• Home
  • AI Techology
    • eNews
    • Posters
  • Company
    • About Us
    • Board of Directors
    • Career Opportunities
    • Press Releases
    • Privacy Policy
    • Trademarks
  • CONTACT US
Sage-N ResearchSage-N Research
  • Home
  • AI Techology
    • eNews
    • Posters
  • Company
    • About Us
    • Board of Directors
    • Career Opportunities
    • Press Releases
    • Privacy Policy
    • Trademarks
  • CONTACT US

US HUPO 2019

Friday, February 15, 2019 Posted by Admin
Was this article helpful?

Model-Free SILAC Data Analysis is Possible, Reproducible, and Essential

David Chiang; Patrick Chu
Sage-N Research, Milpitas, CA

ABSTRACT:  (View the Poster Here)

Using SILAC as a case study, we aim to solve this paradox: How proteomics — an analytical science with high-accuracy data — can produce irreproducible results.

One factor is that labs rely on complex models (search engines, discriminant scores, posterior probabilities) within often-opaque software to interpret m/z data. Increased complexity generally means narrower applicability. Because mass spectrometry data have wide signal-to-noise, any complex model is prone to fitting problems for some data subset while depriving researchers of data insight.

For SILAC differential quantitation, a conventional workflow uses a statistical formula to calculate a “probability” (actually p-value) to evaluate a peptide ID, then fit a Gaussian area-under-curve model to quantify light and heavy peptides to derive their ratio. It’s simple in concept but tricky in practice.

Experienced users often find that while 90% or more of the protein ratios are generally correct (if imprecise), a small portion (but often the most critical) have essentially random ratios.

Two susceptibilities explain this: (1) Over-fitted p-values randomizes IDs and (2) under-fitted areas can randomize quantitation. A Gaussian fit requires at least one data-point near the maximum. In any large dataset, some small percentage would lack such points to yield random ratios.

Counter to complexity bias, a robust foundation requires direct analysis of raw m/z data with model-free peptide significance and SILAC quantitation.

Specifically, each peptide-sequence match (PSM) can be distilled to (1) number of matched fragments and (2) their average RMS error. For SILAC quantitation, each precursor scan is essentially an independent ratio-sampling experiment whose median approximates the true ratio. We believe this model-free approach is novel and fundamentally solves irreproducibility.

 

Share

Leave a Reply

Send Us Your Thoughts On This Post.
Cancel Reply

Contact Us

Send us an email and we'll get back to you soon.

Send Message

Site Search

Privacy Policy

View Our Data Protection Policy Regarding Use of this Website

© 2025 Sage-N Research, Inc. All Rights Reserved.

Prev Next