In biobakery/SparseDOSSA2: A Statistical Model for Describing and Simulating Microbial Community Profiles

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title: "Simulating realistic microbial observations with SparseDOSSA2" author: - name: "Siyuan Ma" affiliation: - Harvard T.H. Chan School of Public Health - Broad Institute email: [e-mail] package: SparseDOSSA2 date: "12/01/2020" output: BiocStyle::html_document vignette: > %\VignetteIndexEntry{SparseDOSSA2} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} bibliography: references.bib

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Introduction

SparseDOSSA2 an R package for fitting to and the simulation of realistic microbial abundance observations. It provides functionlaities for: a) generation of realistic synthetic microbial observations, b) spiking-in of associations with metadata variables for e.g. benchmarking or power analysis purposes, and c) fitting the SparseDOSSA 2 model to real-world microbial abundance observations that can be used for a). This vignette is intended to provide working examples for these functionalities.

library(SparseDOSSA2)
# tidyverse packages for utilities
library(magrittr)
library(dplyr)
library(ggplot2)

Installation

SparseDOSSA2 is a Bioconductor package and can be installed via the following command.

# if (!requireNamespace("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install("SparseDOSSA2")

Simulating realistic microbial observations with SparseDOSSA2

The most important functionality of SparseDOSSA2 is the simulation of realistic synthetic microbial observations. To this end, SparseDOSSA2 provides three pre-trained templates, "Stool", "Vaginal", and "IBD", targeting continuous, discrete, and diseased population structures.

Stool_simulation <- SparseDOSSA2(template = "Stool", 
                                 n_sample = 100, 
                                 n_feature = 100,
                                 verbose = TRUE)
Vaginal_simulation <- SparseDOSSA2(template = "Vaginal", 
                                   n_sample = 100, 
                                   n_feature = 100,
                                   verbose = TRUE)

Fitting to microbiome datasets with SparseDOSSA2

SparseDOSSA2 provide two functions, fit_SparseDOSSA2 and fitCV_SparseDOSSA2, to fit the SparseDOSSA2 model to microbial count or relative abundance observations. For these functions, as input, SparseDOSSA2 requires a feature-by-sample table of microbial abundance observations. We provide with SparseDOSSA2 a minimal example of such a dataset: a five-by-five of the HMP1-II stool study.

data("Stool_subset", package = "SparseDOSSA2")
# columns are samples.
Stool_subset[1:2, 1, drop = FALSE]

Fitting SparseDOSSA2 model with fit_SparseDOSSA2

fit_SparseDOSSA2 fits the SparseDOSSA2 model to estimate the model parameters: per-feature prevalence, mean and standard deviation of non-zero abundances, and feature-feature correlations. It also estimates joint distribution of these parameters and (if input is count) a read count distribution.

fitted <- fit_SparseDOSSA2(data = Stool_subset,
                           control = list(verbose = TRUE))
# fitted mean log non-zero abundance values of the first two features
fitted$EM_fit$fit$mu[1:2]

Fitting SparseDOSSA2 model with fitCV_SparseDOSSA2

The user can additionally achieve optimal model fitting via fitCV_SparseDOSSA2. They can either provide a vector of tuning parameter values (lambdas) to control sparsity in the estimation of the correlation matrix parameter, or a grid will be selected automatically. fitCV_SparseDOSSA2 uses cross validation to select an "optimal" model fit across these tuning parameters via average testing log-likelihood. This is a computationally intensive procedure, and best-suited for users that would like accurate fitting to the input dataset, for best simulated new microbial observations on the same features as the input (i.e. not new features).

```r

set.seed(1)

fitted_CV <- fitCV_SparseDOSSA2(data = Stool_subset,

lambdas = c(0.1, 1),

K = 2,

control = list(verbose = TRUE))

the average log likelihood of different tuning parameters

apply(fitted_CV$EM_fit$logLik_CV, 2, mean)

The second lambda (1) had better performance in terms of log likelihood,

and will be selected as the default fit.

```

Parallelization controls with future

SparseDOSSA2 internally uses r BiocStyle::CRANpkg("future") to allow for

parallel computation. The user can thus specify parallelization through future's

interface. See the reference #manual

for future for more details. This is

particularly suited if fitting SparseDOSSA2 in a high-performance computing

environment/

```r

regular fitting

system.time(fitted_regular <-

fit_SparseDOSSA2(data = Stool_subset,

control = list(verbose = FALSE)))

parallel fitting with future:

future::plan(future::multisession())

system.time(fitted_parallel <-

fit_SparseDOSSA2(data = Stool_subset,

control = list(verbose = FALSE)))

For CV fitting, there are three components that can be paralleled, in order:

different cross validation folds, different tuning parameter lambdas,

and different samples. It is usually most efficient to parallelize at the

sample level:

system.time(fitted_regular_CV <-

fitCV_SparseDOSSA2(data = Stool_subset,

lambdas = c(0.1, 1),

K = 2,

control = list(verbose = TRUE)))

future::plan(future::sequential(), future::sequential(), future::multisession())

system.time(fitted_parallel_CV <-

fitCV_SparseDOSSA2(data = Stool_subset,

lambdas = c(0.1, 1),

K = 2,

control = list(verbose = TRUE)))

# Sessioninfo
```r
sessionInfo()

References

biobakery/SparseDOSSA2 documentation built on Dec. 3, 2024, 10:17 p.m.