generate.A | R Documentation |
Turn a data.frame indicating gene sets into the allocation matrix.
generate.A(df, X, Y, verbose=TRUE)
df |
data.frame with mRNAs in its first and miRNAs in its second column. |
X |
Expression matrix of miRNAs whose row names will be used to generate the list of miRNAs. |
Y |
Expression matrix of mRNAs whose row names will be used to generate the list of mRNAs. |
verbose |
Logical. Shall progress be printed? |
Allocation matrix A necessary for "miR.test" function.
Stephan Artmann
####################################### ### 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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