Closed acwarren closed 6 years ago
hi, is demuxlet slow for you even if you use the —group-list flag?
On Feb 7, 2018, at 1:13 PM, acwarren notifications@github.com wrote:
Is there a way to just infer sample identity and turn off double detection to save computational cost for experiments with higher numbers of samples?
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I am using the --group-list flag, which is helpful - but I have single cells that I expect are from one of 25 samples, but which I would like to test against a possible ~1000 samples - which would be a lot of possible doublet combinations. So the computational cost is not from having larger numbers of cells but from having larger numbers of possible samples
got it. we currently don't have a way to turn off the doublet detection. could you provide some motivation for why you want to test against a vcf of 1000 samples? if it's to check the accuracy of demuxlet, you can put in the 25 samples in the pool and perhaps an additional set of 25 samples not in the pool.
~jimmie
On Feb 8, 2018, at 7:18 AM, acwarren notifications@github.com wrote:
I am using the --group-list flag, which is helpful - but I have single cells that I expect are from one of 25 samples, but which I would like to test against a possible ~1000 samples - which would be a lot of possible doublet combinations. So the computational cost is not from having larger numbers of cells but from having larger numbers of possible samples
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I was interested in doing it for evaluation of accuracy and because there is a possibility that a sample is mislabeled, so cells may actually be from one of the other 1000 samples.
ah ok. we will look into supporting turning off doublet detection in the future. for now, we have a few higher order items (tuning for V2 chemistry) that we must look into first.
On Feb 8, 2018, at 10:53 AM, acwarren notifications@github.com wrote:
I was interested in doing it for evaluation of accuracy and because there is a possibility that a sample is mislabeled, so cells may actually be from one of the other 1000 samples.
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Is there a way to just infer sample identity and turn off double detection to save computational cost for experiments with higher numbers of samples?