represents the community is more affected by stochasticity. Reference:https://doi.org/10.1101/2020.06.03.131896
This script was modified from (Reference doi:10.1038/ismej.2015.142) and used to compare observed community composition with that predicted by a neutral assembly model(Reference doi:10.1111/j.1462-2920.2005.00956.x),which assumes that community assembly is only driven by chance and dispersal.The results were output as a visualized figure and a detailed table which contains information of observed and predicted community composition. Reference:https://doi.org/10.1101/2020.06.03.131896
This script was for the checkerboard score variance(C-score-var) analysis of non-random species co-occurrence patterns. Reference:Hu AY, Ju F, Hou LY, Li JW, Yang XY, Wang HJ, Mulla SI, Sun Q, Bürgmann H, Yu CP. 2017. Strong impact of anthropogenic contamination on the co-occurrence patterns of a riverine microbial community. Environmental Microbiology (2017) 19(12), 4993–500
This script first defines the function "co_occurrence_network" to generate a filtered gml-formatted network file based on all pairwise Spearman's correlations and FDR-adjusted P-value calculation and filtration, and then uses input abundance table to generate co-occurrence network which can be visulized in Gephi (https://gephi.org/) and calculate network topological properties. Reference:Ju F, Zhang T. 2015. Bacterial assembly and temporal dynamics in activated sludge of a full-scale municipal wastewater treatment plant. The ISME Journal. 9: 683-695
This script integrates the above four modules which use the same abundance table of microbial entities as input.