I am running computeSumFactors on a rather large expression matrix ( 68K ~ 20K).  Instead of running quickCluster on such a large matrix, I use k-mean clustering result from a previous analysis ( PCA of top 1000 variable genes). However, it returns the following warning upon finished : number of cells in each cluster should be at least twice that of the largest 'sizes'. I was wondering if it will have any adverse effects on the size factor when the number of cells are not evenly distributed in each clusters.   

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.