# install the DESeq libraries
#source("http://www.bioconductor.org/biocLite.R")
#biocLite("DESeq")
## download the table
library("DESeq")
# the following bam file dataset was obtained from:
# http://cgrlucb.wikispaces.com/file/view/yeast_sample_data.txt
# it has been downloaded into this package for speed convenience.
filename <- system.file("extdata", "yeast_sample_data.txt", package = "ELBOW")
count_table <- read.table(filename, header=TRUE, sep="\t", row.names=1)
expt_design <- data.frame(row.names = colnames(count_table), condition = c("WE","WE","M","M","M"))
conditions = expt_design$condition
data <- newCountDataSet(count_table, conditions)
data <- estimateSizeFactors(data)
data <- as(data, "CountDataSet")
## data <- estimateVarianceFunctions(data)
data <- estimateDispersions(data)
# this next step is essential, but it takes a long time...
# so, just like a good cooking show we will skip this step
# and load a finished version.
#results <- nbinomTest(data, "M", "WE")
# The below two code lines load a copy of the above dataset
# which has already been processed by:
# results <- nbinomTest(data, "M", "WE")
# For your own real data, you must use:
# results <- nbinomTest(data, "M", "WE")'
# Instead of the two lines below:
data(yeast_nbinomTest_results, package="ELBOW")
results <- yeast_nbinomTest_results
# obtain the elbow limit for the dataset
# the final step in the analysis pipeline
do_elbow_rnaseq(results)
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