MicroRNA microarray technology is a powerful high-throughput tool capable of monitoring the expression of thousands of microRNAs at once within tens of samples processed in parallel in a single experiment. While many of the same tools for analyzing mRNA expression arrays can be applied to the analysis of miRNA data, there are distinct differences between the two platforms which necessitate special use of some methods.
Provides statistical tools for visualization and analysis of microarrays. GeneSpring GX offers an interactive environment that promotes investigation and enables understanding of Transcriptomics, Genomics, Metabolomics and Proteomics data within a biological context. It can be used for expression arrays, miRNA, exon arrays and genomics copy number data. It also allows to identify targets of interest that are both statistically and biologically meaningful.
A package for the pre-processing and differential expression analysis of Agilent microRNA array data. For the pre-processing of the microRNA signal, AgiMicroRNA incorporates the robust multiarray average algorithm, a method that produces a summary measure of the microRNA expression using a linear model that takes into account the probe affinity effect. To obtain a normalized microRNA signal useful for the statistical analysis, AgiMicroRna offers the possibility of employing either the processed signal estimated by the robust multiarray average algorithm or the processed signal produced by the Agilent image analysis software. It also incorporates different graphical utilities to assess the quality of the data. AgiMicroRna uses the linear model features implemented in the limma package to assess the differential expression between different experimental conditions and provides links to the miRBase for those microRNAs that have been declared as significant in the statistical analysis.
Takes the MiChip miRNA microarray .grp scanner output files and parses these out, providing summary and plotting functions to analyse MiChip hybridizations. A set of hybridizations is packaged into an ExpressionSet allowing it to be used by other BioConductor packages.
Analyses large-scale expression datasets. It utilizes curated sets of 3’ UTRs to attach sequences to these genes and then applies the Sylamer algorithm for detection of miRNA or siRNA signatures in those sequences. SylArray allows researchers from a broad area of expertise to perform fast and accurate detection of small RNA signatures in their gene-expression datasets.
Sorts features based on a criterion that involves all other features. UFFizi is based on the unsupervised feature filtering (UFF) algorithm, that employs an entropy measure applied to singular value decomposition (SVD). It employs information contained in the singular values in order to select the features. This tool can serve to discover rare events in the dataset or filter faulty instances before proceeding with further analysis.
Provides pathway enrichment techniques for miRNA expression data. Specifically, the set of methods handles the many-to-many relationship between miRNAs and the multiple genes they are predicted to target (and thus affect.) miRNApath also handles the gene-to-pathway relationships separately. Both steps are designed to preserve the additive effects of miRNAs on genes, many miRNAs affecting one gene, one miRNA affecting multiple genes, or many miRNAs affecting many genes.
A tool for the analysis and visualization of high-level microarray data from individual or multiple different platforms. Currently, InCroMAP supports mRNA, microRNA, DNA methylation and protein modification datasets. Several methods are offered that allow for an integrated analysis of data from those platforms. The available features of InCroMAP range from visualization of DNA methylation data over annotation of microRNA targets and integrated gene set enrichment analysis to a joint visualization of data from all platforms in the context of metabolic or signalling pathways.