Hi All,
I was wondering whether you could share your experiences and thoughts on the following scenario.
Say you are analyzing someone elses data and as a first point of call you do some quality checking based on whichever guidelines are available (such as the Encode guidelines for Chip-seq for examples). During your quality assessment, you observe that the data is of extreme poor quality (lets continue with the chip-seq example and say you observe 85-95% PCR duplication etc). At this point you think, this experiment probably should be repeated but due to time restrictions, you continue with stringent parameters (including the PCR duplicates) and take the resulting overlapping peaks from 2 different methods (observed with IDR < 0.01 for 2 samples). You present the handful of peaks observed to the owner of the data, but they aren't happy with what they are seeing. They ask you to relax the parameters and flood the system with noise, violating any guidelines, just for the sake of having some data to base future experiments on.
- Have you come across a scenario like this?
- What are your thoughts on using a less stringent methods on an extremely poor data set?
Thanks in advance for any responses
I was wondering whether you could share your experiences and thoughts on the following scenario.
Say you are analyzing someone elses data and as a first point of call you do some quality checking based on whichever guidelines are available (such as the Encode guidelines for Chip-seq for examples). During your quality assessment, you observe that the data is of extreme poor quality (lets continue with the chip-seq example and say you observe 85-95% PCR duplication etc). At this point you think, this experiment probably should be repeated but due to time restrictions, you continue with stringent parameters (including the PCR duplicates) and take the resulting overlapping peaks from 2 different methods (observed with IDR < 0.01 for 2 samples). You present the handful of peaks observed to the owner of the data, but they aren't happy with what they are seeing. They ask you to relax the parameters and flood the system with noise, violating any guidelines, just for the sake of having some data to base future experiments on.
- Have you come across a scenario like this?
- What are your thoughts on using a less stringent methods on an extremely poor data set?
Thanks in advance for any responses
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