Abstract
Analysis of single-cell transcriptomes remains a challenge in that subtle differences of cell types are difficult to resolve. Here we present the self-assembling manifolds (SAM) algorithm, which dynamically rescales gene expression to amplify differences between cells. We demonstrate its advantage over other methods by analyzing stem cells from Schistosoma, a parasite that infects >250 million people. Benchmarking on another 47 datasets, SAM consistently improves cell clustering and marker gene identification.
Copyright
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