fixes spatially variable issues

3_integration_clustering.qmd

@@ -9,6 +9,7 @@ engine: knitr
 library(Seurat)
 library(ggplot2)
 library(clustree)
+library(patchwork)
 ```
 
 ```{r}
@@ -96,21 +97,53 @@ SpatialPlot(seu_int, group.by = res) +
   scale_fill_viridis_d(option = "H")
 ```
 
+```{r}
+Idents(seu_int) <- "integrated_snn_res.0.4"
+
+SpatialDimPlot(seu_int, 
+               alpha = 0.6,
+               cells.highlight = CellsByIdentities(object = seu_int,
+                                                   idents = 7)) + 
+  plot_layout(guides='collect') &
+  theme(legend.position = "none") 
+
+SpatialDimPlot(seu_int, 
+               alpha = 0.6,
+               cells.highlight = CellsByIdentities(object = seu_int,
+                                                   idents = 0)) + 
+  plot_layout(guides='collect') &
+  theme(legend.position = "none") 
+```
+
+```{r}
+seu_int <- PrepSCTFindMarkers(seu_int)
+all_marks <- FindAllMarkers(seu_int, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
+```
+
+```{r}
+SpatialPlot(seu_int, features = "Fth1") 
+# oligodendrocytes, myenilated neurons
+```
+
 
 
 ```{r}
+DefaultAssay(seu_list$Anterior) <- "SCT"
+
 seu_list$Anterior <-
   FindSpatiallyVariableFeatures(
     seu_list$Anterior,
     features = rownames(seu_int),
-    selection.method = "markvariogram"
+    selection.method = "moransi"
   )
 
+sf <- SpatiallyVariableFeatures(seu_list$Anterior)
+
 spatialFeatures <-
-  SVFInfo(seu_int, method = "markvariogram", status = TRUE)
-spatialFeatures <- na.omit(spatialFeatures)
+  SVFInfo(seu_list$Anterior, method = "moransi", status = TRUE)
 spatialFeatures <-
-  spatialFeatures %>% filter(variable == TRUE) %>% arrange(rank)
-spatialFeatures <- spatialFeatures[, c("r.metric.5"), drop = FALSE]
+  spatialFeatures |> arrange(rank)
+
+SpatialPlot(seu_list$Anterior, features = rownames(spatialFeatures)[1:6], ncol = 3, alpha = c(0.1, 1))
 ```