Dataset Information

Reproducibility of SELDI Spectra Across Time and Laboratories.

ABSTRACT: This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited.The reproducibility of mass spectrometry (MS) data collected using surface enhanced laser desorption/ionization-time of flight (SELDI-TOF) has been questioned. This investigation was designed to test the reproducibility of SELDI data collected over time by multiple users and instruments. Five laboratories prepared arrays once every week for six weeks. Spectra were collected on separate instruments in the individual laboratories. Additionally, all of the arrays produced each week were rescanned on a single instrument in one laboratory. Lab-to-lab and array-to-array variability in alignment parameters were larger than the variability attributable to running samples during different weeks. The coefficient of variance (CV) in spectrum intensity ranged from 25% at baseline, to 80% in the matrix noise region, to about 50% during the exponential drop from the maximum matrix noise. Before normalization, the median CV of the peak heights was 72% and reduced to about 20% after normalization. Additionally, for the spectra from a common instrument, the CV ranged from 5% at baseline, to 50% in the matrix noise region, to 20% during the drop from the maximum matrix noise. Normalization reduced the variability in peak heights to about 18%. With proper processing methods, SELDI instruments produce spectra containing large numbers of reproducibly located peaks, with consistent heights.

SUBMITTER: Diao L

PROVIDER: S-EPMC3085423 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Reproducibility of SELDI Spectra Across Time and Laboratories.

Diao Lixia L Clarke Charlotte H CH Coombes Kevin R KR Hamilton Stanley R SR Roth Jack J Mao Li L Czerniak Bogdan B Baggerly Keith A KA Morris Jeffrey S JS Fung Eric T ET Bast Robert C RC

Cancer informatics 20110314

This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited.The reproducibility of mass spectrometry (MS) data collected using surface enhanced laser desorption/ionization-time of flight (SELDI-TOF) has been questioned. This investigation was designed to test the reproducibility of SELDI data collected over time by multiple users and instruments. Five laboratories prepared arrays once every week for six weeks. Spectra were collected o ...[more]

PMID: 21552492

Similar Datasets

Project description:Acute myocardial infarct (AMI) size depicted by late gadolinium enhancement cardiovascular magnetic resonance (CMR) is increasingly used as an efficacy endpoint in randomized trials comparing AMI therapies. Infarct size is quantified using manual planimetry (MANUAL), visual scoring (VISUAL), or automated techniques using signal-intensity thresholding (AUTO). Although AUTO is considered the most reproducible, prior studies did not account for the subjective determination of endocardial/epicardial borders, which all methods require. For MANUAL and VISUAL, prior studies did not address how to treat intermediate signal intensities due to partial volume.To assess sources of variability, AMI size was measured in 30 patients and 12 controls by 3 core-laboratories using 8 methods, each separated by more than 2 months time (n = 720 evaluations). The methods were: (1,2) AUTOSegment, AUTOFWHM (using Segment software or the full-width-at-half-maximum algorithm, respectively); (3,4) AUTO-UCSegment, AUTO-UCFWHM (user correction for endocardial border pixels, no-reflow, etc.); (5) MANUAL; (6) MANUAL-ISI (adjustment for intermediate signal-intensities); (7) VISUAL; (8) VISUAL-ISI.Mean infarct size varied between 16.8% and 27.2% of LV mass depending on method. Even automated techniques with no user interaction for infarct borders resulted in significant within-patient variability given the need to subjectively trace endocardial/epicardial contours. The coefficient-of-variation (CV) was 10.6% and 14.6% for AUTOSegment and AUTOFWHM, respectively. For manual and visual categories, reproducibility was improved when intermediate signal-intensities were considered (MANUAL-ISI vs MANUAL: CV = 8.3% vs 14.4%; p = 0.03; VISUAL-ISI vs VISUAL: CV = 8.4% vs 10.9%; p = 0.01). For AUTO-UCSegment, MANUAL-ISI, and VISUAL-ISI (best technique in each category) within-patient variability due to the quantification method was less than 10% of total variability, and the required sample sizes for detecting a 5% absolute difference in infarct size were 62, 63, and 62 patients, respectively.Among CMR core-laboratories, an important source of variability in infarct size quantification is the subjective delineation of endocardial/epicardial borders. When intermediate signal intensities are considered in manual planimetry and visual scoring, reproducibility and impact on sample size are similar to automated techniques.

Project description:BackgroundMass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result.ResultsIn this paper, we describe the first systematic comparison of a wide range of normalisation methods, using two objectives that should be met by a good method. These objectives are minimisation of inter-spectra variance and maximisation of signal with respect to class separation. The former is assessed using an estimation of the coefficient of variation, the latter using the classification performance of three types of classifiers on real-world datasets representing two-class diagnostic problems. To obtain a maximally robust evaluation of a normalisation method, both objectives are evaluated over multiple datasets and multiple configurations of baseline correction and peak detection methods. Results are assessed for statistical significance and visualised to reveal the performance of each normalisation method, in particular with respect to using no normalisation. The normalisation methods described have been implemented in the freely available MASDA R-package.ConclusionIn the general case, normalisation of mass spectra is beneficial to the quality of data. The majority of methods we compared performed significantly better than the case in which no normalisation was used. We have shown that normalisation methods that scale spectra by a factor based on the dispersion (e.g., standard deviation) of the data clearly outperform those where a factor based on the central location (e.g., mean) is used. Additional improvements in performance are obtained when these factors are estimated locally, using a sliding window within spectra, instead of globally, over full spectra. The underperforming category of methods using a globally estimated factor based on the central location of the data includes the method used by the majority of SELDI users.

Dataset Information

Reproducibility of SELDI Spectra Across Time and Laboratories.

Publications

Reproducibility of SELDI Spectra Across Time and Laboratories.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets