Classification software tools | Gene expression microarray data analysis
Gene expression profiling based on microarray technology has been applied widely on monitoring global transcriptome changes in biological samples. In cancer research, one of the major microarray applications is to identify genes, or features, whose expression patterns can discriminate samples with distinct states (usually defined by the phenotype of samples such as primary or metastatic tumour).
Serves for the functional analysis of gene expression and genomic data. Babelomics offers the possibility to explore the effects of alteration in gene expression levels or changes in genes sequences within a functional context. It provides user-friendly access to a full range of methods that cover: (1) primary data analysis; (2) a variety of tests for different experimental designs; and (3) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context.
Handles information derived from CFX systems for polymerase chain reactions (PCR) detection. CFX Manager is a standalone software dedicated to perform analysis for single nucleotides polymorphisms (SNPs) genotyping studies, as well as gene expression. Besides, the application is able to generate a wide range of plots and includes functions for data analysis that can be customized according the user needs.
Enables model-based clustering, classification, and density estimation based on finite Gaussian mixture modelling. Mclust is an R package that provides a strategy for clustering, density estimation and discriminant analysis. It offers a variety of covariance structures obtained through eigenvalue decomposition, functions for performing single E and M steps and for simulating data for each available model. The software also includes additional ways of displaying and visualizing fitted models along with clustering, classification, and density estimation results.
Implements a method for characterizing cell heterogeneity using RNA mixtures from nearly any tissue. CIBERSORT needs an input matrix of reference gene expression signatures to calculate the relative proportions of each cell type of interest. This software deconvolves mixture via a linear support vector regression (SVR) and a machine learning approach. It aids with large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets.
Allows users to study finite mixture models for various parametric and semiparametric settings. mixtools contains several electron-microscopy (EM) algorithms for determining parameters in a wide range of different mixture-of-regression contexts. More precisely, it supplies techniques for finite mixture model analysis in which components are regressions, multinomial vectors arising from discretization of multivariate data.
Provides a theoretical analysis of the minimal-redundancy-maximal-relevance condition. mRMR is a framework allowing users to minimize redundancy, and it uses a series of intuitive measures of relevance and redundancy to select promising features for both continuous and discrete data sets. The incremental selection scheme of this method avoids the difficult multivariate density estimation in maximizing dependency. It can also be combined with other feature selectors.
Analyses differential gene expression for each cell type and relative cell-type frequencies. CSsam works on biological samples from microarray data. This tool addresses the extensive loss of biological signal in microarray datasets when analyzing complex tissue samples that vary in cellular composition. It localizes the identified differential expression to a particular cellular context, which allows hypothesis formulation for follow-up experiments, and is usable with microarray analysis of any heterogeneous tissue and to other types of molecular measurements.