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Using the generated output file the next steps are applied. It uses the coverage information as well as the strand specific information and only serves CpGs that are on the appropriate strand. If no strand information is available, it considers both.
In addition it has to be noted, although most references stating a sample size of at least 3 is providing actual proof, this is in part because they use sequencing chips like the ddGoT and in part due to a specific successful study using as few as 10 cells (Chen et al. 2013).
But there is a simple solution which would allow to greatly increase the sample size: use pooled sequencing. By pooling multiple biological samples (e.g. patients) into one well we can easily achieve a sample size of up to 10 thousand per well. This would allow a very easy increase in statistical power, when comparing two groups which I consider to be essential for any future studies. In addition to the increased sample size, such a pooled analysis would allow to further benchmark the method and to validate the method itself.
With this the methylation calls can be compared between all samples. Due to the constant removal of outliers we have a lot of methylation calls that are only available for one of the five samples. But those methylation calls are still available in more than one sample. The reason for that is, that each sample might still contain some outliers.
The final (last) step creates the final output. Using a user specified set of target regions and their methylation calls the software produces a final table where each row reflects on one target region in one sample. Using the provided lists and results files derived from the previous steps, the final methylation calls per target/sample are displayed as a table. d2c66b5586
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