Package mirlastic. November 12, 2015

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1 Type Package Title mirlastic Version 1.0 Date Author Steffen Sass, Nikola Mueller Package mirlastic November 12, 2015 Maintainer Steffen Sass mirlastic is a tool to systematically screen for putative mi/mrna interactions using a penalized regression model. mirna target predictions serve as putative interaction graph to be validated by given transcriptome expression measurements. For each mrna a penalized regression model is calculated. As a result, mirlastic returns the validated mi/mrna relationships gathered from the regression models. Depends glmnet License What license is it under? LazyLoad yes RoxygenNote NeedsCompilation no R topics documented: mirlastic-package filter.tables lea lea.scores nw.inference table.readable mirlastic-package mirlastic - a tool to systematically screen for putative mirna/mrna interactions 1

2 2 filter.tables Details mirlastic is a tool to systematically screen for putative mi/mrna interactions using a penalized regression model. mirna target predictions serve as putative interaction graph to be validated by given transcriptome expression measurements. For each mrna a penalized regression model is calculated. As a result, mirlastic returns the validated mi/mrna relationships gathered from the regression models. Package: mirlastic Type: Package Version: 1.0 Date: License: What license is it under? LazyLoad: yes Accepts mrna and mirna expression matrices together with a mi/mrna target network obtained from target predictions. It returns a network of validated mi/mrna target relationships. Maintainer: Steffen Sass <steffen.sass@helmholtz-muenchen.de> See Also glmnet filter.tables Table filter for mirlastic This method can be used to match expression and target matrices for mirlastic. Furthermore, it filters the mirna expression for expressed mirnas. To do so, an expression threshold and a vector of sample conditions has to be supplied. The function then checks for every mirna if the mean expression exceeds the given threshold in at least one condition.

3 filter.tables 3 filter.tables(,mir.expr,target.matrix,conditions=null,mir.threshold=na) mir.expr a matrix of mrna expression values. a matrix of mirna expression values. target.matrix a matrix representing the predicted target network. The row and column names have to be the same as the rownames of the mirna and mrna matrix, respectively. conditions a factor vector indicating the conditions of the samples. The mean expression has to exceed the given threshold in at least one condition. If NULL, the mean over all samples will be checked. mir.threshold the expression threshold a mirna has to exceed. If NA, the mirna data will be not filtered. mir.expr a matrix of matched mrna expression values. a matrix of matched mirna expression values. target.matrix a matrix representing the matched predicted target network. Steffen Sass and Nikola Mueller See Also mirlastic

4 4 lea lea LEA: Local enrichment analysis The primary goal of LEA is to identify regions within a mirna-mrna network which are strongly enriched for a certain biological process, thereby inferring information on the functional role of specific mirnas. We assume that the genes in a locally enriched area are located in close proximity to genes assigned to the respective functional group. We thus use shortest paths as a basis to infer areas of local enrichment for a given functional group. lea(x) x a weighted adjacency matrix as provided by the mirlastic inference. Details Given a mirlastic inference result the lea procedure identifies locally enriched genesets. learesult a learesult object data("funct_geneset",package="mirlastic") lea.result=lea(m,geneset)

5 lea.scores 5 lea.scores Scoring of LEA results For a specified gene set and a given LEA result this function returns individual local enrichment scores for mirnas and mrnas in the network. lea.scores(lea.result,geneset.index=null,geneset.id=null) a weighted adjacency matrix as provided by the mirlastic inference. geneset.index The index of the gene set to test. Must be not NULL if no geneset.id is given. geneset.id The name of the gene set to test. Must be not NULL if no geneset.index is given. list(gene.scores=score,mir.scores=mir.score) a list containing scores for each gene and each mirna in the network. data("funct_geneset",package="mirlastic") lea.result=lea(m) lea.s=lea.scores(lea.result,1)

6 6 nw.inference nw.inference mirlastic inference Accepts mrna and mirna expression matrices together with a mi/mrna target network obtained from target predictions. It returns a network of validated mi/mrna target relationships. nw.inference(,mir.expr,target.matrix) Details a matrix of mrna expression values. mir.expr a matrix of mirna expression values. target.matrix Matrix representing the predicted target network. The row and column names have to be the same as the rownames of the mirna and mrna matrix, respectively. The predicted target network must be supplied in a matrix-like structure of size n x m, where n is the number of measured mirnas and m the number of measured mrnas. The row and column identifiers have to match the row identifiers of the mirna and mrna expression matrices, respectively. A function filter.tables is supplied that matches the rows and columns of expression and target matrices. This matrix must only consist of 0 and 1 entries, which indicate the predicted target relationship. The function returns a matrix in the same format. This matrix can be formatted into a data.frame that is human readable. This can be done using the function table.readable. m a matrix representing the network of validated mi/mrna target relationships with non-zero entries representing the elastic net regression coefficients

7 table.readable 7 table.readable Reformat mirlastic result tables Returns a data.frame representing a mirlastic result network that is human readable. table.readable(m) m a matrix as returned by mirlastic. m.readable a data frame representing the mirlastic result network that is human readable. See Also Steffen Sass and Nikola Mueller mirlastic m=nw.inference(,mir.expr,target.matrix)