Background:　Single-cell　RNA-sequencing　(scRNA-seq)　is　fast　becoming　a　powerful　tool　for　profiling　genome-scale　transcriptomes　of　individual　cells　and　capturing　transcriptome-wide　cell-to-cell　variability.　However,　scRNA-seq　technologies　suffer　from　high　levels　of　technical　noise　and　variability,　hindering　reliable　quantification　of　lowly　and　moderately　expressed　genes.　Since　most　downstream　analyses　on　scRNA-seq,　such　as　cell　type　clustering　and　differential　expression　analysis,　rely　on　the　gene-cell　expression　matrix,　preprocessing　of　scRNA-seq　data　is　a　critical　preliminary　step　in　the　analysis　of　scRNA-seq　data.　Results:　We　presented　scNPF,　an　integrative　scRNA-seq　preprocessing　framework　assisted　by　network　propagation　and　network　fusion,　for　recovering　gene　expression　loss,　correcting　gene　expression　measurements,　and　learning　similarities　between　cells.　scNPF　leverages　the　context-specific　topology　inherent　in　the　given　data　and　the　priori　knowledge　derived　from　publicly　available　molecular　gene-gene　interaction　networks　to　augment　gene-gene　relationships　in　a　data　driven　manner.　We　have　demonstrated　the　great　potential　of　scNPF　in　scRNA-seq　preprocessing　for　accurately　recovering　gene　expression　values　and　learning　cell　similarity　networks.　Comprehensive　evaluation　of　scNPF　across　a　wide　spectrum　of　scRNA-seq　data　sets　showed　that　scNPF　achieved　comparable　or　higher　performance　than　the　competing　approaches　according　to　various　metrics　of　internal　validation　and　clustering　accuracy.　We　have　made　scNPF　an　easy-to-use　R　package,　which　can　be　used　as　a　versatile　preprocessing　plug-in　for　most　existing　scRNA-seq　analysis　pipelines　or　tools.　Conclusions:　scNPF　is　a　universal　tool　for　preprocessing　of　scRNA-seq　data,　which　jointly　incorporates　the　global　topology　of　priori　interaction　networks　and　the　context-specific　information　encapsulated　in　the　scRNA-seq　data　to　capture　both　shared　and　complementary　knowledge　from　diverse　data　sources.　scNPF　could　be　used　to　recover　gene　signatures　and　learn　cell-to-cell　similarities　from　emerging　scRNA-seq　data　to　facilitate　downstream　analyses　such　as　dimension　reduction,　cell　type　clustering,　and　visualization.　©　2019　The　Author(s).