ArticleCancer informatics2007
Detecting outlier microarray arrays by correlation and percentage of outliers spots.
Article in Cancer informatics, 2007. 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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Who cites it
7 citing papers in PubMed.
- Integrated pan-cancer analysis revealed therapeutic targets in the ABC transporter protein family.PloS one · 2025Article
- Decoded cardiopoietic cell secretome linkage to heart repair biosignature.Stem cells translational medicine · 2024Article
- AI-Enhanced evaluation of YouTube content on post-surgical incontinence following pelvic cancer treatment.SSM - population health · 2024Article
- Robust principal component analysis for accurate outlier sample detection in RNA-Seq data.BMC bioinformatics · 2020Article
- Advanced heat map and clustering analysis using heatmap3.BioMed research international · 2014Article
- Dynamics of short-term gene expression profiling in liver following thermal injury.The Journal of surgical research · 2012Article
- MOF: an R function to detect outlier microarray.Genomics, proteomics & bioinformatics · 2008Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed a quality assurance (QA) tool, namely microarray outlier filter (MOF), and have applied it to our microarray datasets for the identification of problematic arrays. Our approach is based on the comparison of the arrays using the correlation coefficient and the number of outlier spots generated on each array to reveal outlier arrays. For a human universal reference (HUR) dataset, which is used as a technical control in our standard hybridization procedure, 3 outlier arrays were identified out of 35 experiments. For a human blood dataset, 12 outlier arrays were identified from 185 experiments. In general, arrays from human blood samples displayed greater variation in their gene expression profiles than arrays from HUR samples. As a result, MOF identified two distinct patterns in the occurrence of outlier arrays. These results demonstrate that this methodology is a valuable QA practice to identify questionable microarray data prior to downstream analysis.
Identifiers
19458777PMC2675485What OpenQuestion holds
Registered trials
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