MDQC: a new quality assessment method for microarrays based on quality control reports | Zendy

Gabriela V. Cohen Freue | Zendy; Zsuzsanna Hollander | Zendy; Enqing Shen | Zendy; Ruben H. Zamar | Zendy; Robert Balshaw | Zendy; Andreas Scherer | Zendy; Bruce M. McManus | Zendy; Paul Keown | Zendy; W. Robert McMaster | Zendy; Raymond T. Ng | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

MDQC: a new quality assessment method for microarrays based on quality control reports

Author(s) -

Gabriela V. Cohen Freue,

Zsuzsanna Hollander,

Enqing Shen,

Ruben H. Zamar,

Robert Balshaw,

Andreas Scherer,

Bruce M. McManus,

Paul Keown,

W. Robert McMaster,

Raymond T. Ng

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm487

Subject(s) - mahalanobis distance , computer science , quality (philosophy) , data mining , bioconductor , outlier , quality assurance , data quality , process (computing) , multivariate statistics , control (management) , quality score , artificial intelligence , metric (unit) , machine learning , engineering , philosophy , biochemistry , chemistry , external quality assessment , operations management , epistemology , gene , operating system

The process of producing microarray data involves multiple steps, some of which may suffer from technical problems and seriously damage the quality of the data. Thus, it is essential to identify those arrays with low quality. This article addresses two questions: (1) how to assess the quality of a microarray dataset using the measures provided in quality control (QC) reports; (2) how to identify possible sources of the quality problems.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research