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For more information about experimental design services, click a question below.
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Yes. Microarrays are quite expensive to perform, so you would want to do them properly from the first step on. It is not uncommon to see trouble due to incorrect design when it comes to the time to interpret the data. Microarray experiments are biological experiments, so the most important considerations will be biological. However, you should always be aware of the basic statistical and philosophical issues involved in any successful experimental design. If you are not familiar with basic statistical experiment design, consulting a statistician is always recommended. In addition, it will be very useful to involve a statistician/bioinformatician in the analysis/interpretation process to prevent common pitfalls, such as underestimating the "multiple testing problem" involved in examining thousands of genes at once.
A good start is to define your expectations (hypotheses)in advance in as much detail as possible. A common mistake is to consider microarray experiment as only explorative. Inventing ad hoc explanations later when you get your results is not best practice. As Ernst Wit writes in his Ethics of Chance: A statistical "method that makes use of a retrospective study of the data cannot [ever] reach the same significance level as a prior formulation of the hypothesis." This is a general issue for the performance and interpretation of scientific experiments and is particularly relevant for microarray studies with their large data sets and surprise observations. There are two types of replicates: biological replicates and technical replicates. If you want to get generalized conclusion (e.g. effect of SOD knockout on B6 mice), you need biological replicates (e.g. several SOD KO mice vs. several WT mice). Generally, using less than 3 replicates is not a good idea. Based on our experience, you will need 3 replicates for replicated experiment on cell lines, 4-5 replicates for inbred mice,
and >10-15 replicates for human tissues. The exact number of replicates depends on several factors, including variation between samples (can be determined by pilot studies. clinical samples between different patients can vary a lot), significant level (P value, e.g. 0.05), desired detectable difference (e.g. 2 fold difference), etc. Please consult us on sample size determination. In addition, equal numbers of replicates for each condition/comparison (balanced design) can make the later analysis simpler. Yes and no. Yes for two-color arrays. A dye-swap replicate is almost always necessary (except some special studies such as classification of tumor types using common reference design). No for Affymetrix Genechips and other single color arrays, unless you are planning a technical instead of a biological study. It is costly to do technical replicates on Genechips, and they are usually highly reproducible (R > 0.99). Technical replication probably doesn't provide any biologically useful information. It is tempting to pool samples to save hybridization costs or lower the required amount of RNA from limited sources. Unless you do single-cell sampling, every samples is already a pool, as it contains mRNA from many cells. Be aware of inter-individual variations (e.g. clinical study, mice model). You might not be able to get any differentially expressed genes due to the large variations in each group. If you feel like pooling, it is
important to pool the biological material (tissue, cells), not the purified RNA or labeled cDNA! In this way, problems are far easier to spot. Check phenotypes of individuals to make sure they are not ourliers before pooling. Don't ever include any sample that looks suspicious. o Keep in mind of the biological question you are trying to answer
o Always talk to people (researchers with microarray experience, statistician, and bioinformatician) before conducting real experiments
o Don’t do microarray experiment without any replicates just because short of money, because you end up with useless data and waste of your money
o Be consistent on sample preparation, hybridization, and scanner settings. |
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One important consideration is that you should compare samples that are similar. Don't try to maximize the number of differentially expressed genes. Microarrays are very sensitive in picking out expression changes. It is impossible to give biological meaning interpretation if almost all the genes on the array to be differentially expressed.
- Knock-out animal models. If you compare two animals, try to take samples from comparable areas. The same tissue may not be the same anymore, after you knock out a major physiological process. E.g., the testis of a steroid receptor knock-out will have little resemblance to a wild-type testis, because spermatogenesis is abolished. Therefore almost all genes will be changed in expression to some extent - and the results may be close to impossible to interpret.
- Tissue comparisons. There are probably very few sensible experiments comparing samples from different tissues (except in the context of large-scale comprehensive expression surveys). It is often even recommendable to restrict analysis to specific cell types within the same tissue, if the quantitative composition changes between conditions.
- Drug treatments. If you are interested in a drug effect, try to examine the earliest time point possible. This minimizes secondary effects and focuses on the drug-specific changes. Of course, a time-series can be helpful, but if you know the physiological time-scale in advance, it is often more efficient to increase the number of replicates on one early time point.
- Stably transfected cell lines. To establish a cell line that has been stably transfected by some DNA, you usually have to go through a rigorous selection process, often even including a single-cell stage. Afterwards the newly established cell line may no longer be comparable to its parent line, especially if the effect of the transfection is rather mild. The observations could be dominated by individual differences between cells that have been "amplified" by the selection process. Therefore, it is important to use a mock transfected cell line as the control, preferably one that expresses an inactive point mutant of the construct of interest. Also, as the transfection process and the subsequent selection and gene integration are not reproducible, it is recommended to use independent transfectants for each replicate, even though this is more laborious.
- Response studies vs. condition studies. If you want to compare two tissues that are quite different (wild type vs. mutant, healthy vs. diseased) it may be more effective to compare their responses to some stimulus (a drug, an hormone, a stressor), rather than comparing their conditions directly. In this setup, each hybridization could compare a single tissue in the stimulated and unstimulated state, which should be more similar, i.e. comparable, than the two different tissues.
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- Standard arrays. If possible use arrays that many other people are using. This facilitates data exchange and the comparison of results. Your data will be much more useful and easier to interpret if you can directly compare them to other people's data.
- Whole-genome arrays. Nowadays, there are few good reasons to restrict your studies to an arbitrary selection of genes. Exceptions are experiments with organisms for which whole-genome arrays are unavailable or very focused specialized studies. However, it would be a mistake to choose a partial array just because a more comprehensive genome-wide study might yield too many unexpected results.
- Single-color arrays (e.g. Affymetrix Genechip® arrays) vs. two-color arrays (e.g. cDNA microarray). Two-colored arrays are generally cheaper than Genechips, and it is well suitable for studies involving comparison of two conditions (mutant vs. wild type; treated vs. untreated; healthy vs. diseased; etc.). However, it has two drawbacks. First of all, because this type of array is usually printed on slides (either with 2D or 3D substrates) by spotting robots with pins, the printing reproducibility between arrays varies a lot (ratio approach only partially solves this problem, because missing spots can not be handled). Secondly, as soon as the study becomes a bit more complex (time-course; comparison of several mutants; inter-patient comparison; multi-factor experiments), designing with two-color arrays is more challenging. The advantage of genechip is the uniformity across chips, experimental procedures, and even scanners. However, Genechip also has its own drawbacks:
1. The probes are designed based on public sequence databases that are error-prone;
2. The probes are short, so cross-hybridization is inevitable;
3. Mismatch probes generate analysis problem and have to be discarded;
4. They are generally more expensive. |
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