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Out of curiosity, how does RNAseqPower compare to Scotty when the same pilot data is used for both? Regardless, that's a nice tool to have! -
I hadn't actually seen it before, but it is a very good looking web app! The power estimates should be similar, but won't be identical for 2 reasons:
1. Scotty assumes that variance follows a lognormal distribution. I think this is a valid and logical assumption. RNASeqPower uses the measured variance from the data instead.
2. Scotty pseudo-randomly selects 200 values from a 2 dimensional matrix (variance and depth) then uses that mean as the power of the dataset. The way we use RNASeqPower is to say that we want to know the minimum requirements to reach the desired power. We need to know two things, how many genes do I want to detect and how much variance to I want to allow. If you only want to detect the top 90% of genes, then you take the 10th percentile of gene counts for your pilot (all other genes will have more than this count). You do the same for variance except you select the 90th percentile of variance. Using the minimum read count and the maximum allowable variance, that is the power you have in your experiment. Granted most genes will have higher power since they will have lower variance and higher counts. So in the end, it comes down to how you define what power actually means.
RNASeqPower can also give you the power for each gene in a dataset.Comment
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Cool, thanks!Comment
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Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
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