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Integrated multi-level quality control for proteomic profiling studies using mass spectrometry.

Cairns DA, Perkins DN, Stanley AJ, Thompson D, Barrett JH, Selby PJ, Banks RE - BMC Bioinformatics (2008)

Bottom Line: Manual inspection of those spectral data that were identified as being of poor quality confirmed the efficacy of the algorithms.Variance components analysis demonstrated the relatively small amount of technical variance attributable to day of profile generation and experimental array.The removal of these spectra at the initial stages of the analysis substantially improves the confidence of putative biomarker identification and allows inter-experimental comparisons to be carried out with greater confidence.

View Article: PubMed Central - HTML - PubMed

Affiliation: Cancer Research UK Clinical Centre, Leeds Institute of Molecular Medicine, St James's University Hospital, Leeds, UK. d.a.cairns@leeds.ac.uk

ABSTRACT

Background: Proteomic profiling using mass spectrometry (MS) is one of the most promising methods for the analysis of complex biological samples such as urine, serum and tissue for biomarker discovery. Such experiments are often conducted using MALDI-TOF (matrix-assisted laser desorption/ionisation time-of-flight) and SELDI-TOF (surface-enhanced laser desorption/ionisation time-of-flight) MS. Using such profiling methods it is possible to identify changes in protein expression that differentiate disease states and individual proteins or patterns that may be useful as potential biomarkers. However, the incorporation of quality control (QC) processes that allow the identification of low quality spectra reliably and hence allow the removal of such data before further analysis is often overlooked. In this paper we describe rigorous methods for the assessment of quality of spectral data. These procedures are presented in a user-friendly, web-based program. The data obtained post-QC is then examined using variance components analysis to quantify the amount of variance due to some of the factors in the experimental design.

Results: Using data from a SELDI profiling study of serum from patients with different levels of renal function, we show how the algorithms described in this paper may be used to detect systematic variability within and between sample replicates, pooled samples and SELDI chips and spots. Manual inspection of those spectral data that were identified as being of poor quality confirmed the efficacy of the algorithms. Variance components analysis demonstrated the relatively small amount of technical variance attributable to day of profile generation and experimental array.

Conclusion: Using the techniques described in this paper it is possible to reliably detect poor quality data within proteomic profiling experiments undertaken by MS. The removal of these spectra at the initial stages of the analysis substantially improves the confidence of putative biomarker identification and allows inter-experimental comparisons to be carried out with greater confidence.

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Related in: MedlinePlus

Demonstration of absolute difference in technical replicate peaks empirical significance methods for 16 conveniently chosen peaks in the 2–4 kDa mass segment. Solid black histograms show the reference distribution in black with the actual differences indicated by the red hatched histogram bars. Values greater than the critical value (indicated by red dotted line from the abscissa) result in a significant difference in a pair of technical replicates being declared. The percentage of peaks declared significantly different out of the total number of peaks in that mass segment is shown in the fifth column of each mass segment in Table 1.
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Figure 3: Demonstration of absolute difference in technical replicate peaks empirical significance methods for 16 conveniently chosen peaks in the 2–4 kDa mass segment. Solid black histograms show the reference distribution in black with the actual differences indicated by the red hatched histogram bars. Values greater than the critical value (indicated by red dotted line from the abscissa) result in a significant difference in a pair of technical replicates being declared. The percentage of peaks declared significantly different out of the total number of peaks in that mass segment is shown in the fifth column of each mass segment in Table 1.

Mentions: The figures in the 5th column for each mass segment in Table 1 show the percentage of common peaks in that region for which the absolute difference between the duplicate pair is significantly different from zero. This is also based on empirical significance tests derived from the reference set of QC spectra. Figure 3 shows histograms similar to Figure 2 for 16 conveniently chosen peaks from the 2–4 kDa region. As for the other QC variables, a value greater than the critical value means declaring a significant absolute difference between duplicate technical replicates for a particular peak. To summarize these results, the percentage of peaks declared significantly different out of the total number of peaks in that mass segment is shown in each fifth column in Table 1.


Integrated multi-level quality control for proteomic profiling studies using mass spectrometry.

Cairns DA, Perkins DN, Stanley AJ, Thompson D, Barrett JH, Selby PJ, Banks RE - BMC Bioinformatics (2008)

Demonstration of absolute difference in technical replicate peaks empirical significance methods for 16 conveniently chosen peaks in the 2–4 kDa mass segment. Solid black histograms show the reference distribution in black with the actual differences indicated by the red hatched histogram bars. Values greater than the critical value (indicated by red dotted line from the abscissa) result in a significant difference in a pair of technical replicates being declared. The percentage of peaks declared significantly different out of the total number of peaks in that mass segment is shown in the fifth column of each mass segment in Table 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2657802&req=5

Figure 3: Demonstration of absolute difference in technical replicate peaks empirical significance methods for 16 conveniently chosen peaks in the 2–4 kDa mass segment. Solid black histograms show the reference distribution in black with the actual differences indicated by the red hatched histogram bars. Values greater than the critical value (indicated by red dotted line from the abscissa) result in a significant difference in a pair of technical replicates being declared. The percentage of peaks declared significantly different out of the total number of peaks in that mass segment is shown in the fifth column of each mass segment in Table 1.
Mentions: The figures in the 5th column for each mass segment in Table 1 show the percentage of common peaks in that region for which the absolute difference between the duplicate pair is significantly different from zero. This is also based on empirical significance tests derived from the reference set of QC spectra. Figure 3 shows histograms similar to Figure 2 for 16 conveniently chosen peaks from the 2–4 kDa region. As for the other QC variables, a value greater than the critical value means declaring a significant absolute difference between duplicate technical replicates for a particular peak. To summarize these results, the percentage of peaks declared significantly different out of the total number of peaks in that mass segment is shown in each fifth column in Table 1.

Bottom Line: Manual inspection of those spectral data that were identified as being of poor quality confirmed the efficacy of the algorithms.Variance components analysis demonstrated the relatively small amount of technical variance attributable to day of profile generation and experimental array.The removal of these spectra at the initial stages of the analysis substantially improves the confidence of putative biomarker identification and allows inter-experimental comparisons to be carried out with greater confidence.

View Article: PubMed Central - HTML - PubMed

Affiliation: Cancer Research UK Clinical Centre, Leeds Institute of Molecular Medicine, St James's University Hospital, Leeds, UK. d.a.cairns@leeds.ac.uk

ABSTRACT

Background: Proteomic profiling using mass spectrometry (MS) is one of the most promising methods for the analysis of complex biological samples such as urine, serum and tissue for biomarker discovery. Such experiments are often conducted using MALDI-TOF (matrix-assisted laser desorption/ionisation time-of-flight) and SELDI-TOF (surface-enhanced laser desorption/ionisation time-of-flight) MS. Using such profiling methods it is possible to identify changes in protein expression that differentiate disease states and individual proteins or patterns that may be useful as potential biomarkers. However, the incorporation of quality control (QC) processes that allow the identification of low quality spectra reliably and hence allow the removal of such data before further analysis is often overlooked. In this paper we describe rigorous methods for the assessment of quality of spectral data. These procedures are presented in a user-friendly, web-based program. The data obtained post-QC is then examined using variance components analysis to quantify the amount of variance due to some of the factors in the experimental design.

Results: Using data from a SELDI profiling study of serum from patients with different levels of renal function, we show how the algorithms described in this paper may be used to detect systematic variability within and between sample replicates, pooled samples and SELDI chips and spots. Manual inspection of those spectral data that were identified as being of poor quality confirmed the efficacy of the algorithms. Variance components analysis demonstrated the relatively small amount of technical variance attributable to day of profile generation and experimental array.

Conclusion: Using the techniques described in this paper it is possible to reliably detect poor quality data within proteomic profiling experiments undertaken by MS. The removal of these spectra at the initial stages of the analysis substantially improves the confidence of putative biomarker identification and allows inter-experimental comparisons to be carried out with greater confidence.

Show MeSH
Related in: MedlinePlus