ArticleBriefings in bioinformatics2011
Exon array data analysis using Affymetrix power tools and R statistical software.
Article in Briefings in bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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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
66 citing papers in PubMed.
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- The R Language: An Engine for Bioinformatics and Data Science.Life (Basel, Switzerland) · 2022Review
- Web tools to perform long non-coding RNAs analysis in oncology research.Database : the journal of biological databases and curation · 2021Review
- Comparative analysis of 1152 African-American and European-American men with prostate cancer identifies distinct genomic and immunological differences.Communications biology · 2021Article
- Anti-S100A4 Antibody Therapy Is Efficient in Treating Aggressive Prostate Cancer and Reversing Immunosuppression: Serum and BiopsyMolecular cancer therapeutics · 2020Article
- Identifying and treating ROBO1The Prostate · 2020Article
- Validation of a genomic classifier for prediction of metastasis and prostate cancer-specific mortality in African-American men following radical prostatectomy in an equal access healthcare setting.Prostate cancer and prostatic diseases · 2020Article
- Genetic Architecture of Gene Expression in European and African Americans: An eQTL Mapping Study in GENOA.American journal of human genetics · 2020Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
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1 author.
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Abstract
The use of microarray technology to measure gene expression on a genome-wide scale has been well established for more than a decade. Methods to process and analyse the vast quantity of expression data generated by a typical microarray experiment are similarly well-established. The Affymetrix Exon 1.0 ST array is a relatively new type of array, which has the capability to assess expression at the individual exon level. This allows a more comprehensive analysis of the transcriptome, and in particular enables the study of alternative splicing, a gene regulation mechanism important in both normal conditions and in diseases. Some aspects of exon array data analysis are shared with those for standard gene expression data but others present new challenges that have required development of novel tools. Here, I will introduce the exon array and present a detailed example tutorial for analysis of data generated using this platform.
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