Design consideration:
-Identify the key aims
constraining factors
protocols for sample isolation and processing
decide analysis
decide validation
aim to randomise nuisance factors
1. Replication
averaging replicates will give better estimates of the mean. replicates allow statistical inferences to be made.
Biological vs Technical Replication. Techincal ccome from the same sample i ndifferent chips. biological came from different samples. replicates is a scale between biological and technical
3. Level of Inference
Always compromise between precision and generality
what level do conclusion need to be made --> to just the technical sample, to all experiment in cell lines, to all mices?
More general solution inferences capture more variance
more variablity mena more rep;licates
4. Stastitical issues
a. Level of variability
statistically significant does not always mean biologically significant
b. Multiple testing and False Discovery Rate (FDR)
Usually applies T-Test for each probesets. For each test, P-Values are the probabilities that the test would produce a result as least as extreme assuming the null hypothesis are true. We expect 5% chance that the test result in false positives for multiple test. The FDR was applied to avoid high false positives. Which accounts for the number of test applied.
c. Effect size
How large of a change we want to detect
d. Power
Our ability to discover truth. More replication more power
Common Design Principles
1. Single Factor
varying single factor at once. example with ot wothout drug. for dual channel place comparison of interest near each other. short time can be treatesd on a single factor experiment
-Paired Samples
Microarray experiments with paired designs are often encountered in a clinical setting where for example, samples are isolated from the same patients before and after treatment. Describe the reasons that it might be attractive to employ paired design in microarray experiment!
reduces variability in biological replicates
still captures variability with respect to response between patients
-Pooling vs Amplification
Mutiple isolation are pooled to give enough biological material of the expression level
gives more robust estimation of the expression level
but it can be dominated by one unusual samples
pool only when necessary and consider amplification as alternative
making sub pools is a compromise, ex: pool 15 into 3 x 5
amplificaiton is alternative to overcame limitation due to sample availability
but its not possible to introduce amplification without bias
-Dual Channel Dye Swaps
-Missing measurement
-Practical Design
-Usually limited by cost and sample availability
-consider other experiment for informal estimation parameters
-usually 3-5 replicate for well known strain
or 30-200 for human population inference
consider extendable desing or pilot experiment
Experimental Design Biological questions: Which genes are expressed in a sample? Which genes are differentially expressed (DE) in a treatment, mutant, etc.? Which genes are co-regulated in a series of treatments? Selection of best biological samples and reference Comparisons with minimum number of variables Sample selection: maximum number of expressed genes Alternative reference: pooled RNA of all time points (saves chips) Develop validation and follow-up strategy for expected expression hits e.g. real-time PCR and analysis of transgenics or mutants Choose type of experiment common reference, e.g.: S1 x S1+T1, S1 x S1+T2 paired references, e.g.: S1 x S1+T1, S2 x S2+T1 loop & pooling designs many other designs At least three (two) biological replicates are essential Biological replicates: utilize independently collected biosamples Technical replicates: utilize often the same biosample or RNA pool
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