ArticleProteome science2007
A method for improving SELDI-TOF mass spectrometry data quality.
Article in Proteome science, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Benchmarking deep learning architectures for MALDI-TOF mass spectrometry in infectious disease diagnostics and vector-borne disease surveillance.NPJ digital medicine · 2026Article
- Towards in vivo estimation of reaction kinetics using high-throughput metabolomics data: a maximum likelihood approach.BMC systems biology · 2015Article
- Investigation of serum protein profiles in scrapie infected sheep by means of SELDI-TOF-MS and multivariate data analysis.BMC research notes · 2013Article
- Alteration of the serum N-glycome of mice locally exposed to high doses of ionizing radiation.Molecular & cellular proteomics : MCP · 2013Article
- Mass spectrometry protein expression profiles in colorectal cancer tissue associated with clinico-pathological features of disease.BMC cancer · 2010Article
- Imaging mass spectrometry of gastric carcinoma in formalin-fixed paraffin-embedded tissue microarray.Cancer science · 2010Article
- Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments.BioData mining · 2009Article
- Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data.Bioinformatics (Oxford, England) · 2008Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSurface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a powerful tool for rapidly generating high-throughput protein profiles from a large number of samples. However, the events that occur between the first and last sample run are likely to introduce technical variation in the results.
methodsWe fractionated and analyzed quality control and investigational serum samples on 3 Protein Chips and used statistical methods to identify poor-quality spectra and to identify and reduce technical variation.
resultsUsing diagnostic plots, we were able to visually depict all spectra and to identify and remove those that were of poor quality. We detected a technical variation associated with when the samples were run (referred to as batch effect) and corrected for this variation using analysis of variance. These corrections increased the number of peaks that were reproducibly detected.
conclusionBy removing poor-quality, outlier spectra, we were able to increase peak detection, and by reducing the variance introduced when samples are processed and analyzed in batches, we were able to increase the reproducibility of peak detection.
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Registered trials
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.