arab: The Arabidopsis RNA-Seq Dataset

In gu-mi/NBGOF: Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data

Description

An RNA-Seq dataset from a pilot study of the defense response of Arabidopsis to infection by bacteria. We performed RNA-Seq experiments on three independent biological samples from each of the two treatment groups. The matrix contains the frequencies of RNA-Seq reads mapped to genes in a reference database. Rows correspond to genes and columns correspond to independent biological samples.

Usage

data(arab)

Format

A 26222 by 6 matrix of RNA-Seq read frequencies.

Details

We challenged leaves of Arabidopsis with the defense-eliciting ΔhrcC mutant of Pseudomonas syringae pathovar tomato DC3000. We also infiltrated leaves of Arabidopsis with 10mM MgCl2 as a mock inoculation. RNA was isolated 7 hours after inoculation, enriched for mRNA and prepared for RNA-Seq. We sequenced one replicate per channel on the Illumina Genome Analyzer (http://www.illumina.com). The length of the RNA-Seq reads can vary in length depending on user preference and the sequencing instrument. The dataset used here are derived from a 36-cycle sequencing reaction, that we trimmed to 25mers. We used an in-house computational pipeline to process, align, and assign RNA-Seq reads to genes according to a reference database we developed for Arabidopsis.

References

Di Y, Schafer DW, Cumbie JS, and Chang JH (2011): "The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq", Statistical Applications in Genetics and Molecular Biology, 10 (1).

See https://github.com/gu-mi/NBGOF/wiki/ for more details.

Examples

## Load the dataset into R session:
library(NBGOF)
data(arab)

## simulation set-up:
seed = 539
sim = 999
conf.env = 0.95
nc = detectCores() - 1
set.seed(seed)

## subset read counts to exclude all zero rows within each treatment:
good.arab = arab[(rowSums(arab[ ,1:3]) != 0 & rowSums(arab[ ,4:6]) != 0), ]
m = 500    # number of genes retained
# consider a single group with 3 replicates
samp.idx = sample(1:dim(good.arab)[1], m)
arab.sub = good.arab[samp.idx,1:3] 
lib.sizes = colSums(arab.sub)
y = arab.sub

## model matrix for arab.sub:
x = as.matrix(rep(1,3))

## GOF tests for different dispersion models:
fnbp.arab = nb.gof.m(counts=y, x=x, sim=sim, model="NBP", seed=1, ncores=nc)
fnbq.arab = nb.gof.m(counts=y, x=x, sim=sim, model="NBQ", seed=1, ncores=nc)
fcom.arab = nb.gof.m(counts=y, x=x, sim=sim, model="Common", seed=1, ncores=nc)
fgen.arab = nb.gof.m(counts=y, x=x, sim=sim, model="Genewise", seed=1, ncores=nc)
ftgc.arab = nb.gof.m(counts=y, x=x, sim=sim, model="Tagwise-Common", prior.df=10, seed=1, ncores=nc)
ftgt.arab = nb.gof.m(counts=y, x=x, sim=sim, model="Tagwise-Trend", prior.df=10, min.n=100, seed=1, ncores=nc)
ftrd.arab = nb.gof.m(counts=y, x=x, sim=sim, model="Trended", min.n=100, seed=1, ncores=nc)

## summarize the GOF test results:
summary(fnbp.arab, conf.env=conf.env, data.note="arab")
summary(fnbq.arab, conf.env=conf.env, data.note="arab")
summary(fcom.arab, conf.env=conf.env, data.note="arab")
summary(fgen.arab, conf.env=conf.env, data.note="arab")
summary(ftgc.arab, conf.env=conf.env, data.note="arab")
summary(ftgt.arab, conf.env=conf.env, data.note="arab")
summary(ftrd.arab, conf.env=conf.env, data.note="arab")

## mean-dispersion plot with some of the fitted dispersion models:
# model.vec=c("Common", "NBP", "NBQ", "Trended", "Tagwise-Common", "Tagwise-Trend")
# pl.arab = MDPlot(model.vec, y, x, title="Mean-Dispersion Plot with Fitted Dispersion Models (Arabidopsis Data)")
# pdf(file="arab-md-plot-subset.pdf", width=7, height=7)
# print(pl.arab)
# dev.off()

## diagnostic plot (histogram or uniform QQ plot of GOF p-values):
# dp1 = diagPlot(fnbq.arab, type="hist", binwidth=0.1)
# print(dp1)

gu-mi/NBGOF documentation built on Oct. 25, 2020, 3:30 a.m.