deML is a maximum likelihood demultiplexing algorithm that allows probabilistic sample assignments. We compute the likelihood of assignment to various samples and assign to the most likely sample. We then compute the assignment quality to avoid ambiguous assignments. This is similar to how mapping quality solves the problem of ambiguous assignments.
Our paper describing our algorithm was published in Bioinformatics. To cite us:
Please find the documentation in the README with the package.