Single-cell　RNA　sequencing　(scRNA-seq)　provides　a　powerful　tool　to　analyze　the　expression　level　of　tissues　at　a　cellular　resolution.　However,　it　could　not　capture　the　spatial　organization　of　cells　in　a　tissue.　The　spatially　resolved　transcriptomics　technologies　(ST)　have　been　developed　to　address　this　issue.　However,　the　emerging　STs　are　still　inefficient　at　single-cell　resolution　and/or　fail　to　capture　the　sufficient　reads.　To　this　end,　we　adopted　a　partial　least　squares-based　method　(spatial　modular　patterns　[SpaMOD])　to　simultaneously　integrate　the　two　data　modalities,　as　well　as　the　networks　related　to　cells　and　spots,　to　identify　the　cell-spot　comodules　for　deciphering　the　SpaMOD　of　tissues.　We　applied　SpaMOD　to　three　paired　scRNA-seq　and　ST　datasets,　derived　from　the　mouse　brain,　granuloma,　and　pancreatic　ductal　adenocarcinoma,　respectively.　The　identified　cell-spot　comodules　provide　detailed　biological　insights　into　the　spatial　relationships　between　cell　populations　and　their　spatial　locations　in　the　tissue.