Hi Katia,
For ssGSEA we'd recommend a metric that has a gene length normalization built in, such as gene level TPM, or FPKM, since ssGSEA compares within a sample for relative enrichment. Normalize counts is more appropriate for standard GSEA which ranks per-gene differentially between samples.
Zero values are allowed but as they are considered a "tie" in the ranking, when the gene list is sorted, the order ends up arbitrary. This can have a variable effect on the score depending on how extensive the zeros are. The issue here is largest with single-cell data where we don't necessarily recommend ssGSEA for individual cell-level quantitation, but binning to cluster level seems to work reasonably well, as does bulk sequencing data.
-Anthony
Anthony S. Castanza, PhD
Curator, Molecular Signatures Database
Mesirov Lab, Department of Medicine
University of California, San Diego