ArticleBMC bioinformatics2009
Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Comparative Meta-Analysis of Transcriptomics Data during Cellular Senescence and In Vivo Tissue Ageing.Oxidative medicine and cellular longevity · 2015Pooled it
- A comparative genomic study in schizophrenic and in bipolar disorder patients, based on microarray expression profiling meta-analysis.TheScientificWorldJournal · 2013Pooled it
- MiR-92 Controls Synaptic Development Through Glial Vha55 Regulation.Biomolecules · 2025Article
- Identification of Sex-Specific Transcriptome Responses to Polychlorinated Biphenyls (PCBs).Scientific reports · 2019Article
- Systems biology combining human- and animal-data miRNA and mRNA data identifies new targets in ureteropelvic junction obstruction.BMC systems biology · 2017Article
- Article
- Emergent Transcriptomic Technologies and Their Role in the Discovery of Biomarkers of Liver Transplant Tolerance.Frontiers in immunology · 2015Review
- Article
- HuR-regulated mRNAs associated with nuclear hnRNP A1-RNP complexes.International journal of molecular sciences · 2013Article
- Performance in omics analyses of blood samples in long-term storage: opportunities for the exploitation of existing biobanks in environmental health research.Environmental health perspectives · 2013Article
- Application of an integrative computational framework in trancriptomic data of atherosclerotic mice suggests numerous molecular players.Advances in bioinformatics · 2012Article
- Coactivation of GR and NFKB alters the repertoire of their binding sites and target genes.Genome research · 2011Article
- SYSGENET: a meeting report from a new European network for systems genetics.Mammalian genome : official journal of the International Mammalian Genome Society · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods.
resultsWe describe here Gene ARMADA (Automated Robust MicroArray Data Analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form (Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime.
conclusionGene ARMADA provides a highly adaptable, integrative, yet flexible tool which can be used for automated quality control, analysis, annotation and visualization of microarray data, constituting a starting point for further data interpretation and integration with numerous other tools.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.