README.md

In cammiller/imagingPC: Analysis of Imaging Mass Spectrometry Data using a Process Convolution Approach

imagingPC: Analysis of imaging mass spectrometry (IMS) data using a process convolution (PC) approach

In this R package, we employ a discrete PC approach in which a zero-centered latent process is convolved with a smoothing kernel function to account for spatial information. For computational efficiency with large datasets, this package implements a semivariogram-based approach to estimate and fix the smoothing kernel function. In the context of a Bayesian mixed model framework, this package writes and fits models to estimate the latent process at a limited set of locations called cupport istes while also incorporating covariates of interest. Furthermore, this package incorporates the PC approach into left-censored and marginalized two-part models to account for different zero-generating processes. This package was designed for IMS data, but the methods can be extended to imaging data collected over a regular grid.

The imagingPC package requires the R packages geoR, plyr, coda, ggplot2, gridExtra, cowplot, and nimble. The last package, nimble, also requires the installation of Rtools to write C++ code.

The research for which this R package was developed will be published in multiple papers in peer-reveiwed journals. As soon as those papers are published, I will reference those publications to provide insight into and justification for the methods we use.

Installation

To install the imagingPC package, you need to have the devtools package installed and loaded. Then you can just use the install_github function.

# install.packages("devtools")
library(devtools)
install_github("cammiller/imagingPC")

cammiller/imagingPC documentation built on June 28, 2019, 12:04 a.m.