In cuiyingbeicheng/Foldseq: Log fold change detection based on an empirical bayesian method.

Introduction

This manual provides a walk through tutorial on how to use Foldseq, which implements an empirical Bayesian method to substantially improve the power and accuracy in fold change detection.

Installation

To run the entire deconvolution tutorial, users need to install the Foldseq package.

# install devtools if necessary
install.packages('devtools')

# install the Foldseq package
devtools::install_github('cuiyingbeicheng/Foldseq')

# load
library(Foldseq)

Example

library(Foldseq)

# An example to detect log fold change
data(smalldata)
res = Foldseq(ctrl=smalldata$Ctrl, trt=smalldata$Trt, cis.chr="chr1", cis.null.lfc = 0, trans.null.lfc = log2(1.5))

# Users can output significant highly expressed cis genes 
res$cis.high
# (Things are the same for the rest types of genes,i.e.,significant highly expressed trans genes,
# significant lowly expressed cis genes, significant lowly expressed trans genes,
# cis genes not significantly expressed and trans genes not significantly expressed.)
# Users can also output summary statistics of the fold change detection results
res$sumStat

cuiyingbeicheng/Foldseq documentation built on May 18, 2020, 6:31 a.m.