The experimental design is fundamental.
Make a plan to maximize the output, robust against failures.
$100 can save thousands of dollars.
If you don’t have any limitations in budget, time, and human resources, you doubtlessly follow statistical theory. However, you have constraints in the real world. The budget determines the maximum number of samples, which is inarguably too few, especially in microarray or RNA-Seq experiments, against the requirement from theory. If so, blindly following the principle is helpless. We dare to say from our experiences that assigning experimental parameters based on biological speculation about the background mechanism is far more crucial.
Forgive us that we can't say anything more concretely because it depends on each case. Please contact us for more details.
Everybody follows the trend. We admit that booming is necessary for technology evolution. On the other hand, we also know that newer is not always better and that ads exaggerate the actual capability. So it is no doubt that you'd better assess the latest technology before you decide to take it.
You need data for assessment. ut you should not too much rely on sample data provided by makers because the quality of the champion data is much higher than that of you will get. So we recommend you take a look at several data sets of other researchers which are publicly available from the internet. They don't need to be of the same organism, organs or cells, diseases, etc., because you can somewhat grasp the ability, limits, and problems of the technology.
We offer the Data Analysis Service for assessment. Knowing how to analyze the data, and what output you will get gives vivid and tangible hints for your experimental planning. Even if you spent some time and money on assessment, it worth it because you can avoid far more massive loss.
When you are planning, you often ignore the fact that the experiment can sometimes fail. Yes, failure is a part of the research. We understand you want to remove all risks, though, it is impossible. So we recommend you think from two points. (1) What kind of failures are likely to happen. And (2) What type of failure is disastrous. It makes you reasonably select risks to be taken care of.
Everybody can easily imagine the second point. Contrary, the first point is difficult if you don't have a lot of experience. Moreover, failures that are likely to occur are different between experiments that you do and that you outsource. The reason you order this service is to let us complement your lack of experience with ours.
Embedding risk hedge methods in the experimental plan is not free because you have to assign some samples for this purpose. So you have to consider the cost is reasonable or not. On the other hand, there are free tricks to reduce risks, like sample labeling or how to order the outsource. Combining various means, you brush up the plan to be robust, and negative-effect-limited in case something happens. We do the best for it by our know-how.
|Experimental Planning Support Service||100 USD||Assessment of Technologies Not included. Please use the Data Analysis Service.|
|Experimental Planning Support Service||100 EUR||Assessment of Technologies Not included. Please use the Data Analysis Service.|
|Experimental Planning Support Service||10,000 JPY||Assessment of Technologies Not included. Please use the Data Analysis Service.|
Describe the study (comment input field)