Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

arrayMagic: two-colour cDNA microarray quality control and preprocessing.

Andreas Buness1, Wolfgang Huber, Klaus Steiner

  • 1Department of Molecular Genome Analysis, German Cancer Research Center INF 580, Heidelberg, 69120, Germany. a.buness@dkfz.de

Bioinformatics (Oxford, England)
|September 30, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Towards clinically interpretable machine learning in emergency surgery: feature importance and insights across clinical time points in abdominal pain cases.

Langenbeck's archives of surgery·2026
Same author

Multimodal plasma and urinary cell-free DNA profiling improves risk stratification in newly diagnosed prostate cancer.

NPJ precision oncology·2026
Same author

Differentiating Main-Duct IPMN from Chronic Pancreatitis Using Next-Generation Sequencing of Main Pancreatic Duct Fluid: A Pilot Study.

Diagnostics (Basel, Switzerland)·2025
Same author

Digital spatial profiling identifies phospho-JNK as a biomarker for early risk stratification of aggressive prostate cancer.

Frontiers in oncology·2025
Same author

Cancer-associated fibroblasts promote drug resistance in ALK-driven lung adenocarcinoma cells by upregulating lipid biosynthesis.

Cancer & metabolism·2025
Same author

Coupling Immunoprecipitation with Multiplexed Digital PCR for Cell-Free DNA Methylation Detection in Small Plasma Volumes of Early-Onset Colorectal Cancer.

Analytical chemistry·2025
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
See all related articles

arrayMagic is a software package designed for quality control and preprocessing of two-colour cDNA microarray data. It offers an automated pipeline for efficient, high-throughput analysis, ensuring quality-assured data for downstream research.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Two-colour cDNA microarray technology is widely used for gene expression profiling.
  • Quality control and preprocessing are critical steps for reliable microarray data analysis.
  • Existing software solutions may lack comprehensive features or user-friendliness.

Purpose of the Study:

  • To introduce arrayMagic, an R package for automated quality control and preprocessing of two-colour cDNA microarray data.
  • To provide a flexible and reproducible analysis pipeline for high-throughput microarray experiments.
  • To ensure the generation of quality-assured and preprocessed data suitable for advanced analyses.

Main Methods:

  • Development of an automated analysis pipeline within the R environment.

Related Experiment Videos

  • Implementation of key steps including data import, normalization, replica merging, and quality diagnostics.
  • Utilisation of a script-based approach for reproducibility and flexibility.

    • Main Results:

    • arrayMagic offers a comprehensive solution for microarray data preprocessing.
    • The automated pipeline streamlines the workflow from raw data to analysis-ready output.
    • The software ensures high-throughput processing with robust quality control measures.

    Conclusions:

    • arrayMagic provides a valuable tool for researchers working with two-colour cDNA microarray data.
    • The package enhances data quality and facilitates efficient downstream analysis.
    • Its script-based nature promotes reproducibility in high-throughput genomic studies.