PMCosm: Probabilistic Models for Microbial-Cosmos

This PMCosm package implements statistical inferences for microbial ecology analysis. This package provides methods for

• categorizing the admixed structure of microbial communities,
• detecting influential microbes in both engineered and natural environments,
• evaluating sub-communities' preferences of life strategies,
• identifying causal relationship between microbes' metabolism functions and fluctuations of surrounding environments,
• predicting the community composition and structure for a given local environment.

Installation and documentation

To install, open R and type:

install.packages("devtools")
devtools::install_github("YushiFT/PMCosm")

Package overview

Functions

• calc_mle_trio: Calculate MLE estimates for universal parameter trio for inferences about the admixed community structure.
• classify_taxa: Traditional relative-abundance-based methods to classify microbial taxa into rare or abundant biospheres.
• classify_vag_lag: Define a model-driven testable boundary between dispersal vangguards and dispersal laggards.
• is_dispersion: Hypothesis test for the existence of dispersion.
• is_overdispersion: Hypothesis test for the existence of over-dispersion.
• is_zero_infla: Check the existence of inflated zeros.
• pca_simple: A simplified principle component analysis for microbial data.
• plot_trio: Graphic display of trio estimates to observe the Two-Wing pattern.

Quick start guide

Deriving MLE estimates for the admixed structure of environmental microbial communities

library(PMCosm)
# import sample data from microbial communities in hangzhou bay
data(hzmicrobe)
param_trio_bay <- calc_mle_trio(mic_bay, n_sample=10, replicates=3)
param_trio_era <- calc_mle_trio(mic_era, n_sample=12, replicates=3)

Visualize the Two-Wing admixed structure by trio parameters estimates

library(ggplot2)
library(latex2exp)
# for bay
plot_trio(param_trio_bay, point_size=0.8, a=0.6)
# zoom in for the majority 
plot_trio(param_trio_bay, point_size=0.8, a=0.6, zoom_in=TRUE)
# for era 
plot_trio(param_trio_era, point_size=0.8, a=0.6)

Categorize dispersal vanguards and laggards by identifying the model-driven testable boundary

id_vag_lag_bay <- classify_vag_lag(mic_bay, param_trio_bay)
id_vag_lag_era <- classify_vag_lag(mic_era, param_trio_era)
# print ids of dispersal vanguards in bay
print(id_vag_lag_bay$vanguards)
# print ids of dispersal laggards in bay
print(id_vag_lag_bay$laggards)
# print ids of dispersal vanguards in era
print(id_vag_lag_era$vanguards)
# print ids of dispersal laggards in era
print(id_vag_lag_era$laggards)

Visualize community structure as an admixture of dispersal vanguards and laggards

plot_vag_lag(param_trio_bay, id_vag_lag_bay)
plot_vag_lag(param_trio_era, id_vag_lag_era)