Limits...
Characterising phase variations in MALDI-TOF data and correcting them by peak alignment.

Lin SM, Haney RP, Campa MJ, Fitzgerald MC, Patz EF - Cancer Inform (2005)

Bottom Line: With the help of principal component analysis, we demonstrated that after peak alignment, the differences among replicates are reduced.We compared this approach to peak alignment with a model-based calibration approach in which there was known information about peaks in common among all spectra.Finally, we examined the potential value at each point in an analysis pipeline of having a set of methods available that includes parametric, semiparametric and nonparametric methods; among such methods are those that benefit from the use of prior information.

View Article: PubMed Central - PubMed

Affiliation: Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA. S-Lin2@northwestern.edu

ABSTRACT
The use of MALDI-TOF mass spectrometry as a means of analyzing the proteome has been evaluated extensively in recent years. One of the limitations of this technique that has impeded the development of robust data analysis algorithms is the variability in the location of protein ion signals along the x-axis. We studied technical variations of MALDI-TOF measurements in the context of proteomics profiling. By acquiring a benchmark data set with five replicates, we estimated 76% to 85% of the total variance is due to phase variation. We devised a lobster plot, so named because of the resemblance to a lobster claw, to help detect the phase variation in replicates. We also investigated a peak alignment algorithm to remove the phase variation. This operation is analogous to the normalization step in microarray data analysis. Only after this critical step can features of biological interest be clearly revealed. With the help of principal component analysis, we demonstrated that after peak alignment, the differences among replicates are reduced. We compared this approach to peak alignment with a model-based calibration approach in which there was known information about peaks in common among all spectra. Finally, we examined the potential value at each point in an analysis pipeline of having a set of methods available that includes parametric, semiparametric and nonparametric methods; among such methods are those that benefit from the use of prior information.

No MeSH data available.


Five replicates from sample A. Shown are the spectrum (a and b) and corresponding lobster plot (c and d) before (a and c) and after (b and d) time warping.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC2657651&req=5

f2-cin-01-32: Five replicates from sample A. Shown are the spectrum (a and b) and corresponding lobster plot (c and d) before (a and c) and after (b and d) time warping.

Mentions: So far we have discussed the phase variation problem and its solution in a pair of replicates. This methodology can easily be extended to handle multiple replicates. Similar to the extension of microarray normalization beyond pair wise, we can devise three solutions. As a first solution, we can designate one spectrum as the template, and then align the other spectra to this template. As a variation of the first method, we can derive the second method by using the average of all spectra as the template. We used this second solution in this paper. Furthermore, we can implement a more computationally intensive third solution by alternating the averaging and aligning steps until some convergence criterion is met. The lobster plot can also be extended to handle multiple spectra by a pair wise strategy similar to pair wise scatter plots (Figure 2b and Figure 2d).


Characterising phase variations in MALDI-TOF data and correcting them by peak alignment.

Lin SM, Haney RP, Campa MJ, Fitzgerald MC, Patz EF - Cancer Inform (2005)

Five replicates from sample A. Shown are the spectrum (a and b) and corresponding lobster plot (c and d) before (a and c) and after (b and d) time warping.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2657651&req=5

f2-cin-01-32: Five replicates from sample A. Shown are the spectrum (a and b) and corresponding lobster plot (c and d) before (a and c) and after (b and d) time warping.
Mentions: So far we have discussed the phase variation problem and its solution in a pair of replicates. This methodology can easily be extended to handle multiple replicates. Similar to the extension of microarray normalization beyond pair wise, we can devise three solutions. As a first solution, we can designate one spectrum as the template, and then align the other spectra to this template. As a variation of the first method, we can derive the second method by using the average of all spectra as the template. We used this second solution in this paper. Furthermore, we can implement a more computationally intensive third solution by alternating the averaging and aligning steps until some convergence criterion is met. The lobster plot can also be extended to handle multiple spectra by a pair wise strategy similar to pair wise scatter plots (Figure 2b and Figure 2d).

Bottom Line: With the help of principal component analysis, we demonstrated that after peak alignment, the differences among replicates are reduced.We compared this approach to peak alignment with a model-based calibration approach in which there was known information about peaks in common among all spectra.Finally, we examined the potential value at each point in an analysis pipeline of having a set of methods available that includes parametric, semiparametric and nonparametric methods; among such methods are those that benefit from the use of prior information.

View Article: PubMed Central - PubMed

Affiliation: Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA. S-Lin2@northwestern.edu

ABSTRACT
The use of MALDI-TOF mass spectrometry as a means of analyzing the proteome has been evaluated extensively in recent years. One of the limitations of this technique that has impeded the development of robust data analysis algorithms is the variability in the location of protein ion signals along the x-axis. We studied technical variations of MALDI-TOF measurements in the context of proteomics profiling. By acquiring a benchmark data set with five replicates, we estimated 76% to 85% of the total variance is due to phase variation. We devised a lobster plot, so named because of the resemblance to a lobster claw, to help detect the phase variation in replicates. We also investigated a peak alignment algorithm to remove the phase variation. This operation is analogous to the normalization step in microarray data analysis. Only after this critical step can features of biological interest be clearly revealed. With the help of principal component analysis, we demonstrated that after peak alignment, the differences among replicates are reduced. We compared this approach to peak alignment with a model-based calibration approach in which there was known information about peaks in common among all spectra. Finally, we examined the potential value at each point in an analysis pipeline of having a set of methods available that includes parametric, semiparametric and nonparametric methods; among such methods are those that benefit from the use of prior information.

No MeSH data available.