Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Impact of experimental noise and annotation imprecision on data quality in microarray experiments.

Methods in molecular biology (Clifton, N.J.) - 1 Jan 2013

Scherer Andreas, Dai Manhong, Meng Fan

Abstract excerpt

Data quality is intrinsically influenced by design, technical, and analytical parameters. Quality parameters have not yet been well defined for gene expression analysis by microarrays, though ad interim, following recommended good experimental practice guidelines should ensure generation of reliable and reproducible data. Here we summarize essential practical recommendations for experimental design, technical...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Data Interpretation, Statistical
Share to DNA Probes
Share to Molecular Sequence Annotation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Observer Variation

Share to Quality Control

Share to Reference Standards

Share to Research Design

Share to Specimen Handling

Source

Identifiers

PMID: 23385537
DOI: 10.1007/978-1-60327-337-4_10

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Impact of experimental noise and annotation imprecision on data quality in microarray experiments.PMID 23385537Read ↗

There is no silver bullet--a guide to low-level data transforms and normalisation methods for microarray data.Semantic proximity Read ↗

Probabilistic analysis of probe reliability in differential gene expression studies with short oligonucleotide arrays.Semantic proximity Read ↗

Statistical considerations for analysis of microarray experiments.Semantic proximity Read ↗

Microarray data quality analysis: lessons from the AFGC project. Arabidopsis Functional Genomics Consortium.Semantic proximity Read ↗

A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data.Semantic proximity Read ↗

Microarray-based expression profiling and informatics.Semantic proximity Read ↗

Trends in microarray analysis.Semantic proximity Read ↗

Batch effect removal methods for microarray gene expression data integration: a survey.Semantic proximity Read ↗

Beyond library size: a field guide to NGS normalizationSemantic proximity Read ↗

Re-Annotator: Annotation Pipeline for MicroarraysSemantic proximity Read ↗

Microarray results: how accurate are they?Semantic proximity Read ↗

Assessing sources of variability in microarray gene expression data.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.