Functional Enrichment for a Table of Normalized Gene Expression Values

Share:

Description

Bi-Directional FUNCtional enrichment [1] compares expression values for up- and down-regulated genes are compared for at least one gene set, using normalized expression values. Gene sets are already defined for human and mouse gene symbols. All other gene sets must be specified by the user. The user can optionally output box-plots to visualze enrichment scores across samples in different groups.

Usage

1
RNA.bdfunc.signal(expression.table, sample.file, project.name, project.folder, species = NULL, enrichment.file = NULL, p.method = "t-test", p.adjust.method = "fdr", plot.flag = TRUE, color.palette = c("red", "blue", "green", "orange", "purple", "cyan", "pink", "maroon", "yellow", "grey", "black", colors()))

Arguments

expression.table

Data frame with genes in columns and samples in rows. Data should be log2 transformed. The RNA.norm function automatically creates this file.

sample.file

Tab-delimited text file providing group attributions for all samples considered for analysis.

project.name

Name for sRAP project. This determines the names for output files.

project.folder

Folder for sRAP output files

species

Name for species used for analysis. If species is set to "human" or "mouse," then pre-defined gene lists provided by the sRAP package are used.

The default human gene list is created from gene ontology [2] and MSigDB [3] databases. The default mouse gene list is created from the gene ontology [2] database.

enrichment.file

Table of gene lists including up- and down-regulated genes.

This is only necessary when defining a custom species. This parameter is ignored when the species is set to "human" or "mouse".

p.method

Method for calculating p-values

"t-test" (Default) = t-test between up-regulated and down-regualted genes "mann-whitney" = Non-parametric Mann-Whitney U test between up-and down-regulated genes "ks" = Kolmogorov-Smirnov test between up- and down-regulated genes

p.adjust.method

Method for calculating false discovery rate (FDR):

"fdr" (Default)= B-H "Step-Up" FDR [4] "q-value" = Storey q-value [5] "none" = use unadjusted p-value without multiple hypothesis correction

plot.flag

Logical value: Should box-plots be created for all gene sets?

If primary variable is two groups called "positive" and "negative", this value also determines if ROC plot will be created.

color.palette

Colors for primary variable (specified in the second column of the sample file).

If method is set to "t-test," this variable is ignored. In this special case, groups with an average t-test statistic above 2 are colored red, groups with an average t-test statistic below -2 are colored green, and all other groups are colored grey.

Author(s)

Charles Warden <cwarden45@gmail.com>

References

[1] Warden CD, Kanaya N, Chen S, and Yuan Y-C. (2013). BD-Func: A Streamlined Algorithm for Predicting Activation and Inhibition of Pathways. peerJ, 1:e159

[2] Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, and Sherlock G.(2000). Gene Ontology: tool for the unification of biology Nat Genet, 25:25-29

[3] Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, and Mesirov JP.(2011). Molecular signatures database (MSigDB) 3.0. Bioinformatics, 27:1739-1740.

[4] Benjamini Y, and Hochberg Y.(1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B, 57:289-300.

[5] Storey JD, and Tibshirani R. (2003). Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences, 100:9440-9445.

See Also

sRAP goes through an entire analysis for an example dataset provided with the sRAP package.

Please post questions on the sRAP discussion group: http://sourceforge.net/p/bdfunc/discussion/srap/

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
	
library("sRAP")

dir <- system.file("extdata", package="sRAP")
expression.table <- file.path(dir,"MiSeq_cufflinks_genes_truncate.txt")
sample.table <- file.path(dir,"MiSeq_Sample_Description.txt")
project.folder <- getwd()
project.name <- "MiSeq"

expression.mat <- RNA.norm(expression.table, project.name, project.folder)

stat.table <- RNA.deg(sample.table, expression.mat, project.name, project.folder, box.plot=FALSE, ref.group=TRUE, ref="scramble",method="aov", color.palette=c("green","orange"))

RNA.bdfunc.signal(expression.mat, sample.table, plot.flag=FALSE, project.name, project.folder, species="human")