Zⁱ_g Z^a_g v1.0.0

Quick Installation Guide and Simple Analysis

The zigzag R package is designed to compute the posterior probability that genes are actively expressed, given a set of RNA-seq relative expression estimates. Requires at least 2 replicates (expression estimates from RNA-seq libraries).

Installation

To install the zigzag package, you can use the following commands in your R environment:

if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")
devtools::install_github("ammonthompson/zigzag")

or you can clone the repository and install it locally:

install.packages("path/to/zigzag", type = "source", repos = NULL)

Simple Analysis Example

You can perform a basic analysis to get a feel for zigzag's functionality. Here's a simple example to get you started:

library(zigzag)

# Load your RNA-seq expression data with at least 2 replicate libraries.
expression_data <- read.csv('path/to/your/data.csv', header = T, row.names = 1)
gene_length_data <- read.csv('/path/to/your/gene_length_data.csv', header = T, row.names = 1)

# Load data into a zigzag object
# Compute the posterior probability of active expression by
# first running burnin until the chain is stationary,
# then run mcmc to sample from the posterior distribution.
my_zigzag <- zigzag$new(expression_data, 
                        gene_lengths_data, 
                        output_dir = "my_output")
my_zigzag$burnin()
my_zigzag$mcmc()

View the results located in "my_output/" and evaluate quality of the MCMC samples and priors in "my_output/*mcmc_output/mcmc_report".

You will likely not want to use all default settings. Use ? to view documentation on functions. For example

?zigzag
?zigzag::burnin 
?zigzag::mcmc

Documentation and Tutorial

For a more comprehensive guide and detailed tutorials on how to use zigzag, please visit our tutorial page.

Citation

If you use zigzag in your research, please cite our work using the following reference:

Thompson et al., 2020, PNAS