upload

DESCRIPTION

@@ -30,7 +30,6 @@ Imports:
     pheatmap,
     corrplot,
     circlize,
-    ggalluvial,
     ggraph,
     ggrepel,
     igraph

R/demo.R

@@ -130,9 +130,9 @@ demo_pathways <- function() {
     dest <- c("CDK2", "CDK4", "TP53", "Atf2")
     pathway <- c("p53 signaling pathway", "p53 signaling pathway", "p53 signaling pathway",
         "PI3K-Akt signaling pathway")
-    sourcename <- c("Process(activation)", "Process(activation)", "Process(binding)",
+    type <- c("Process(activation)", "Process(activation)", "Process(binding)",
         "Process(association)")
-    type <- rep("KEGG", 4)
+    sourcename <- rep("KEGG", 4)
     src_tf <- c("NO", "NO", "NO", "NO")
     dest_tf <- c("NO", "NO", "YES", "YES")
     species <- c("Human", "Human", "Human", "Mouse")

R/plot.R

@@ -338,7 +338,6 @@ plot_st_celltype_all <- function(object, size = 1, color = NULL) {
 #' @param color_low Color for the lowest value.
 #' @param color_mid Color for the middle value for using \code{scale_color_gradient2}. Default is \code{NULL}.
 #' @param color_high Color for the highest value.
-#' @param if_use_newmeta Whether to use newmeta o plot the spatial distribution of gene after \code{\link{dec_celltype}} for spot-based data. Default is \code{FALSE}.
 #' @param scale Character indicating if the values should be centered and scaled in either the row direction or the column direction, or none. Corresponding values are 'row', 'column' and 'none'.
 #' @param if_show_top Whether to plot a symbol to the highest value across rows or columns. Default is \code{TRUE}.
 #' @param top_direction Direction to identify the highest value, select \code{'row'} or \code{'column'}.

inst/CITATION

@@ -17,6 +17,7 @@ citEntry(
              as.person("Xiao Xu"),
              as.person("Xiaohui Fan")
              ),
+  journal  = "Under submission",
   year     = "2022",
   textVersion = paste("Shao et al. Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk. 2022."
   )

vignettes/sc_tutorial.Rmd

@@ -1,13 +1,13 @@
 ---
-title: "SpaTalk tutorial (spot-based ST data)"
+title: "SpaTalk tutorial (single-cell ST data)"
 author: "Xin Shao"
 date: "`r Sys.Date()`"
 output:
   prettydoc::html_pretty:
     theme: cayman
     highlight: github
 vignette: >
-  %\VignetteIndexEntry{SpaTalk tutorial (spot-based ST data)}
+  %\VignetteIndexEntry{SpaTalk tutorial (single-cell ST data)}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -46,23 +46,23 @@ obj <- dec_celltype(object = obj,
 
 ### Use `plot_st_celltype_percent()` to view cell-type percent
 
-```{r plot_st_celltype_percent, fig.width=6, fig.height=6, fig.align='center', echo=TRUE}
+```{r plot_st_celltype_percent, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # plot cell-type percent across spatial cells
-plot_st_celltype_percent(object = obj, celltype = 'eL2_3',size = 3)
+plot_st_celltype_percent(object = obj, celltype = 'Oligo',size = 2)
 
 ```
 
 ### Use `plot_st_gene()` to view gene expression
 
-```{r plot_st_gene, fig.width=6, fig.height=6, fig.align='center', echo=TRUE}
+```{r plot_st_gene, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # plot marker gene expression across spatial cells
-plot_st_gene(object = obj, gene = 'Lamp5',size = 3, if_use_newmeta = F)
+plot_st_gene(object = obj, gene = 'Plp1',size = 2, if_use_newmeta = F)
 
 ```
 
 ### Use `plot_st_cor_heatmap()` to view correlation heatmap
 
-```{r plot_st_cor_heatmap, fig.width=7, fig.height=6, fig.align='center', echo=TRUE}
+```{r plot_st_cor_heatmap, fig.width=4, fig.height=3, fig.align='center', echo=TRUE}
 # correlation between marker gene expression and cell type percent across spatial cells
 plot_st_cor_heatmap(object = obj,
                     marker_genes = c("Plp1","Vip","Sst","Lamp5","Pcp4","Mfge8","Pvalb"),
@@ -76,42 +76,42 @@ plot_st_cor_heatmap(object = obj,
 ## **ST at single-cell resolution**
 ### Use `plot_st_celltype()` to view cell type in space
 
-```{r plot_st_celltype, fig.width=6, fig.height=6, fig.align='center', echo=TRUE}
+```{r plot_st_celltype, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # plot cell type in reconstructed ST atlas
-plot_st_celltype(object = obj, celltype = 'eL2_3')
+plot_st_celltype(object = obj, celltype = 'Oligo', size = 2)
 
 ```
 
 ### Use `plot_st_gene()` to view gene expression in space
 
-```{r plot_st_gene2, fig.width=6, fig.height=6, fig.align='center', echo=TRUE}
+```{r plot_st_gene2, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # plot marker gene expression in single-cell ST data
-plot_st_gene(object = obj,gene = 'Lamp5', if_use_newmeta = T)
+plot_st_gene(object = obj,gene = 'Plp1', if_use_newmeta = T, size = 2)
 
 ```
 
 ### Use `plot_st_celltype_density()` to view cell-type density in space
 
-```{r plot_st_celltype_density, fig.width=6, fig.height=6, fig.align='center', echo=TRUE}
+```{r plot_st_celltype_density, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # plot cell-type density in single-cell ST data
 plot_st_celltype_density(object = obj,
-                         celltype = 'eL2_3',
+                         celltype = 'Oligo',
                          type = 'raster',
                          color_low = 'purple',
                          color_high = 'yellow')
 
 plot_st_celltype_density(object = obj,
-                         celltype = 'eL2_3',
+                         celltype = 'Oligo',
                          type = 'contour',
                          color_low = 'purple',
                          color_high = 'yellow')
 ```
 
 ### Use `plot_st_celltype_all()` to view all cell types in space
 
-```{r plot_st_celltype_all, fig.width=6, fig.height=6, fig.align='center', echo=TRUE}
+```{r plot_st_celltype_all, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # plot all cell types in single-cell ST data
-plot_st_celltype_all(object = obj,size = 2)
+plot_st_celltype_all(object = obj, size = 2)
 
 ```
 
@@ -143,7 +143,11 @@ obj_lr_path$path_pvalue
 
 ```{r plot_ccdist, fig.width=6, fig.height=6, fig.align='center', echo=TRUE}
 # Point plot with spatial distribution of celltype_sender and celltype_receiver
-plot_ccdist(object = obj, celltype_sender = 'eL5', celltype_receiver = 'Astro')
+plot_ccdist(object = obj,
+            celltype_sender = 'eL5',
+            celltype_receiver = 'Astro',            
+            size = 2,
+            arrow_length = 0.1)
 
 ```
 
@@ -157,14 +161,16 @@ plot_cci_lrpairs(object = obj, celltype_sender = 'eL5', celltype_receiver = 'Ast
 
 ### Use `plot_lrpair()` to view the specific LRI
 
-```{r plot_lrpair, fig.width=6, fig.height=6, fig.align='center', echo=TRUE}
+```{r plot_lrpair, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # Point plot with LR pair from celltype_sender to celltype_receiver
 plot_lrpair(object = obj,
             celltype_sender = 'eL5',
             ligand = 'Cort',
             celltype_receiver = 'Astro',
             receptor = 'Sstr2',
-            if_plot_density = F)
+            if_plot_density = F,
+            size = 2,
+            arrow_length = 0.1)
 
 ```
 
@@ -196,7 +202,6 @@ plot_lr_path(object = obj,
 
 ```{r plot_path2gene, fig.width=8, fig.height=6, fig.align='center', echo=TRUE}
 # River plot of significantly activated pathways and related downstream genes of receptors
-library(ggalluvial)
 plot_path2gene(object = obj,                
                celltype_sender = 'eL5',
                ligand = 'Cort',
@@ -210,7 +215,7 @@ plot_path2gene(object = obj,
 To infer all paired cell-cell communications, use `dec_cci_all()` instead of `dec_cci()`
 
 ```{r dec_cci_all, echo=TRUE}
-# Infer cell-cell communications from SST to PVALB neurons
+# Infer all cell-cell communications
 # obj <- dec_cci_all(object = obj)
 ```
 

vignettes/spot_tutorial.Rmd

@@ -46,7 +46,7 @@ obj <- createSpaTalk(st_data = as.matrix(st_data),
 
 ### Use `plot_st_pie_generate()` to view the cell-type composition
 
-```{r plot_st_pie_generate, echo=TRUE}
+```{r plot_st_pie_generate, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 plot_st_pie_generate(st_meta = st_meta, pie_scale = 1.3, xy_ratio = 1.8)
 
 ```
@@ -65,31 +65,31 @@ obj <- dec_celltype(object = obj,
 
 ### Use `plot_st_pie()` to view predicted cell-type composition
 
-```{r plot_st_pie, echo=TRUE}
+```{r plot_st_pie, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # Scatter pie plot for each spot
 plot_st_pie(object = obj, pie_scale = 1.3, xy_ratio = 1.8)
 
 ```
 
 ### Use `plot_st_celltype_percent()` to view cell-type percent
 
-```{r plot_st_celltype_percent, echo=TRUE}
+```{r plot_st_celltype_percent, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # plot cell-type percent across spatial spots
-plot_st_celltype_percent(object = obj, celltype = 'Oligo',size = 3)
+plot_st_celltype_percent(object = obj, celltype = 'Oligo',size = 4)
 
 ```
 
 ### Use `plot_st_gene()` to view gene expression
 
-```{r plot_st_gene, echo=TRUE}
+```{r plot_st_gene, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # plot marker gene expression across spatial spots
-plot_st_gene(object = obj, gene = 'Plp1',size = 3, if_use_newmeta = F)
+plot_st_gene(object = obj, gene = 'Plp1',size = 4, if_use_newmeta = F)
 
 ```
 
 ### Use `plot_st_cor_heatmap()` to view correlation heatmap
 
-```{r plot_st_cor_heatmap, echo=TRUE}
+```{r plot_st_cor_heatmap, fig.width=4, fig.height=3, fig.align='center', echo=TRUE}
 # correlation between marker gene expression and cell type percent across spatial spots
 plot_st_cor_heatmap(object = obj,
                     marker_genes = c("Plp1","Vip","Sst","Lamp5","Pcp4","Mfge8","Pvalb"),
@@ -103,23 +103,23 @@ plot_st_cor_heatmap(object = obj,
 ## **ST at single-cell resolution**
 ### Use `plot_st_celltype()` to view cell type in space
 
-```{r plot_st_celltype, echo=TRUE}
+```{r plot_st_celltype, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # plot cell type in reconstructed ST atlas
-plot_st_celltype(object = obj, celltype = 'Oligo')
+plot_st_celltype(object = obj, celltype = 'Oligo', size = 2)
 
 ```
 
 ### Use `plot_st_gene()` to view gene expression in space
 
-```{r plot_st_gene2, echo=TRUE}
+```{r plot_st_gene2, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # plot marker gene expression in reconstructed ST atlas
-plot_st_gene(object = obj,gene = 'Plp1', if_use_newmeta = T)
+plot_st_gene(object = obj,gene = 'Plp1', if_use_newmeta = T, size = 2)
 
 ```
 
 ### Use `plot_st_celltype_density()` to view cell-type density in space
 
-```{r plot_st_celltype_density, echo=TRUE}
+```{r plot_st_celltype_density, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # plot cell-type density in reconstructed ST atlas
 plot_st_celltype_density(object = obj,
                          celltype = 'Oligo',
@@ -136,9 +136,9 @@ plot_st_celltype_density(object = obj,
 
 ### Use `plot_st_celltype_all()` to view all cell types in space
 
-```{r plot_st_celltype_all, echo=TRUE}
+```{r plot_st_celltype_all, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # plot all cell types in reconstructed ST atlas
-plot_st_celltype_all(object = obj)
+plot_st_celltype_all(object = obj, size = 2)
 
 ```
 
@@ -167,9 +167,13 @@ obj_lr_path$path_pvalue
 
 ### Use `plot_ccdist()` to view distribution of senders and receivers
 
-```{r plot_ccdist, echo=TRUE}
+```{r plot_ccdist, fig.width=6, fig.height=6, fig.align='center', echo=TRUE}
 # Point plot with spatial distribution of celltype_sender and celltype_receiver
-plot_ccdist(object = obj, celltype_sender = 'SST', celltype_receiver = 'PVALB')
+plot_ccdist(object = obj, 
+            celltype_sender = 'SST', 
+            celltype_receiver = 'PVALB',            
+            size = 2,
+            arrow_length = 0.1)
 
 ```
 
@@ -185,20 +189,22 @@ Given the limited LR pairs, we do not show the result here
 
 ### Use `plot_lrpair()` to view the specific LRI
 
-```{r plot_lrpair, echo=TRUE}
+```{r plot_lrpair, fig.width=5, fig.height=6, fig.align='center', echo=TRUE}
 # Point plot with LR pair from celltype_sender to celltype_receiver
 plot_lrpair(object = obj,
             celltype_sender = 'SST',
             ligand = 'Sst',
             celltype_receiver = 'PVALB',
             receptor = 'Sstr2',
-            if_plot_density = F)
+            if_plot_density = F,            
+            size = 2,
+            arrow_length = 0.1)
 
 ```
 
 ### Use `plot_lrpair_vln()` to view violin plot with spatial distance of LRI
 
-```{r plot_lrpair_vln, echo=TRUE}
+```{r plot_lrpair_vln, fig.width=8, fig.height=6, fig.align='center', echo=TRUE}
 # Violin plot with spatial distance of LR pair between senders and receivers and between all cell-cell pairs
 plot_lrpair_vln(object = obj,
                 celltype_sender = 'SST',
@@ -210,7 +216,7 @@ plot_lrpair_vln(object = obj,
 
 ### Use `plot_lr_path()` to view network with LR and downstream pathways
 
-```{r plot_lr_path, echo=TRUE}
+```{r plot_lr_path, fig.width=6, fig.height=6, fig.align='center', echo=TRUE}
 # Plot network with LR and downstream pathways
 plot_lr_path(object = obj,                
              celltype_sender = 'SST',
@@ -222,7 +228,7 @@ plot_lr_path(object = obj,
 
 ### Use `plot_path2gene()` to view river plot of significantly activated pathways
 
-```{r plot_path2gene, echo=TRUE}
+```{r plot_path2gene, fig.width=8, fig.height=6, fig.align='center', echo=TRUE}
 # River plot of significantly activated pathways and related downstream genes of receptors
 plot_path2gene(object = obj,                
                celltype_sender = 'SST',
@@ -237,7 +243,7 @@ plot_path2gene(object = obj,
 To infer all paired cell-cell communications, use `dec_cci_all()` instead of `dec_cci()`
 
 ```{r dec_cci_all, echo=TRUE}
-# Infer cell-cell communications from SST to PVALB neurons
+# Infer all cell-cell communications
 # obj <- dec_cci_all(object = obj)
 ```