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Experimental design part 2 measurements

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Part 2 of a 5 part lecture series on experimental design. This section deals with direct and indirect measurements, experimental units, independent and dependent variables, pilot studies.

Text form of some of these points are hosted at www.lantsandlaminins.com

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Experimental design part 2 measurements

  1. 1. Measurements Experimental design: part 2
  2. 2. Right. It is time to decide what data you will actually need to test your hypothesis What will you actually measure? How will the data be collected? Do you need a pilot study? What are the experimental variables? And what controls do you need? Sounds like a lot It can seem that way, but if you work methodically through the questions you will end up with a solid plan
  3. 3. One key point before we start. Think about how the figure will look in the paper once you’re finished %ofpopulation Green Purple w/green A B Its always a good idea to have an idea where you are going Keep this in mind as you move forward
  4. 4. What will you actually measure? Are the measurements direct or indirect? If you can directly measure the outcome: great! Quite often you won’t be able to directly measure what you want. If that is the case, will the indirect measurement still allow you to test your hypothesis? Examples please! Or will they actually provide data that answers a different question?
  5. 5. Hypothesis: miR 162 drives degradation of Pax3 mRNA A direct measurement would be to treat cells with miR 162 and measure Pax3 mRNA degradation rates miR 162 treated miR 162 untreated Pax3 mRNA time Block transcription and acquire RNA samples over time, then work out relative rates of Pax3 mRNA decay
  6. 6. Hypothesis: miR 162 drives degradation of Pax3 mRNA An indirect experiment would be to measure Pax3 mRNA abundance after treatment The measurement would be at a selected time point only miR 162 You would then infer from a reduction in Pax3 mRNA that degradation had occurredAlthough this would be cheaper, quicker and easier. There are other reasons why Pax3 mRNA levels would decrease. E.g. reduced transcription So, you would have to decide if the indirect measurement is good enough for your question
  7. 7. If you were studying a new mouse model of glaucoma where you have increased extracellular matrix production in the drainage channel of the eye A direct measurement would be to kill the mice and examine their eyes by histology – making measurements of matrix build up
  8. 8. An indirect measurement would be to record the intraocular pressure in live animals. Under the assumption that increased matrix material leads to increased IOP Perhaps do both? Measure IOP over time, then do histology at the end of the experiment to validate the indirect approach But, IOP might change for a reason other than a change in matrix build up IOP Drug added Time But by using IOP measurement you can take measurements through time and possibly reduce animal numbers used
  9. 9. Qualitative or quantitative? Simple message: If you can quantify something you should. But, numerical data will indicate how robust your conclusions are Numerical data will therefore be required for any meaningful impact e.g. policy change, to arise from your work Qualitative (descriptive) work can give depth to a data set, can indicate reasons why things happen What will you actually measure?
  10. 10. Descriptive: Cells were either green, or purple with green squiggles You could put numbers to the same observation %ofpopulation Green Purple w/green The graph makes your story stronger. So, if possible, plan a way to quantify Got it
  11. 11. Do you need a pilot study? Simple answer: Probably yes! OK, I’m on it!
  12. 12. That’s not quite what we mean!
  13. 13. Pilot studies are small scale versions of your full study Use them to iron out any methodological issues This could be dilutions, incubation times etc. in wet lab studies Equally this could allow you to refine your questions, identifying ambiguities in survey based studies etc
  14. 14. Pilot studies can also tell you two key pieces of information that you need to determine what sample size you need 1. How big a difference do you expect to observe 2. How much variability will exist within your study populations We’ll come back to discuss how these numbers are used a bit later
  15. 15. Pilot studies also help you test your controls and identify appropriate reference points. We’ll also come back to this shortly
  16. 16. What are the experimental variables? Let’s introduce some of the terms you will see in stats books Oh no! Don’t worry, we’ll be gentle! Let’s move on
  17. 17. Experimental Unit This is the thing that will be studied. E.g. the person, the mouse, the flask of cells. Experimental unit Each experimental unit should be an independent entity. This is an important point so we’ll come back to it later
  18. 18. Let’s talk about variables There are two classes of variable 1. Dependent variables Dependent variables are what you actually measure in your experiment Dependent Variables Heart rate Muscle mass Cancer metastasis Tail Length Cell numbers Skin strength mRNA levels
  19. 19. Dependent Variables Heart rate Muscle mass Cancer metastasis Tail Length Cell numbers Skin strength mRNA levels Independent variables are the things that are set by the experimenter These are the groups that you are going to compare 2. Independent variables Independent Variables
  20. 20. Dependent Variables Heart rate Muscle mass Cancer metastasis Tail Length Cell numbers Skin strength Things like genetic strains or the different treatments used Genetic background Diet Age Drug Treatment Independent Variables Terms you might hear to describe the independent variable are “treatment”, “factor” and “level”
  21. 21. Treatments These are what are done to the experimental unit A treatment can have multiple levels e.g. 5 mg, 10 mg, 20 mg, etc A B C Treatments Experimental unit Factor
  22. 22. Experimental unit Factors are broad sub populations within the experimental units Examples are breaking the experimental units into different ages or genders Factor A B C Treatments
  23. 23. A B If you are doing a study to investigate the effect of a genetic modification. Then the different strains are your independent variables +/+ +/- -/- In you then treat the 3 strains with 2 compounds then a statistician might say your study has 3 factors (the strains) and two levels (two treatments per strain) Factor The treatments are the main study question, the factors are things that are relevant to your interpretation
  24. 24. Example Let’s look at one of our examples Squamous cell carcinoma cells will either be induced to express protein B, a control protein “C”, or not treated then seeded onto either a skin substitute or onto pure collagen. After 48 hours the distance migrated into each substrate will be measured Experiment Hypothesis Squamous cell carcinoma cells induced to overexpress protein B display increased invasion compared with control treated cells. Skin substitute Collagen Normal cells + protein B + protein C Normal cells + protein B + protein C Can you identify: • The Dependent variable • The Treatment • The experimental Unit • And any factors
  25. 25. ExampleSquamous cell carcinoma cells will either be induced to express protein B, a control protein “C”, or not treated then seeded onto either a skin substitute or onto pure collagen. After 48 hours the distance migrated into each substrate will be measured Skin substitute Collagen Normal cells + protein B + protein C Normal cells + protein B + protein C Treatments Experimental unit Factors Untreated +Protein B +Protein C Each block of skin substitute/collagen and associated cells within it Skin substitute or Collagen Dependent variable Distance migrated into substrate What you measure What you care about Sub-groupingsWhere the data comes from
  26. 26. A B C In practical terms, identifying what’s what in terms of your study is the first step toward assigning groups We’ll come back to this point later
  27. 27. A B C Treatment Experimental unit Factor But the more variables you have, the more samples you will need to obtain robust statistics It is always tempting to add every possible variable So, make sure to think carefully and only include variables that are truly required or will add value to the analysis Remember, answering one question well is better than trying to answer too many questions and getting ambiguous data
  28. 28. Part 2 Recap. Plan your measurements based on the hypothesis and plan the figure you think your data will generate Choose direct measurements whenever you can. If you will use indirect make sure what their interpretation will fit the study question Conduct a pilot experiment to work out exactly how the experiment should be done Clearly define what all your experimental variables will be before you go any further
  29. 29. Videos: SamJess DanielleLiam

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