miRtest {miRtest}

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:	2.1
Date:	2024-02-04
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 <[e-mail]>, Klaus Jung, Tim Beissbarth

Maintainer: Stephan Artmann <[e-mail]>

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