ArticleBMC research notes2010
Analysing time course microarray data using Bioconductor: a case study using yeast2 Affymetrix arrays.
Article in BMC research notes, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed.
- Exposure to the lampricide TFM elicits an environmental stress response in yeast.FEMS yeast research · 2019Article
- Knockout of the Hmt1p Arginine Methyltransferase inMolecular & cellular proteomics : MCP · 2018Article
- Exposure to the lampricide 3-trifluoromethyl-4-nitrophenol results in increased expression of carbohydrate transporters in Saccharomyces cerevisiae.Environmental toxicology and chemistry · 2016Article
- Histone Sprocket Arginine Residues Are Important for Gene Expression, DNA Repair, and Cell Viability in Saccharomyces cerevisiae.Genetics · 2015Article
- Neuronal calcium signaling pathways are associated with the development of epilepsy.Molecular medicine reports · 2015Article
- Transcriptional response to cardiac injury in the zebrafish: systematic identification of genes with highly concordant activity across in vivo models.BMC genomics · 2014Article
- Interplay of microRNAs, transcription factors and target genes: linking dynamic expression changes to function.Nucleic acids research · 2013Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLarge scale microarray experiments are becoming increasingly routine, particularly those which track a number of different cell lines through time. This time-course information provides valuable insight into the dynamic mechanisms underlying the biological processes being observed. However, proper statistical analysis of time-course data requires the use of more sophisticated tools and complex statistical models.
findingsUsing the open source CRAN and Bioconductor repositories for R, we provide example analysis and protocol which illustrate a variety of methods that can be used to analyse time-course microarray data. In particular, we highlight how to construct appropriate contrasts to detect differentially expressed genes and how to generate plausible pathways from the data. A maintained version of the R commands can be found at http://www.mas.ncl.ac.uk/~ncsg3/microarray/.
conclusionsCRAN and Bioconductor are stable repositories that provide a wide variety of appropriate statistical tools to analyse time course microarray data.
Identifiers
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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.