hpca.se <- HumanPrimaryCellAtlasData()
# Note that it is poor practice to use '@' in analysis code. Use
# assay(hpca, "logcounts") instead, or even simpler:
out <- pairwiseWilcox( hpca.se, hpca.se$label.main, direction="up")
markers <- getTopMarkers(out$statistics, out$pairs, n=10)
##  "ANK2" "ARHGEF40" "CDH2" "EFR3B" "FAM168A" "HEY1"
##  "INTU" "KIF21A" "LRP11" "MBOAT2"
I should add that, in my opinion, there's not much point in using the Wilcoxon rank sum test for the HPCA data; this is a bulk microarray reference and there's not enough samples to give you a fine-grained ordering of candidate markers. For example, I reckon if you looked inside out$statistics, you would find many of the top genes stuck on the same p-value because there's just not enough permutations of ranks to distinguish them. You'll just end up with an arbitrary choice of the top n=10 in such cases - better to use pairwiseTTests(), which is more responsive to the effect size.