The package can be loaded with the command:
We set the argument variable.type=‘HVGs’ (default option) to use the highly variable genes.
Show the tSNE plot based on the extracted features from DR-SC.
Show the UMAP plot based on the extracted features from DR-SC.
We set the argument variable.type=‘SVGs’ (default option) to use the spatially variable genes.
Show the spatial scatter plot for clusters
Show the tSNE plot based on the extracted features from DR-SC.
Show the UMAP plot based on the extracted features from DR-SC.
Find the marker genes for each clusters
dat <- FindAllMarkers(seus)
head(dat)
library(dplyr, verbose=F)
top2 <- dat %>%
group_by(cluster) %>%
top_n(n = 2, wt = avg_log2FC)
top2
Visualize single cell expression distributions in each cluster from Seruat.
Visualize single cell expression distributions in each cluster
We extract tSNE based on the features from DR-SC and then visualize feature expression in the low-dimensional space
The size of the dot corresponds to the percentage of cells expressing the feature in each cluster. The color represents the average expression level
Single cell heatmap of feature expression
We set the argument variable.type=‘SVGs’ (default option) to use the spatially variable genes.
Plot the MBIC curve
Show the spatial scatter plot for clusters
Show the tSNE plot based on the extracted features from DR-SC.