2.3 years ago by
Cambridge, United Kingdom
No, it's fine. We threw in warnings when we were developing the method, but later on, we found that it didn't really matter (as long as you have enough cells - usually at least 100 - in each cluster, which is ensures you get precise estimates). As of the next release, this particular warning will be removed; the function will also be more tolerant of number of cells in each cluster below sizes
, producing warnings rather than errors.
Note that clustering prior to computeSumFactors
should be done in a way that is insensitive to the size factors. Otherwise, in extreme cases, you cluster cells that have similar library sizes, rather than those with similar expression profiles. We suggest doing something like computing ranks (e.g., with quickCluster
and get.ranks=TRUE
) and running a clustering algorithm on that instead. You can use k-means, or you can try quickCluster
with method="igraph"
(parallelizable via BiocParallel). This uses a community-based detection algorithm for clustering, which avoids constructing the distance matrix for large numbers of cells.
Also, you misspelt the tag, which is why I didn't see this post until now.
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modified 2.3 years ago
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2.3 years ago by
Aaron Lun • 25k