Phinney varibility workshop

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This document discusses measuring and controlling variability in a core facility environment. It notes that increased variability decreases statistical power to detect real effects. Common sources of variability include biological differences, sample preparation inconsistencies, and technical issues. The document asks how best to communicate these issues to customers given limited funding to directly measure variability. It discusses challenges like analyzing samples over long periods when preparation methods may differ, having no control over initial sample collection, and potential sources of variability like pipetting errors. Finally, it provides an example method using pooled and individual samples to empirically measure variability introduced through sample processing.

Controlling or at least
measuring
Variability In a core facility environment

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

Common sources of
variability
• Biological
• Sample preparation
• Technical
• Data Analysis

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

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?

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

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

Empirical Nulls
• Are empirical Null’s a good way to measure
variability?

$Is peptide or protein fractionation worth it? • Does the fractionation kill your power? • Is it better not to fractionate ? • What is the least variable fractionation method for proteomics? • How do you measure the variability your fractionation causes?$

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

KCAS' flow cytometry team supports assay development, validation, and sample processing for preclinical and clinical studies. They helped a client design and perform a pharmacodynamic assay to measure T cell, B cell, and NK cell counts in rats treated with an antibody therapeutic within 4 weeks. KCAS recommended using CD45 lymphocyte gating and BD Trucount tubes for absolute counts. Results showed preserved samples had less than 20% variability in cell counts and percentages compared to unpreserved samples over 5 days. KCAS successfully completed the study in under 4 weeks as requested by the client.

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Sandeep kumarsksurya

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Obsity9993664147

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Pharmacokinetic variability exists both within and between individuals. The dose required to produce a therapeutic effect varies widely between patients due to factors like age, sex, weight, and drug interactions. Obesity affects the pharmacokinetic parameters of volume of distribution and clearance for some drugs. For highly lipophilic drugs, the volume of distribution is often increased in obese patients, while metabolism may be decreased. In neonates, drug metabolism pathways are generally less developed compared to adults, resulting in lower clearance and longer half-lives for many drugs. Gender can also influence pharmacokinetics due to differences in factors like muscle mass, vascular resistance and metabolic rates between males and females.

Pharmacokinetic variability9993664147

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Pharmacokinetic variability occurs due to differences in drug absorption, distribution, metabolism, and excretion between individuals. Several factors influence this variability including age, body weight, pregnancy, and disease states. Drug metabolism and excretion are often lower in newborns compared to adults due to immature organ systems and lower enzyme levels. Plasma protein binding is also lower in newborns which increases drug distribution. Renal function is reduced in newborns leading to slower drug clearance. These developmental differences can increase the risk of adverse drug reactions in newborns if dosages are not appropriately adjusted.

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This document discusses ocular drug delivery systems. It begins with an overview of eye anatomy and then introduces various ocular drug delivery formulations including solutions, suspensions, ointments, emulsions, and gels. It describes the advantages of controlled delivery systems for ocular drugs in increasing bioavailability and residence time. Various controlled delivery technologies are classified and evaluated, with examples like inserts, shields, and iontophoresis. Emerging areas like carbon nanotubes, pseudolatices, and vesicular systems are presented. The document concludes that controlled delivery can improve treatment effectiveness but that devices need further development for patient comfort.

ocular drug delivery vsrujanav

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The document discusses ocular drug delivery systems. It begins with an introduction to eye anatomy and factors affecting drug absorption in the eye. It then describes various ophthalmic formulations like solutions, suspensions, and ointments. It discusses advances in controlled release ocular systems including inserts, contact lenses, and nanoparticles to prolong drug release. Finally, it outlines new approaches in ocular drug delivery research focusing on combining technologies for targeted and sustained drug delivery to the eye.

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Sampling designPRIYAN SAKTHI

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Non-probability sampling methods do not allow researchers to determine sampling errors because selections are non-random. There are several types of non-probability sampling including convenience sampling, purposive sampling, quota sampling, and snowball sampling. When determining sample size, researchers must consider the study goals, desired precision, confidence level, population variability, and anticipated response rate to obtain a sample that accurately represents the target population.

Prosit google-cloudUC Davis

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1) The document describes how to set up a Google Cloud virtual machine to use Prosit, a tool for peptide MS/MS and retention time prediction. It provides step-by-step instructions for installing the necessary software, downloading pre-trained Prosit models, and running examples. 2) Setup is estimated to take around 20 minutes. The document recommends using the cheapest GPU option (Tesla P100) as Prosit does not heavily utilize the GPU during prediction. At least 8 CPU cores and 100GB RAM are suggested. 3) Benchmarking showed that 100,000 peptides can be predicted in 10 minutes on a Tesla P100 VM, while 1 million peptides would take around 100 minutes.

Phinney 2019 ASMS Proteome software Users group TalkUC Davis

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The document summarizes different methods for optimizing data-independent acquisition (DIA) workflows in a proteomics core facility. It compares the 2x green fluorescent protein (GFP) method to using a narrow chromatogram library and wide experimental window, finding the 2x GFP method works well for 2-5 samples. An example experiment profiling several proteins across conditions is described. Different permutations are tested on a subset, comparing public libraries to in-house libraries for building chromatogram libraries. The 2x GFP method using public libraries works best for depth and missing values. Challenges with ScaffoldDIA software are noted. In conclusion, DIA provides better coverage than data-dependent acquisition, especially for human samples, and can work

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Phinney varibility workshop

1. Controlling or at least measuring Variability In a core facility environment

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. Common sources of variability • Biological • Sample preparation • Technical • Data Analysis

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. 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. 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. 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. Empirical Nulls • Are empirical Null’s a good way to measure variability?

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. 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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Phinney varibility workshop

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