Signal Conditioning and Filtering of SELDI MS Time Series Dariya - PowerPoint PPT Presentation

Sep 24, 2023 •264 likes •426 views

Signal Conditioning and Filtering of SELDI MS Time Series Dariya Malyarenko College of William and Mary & INCOGEN, Inc. new data familiar problems known techniques SELDI Profiling: Sources of Error S ij D S ij 100 m LONG WAY! Signal

Signal Conditioning and Filtering of SELDI MS Time Series Dariya Malyarenko College of William and Mary & INCOGEN, Inc. new data familiar problems known techniques
SELDI Profiling: Sources of Error S ij D S ij 100 µ m LONG WAY!
Signal Conditioning Issues • Peak relevance: biological or artifact? - noise characterization • Peak location: which column entry? S ij - deconvolution filtering • Peak intensity: which value entry? - background removal
Baseline Correction Peak intensity in the data matrix � Baseline is predictable from RC modeling � Single shot or average data
Nonlinear Baseline Peak intensity and biological relevance � Length is proportional to the number of overload points � Single shot correction only
Baseline Ringing 0.2 Peak relevance Frequency Intensity CHIRP 0.1 2000 4000 0.0 Time Time � Damped coherent oscillation after overload � Should be subtracted after each single shot
Smoothing & Rescaling Peak relevance and intensity MH + MH 2+
Rescaling & Noise Peak relevance � Provides constant noise amplitude � Filtered noise is “colored”
Subposition De-Jittering Peak location, intensity, relevance � Subposition de-jittering during acquisition
SELDI Resolution in Time Peak size and location � σ t = const (< 12 kDa): monoisotopic target (Na) can be used in time
Shaping and Spiking Target Filter Peak location, intensity shaping W - 1 denoising W deconvolution
Deconvolution of SELDI TOF Peak location, intensity, relevance MH + +SA -H 2 O TARGET +Na -CO 2 M/z � Na & SA adducts and H 2 O, NH 4 & CO 2 (neutral loss) peaks are detected up to 9 kDa � Adduct peaks are correlated
Spiking Accuracy Peak location and size M/z, Da � Target filter preserves total intensity � Experimental filter detects peak shifts < σ t in simulated data
Conclusions: � Noise characterization and reduction: - baseline removal - detector saturation correction - SNR rescaling � Location calibration: - correlative dejittering � Resolution enhancement; - target filter deconvolution
Acknow ledgements This work was supported by Virginia’s Commonwealth Research Technology Fund #IN2002-03, and Phase I SBIR grant from the National Cancer Institute CA101479 We thank our collaborators and coauthors from EVMS, W&M and INCOGEN for assistance with acquisition and analysis of calibration data for SELDI PBSII instrument We are grateful to Dr. Stacy Moore and Dr. Scott Weinberger from Ciphergen, Inc. for their help in clarifying instrumental specifications and parameters Contact info: filter@incogen.com

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