Gene　set　scoring　(GSS)　has　been　routinely　conducted　for　gene　expression　analysis　of　bulk　or　single-cell　RNA　sequencing　(RNA-seq)　data,　which　helps　to　decipher　single-cell　heterogeneity　and　cell　type-specific　variability　by　incorporating　prior　knowledge　from　functional　gene　sets.　Single-cell　assay　for　transposase　accessible　chromatin　using　sequencing　(scATAC-seq)　is　a　powerful　technique　for　interrogating　single-cell　chromatin-based　gene　regulation,　and　genes　or　gene　sets　with　dynamic　regulatory　potentials　can　be　regarded　as　cell　type-specific　markers　as　if　in　single-cell　RNA-seq　(scRNA-seq).　However,　there　are　few　GSS　tools　specifically　designed　for　scATAC-seq,　and　the　applicability　and　performance　of　RNA-seq　GSS　tools　on　scATAC-seq　data　remain　to　be　investigated.　Here,　we　systematically　benchmarked　ten　GSS　tools,　including　four　bulk　RNA-seq　tools,　five　scRNA-seq　tools,　and　one　scATAC-seq　method.　First,　using　matched　scATAC-seq　and　scRNA-seq　datasets,　we　found　that　the　performance　of　GSS　tools　on　scATAC-seq　data　was　comparable　to　that　on　scRNA-seq,　suggesting　their　applicability　to　scATAC-seq.　Then,　the　performance　of　different　GSS　tools　was　extensively　evaluated　using　up　to　ten　scATAC-seq　datasets.　Moreover,　we　evaluated　the　impact　of　gene　activity　conversion,　dropout　imputation,　and　gene　set　collections　on　the　results　of　GSS.　Results　show　that　dropout　imputation　can　significantly　promote　the　performance　of　almost　all　GSS　tools,　while　the　impact　of　gene　activity　conversion　methods　or　gene　set　collections　on　GSS　performance　is　more　dependent　on　GSS　tools　or　datasets.　Finally,　we　provided　practical　guidelines　for　choosing　appropriate　preprocessing　methods　and　GSS　tools　in　different　application　scenarios.