The sequencing core here is including a control sample(technical replicate) onto a multiplexed lane for every RNA-seq run. This is so that if the run goes well, there should be a high correlation in the control data between runs. In the past runs, I have looked at Spearmans correlation test and scatter plots to check for high concordance. However, the results from these tests varied and interpreting them was not the easiest.
The Marioni et al. paper focuses on technical replicability of samples of a single flow cell. Could this be extended to withing run sample concordance?
If so, I could think of 3 ways in which sample concordance could be measured.
a) Spearmans correlation of counts
b) Poisson modelling the counts should show little differential expression as shot noise should be modelled by Poisson
c) Use a hyper-geometric distribution model to compute P-value testing whether the number of counts differed than expected by random sampling.
Are any of the above methods appropriate for answering the question? If not what could be a possible way.
The Marioni et al. paper focuses on technical replicability of samples of a single flow cell. Could this be extended to withing run sample concordance?
If so, I could think of 3 ways in which sample concordance could be measured.
a) Spearmans correlation of counts
b) Poisson modelling the counts should show little differential expression as shot noise should be modelled by Poisson
c) Use a hyper-geometric distribution model to compute P-value testing whether the number of counts differed than expected by random sampling.
Are any of the above methods appropriate for answering the question? If not what could be a possible way.
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