miRtest (version 2.2)

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.

Author

Stephan Artmann <[e-mail]>, Klaus Jung, Tim Beissbarth

Maintainer: Stephan Artmann <[e-mail]>

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.

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