The　spatial　transcriptomics　is　a　rapidly　evolving　biological　technology　that　simultaneously　measures　the　gene　expression　profiles　and　the　spatial　locations　of　spots.　With　progressive　advances,　current　spatial　transcriptomic　techniques　can　achieve　the　cellular　or　even　the　subcellular　resolution,　making　it　possible　to　explore　the　fine-grained　spatial　pattern　of　cell　types　within　one　tissue　section.　However,　most　existing　cell　spatial　clustering　methods　require　a　correct　specification　of　the　cell　type　number,　which　is　hard　to　determine　in　the　practical　exploratory　data　analysis.　To　address　this　issue,　we　present　a　nonparametric　Bayesian　model　BACT　to　perform　BAyesian　Cell　Typing　by　utilizing　gene　expression　information　and　spatial　coordinates　of　cells.　BACT　incorporates　a　nonparametric　Potts　prior　to　induce　neighboring　cells＇　spatial　dependency,　and,　more　importantly,　it　can　automatically　learn　the　cell　type　number　directly　from　the　data　without　prespecification.　Evaluations　on　three　single-cell　spatial　transcriptomic　datasets　demonstrate　the　better　performance　of　BACT　than　competing　spatial　cell　typing　methods.　The　R　package　and　the　user　manual　of　BACT　are　publicly　available　at　https://github.com/yinqiaoyan/BACT.