update paper DOI number

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: PRECAST
 Type: Package
 Title: Embedding and Clustering with Alignment for Spatial Datasets
-Version: 1.3
-Date: 2022-10-18
+Version: 1.4
+Date: 2023-02-05
 Authors@R: 
     c(person(given = "Wei",
              family = "Liu",
@@ -16,7 +16,7 @@ Authors@R:
              role = "aut"))
 Author: Wei Liu [aut, cre], Yi Yang [aut], Jin Liu [aut]
 Maintainer: Wei Liu <[email protected]>
-Description: An efficient data integration method is provided for multiple spatial transcriptomics data with non-cluster-relevant effects such as the complex batch effects. It unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, requiring only partially shared cell/domain clusters across datasets. More details can be referred to Wei Liu, et al. (2022) <doi:10.1101/2022.06.26.497672>.
+Description: An efficient data integration method is provided for multiple spatial transcriptomics data with non-cluster-relevant effects such as the complex batch effects. It unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, requiring only partially shared cell/domain clusters across datasets. More details can be referred to Wei Liu, et al. (2023) <doi:10.1038/s41467-023-35947-w>.
 License: GPL-3
 Depends: 
     parallel,

R/zzz.R

@@ -0,0 +1,6 @@
+.onAttach <- function(libname, pkgname) {
+  # output the specific message when package is loaded!
+  RFver <- read.dcf(file=system.file("DESCRIPTION", package=pkgname),
+                    fields=c("Description"))
+  packageStartupMessage(paste(pkgname,': ', RFver))
+}

README.md

@@ -13,7 +13,7 @@ PRECAST: a probabilistic embedding and clustering with alignment for spatial tra
 
 PRECAST  is a package for integrating and analyzing multiple spatially resolved transcriptomics (SRT) datasets, developed by the Jin Liu's lab. It unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, requiring only partially shared cell/domain clusters across datasets.
 
-Check out our [bioRxiv paper](https://doi.org/10.1101/2022.06.26.497672) and  our [Package Website](https://feiyoung.github.io/PRECAST/index.html) for a more complete description of the methods and analyses. 
+Check out our [Nature Comunications paper](https://www.nature.com/articles/s41467-023-35947-w) and  our [Package Website](https://feiyoung.github.io/PRECAST/index.html) for a more complete description of the methods and analyses. 
 
 PRECAST can be used to compare and contrast experimental datasets in a variety of contexts, for instance:
 

man/ICM.EM_structure.Rd

@@ -31,7 +31,7 @@
 \item{loglik}{pseudo observed log-likelihood.}
 }
 \references{
-\href{https://www.biorxiv.org/content/10.1101/2021.12.25.474153v4}{Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) Joint dimension reduction and clustering analysis for single-cell RNA-seq and spatial transcriptomics data}
+\href{https://www.nature.com/articles/s41467-023-35947-w}{Wei Liu, Liao, X., Luo, Z. et al, Jin Liu* (2023). Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST. Nature Communications, 14, 296}
 }
 \author{
 Wei Liu

man/PRECAST.Rd

@@ -19,6 +19,10 @@
   Return a revised PRECASTObj object.
 }
 
+\references{
+\href{https://www.nature.com/articles/s41467-023-35947-w}{Wei Liu, Liao, X., Luo, Z. et al, Jin Liu* (2023). Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST. Nature Communications, 14, 296}
+}
+
 \author{
   Wei Liu
 }

man/idrsc.Rd

@@ -53,7 +53,7 @@
 \item{loglik}{pseudo observed log-likelihood.}
 }
 \references{
-\href{https://www.biorxiv.org/content/10.1101/2021.12.25.474153v4}{Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) Joint dimension reduction and clustering analysis for single-cell RNA-seq and spatial transcriptomics data}
+\href{https://www.nature.com/articles/s41467-023-35947-w}{Wei Liu, Liao, X., Luo, Z. et al, Jin Liu* (2023). Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST. Nature Communications, 14, 296}
 }
 \author{
 Wei Liu

man/selectIntFeatures.Rd

@@ -18,6 +18,10 @@
   Return a string vector, the selected gene list for integration in PRECAST.
 }
 
+\references{
+\href{https://www.nature.com/articles/s41467-023-35947-w}{Wei Liu, Liao, X., Luo, Z. et al, Jin Liu* (2023). Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST. Nature Communications, 14, 296}
+}
+
 \author{
   Wei Liu
 }

src/idrsc2.cpp

@@ -798,45 +798,88 @@ Rcpp:: List idrsc2Cpp(const Rcpp::List& Xlist, const Rcpp::List& Adjlist, const
     }
 
 
-
-    //set parallel structure object
-    par_iDRSC2 parObj(Xf, Adjf, Adjf_car, yf,  Mu0, Sigma0, W_int,  Lam0, Psi0,
+    if(lengthK==1){
+      field<ivec> yf_int(r_max);
+      for (int r = 0; r < r_max; r++){
+        ivec tmp_yf = yf(r).col(0);
+        yf_int(r) = tmp_yf;
+      }
+      MATTYPE Mu_int = Mu0(0);
+      MATTYPE Lam_int = Lam0;
+      CUBETYPE Psi_int = Psi0;   
+      VECTYPE alpha_int = alpha0(0);
+      CUBETYPE Sigma_int = Sigma0(0);
+      Objidrsc2 output = idrsc2(Xf, Adjf, Adjf_car, yf_int,
+                         Mu_int, Sigma_int,  W_int,
+                         Lam_int, Psi_int,
+                         alpha_int,  beta0,  beta_grid,
+                         maxIter_ICM,  maxIter, epsLogLik, verbose,
+                         homo, homoClust, Sigma_diag, mix_prop_heter, Sp2); 
+
+      List Objidrsc2Rcpp(lengthK);
+
+        // output return value
+        Objidrsc2Rcpp[0] = List::create(
+          Rcpp::Named("cluster") = output.yf,
+          Rcpp::Named("hZ") = output.Ezz,
+          Rcpp::Named("hV") = output.Vf,
+          Rcpp::Named("Rf") = output.Rf,
+          Rcpp::Named("beta") = output.beta0,
+          Rcpp::Named("Mu") = output.Mu0,
+          Rcpp::Named("Sigma") = output.Sigma0,
+          Rcpp::Named("Psi") = output.Psi0,
+          Rcpp::Named("W") = output.W0,
+          Rcpp::Named("Lam") = output.Lam0,
+          Rcpp::Named("loglik") = output.loglik,
+          Rcpp::Named("loglik_seq") = output.loglik_seq);
+
+
+      return(Objidrsc2Rcpp);
+
+
+    }else{
+      //set parallel structure object
+      par_iDRSC2 parObj(Xf, Adjf, Adjf_car, yf,  Mu0, Sigma0, W_int,  Lam0, Psi0,
                         alpha0, beta0, beta_grid, maxIter_ICM, maxIter, epsLogLik, verbose,
                         homo, homoClust, Sigma_diag, mix_prop_heter, Sp2, maxK, minK);
-
-	const int n_thread = coreNum;
-	std::vector<std::thread> threads(n_thread);
-
-	for (int i_thread = 0; i_thread < n_thread; i_thread++){
-		threads[i_thread] = std::thread(&par_iDRSC2::update_by_thread_idrsc2, &parObj, i_thread);
-	}
-	for (int i = 0; i < n_thread; i++){
-		threads[i].join();
-	}
-
-
-    List Objidrsc2Rcpp(maxK-minK+1);
-
-
-    for (int k = 0; k < maxK - minK + 1; k++){
+      
+      const int n_thread = coreNum;
+      std::vector<std::thread> threads(n_thread);
+      
+      for (int i_thread = 0; i_thread < n_thread; i_thread++){
+        threads[i_thread] = std::thread(&par_iDRSC2::update_by_thread_idrsc2, &parObj, i_thread);
+      }
+      for (int i = 0; i < n_thread; i++){
+        threads[i].join();
+      }
+      
+      
+      List Objidrsc2Rcpp(maxK-minK+1);
+      
+      
+      for (int k = 0; k < maxK - minK + 1; k++){
         // output return value
         Objidrsc2Rcpp[k] = List::create(
-        Rcpp::Named("cluster") = parObj.output[k].yf,
-        Rcpp::Named("hZ") = parObj.output[k].Ezz,
-        Rcpp::Named("hV") = parObj.output[k].Vf,
-        Rcpp::Named("Rf") = parObj.output[k].Rf,
-        Rcpp::Named("beta") = parObj.output[k].beta0,
-        Rcpp::Named("Mu") = parObj.output[k].Mu0,
-        Rcpp::Named("Sigma") = parObj.output[k].Sigma0,
-        Rcpp::Named("Psi") = parObj.output[k].Psi0,
-        Rcpp::Named("W") = parObj.output[k].W0,
-        Rcpp::Named("Lam") = parObj.output[k].Lam0,
-        Rcpp::Named("loglik") = parObj.output[k].loglik,
-        Rcpp::Named("loglik_seq") = parObj.output[k].loglik_seq);
+          Rcpp::Named("cluster") = parObj.output[k].yf,
+          Rcpp::Named("hZ") = parObj.output[k].Ezz,
+          Rcpp::Named("hV") = parObj.output[k].Vf,
+          Rcpp::Named("Rf") = parObj.output[k].Rf,
+          Rcpp::Named("beta") = parObj.output[k].beta0,
+          Rcpp::Named("Mu") = parObj.output[k].Mu0,
+          Rcpp::Named("Sigma") = parObj.output[k].Sigma0,
+          Rcpp::Named("Psi") = parObj.output[k].Psi0,
+          Rcpp::Named("W") = parObj.output[k].W0,
+          Rcpp::Named("Lam") = parObj.output[k].Lam0,
+          Rcpp::Named("loglik") = parObj.output[k].loglik,
+          Rcpp::Named("loglik_seq") = parObj.output[k].loglik_seq);
+      }
+
+
+      return(Objidrsc2Rcpp);
     }
 
 
-  return(Objidrsc2Rcpp);
+    
 }