Evidence map›Paper›PMID 27605336›Full record

ReviewMicroarrays (Basel, Switzerland)2012

Data Analysis Strategies for Protein Microarrays.

Paula Díez, Noelia Dasilva, María González-González, Sergio Matarraz, Juan Casado-Vela, Alberto Orfao, Manuel Fuentes

Abstract readReview
In one paragraph

Review in Microarrays (Basel, Switzerland), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Immunoproteomics: The Evolution from Classical Methods to Emerging Techniques.Methods in molecular biology (Clifton, N.J.) · 2026
    Review
  2. Article
  3. Article
  4. Unravelling Immunogenic Cell Death Mechanisms by Functional Proteomics Arrays.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Human Mesenchymal Stem Cell Secretome Exhibits a Neuroprotective Effect overMolecular therapy. Methods & clinical development · 2020
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Paula DíezCentro de Investigación del Cáncer/IBMCC (USAL/CSIC), IBSAL, Departamento de Medicina and Servicio General de Citometría, University of Salamanca, Salamanca 37007, Spain. pauladg@usal.es.
Noelia DasilvaCentro de Investigación del Cáncer/IBMCC (USAL/CSIC), IBSAL, Departamento de Medicina and Servicio General de Citometría, University of Salamanca, Salamanca 37007, Spain. noeliadf@usal.es.
María González-GonzálezCentro de Investigación del Cáncer/IBMCC (USAL/CSIC), IBSAL, Departamento de Medicina and Servicio General de Citometría, University of Salamanca, Salamanca 37007, Spain. mariagg@usal.es.
Sergio MatarrazCentro de Investigación del Cáncer/IBMCC (USAL/CSIC), IBSAL, Departamento de Medicina and Servicio General de Citometría, University of Salamanca, Salamanca 37007, Spain. smats@usal.es.
Juan Casado-VelaTranslational Oncology Unit, Instituto de Investigaciones Biomédicas 'Alberto Sols', Spanish National Research Council (CSIC-UAM), 28029 Madrid, Spain. jcasado@iib.uam.es.
Alberto OrfaoCentro de Investigación del Cáncer/IBMCC (USAL/CSIC), IBSAL, Departamento de Medicina and Servicio General de Citometría, University of Salamanca, Salamanca 37007, Spain. orfao@usal.es.
Manuel FuentesCentro de Investigación del Cáncer/IBMCC (USAL/CSIC), IBSAL, Departamento de Medicina and Servicio General de Citometría, University of Salamanca, Salamanca 37007, Spain. mfuentes@usal.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microarrays constitute a new platform which allows the discovery and characterization of proteins. According to different features, such as content, surface or detection system, there are many types of protein microarrays which can be applied for the identification of disease biomarkers and the characterization of protein expression patterns. However, the analysis and interpretation of the amount of information generated by microarrays remain a challenge. Further data analysis strategies are essential to obtain representative and reproducible results. Therefore, the experimental design is key, since the number of samples and dyes, among others aspects, would define the appropriate analysis method to be used. In this sense, several algorithms have been proposed so far to overcome analytical difficulties derived from fluorescence overlapping and/or background noise. Each kind of microarray is developed to fulfill a specific purpose. Therefore, the selection of appropriate analytical and data analysis strategies is crucial to achieve successful biological conclusions. In the present review, we focus on current algorithms and main strategies for data interpretation.

Indexed as

algorithmbackground correctionbiomarkerfluorescence intensitymicroarraynormalizationproteome

Identifiers

PMID27605336
PMCPMC5003438

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.