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Phinney varibility workshop

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ASMS 2014 Analytical Core Directors Workshop.

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Phinney varibility workshop

  1. 1. Controlling or at least measuring Variability In a core facility environment
  2. 2. Variability • Increased variability = decreased power • Power = probability of find an effect that is there • You can fight this by increasing the sample size but often it is much cheaper to decrease variability instead
  3. 3. Common sources of variability • Biological • Sample preparation • Technical • Data Analysis
  4. 4. What is possible in a core facility? • Almost no one who sends me samples has enough money to measure variability or wants to pay for it • What are the best ways to communicate these issues to customers? • How do you know what your variability is if there are no resources to measure it? • How do you measure variability when you have a large number of different types experiments? • How much QC do you bundle into your costs if you have to charge people
  5. 5. Some issues I routinely have • Analyzing samples over months at a time…. • Sample preparation of Plant tissue may be completely different than human cells or Plasma, or Milk In terms of how consistently you can prepare it • How do I know how consistently I can prepare a sample • Often I have no control over how the sample is prepared. How do I deal with that?
  6. 6. Common ways to decrease variability during sample prep • Process all samples on the same day by the same person o Person can still get tired or make mistakes…Variability may not be consistent beginning to end o May not be possible • Use Robotics for part of the sample prep o Many things still cannot be done well by robots • In gel digestion of proteins is not optimal • Decrease the things you do to a sample o Fractionation, precipitation, SPE • Label proteins or peptides upstream and multiplex
  7. 7. Common sources of variability you may not be thinking about • Pipetting errors o Can be vary large for small volumes • Eppendorf 2 ul = 12% Systemic 6% random error o Hard to get tight cv’s on your spiked peptides • Variability due to SPE material lots o The SPE material you use today may not be the same the next time you buy it • Variability due to software o manual integration o Normalization
  8. 8. Empirical Nulls • Are empirical Null’s a good way to measure variability?
  9. 9. Is peptide or protein fractionation worth it? • Does the fractionation kill your power? • Is it better not to fractionate ? • What is the least variable fractionation method for proteomics? • How do you measure the variability your fractionation causes?
  10. 10. Example method to measure variability • From Chris Becker (Proteometrics) Pooled human serum 1 2 3 4 5 n Sample aliquots are processed Processed samples are pooled before analysis and replicates are run Processed samples are run individually Sample Processing

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