Gene expression clustering software tools | Transcription data analysis
Microarray technology has been widely applied in biological and clinical studies for simultaneous monitoring of gene expression in thousands of genes. Gene clustering analysis is found useful for discovering groups of correlated genes potentially co-regulated or associated to the disease or conditions under investigation.
Allows users to collect, manage, and effectively analyze data from microarray experiments. TM4 Microarray Software Suite is composed of a set of four tools: Madam, Spotfinder, Midas and MeV. It offers functions that allow users to record their experimental parameters and data; permit researchers to load the output of a microarray scanning operation and to adjust the placement of each grid cell manually allow for accurate spot detection.
Allows users to build large attractor networks from static gene-expression data. Hclust uses Hopfield networks for clustering, feature selection and network inference and did not require the data relative to pathway or kinetic information. The application is able to generate plots depicting the weight matrix of the network, the relaxation of the state matrix and the energy landscape.
A widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. In response to the rapid increase in the amount of sequencing data produced by the next-generation sequencing technologies, a new CD-HIT program accelerated with a novel parallelization strategy and some other techniques has been developed to allow efficient clustering of such datasets.
Assess the uncertainty in hierarchical cluster analysis. Pvclust is designed for general hierarchical clustering problems. It permits users to easily obtain bootstrap-based p-values for their own dataset and preferred clustering method. The tool provides AU (approximately unbiased) p-value as well as BP (bootstrap probability) value for each cluster in a dendrogram.
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.
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.
Serves to parse metric binary outputs by Illumina sequencers. Illuminate is a Python module that parses metric binaries from Illumina sequencer runs and provides usable data in the form of python dictionaries and dataframes. Users can print sequencing run metrics to the command line and work with the data programmatically at the same time. The reading of active sequencing runs for tile, index and quality metrics is also supported.