Description Usage Arguments Value Examples
Performs the ELBOW fold change test on an CountDataSet from DESeq object. This is a wrapper class to help integrate the ELBOW method into Bioconductor. followed tutorial from: http://cgrlucb.wikispaces.com/Spring+2012+DESeq+Tutorial
1 | do_elbow_rnaseq(rnaSeq)
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rnaSeq |
is the CountDataSet object to analyze. |
a matrix specified as follows
columns — (1) “up_limit”, the upper ELBOW fold-change cut-off value; (2) “low_limit”, the lower ELBOW fold-change cut-off value
rows — one row per sample, specified by the parameter “columns.”
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | # install the DESeq libraries
#if (!requireNamespace("BiocManager", quietly=TRUE))
#install.packages("BiocManager")
#BiocManager::install("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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