ArticleBriefings in bioinformatics2014
Reproducible probe-level analysis of the Affymetrix Exon 1.0 ST array with R/Bioconductor.
Article in Briefings in bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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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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.
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Who cites it
7 citing papers in PubMed.
- Neuronal activity regulates alternative exon usage.Molecular brain · 2020Article
- Alternative transcription of a shorter, non-anti-angiogenic thrombospondin-2 variant in cancer-associated blood vessels.Oncogene · 2018Article
- Splice variants as novel targets in pancreatic ductal adenocarcinoma.Scientific reports · 2017Article
- A random effects model for the identification of differential splicing (REIDS) using exon and HTA arrays.BMC bioinformatics · 2017Article
- iGEMS: an integrated model for identification of alternative exon usage events.Nucleic acids research · 2016Article
- MYCN controls an alternative RNA splicing program in high-risk metastatic neuroblastoma.Cancer letters · 2016Article
- Algorithms for differential splicing detection using exon arrays: a comparative assessment.BMC genomics · 2015Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The presence of different transcripts of a gene across samples can be analysed by whole-transcriptome microarrays. Reproducing results from published microarray data represents a challenge owing to the vast amounts of data and the large variety of preprocessing and filtering steps used before the actual analysis is carried out. To guarantee a firm basis for methodological development where results with new methods are compared with previous results, it is crucial to ensure that all analyses are completely reproducible for other researchers. We here give a detailed workflow on how to perform reproducible analysis of the GeneChip®Human Exon 1.0 ST Array at probe and probeset level solely in R/Bioconductor, choosing packages based on their simplicity of use. To exemplify the use of the proposed workflow, we analyse differential splicing and differential gene expression in a publicly available dataset using various statistical methods. We believe this study will provide other researchers with an easy way of accessing gene expression data at different annotation levels and with the sufficient details needed for developing their own tools for reproducible analysis of the GeneChip®Human Exon 1.0 ST Array.
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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.