Offers powerful yet simple visualization, normalization and significance testing tools. DetectiV uses simple and well established visualization and statistical techniques to analyze data from pathogen detection microarrays. It enables researchers to quickly and easily identify possible infectious agents. The tool performs better than previously published software on a publicly available microarray dataset.
Allows the user to select genes that represent given functional categories. Onto-Design permits to create customized array representing some chosen biological processes. It provides the means to select genes based on their biological process, molecular function or cellular component. The tool includes on a certain microarray is a very laborious process requiring a high level of expertise.
Enables cross-platform comparisons, and proposes a comprehensive procedure for assessments based on spike-in experiments. spkTools is implemented as a user friendly Bioconductor package. It contains functions that can be used to compare expression measures on different array platforms. Its utility has been demonstrated by presenting a spike-in-based comparison of the three major platforms: Affymetrix, Agilent and Illumina.
Estimates gene and eQTL networks from high-throughput expression and genotyping assays. qpgraph is based in the so-called q-order limited partial correlation graphs, qp-graphs, which is specifically tailored towards molecular network discovery from microarray expression data. qp-graphs yield more stable performance figures than other state-of-the-art methods when the ratio of genes to experiments exceeds one order of magnitude. More importantly, the better performance of the qp-graph method on such a gene-to-sample ratio has a decisive impact on the functional coherence of the reverse-engineered transcriptional regulatory modules and becomes crucial in such a challenging situation in order to enable the discovery of a network of reasonable confidence that includes a substantial number of genes relevant to the essayed conditions.
A compliant library for computing principal component analysis on incomplete data sets. pcaMethods combines an expectation maximization approach with a probabilistic model. It also offers methods for visualization of the results, e.g. for plotting an arbitrary number of scores/loadings side by side. pcaMethods is especially suitable for data from experiments where the studied response is non-linear.
Enables biology researchers to explore the entirety of very large microarray compendia in a biologically meaningful manner. SPELL is a scalable, context-specific search methodology implemented in an interactive, web-based search engine. The web-interface allows a researcher to provide a list of query genes, then the search engine reports which datasets are most relevant to that query, lists additional genes related to the query within the relevant conditions and displays the expression levels of these genes.
A statistical method for deconvolving mixed cancer transcriptomes which addresses the aforementioned issues in array-based expression data. DeMix can be applied to ongoing biomarker-based clinical studies and to the vast expression datasets previously generated from mixed tumor and stromal cell samples.
Assists users in research on microarrays, proteomics and metabolomics. irMF uses non-negative matrix factorization to create groups of genes and sets of predictors. It uses non-negative matrix factorization (NMF) to capture correlation structures in the dataset and then utilizes a sequential form of testing to control the error rate of the procedure. Several functions are permitted with this tool, it allows visualization, heatmaps, score plots of the factorization or provides association statistics between NMF clusters and actual groups.
A Windows-program for the analysis of experiments with hierarchical probe-sets. PhylochipAnalyzer operates in two modes: first, the hierarchy of probes is defined interactively; second, the intensity data of a hybridized chip is loaded and analyzed according to the hierarchy. The program can export hierarchy trees to Newick-format and analyzed data to Excel. It contains a Delphi-script that makes it configurable with respect to different criteria for positive signals.
Evaluates transcription factor (TF) activities according to gene-expression data combined with architectural information about the regulatory network. TFInfer allows users to visualize TF activity, a time series activity profile with associated error bars and graphs which can be saved in a variety of formats. Besides, the software provides extra functionalities such as handling both time-series data and data from several independent conditions, possibly with replicates.
An R package that can be used to automatically detect cell-specific marker genes (MGs) located on the scatter radii of mixed gene expressions, estimate cellular proportions in each sample and deconvolute mixed expressions into cell-specific expression profiles. Although the principal application here involves microarray gene expressions, this methodology can be readily applied to other types of quantitative molecular profiling data.
An open-source, platform-independent software pipeline for two-channel microarray spot quality control. MASQOT-GUI hosts a set of independent applications for gridding, segmentation, quantification, quality assessment and data visualization. All required steps, from raw data to final quantification and quality assessment, can be performed within MASQOT-GUI. Optionally, the user can utilize external software for gridding and subsequently import spot coordinates into MASQOT-GUI prior to segmentation.
Isolates and eliminates blobs from Affymetrix microarrays. MBR offers a platform, using a two-step algorithm, able to pinpoint and screens affected probes from a given set. The application includes several detection and refinement settings to detect blobs of various qualities and sizes. It aims to assist users in increasing false rate discovery (FDR) as well as sensitivity in tiling array analysis.
Provides graphical comparison results between normal and cancer with student t-test statistics. Oncopression is focused on microarray-based gene expression profiles for normal and cancer. This web application contains more of 6080 normal and 20 640 cancer expression profiles from 269 and 389 datasets, respectively for 25 types of tissues. Cancers are further categorized according to features found in annotation such as tumor grade, tumor stage, mutation type, etc.
Consists of a dimension reduction technique designed for complex data sets with multiple overlaid signals observed in noisy conditions. SMSSVD is a parameter-free unsupervised dimension reduction technique primarily designed to reduce noise, adaptively for each low-rank-signal in a data matrix. The software represents the data in a way that enables unbiased exploratory analysis and reconstruction of the multiple overlaid signals, including finding the variables that drive the different signals. It was evaluated on several gene expression and synthetic data sets.
Provides methods for computing different p-value based correction methods. Myriads incorporates a simulator for two sample t-tests and Cochran-Armitage case-control tests. The simulator can be used to obtain estimates of the variance of the number of false discoveries jointly with the per family error rate (PFER) committed by any of the correction methods. This software presents three main aspects: (i) correction methods, (ii) dependence test and (iii) the incorporation of an autocorrelation test based on the generalized Durbin-Watson statistic.
Implements a general pilot data-based method for power and sample size determination for high-dimensional genomic data. SSPA allows users to read data as a vector of test statistics and to process the desired estimates. This software offers functions to ease interpretation of results. It can deal with any type of test statistic distribution family so long as both null and alternative are known.
Uses as submission tool for Microarray experiments. AtMIAMExpress is an open-source Web-based software application for the submission of Arabidopsis-based microarray data to ArrayExpress. It was designed as a tool for users with minimal bioinformatics expertise, has comprehensive help and user support, and represents a simple solution to meeting the MIAME guidelines for the Arabidopsis community. It allows three types of submissions: protocols, array designs, and experiments.
Provides a prediction method for transcription units (TU prediction) in the bacterial whole genome. TREBAX uses integrated analysis of microarray and DNA sequence data to identify unknown TUs and detect internal operons in the known operons. Moreover, by modifying the respective input format, the software can also be employed to analyze different data.
Predicts tissue/cell type marker genes using microarray gene expression data. The main advantages of MGFM are i) a short running time of some seconds per analysis. This is achieved by sorting the gene expression values instead of using gene differential expression. ii) MGFM offers the user the possibility to modify the set of samples by easily removing or adding new samples. iii) MGFM is available as a standalone version (R-package) as well as a web application integrated into the CellFinder platform.
Combines connectivity and expression data to produce a robust summary of an experiment. GeneRank is based on the PageRank algorithm and increase its capabilities to rank genes in microarray experiments. It is able to highlight the transcription factor. This tool uses graph or an undirected graph where a node represents a gene and the edges can be defined by some other "previous knowledge" as input.
Provides methods for combining microarray data from different studies. Cross-study normalization is an ArrayMining module that includes five cross-study normalization methods to combine samples from two different studies: an approach based on linked gene and sample-clustering (XPN), an empirical Bayes method (EB), a median rank score-based method (MRANK), an outlier-removing discretization technique (NorDi) and a quantile discretization procedure (QDISC).
Performs a supervised analysis for a pre-normalized input matrix. Class assignment is an ArrayMining module dedicated to supervised learning methods, including methods for microarray sample classification (SVM, RF, PAM and kNN). The results for an analysis contain various performance measures for evaluation and Z-scores for the genes that were most frequently selected across different cross-validation cycles.
Identifies whether sets of functionally related genes are significantly differentially expressed in different microarray sample classes. GSA is an ArrayMining module that provides access to three functional annotation sources to identify functionally related genes in a data set and extract corresponding gene sets: the Gene Ontology Database, the KEGG database, and a collection of more than 30 cancer-related gene sets from the van Andel Institute in Michigan.
Supports blood genomics studies with general applicability to basic and translational research. SPEC predicts the cellular source of predictive gene expression signatures using transcriptional profiling data from total peripheral blood mononuclear cells (PBMCs). The software takes advantage of the fluctuations of cell proportions in a mixed cell sample to find the most likely source for a gene expression signal. It was applied to data from chronic Hepatitis C Virus (HCV) patients.
Permits graphical representation of a wide range of Sample and Data Relationship Format (SDRF) files. SDRF2GRAPH assists researchers to describe and exchange information about experiments. It facilitates comprehension of experimental process described in SDRF and provides prompt visual feedback. This tool underlines each step of experiments even if users are not familiar with the experimental design or technologies.
An alternative approach to gene expression estimation that uses a machine learning approach. MaLTE relies on a gold standard (such as RNA-Seq) to infer gene expression models that make better use of microarray fluorescence probe intensities. This approach can be used to accurately estimate absolute expression levels from microarray data, at both gene and transcript level, which has not previously been possible. This methodology will facilitate re-analysis of archived microarray data and broaden the utility of the vast quantities of data still being generated.
Serves for controlling false discovery rates. Bon-EV performs and controls false discovery rates through controlling the expected number of false discoveries. This program can be used for microarray and sequencing data analysis. It also offers a method that can maintain the high power of the Storey’s q-value procedure and also result in false discovery rate (FDR) control and stability.
A meta-analysis method able to efficiently identify associated biomarkers (ABs) from different independently performed array-based datasets. MiningABs quantifies the similarity between paired probe sequences as a bridge to connect these datasets together. The ABs can be subsequently identified from an “improved” common logit model (c-LM) by combining several sibling-like LMs in a heuristic genetic algorithm selection process. Investigating the ABs using Gene Ontology (GO) enrichment, literature survey, and network analyses indicated that ABs are not only strongly related to cancer development but also highly connected in a diverse network of biological interactions.
Allows users to check genes clusters’ trustworthiness. BCLUST uses a parametric bootstrap resampling approach to include information relative to variations in gene expression levels. It permits to investigate clusters deduced from various hierarchical clustering method and highlights those with high confidence values. The application can also be coupled with other approaches to rank gene clusters for further analysis.
Allows users to manage microarray core. SLIMarray serves for the management of inventory, sample processing and charging information. It automates the construction of interrelated data records where appropriate. This tool can track multiple lab groups' inventories of different chip types, with records of every transaction such as purchasing or using microarrays. It permits users to create records of hybridizations.
Provides a basis matrix. immunoStates is constructed by extracting over 6200 microarray samples from 166 public gene expression datasets with the aims of decreasing biological and technical biases, and, subsequently expanse deconvolution efficiency.
Encodes the immune cell lineage relationships and produces rich confidence scores at all levels of the hierarchy of immune cell types. Infino is a Bayesian regression mixture deconvolution method that incorporates the inter-cell-type relationships called “hierarchy”, and even learns these relationships directly from the data. It produces posterior probability distributions, which are much richer confidence scores than the point estimates and hypotheses tests.
Provides a platform for storage and investigation of microarray data. [email protected] is divided into five main features: (i) “storage”, that is able to handle both commercial and personal microarray experiments; (ii) “processing” includes features for filter and standardize data; (iii) the “analysis” panel allows users to perform a SAM analysis and to cluster data ; (iv) “data mining” furnishes module for annotation and (v) “main” gives access to tools for managing gene lists or gene expression matrix.
Allows storage of matrices of experimental results, which are commonly produced by sequencing and microarray experiments. SummarizedExperiment is an R package that contains two classes: (1) SummarizedExperiment, a matrix-like container and (2) RangedSummarizedExperiment, the child of the SummarizedExperiment. The software is suitable to a variety of experiments, particularly sequencing based experiments such as RNA-Seq and ChIp-Seq.
Facilitates the submission of large microarray experiment datasets to the public repository database ArrayExpress. Tab2MAGE uses a flexible spreadsheet format for MIAME annotation of microarray experiments. The software provides a script that can parse data files in several formats, and a data file checking script. It can also generate MAGE-ML for data exchange.
Provides an environment for analyzing both transcriptome data (gene expression profiles) and metabolome data (compound profiles). KegArray is a Java application that enables users to easily map those data to KEGG resources including PATHWAY, BRITE and genome maps. It can read data format of the EXPRESSION database or tab-delimitated text similar to the EXPRESSION format. It can convert the external database IDs to the KEGG GENES IDs, which are necessary for mapping the array data to the KEGG resources such as pathway maps.
Allows researchers to easily conduct a comprehensive and objective performance evaluation of their newly developed missing value imputation algorithm for microarray data or any data which can be represented as a matrix form (e.g. Next Generation Sequencing (NGS) data or proteomics data). MVIAeval provides a user-friendly interface allowing users to upload the R code of their new algorithm and select (i) the test datasets among 20 benchmark microarray (time series and non-time series) datasets, (ii) the compared algorithms among 12 existing algorithms, (iii) the performance indices from three existing ones, (iv) the comprehensive performance scores from two possible choices, and (v) the number of simulation runs.
Optimizes process with least square. LLSimpute is built on the Pearson correlation coefficient (PCC) and exploits the coherent genes. The method inherent of this software proposes a local least squares imputation and that represents a target gene. The similar genes carry missing values as a linear combination of similar gene and are selected by k-nearest neighbors that have large absolute values of PCC.
Allows the user to convert id lists in order to unify heterogeneous gene sets. MADGene is a software environment comprising a web-based database and a java application. This database creates bridges across 13 different identifier types: Unigene, GenBank, RefSeq, clone ids, official gene symbols, aliases, EntrezGene, probe identifiers (Affymetrix, Agilent, Illumina), UNIPROT, Ensembl (genes and transcripts).
Enables a range of flexible query mechanisms for Allen Brain Atlas (ABA). ALLENMINER allows users to define custom regions of interest, search for genes that are graded or patterned in regions of interest, and view 3D ABA data on platforms where the BrainExplorer is not available. It can serve for identification of genes or combinations of genes that express in a specific region or cell type of the mouse brain.
Retrieves data from different microarray databases and combines microarray data with breast cancer image analysis and clinical data for correlation studies. Microsoap consists of an information framework based on Microarray and Gene Expression Markup Language (MAGE-ML). The software can assist in improving the transition of microarray data from basic biological research to clinical applications.
Analyzes microarray experiments. FSPMA doesn’t require computer programming because the entire process is controlled by a definition file that defines all steps to produce analysis results from microarray and is based on a mixed model ANOVA library. This software permits users to make normalization based on RNAs of known concentration where the spiked into the RNA samples where the spike residual log ration is utilized to normalize the data. By providing pre-defined definition files, it allows to perform analysis of balanced reference designs.
Proposes a method for the determination of edging genes (EGs) for different classes. The algorithm aims to assists researchers in discovery of EG sets to improve the classification of normal and disease tissues. The application also allows users to detect those which are biologically co-regulated or repressed by some siRNA or miRNA genes. It was tested on five microarray datasets.
Makes accessible the contents of maxdLoad2 database. maxdBrowse is a program available through the web-browser, the command-line and the web-service environments. It allows users not familiar with maxdLoad2 to browse and export microarray data from the database for their own analysis. This web interface also permits users to review annotated experiments in a simple manner at any time, without needing maxdLoad2.