Updates for compiling parameters

DESCRIPTION

@@ -15,8 +15,8 @@ Description: A novel spatial topic model to integrate both cell type and spatial
              information for every single cell.
 License: GPL (>= 3)
 Depends: R (>= 3.5.0),
-         Rcpp (>= 0.12.0),
-Imports: RANN (>= 2.6.0),
+Imports: Rcpp (>= 0.12.0),
+         RANN (>= 2.6.0),
          sf (>= 1.0-12),
          methods (>= 3.4),
          foreach (>= 1.5.0),

NAMESPACE

@@ -4,8 +4,10 @@ export(Seurat5obj_to_SpaTopic)
 export(SpaTopic_inference)
 export(stratified_sampling_sf)
 import(foreach)
+import(iterators)
+import(methods)
 importFrom(RANN,nn2)
-importFrom(Rcpp,evalCpp)
+importFrom(Rcpp,sourceCpp)
 importFrom(sf,st_as_sf)
 importFrom(sf,st_cast)
 importFrom(sf,st_crs)

R/Gibbs_sampler.R

@@ -97,8 +97,13 @@ gibbs_spatial_LDA_multiple<-function(...){
 #' @seealso \code{\link{gibbs.res-class}}
 #' 
 #' @importFrom RANN nn2
+#' 
 #' @import foreach
 #' 
+#' @import methods
+#' 
+#' @import iterators
+#' 
 #' @examples 
 #' 
 #' ## tissue is a data frame containing cellular information from one image or

R/SpaTopic-package.R

@@ -26,7 +26,7 @@
 #'
 #' @keywords package
 #' 
-#' @importFrom Rcpp evalCpp
+#' @importFrom Rcpp sourceCpp
 #' 
 #' @useDynLib SpaTopic
 NA

docs/pkgdown.yml

@@ -5,5 +5,5 @@ articles:
   Model_Selection: Model_Selection.html
   SpaTopic: SpaTopic.html
   Intro_SpaTopic: Intro_SpaTopic.html
-last_built: 2023-12-12T19:28Z
+last_built: 2023-12-12T20:53Z
 

src/Makevars

@@ -15,9 +15,9 @@
 ## set the appropriate value, possibly CXX17.
 #CXX_STD = CXX11
 
-PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS)
-PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
-CXXFLAGS=-O3
+PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) -Ilibsrc $(SHLIB_OPENMP_CXXFLAGS) ‘$(R_HOME)/bin/Rscript -e "RcppProgress:::CxxFlags()"‘
+PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) ‘$(R_HOME)/bin/Rscript -e "Rcpp:::LdFlags()"‘ $(SHLIB_OPENMP_CXXFLAGS) ‘$(R_HOME)/bin/Rscript -e "RcppProgress:::CxxFlags()"‘
+#CXXFLAGS=-O3
 CC=ccache clang -Qunused-arguments
 CXX=ccache clang++ -Qunused-arguments
 CCACHE_CPP2=yes

src/Makevars.win

@@ -17,7 +17,7 @@
 
 PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS)
 PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
-CXXFLAGS=-O3
+#CXXFLAGS=-O3
 CC=ccache clang -Qunused-arguments
 CXX=ccache clang++ -Qunused-arguments
 CCACHE_CPP2=yes

vignettes/articles/SpaTopic.Rmd

@@ -40,7 +40,7 @@ The Lung5-1 sample contains 38 annotated cell types.
 Since we used healthy lung tissue as the reference, tumor cells were labeled as ’basal’ cells.
 More informaion can be found [here](https://satijalab.org/seurat/articles/seurat5_spatial_vignette_2.html#human-lung-nanostring-cosmx-spatial-molecular-imager).
 
-```{r,message=FALSE,warning=FALSE}
+```{r seurat,message=FALSE,warning=FALSE}
 ## We use Seurat v5 package to visualze the results
 library(Seurat, quietly = TRUE);packageVersion("Seurat")
 ## Load the Seurat object for the image
@@ -51,7 +51,7 @@ options(future.globals.maxSize = 1e9)
 
 We can use the Seurat function `ImageDimPlot` to visualize the distribution of cell types on the image.
 
-```{r,fig.width=7, fig.height=13,message=FALSE,warning=FALSE}
+```{r celltype,fig.width=7, fig.height=13,message=FALSE,warning=FALSE}
 library(ggplot2)
 celltype.plot <-ImageDimPlot(nano.obj, fov = "lung5.rep1", axes = TRUE, cols = "glasbey",dark.background = T)
 celltype.plot+theme(legend.position = "bottom",legend.direction = "vertical")
@@ -69,7 +69,7 @@ The required input of SpaTopic is a data frame containing cells within on a sing
 You may use the function `Seurat5obj_to_SpaTopic()` to extract input data from a typical Seurat v5 object. 
 The column name for cell type information need to be provided via option `group.by`.
 
-```{r,message=FALSE,warning=FALSE}
+```{r reformat, message=FALSE,warning=FALSE}
 library(SpaTopic);packageVersion("SpaTopic")
 ## Prepare input from Seurat Object
 dataset<-Seurat5obj_to_SpaTopic(object = nano.obj, group.by = "predicted.annotation.l1",image = "image1")
@@ -80,7 +80,7 @@ head(dataset)
 
 This step takes around 90 seconds on a regular laptop
 
-```{r}
+```{r inference}
 ## Gibbs sampling for SpaTopic
 system.time(gibbs.res<-SpaTopic_inference(dataset, ntopics = 7, sigma = 50, region_radius = 400))
 ```
@@ -91,7 +91,7 @@ system.time(gibbs.res<-SpaTopic_inference(dataset, ntopics = 7, sigma = 50, regi
 SpaTopic identify seven topics from the image. 
 Below we use the heatmap to show the cell type composition within each topic.
 
-```{r,fig.width=7,fig.height=4}
+```{r heatmap,fig.width=7,fig.height=4}
 library(pheatmap)
 m <- as.data.frame(gibbs.res$Beta)
 colnames(m)<-paste0("topic",1:7)
@@ -102,7 +102,7 @@ pheatmap::pheatmap(t(m))
 We assign each cell to a topic with the highest posterior probability
 and visualize the distribution of cell topics over the image.
 
-```{r,fig.width=7, fig.height=8}
+```{r topics,fig.width=7, fig.height=8}
 prob<-as.matrix(gibbs.res$Z.trace)
 nano.obj$Topic<-as.factor(apply(prob,1,which.max))
 library(ggplot2)
@@ -115,15 +115,15 @@ ImageDimPlot(nano.obj, fov = "lung5.rep1", group.by = "Topic", axes = TRUE,
 We compare `SpaTopic` to the function `BuildNicheAssay()` in Seurat v5. 
 It took around 5 min on the same laptop.
 
-```{r,eval=FALSE}
+```{r niches,eval=FALSE}
 ### NOT RUN!! We use the pre-computed result
 system.time(nano.obj <- BuildNicheAssay(object = nano.obj, "lung5.rep1", group.by = "predicted.annotation.l1",
                             niches.k = 7, neighbors.k = 100))
 ```
 
 We also visualize the distribution of seven niches over the same image.
 
-```{r,fig.width=7, fig.height=8}
+```{r niches_vis,fig.width=7, fig.height=8}
 ImageDimPlot(nano.obj, fov = "lung5.rep1", group.by = "niches", axes = TRUE, dark.background = T,cols = "glasbey") + ggtitle("Niches") 
 ```