Introduction

The fbseq package part of a collection of packages for the fully Bayesian analysis of RNA-sequencing count data, where a hierarchical model is fit with Markov chain Monte Carlo (MCMC). fbseq is the user interface, and it contains top level functions for calling the MCMC and analyzing output. The other packages, fbseqOpenMP and fbseqCUDA, are backend packages that run the MCMC behind the scenes. Only one is required. fbseqOpenMP can run on most machines, but it is slow for large datasets. fbseqCUDA requires special hardware (i.e. a CUDA general-purpose graphics processing unit), but it's much faster due to parallel computing.

Read the model vignette first.

An understanding of the underlying hierarchical model is important for understanding how to use this package. For best viewing, download the html file to your desktop and then open it with a browser.

Check your system.

See the "Depends", "Imports", and "Suggests" fields of the "package's DESCRIPTION R version and R package requirements.

Install fbseq

Option 1: install a stable release (recommended).

For fbseq , you can navigate to a list of stable releases on the project's GitHub page. Download the desired tar.gz bundle, then install it either with install.packages(..., repos = NULL, type="source") from within R R CMD INSTALL from the Unix/Linux command line.

Option 2: use install_github to install the development version.

For this option, you need the devtools package, available from CRAN or GitHub. Open R and run

library(devtools)
install_github("wlandau/fbseq")

Option 3: build the development version from the source.

Open a command line program such as Terminal in Mac/Linux and enter the following commands.

git clone [email protected]:wlandau/fbseq.git
R CMD build fbseq
R CMD INSTALL ...

where ... is replaced by the name of the tarball produced by R CMD build.

Install an MCMC backend package

fbseqOpenMP and fbseqCUDA , are backend packages that run the MCMC behind the scenes. Only one is required. fbseqOpenMP can run on most machines, but it is slow for large datasets. fbseqCUDA requires special hardware (i.e. a CUDA general-purpose graphics processing unit), but it's much faster due to parallel computing. Installation is similar to that of fbseq and is detailed in their respective README.md files and package vignettes.

Quick start

After installing fbseq and fbseqOpenMP, the following should take a couple seconds to run. The example walks through an example data analysis and shows a few key features of the package. For more specific operational details, see the tutorial vignette.

library(fbseq)

back_end = "OpenMP" # change this to "CUDA" to use fbseqCUDA as the backend

# Example RNA-seq dataset wrapped in an S4 class.
data(paschold) 

# Use a small subset of the data (few genes).
paschold@counts = head(paschold@counts, 20) 

# Run the MCMC for only a few iterations.
configs = Configs(burnin = 10, iterations = 10, thin = 1) 

# Set up the MCMC.
chain = Chain(paschold, configs) 

# Run 4 dispersed independent Markov chains.
chain_list = fbseq(chain, backend = back_end)

# Get total runtime.
attr(chain_list, "runtime")

# Monitor convergence with in all parameters with 
# Gelman-Rubin potential scale reduction factors
head(psrf(chain_list)) 

# Means, standard deviations, and credible intervals of all parameters 
head(estimates(chain_list))

# Means, standard deviations, and credible intervals of 
# linear combinations of the "beta" parameters used to calculate
# gene-specific posterior probabilities.
head(contrast_estimates(chain_list))

# Gene-specific (row-specific) posterior probabilities
head(probs(chain_list))

# Monte Carlo samples on a small subset of parameters
m = mcmc_samples(chain_list) 
dim(m)
m[1:5, 1:5]

# Set max_iter=Inf below to automatically run until convergence.
max_iter = 3
iter = 1
chain@verbose = as.integer(0) # turn off console messages
chain_list = fbseq(chain, backend = back_end)
# saveRDS(chain_list, "chain_list_so_far.rds") # option to save progress
gelman = psrf(chain_list)
if(any(gelman >= 1.1)) while(iter < max_iter){
  chain_list = lapply(chain_list, fbseq, additional_chains = 0, backend = back_end)
  gelman = psrf(chain_list)
  # saveRDS(chain_list, "chain_list_so_far.rds") # option to save progress
  if(all(gelman < 1.1)) break
  iter = iter + 1
}

Activate parallel computing

There are multiple options for activating parallel computing.

• Select backend = "CUDA" rather than backend = "OpenMP in the fbseq function. (The fbseqCUDA package must be installed.) This option uses CUDA to massively parallelize each individual MCMC chain if CUDA is installed on your machine.
• Select backend = "OpenMP" and threads = n in the fbseq function, where n is greater than 1. (The fbseqOpenMP package must be installed.) This option uses OpenMP to parallelize each individual MCMC chain if OpenMP is available on your machine.
• Select backend = "OpenMP" and processes = n in the fbseq function, where n is greater than 1. (The fbseqOpenMP package must be installed.) Whether OpenMP is installed or not, this option will distribute some of the MCMC chains over n parallel processes. Cannot combine with backend = "CUDA" or threads = n (n > 1).