Step 2: After transformation, there is very little variability left in my data.
The cofactor should be chosen carefully, according to the properties of the datasets. We suggest that several different cofactors are tested, as well as different cofactors for different measured flow cytometry parameters, and that the analysis is repeated until an optimal cofactor is found. For stain-free flow cytometry data presented in our study, a cofactor of 150 was chosen. For mass cytometry datasets, a cofactor of 5 is usually used.
Step 3: My viSNE maps look crowded.
Crowding becomes an issues at ca. 150000 cells analyzed. Optimal visual results are usually achieved between 50000 and 150000 cells.
Step 6: I don't see any clusters.
Check your transformation and crowding.
Step: 10. I have many cells, but see only a few on the viSNE map.
It is possible that the cells whose properties you are projecting are very different from the cells in the viSNE map. In that case, many different cells would be projected onto the same parts of the viSNE and create the illusion that only a few have been projected. If this is the case, the projection should not be trusted.