R: Package Description: Two-group combined miRNA- and mRNA-...
miRtest
R Documentation
Package Description: Two-group combined miRNA- and mRNA- expression testing.
Description
Looking for differential expression in miRNA-data can have low power. Taking their respective mRNA-gene sets on the other hand
can lead to too liberal results. In Artmann et al. we proposed a method to combine both information sources and generate p-values
that can detect either miRNA- and target gene set expression differences.
Details
Package:
miRtest
Type:
Package
Version:
1.8
Date:
2014-11-25
License:
GPL
LazyLoad:
yes
URL:
http://www.ncbi.nlm.nih.gov/pubmed/22723856
For a detailed help check vignette("miRtest")
You can start the test with the "miR.test" function, which needs the expression matrix X
of miRNAs, the expression matrix Y of mRNAs and the allocation matrix.
Author(s)
Stephan Artmann <stephanartmann@gmx.net>,
Klaus Jung,
Tim Beissbarth
Maintainer: Stephan Artmann <stephanartmann@gmx.net>
References
Artmann, Stephan and Jung, Klaus and Bleckmann, Annalen and Beissbarth, Tim (2012).
Detection of simultaneous group effects in microRNA expression and
related functional gene sets. Plos ONE, PMID: 22723856.
Brunner, E. (2009) Repeated measures under non-sphericity.
Proceedings of the 6th St. Petersburg Workshop on Simulation,
605-609.
Jelle J. Goeman, Sara A. van de Geer, Floor de Kort, Hans C. van
Houwelingen (2004) A global test for groups of genes: testing
association with a clinical outcome. Bioinformatics 20, 93-99.
Jung, Klaus and Becker, Benjamin and Brunner, Edgar and Beissbarth, Tim (2011).
Comparison of Global Tests for Functinoal Gene Sets in
Two-Group Designs and Selection of Potentially
Effect-causing Genes. Bioinformatics, 27: 1377-1383.
Majewski, IJ, Ritchie, ME, Phipson, B, Corbin, J, Pakusch, M,
Ebert, A, Busslinger, M, Koseki, H, Hu, Y, Smyth, GK, Alexander,
WS, Hilton, DJ, and Blewitt, ME (2010). Opposing roles of polycomb
repressive complexes in hematopoietic stem and progenitor cells.
_Blood_, published online 5 May 2010.
Mansmann, U. and Meister, R., 2005, Testing differential gene
expression in functional groups, _Methods Inf Med_ 44 (3).
Smyth, G. K. (2004). Linear models and empirical Bayes methods for
assessing differential expression in microarray experiments.
_Statistical Applications in Genetics and Molecular Biology_,
Volume *3*, Article 3.
Wu, D, Lim, E, Francois Vaillant, F, Asselin-Labat, M-L, Visvader,
JE, and Smyth, GK (2010). ROAST: rotation gene set tests for
complex microarray experiments. _Bioinformatics_, published online
7 July 2010.
See Also
Function "generate.A" as well as main function "miR.test"
Examples
#######################################
### Generate random expression data ###
#######################################
# Generate random miRNA expression data of 3 miRNAs
# with 8 replicates
set.seed(1)
X = rnorm(24);
dim(X) = c(3,8);
rownames(X) = 1:3;
# Generate random mRNA expression data with 20 mRNAs
# and 10 replicates
Y = rnorm(200);
dim(Y) = c(20,10);
rownames(Y) = 1:20;
# Let's assume that we want to compare 2 miRNA groups, each of 4 replicates:
group.miRNA = factor(c(1,1,1,1,2,2,2,2));
# ... and that the corresponding mRNA experiments had 5 replicates in each group
group.mRNA = factor(c(1,1,1,1,1,2,2,2,2,2));
####################
### Perform Test ###
####################
library(miRtest)
#Let miRNA 1 attack mRNAs 1 to 9 and miRNA 2 attack mRNAs 10 to 17.
# mRNAs 18 to 20 are not attacked. miRNA 3 has no gene set.
miR = c(rep(1,9),c(rep(2,8)));
mRNAs = 1:17;
A = data.frame(mRNAs,miR); # Note that the miRNAs MUST be in the second column!
A
set.seed(1)
P = miR.test(X,Y,A,group.miRNA,group.mRNA)
P
#####################################################
### For a faster result: use other gene set tests ###
#####################################################
# Wilcoxon two-sample test is recommended for fast results
# Note that results may vary depending on how much genes correlate
P.gsWilcox = miR.test(X,Y,A,group.miRNA,group.mRNA,gene.set.tests="W")
P.gsWilcox
############################################
### We can use an allocation matrix as A ###
############################################
A = generate.A(A,X=X,Y=Y,verbose=FALSE);
A
# Now we can test as before
set.seed(1)
P = miR.test(X,Y,A,group.miRNA,group.mRNA,allocation.matrix=TRUE)
P
#####################
### Other Designs ###
#####################
# Some more complicated designs are implemented, check the vignette "miRtest" for details.
group.miRNA = 1:8
group.mRNA = 1:10
covariable.miRNA = factor(c(1,2,3,4,1,2,3,4)) ### A covariable in miRNAs.
covariable.mRNA = factor(c(1,2,3,4,5,1,2,3,4,5)) ### A covariable in mRNAs.
library(limma)
design.miRNA = model.matrix(~group.miRNA + covariable.miRNA)
design.mRNA = model.matrix(~group.mRNA + covariable.mRNA)
P = miR.test(X,Y,A,design.miRNA=design.miRNA,design.mRNA=design.mRNA,allocation.matrix=TRUE)
P