The goal of
genBart is to streamline the process of statistical analysis in
high dimensional studies (e.g. RNA-seq and microarray) to a final and
comprehensive results object that can be uploaded into the BART (Biostatistical
Analysis Reporting Tool) shiny app. The app provides users with tools to
interactively visualize and efficiently sift through large amounts of data and
genBart package accomplishes its goal by providing a set of functions
that modularize every step of the analysis workflow:
Though one strength of BART is its ability to report the entire analysis
workflow in one session, it is often useful to use BART before all of the
analysis is completed (e.g. viewing heat maps before any statistical analysis is
genBart makes this possible by allowing users to easily update and/or
add to existing BART result objects. Conveniently, BART will only populate with
tools based on the information contained in the object generated by
You can install genBART from github with:
# install.packages("devtools") devtools::install_github("jcardenas14/genBart")
I demonstrate a simple example below of how BART can be used for unsupervised
analysis. The data used for this example is available in
genBart and is taken
from a longitudinal microarray experiment monitoring the gene expression changes
in cynomolgus macaques infected with M.tuberculosis (Skinner et al.). To speed
up the hierarchical clustering step, I randomly selected a subset (1000) of the
probes for the example. Please see the genBart vignette
for a full analysis workflow walk-through from the same microarray study.
## Call data library(genBart) data(tb.expr) data(tb.design) # declare design information meta <- metaData(y = tb.expr, design = tb.design, data.type = "microarray", columnname = "columnname", long = TRUE, subject.id = "monkey_id", baseline.var = "timepoint", baseline.val = 0, time.var = "timepoint", sample.id = "sample_group") # normalize data and cluster norm.data <- normalizeData(meta = meta, norm.method = "mean") dendros <- clusterData(norm.data = norm.data) # create BART result object genFile(meta = list(meta), dendrograms = dendros, project.name = "TB Unsupervised Analysis")
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