miRtest | R Documentation |
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.
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.
Stephan Artmann <stephanartmann@gmx.net>, Klaus Jung, Tim Beissbarth
Maintainer: Stephan Artmann <stephanartmann@gmx.net>
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.
Function "generate.A" as well as main function "miR.test"
####################################### ### 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
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