ELBOW {ELBOW} | R Documentation

## Evaluating foLd change By the lOgit Way

### Description

The "elbow" method an improved fold change test for determining cut off for biologically significant changes in expression levels in transcriptomics.

### Details

Package: | ELBOW
Type: | Package
Version: | 1.0
Date: | 2013-08-08
License: | Creative Commons 3.0 Attribution + ShareAlike
| (see: http://creativecommons.org/licenses/by-sa/3.0/)

Elbow an improved fold change test that uses cluster analysis and pattern recognition to set cut off limits that are derived directly from intrareplicate variance without assuming a normal distribution for as few as 2 biological replicates. Elbow also provides the same consistency as fold testing in cross platform analysis. Elbow has lower false positive and false negative rates than standard fold testing when both are evaluated using T testing and Statistical Analysis of Microarray using 12 replicates (six replicates each for initial and final conditions). Elbow provides a null value based on initial condition replicates and gives error bounds for results to allow better evaluation of significance.

Abstract Reference:
Conference Proceeding: Zhang, X., Bjorklund, N. K., Rydzak, T., Sparling, R., Alvare, G., Fristensky, B. (April 2013) “The Elbow Method for deciding significant fold change cutoffs of differentially expressed genes.” Recomb 2013 17th International Conference on Research in Computational Biology.

Paper Reference:
Zhang, X., Bjorklund, N. K., Alvare, G., Rydzak, T., Sparling, R., Fristensky, B. (2013) “Elbow, an improved fold test method for transcriptomics.” Departments of Plant Science and Microbiology, University of Manitoba, Winnipeg, Canada, R3T 2N2

The corresponding author: Brian Fristensky frist@cc.umanitoba.ca

### Author(s)

Xiangli Zhang, Natalie Bjorklund, Graham Alvare, Tom Ryzdak, Richard Sparling, Brian Fristensky

Maintainers: Graham Alvare alvare@cc.umanitoba.ca, Xiangli Zhang justinzhang.xl@gmail.com

### References

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### See Also

See analyze_elbow for doing a full ELBOW analysis and plot.
See do_elbow if you want to extract only the ELBOW cut-off values.

### Examples

    	# read in the EcoliMutMA sample data from the package
    	data(EcoliMutMA, package="ELBOW")
    	csv_data <- EcoliMutMA
    	# - OR - Read in a CSV file (uncomment - remove the #'s
    	#        - from the line below and replace 'filename' with
    	#        the CSV file's filename)
    	# csv_data <- read.csv(filename)

    	# set the number of initial and final condition replicates both to three
    	init_count  <- 3
    	final_count <- 3

    	# Parse the probes, intial conditions and final conditions
    	# out of the CSV file.  Please see: extract_working_sets
    	# for more information.
    	#
    	# init_count should be the number of columns associated with
    	#       the initial conditions of the experiment.
    	# final_count should be the number of columns associated with
    	#       the final conditions of the experiment.
    	working_sets <- extract_working_sets(csv_data, init_count, final_count)

    	probes <- working_sets[[1]]
    	initial_conditions <- working_sets[[2]]
    	final_conditions <- working_sets[[3]]

    	# Uncomment to output the plot to a PNG file (optional)
    	# png(file="output_plot.png")

    	# Analyze the elbow curve.
    	sig <- analyze_elbow(probes, initial_conditions, final_conditions)

    	# write the significant probes to 'signprobes.csv'
    	write.table(sig,file="signprobes.csv",sep=",",row.names=FALSE)

[Package ELBOW version 1.16.0 Index]