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Experimental Design

Experimental Design

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Experimental Design

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  1. Experimental Design EPP 245/298 Statistical Analysis of Laboratory Data

  2. Basic Principles of Experimental Investigation • Sequential Experimentation • Comparison • Manipulation • Randomization • Blocking • Simultaneous variation of factors • Main effects and interactions • Sources of variability • Issues with two-color arrays EPP 245 Statistical Analysis of Laboratory Data

  3. Sequential Experimentation • No single experiment is definitive • Each experimental result suggests other experiments • Scientific investigation is iterative. • “No experiment can do everything; every experiment should do something,” George Box. EPP 245 Statistical Analysis of Laboratory Data

  4. EPP 245 Statistical Analysis of Laboratory Data

  5. Comparison • Usually absolute data are meaningless, only comparative data are meaningful • The level of mRNA in a sample of liver cells is not meaningful • The comparison of the mRNA levels in samples from normal and diseased liver cells is meaningful EPP 245 Statistical Analysis of Laboratory Data

  6. Internal vs. External Comparison • Comparison of an experimental results with historical results is likely to mislead • Many factors that can influence results other than the intended treatment • Best to include controls or other comparisons in each experiment EPP 245 Statistical Analysis of Laboratory Data

  7. Manipulation • Different experimental conditions need to be imposed by the experimenters, not just observed, if at all possible • The rate of complications in cardiac artery bypass graft surgery may depend on many factors which are not controlled and may be hard to measure EPP 245 Statistical Analysis of Laboratory Data

  8. EPP 245 Statistical Analysis of Laboratory Data

  9. Randomization • Randomization limits the difference between groups that are due to irrelevant factors • Such differences will still exist, but can be quantified by analyzing the randomization • This is a method of controlling for unknown confounding factors EPP 245 Statistical Analysis of Laboratory Data

  10. Suppose that 50% of a patient population is female • A sample of 100 patients will not generally have exactly 50% females • Numbers of females between 40 and 60 would not be surprising • In two groups of 100, the disparity between the number of females in the two groups can be as big as 20% simply by chance • This also holds for factors we don’t know about EPP 245 Statistical Analysis of Laboratory Data

  11. Randomization does not exactly balance against any specific factor • To do that one should employ blocking • Instead it provides a way of quantifying possible imbalance even of unknown factors • Randomization even provides an automatic method of analysis that depends on the design and randomization technique. EPP 245 Statistical Analysis of Laboratory Data

  12. The Farmer from Whidbey Island • Visited the University of Washington with a Whalebone water douser • 10 Dixie cups, 5 with water, 5 empty, covered with plywood • If he gets all 10 right, is chance a reasonable explanation? EPP 245 Statistical Analysis of Laboratory Data

  13. The randomness is produced by the process of randomly choosing which 5 of the 10 are to contain water • There are no other assumptions EPP 245 Statistical Analysis of Laboratory Data

  14. If the randomization had been to flip a coin for each of the 10 cups, then the probability of getting all 10 right by chance is different • There are 210 = 1024 ways for the randomization to come out, only one of which is right, so the chance is 1/1024 = .001 • The method of randomization matters EPP 245 Statistical Analysis of Laboratory Data

  15. Randomization Inference • 20 tomato plants are divided 10 groups of 2 placed next to each other in the greenhouse • In each group of 2, one is chosen to receive fertilizer A and one to receive fertilizer B • The yield of each plant is measured EPP 245 Statistical Analysis of Laboratory Data

  16. EPP 245 Statistical Analysis of Laboratory Data

  17. Average difference is 4.1 • Could this have happened by chance? • Is it statistically significant? • If A and B do not differ in their effects (null hypothesis is true), then the plants’ yields would have been the same either whether A or B is applied • The difference would be the negative of what it was if the coin flip had come out the other way EPP 245 Statistical Analysis of Laboratory Data

  18. In pair 1, the yields were 132 and 140. • The difference was 8, but it could have been -8 • With 10 coin flips, there are 210 = 1024 possible outcomes of + or – on the difference • These outcomes are possible outcomes from our action of randomization, and carry no assumptions EPP 245 Statistical Analysis of Laboratory Data

  19. Of the 1024 possible outcomes that are all equally likely under the null hypothesis, only 3 had greater values of the average difference, and only four (including the one observed) had the same value of the average difference • The likelihood of this happening by chance is [3+4/2]/1024 = .005 • This does not depend on any assumptions other than that the randomization was correctly done EPP 245 Statistical Analysis of Laboratory Data

  20. EPP 245 Statistical Analysis of Laboratory Data

  21. > tomato <- edit(data.frame()) > tomato A B 1 132 140 2 82 88 3 109 112 4 143 142 5 107 118 6 66 64 7 95 98 8 108 113 9 88 93 10 133 136 > tomato[,2]-tomato[,1] [1] 8 6 3 -1 11 -2 3 5 5 3 > mean(tomato[,2]-tomato[,1]) [1] 4.1 EPP 245 Statistical Analysis of Laboratory Data

  22. pmtest <- function() { dvec <- tomato[,2] - tomato[,1] actual <- sum(dvec) nbigger <- 0 nsame <- 0 for (i in 1:10000) { svec <- rbinom(10,1,.5)*2-1 tmp <- sum(svec*dvec) if(tmp > actual) nbigger <- nbigger+1 if(tmp == actual) nsame <- nsame+1 } pvalue <- (nbigger+nsame/2)/10000 return(pvalue) } EPP 245 Statistical Analysis of Laboratory Data

  23. > rbinom(10,1,.5)*2 - 1 [1] -1 1 -1 -1 -1 -1 -1 -1 -1 -1 > pmtest() [1] 0.0057 > pmtest() [1] 0.00455 > pmtest() [1] 0.0043 EPP 245 Statistical Analysis of Laboratory Data

  24. EPP 245 Statistical Analysis of Laboratory Data

  25. Randomization and in practice • Whenever there is a choice, it should be made using a formal randomization procedure, such as Excel’s rand() function. • This protects against unexpected sources of variability such as day, time of day, operator, reagent, etc. EPP 245 Statistical Analysis of Laboratory Data

  26. EPP 245 Statistical Analysis of Laboratory Data

  27. EPP 245 Statistical Analysis of Laboratory Data

  28. =rand() in first cell • Copy down the column • Highlight entire column • ^c (Edit/Copy) • Edit/Paste Special/Values • This fixes the random numbers so they do not recompute each time • =IF(C3<0.5,"A","B") goes in cell C2, then copy down the column EPP 245 Statistical Analysis of Laboratory Data

  29. EPP 245 Statistical Analysis of Laboratory Data

  30. To randomize run order, insert a column of random numbers, then sort on that column • More complex randomizations require more care, but this is quite important and worth the trouble • Randomization can be done in Excel, R, or anything that can generate random numbers EPP 245 Statistical Analysis of Laboratory Data

  31. Blocking • If some factor may interfere with the experimental results by introducing unwanted variability, one can block on that factor • In agricultural field trials, soil and other location effects can be important, so plots of land are subdivided to test the different treatments. This is the origin of the idea EPP 245 Statistical Analysis of Laboratory Data

  32. If we are comparing treatments, the more alike the units are to which we apply the treatment, the more sensitive the comparison. • Within blocks, treatments should be randomized • Paired comparisons are a simple example of randomized blocks as in the tomato plant example EPP 245 Statistical Analysis of Laboratory Data

  33. Simultaneous Variation of Factors • The simplistic idea of “science” is to hold all things constant except for one experimental factor, and then vary that one thing • This misses interactions and can be statistically inefficient • Multi-factor designs are often preferable EPP 245 Statistical Analysis of Laboratory Data

  34. Interactions • Sometimes (often) the effect of one variable depends on the levels of another one • This cannot be detected by one-factor-at-a-time experiments • These interactions are often scientifically the most important EPP 245 Statistical Analysis of Laboratory Data

  35. Experiment 1. I compare the room before and after I drop a liter of gasoline on the desk. Result: we all leave because of the odor. EPP 245 Statistical Analysis of Laboratory Data

  36. Experiment 1. I compare the room before and after I drop a liter of gasoline on the desk. Result: we all leave because of the odor. • Experiment 2. I compare the room before and after I drop a lighted match on the desk. Result: no effect other than a small scorch mark. EPP 245 Statistical Analysis of Laboratory Data

  37. Experiment 1. I compare the room before and after I drop a liter of gasoline on the desk. Result: we all leave because of the odor. • Experiment 2. I compare the room before and after I drop a lighted match on the desk. Result: no effect other than a small scorch mark. • Experiment 3. I compare all four of ±gasoline and ±match. Result: we are all killed. • Large Interaction effect EPP 245 Statistical Analysis of Laboratory Data

  38. Statistical Efficiency • Suppose I compare the expression of a gene in a cell culture of either keratinocytes or fibroblasts, confluent and nonconfluent, with or without a possibly stimulating hormone, with 2 cultures in each condition, requiring 16 cultures EPP 245 Statistical Analysis of Laboratory Data

  39. I can compare the cell types as an average of 8 cultures vs. 8 cultures • I can do the same with the other two factors • This is more efficient than 3 separate experiments with the same controls, using 48 cultures • Can also see if cell types react differently to hormone application (interaction) EPP 245 Statistical Analysis of Laboratory Data

  40. Fractional Factorial Designs • When it is not known which of many factors may be important, fractional factorial designs can be helpful • With 7 factors each at 2 levels, ordinarily this would require 27 = 128 experiments • This can be done in 8 experiments instead EPP 245 Statistical Analysis of Laboratory Data

  41. EPP 245 Statistical Analysis of Laboratory Data

  42. EPP 245 Statistical Analysis of Laboratory Data

  43. EPP 245 Statistical Analysis of Laboratory Data

  44. EPP 245 Statistical Analysis of Laboratory Data

  45. Main Effects and Interactions • Factors Cell Type (C), State (S), Hormone (H) • Response is expression of a gene • The main effect C of cell type is the difference in average gene expression level between cell types EPP 245 Statistical Analysis of Laboratory Data

  46. For the interaction between cell type and state, compute the difference in average gene expression between cell types separately for confluent and nonconfluent cultures. The difference of these differences is the interaction. • The three-way interaction CSH is the difference in the two way interactions with and without the hormone stimulant. EPP 245 Statistical Analysis of Laboratory Data

  47. Sources of Variability in Laboratory Analysis • Intentional sources of variability are treatments and blocks • There are many other sources of variability • Biological variability between organisms or within an organism • Technical variability of procedures like RNA extraction, labeling, hybridization, chips, etc. EPP 245 Statistical Analysis of Laboratory Data

  48. Replication • Almost always, biological variability is larger than technical variability, so most replicates should be biologically different, not just replicate analyses of the same samples (technical replicates) • However, this can depend on the cost of the experiment vs. the cost of the sample • 2D gels are so variable replication is required EPP 245 Statistical Analysis of Laboratory Data

  49. Quality Control • It is usually a good idea to identify factors that contribute to unwanted variability • A study can be done in a given lab that examines the effects of day, time of day, operator, reagents, etc. • This is almost always useful in starting with a new technology or in a new lab EPP 245 Statistical Analysis of Laboratory Data

  50. Possible QC Design • Possible factors: day, time of day, operator, reagent batch • At two levels each, this is 16 experiments to be done over two days, with 4 each in morning and afternoon, with two operators and two reagent batches • Analysis determines contributions to overall variability from each factor EPP 245 Statistical Analysis of Laboratory Data