DR-SC: DLPFC Data Analysis

Prepare Seurat object for DR-SC

We start this tutorial with creating the Seurat object. Users who are familar with Seurat object, can skip this subsection and go to next subsection.

First, we load the subset data of DLPFC 151510, dlpfc151510. Although it is saved as a Seurat object prepared for DR-SC, we re-create the Seurat object to show how to prepare the Seurat object for DR-SC.

data("dlpfc151510", package = 'DR.SC')

Second, we create a Seurat object using the count matrix in dlpfc151510 and the spatial coordinates in the meta.data slot.

library(Seurat)
count <- dlpfc151510@assays$RNA@counts
meta_data <- data.frame(row=dlpfc151510@meta.data$row, col=dlpfc151510@meta.data$col, annotation=dlpfc151510$annotation)
row.names(meta_data) <- colnames(count)
## create Seurat object
dlpfc151510 <- CreateSeuratObject(counts=count, meta.data = meta_data)
head(dlpfc151510)

Until now, the data preparation with Seurat object format is finished, and we can go to next step: preprocessing.

Data preprocessing

This preprocessing includes Log-normalization and feature selection. Here we select highly variable genes for example first. The selected genes’ names are saved in “seu@assays$RNA@var.features”

# standard log-normalization
dlpfc151510 <- NormalizeData(dlpfc151510, verbose = F)
# choose 500 highly variable features
seu <- FindVariableFeatures(dlpfc151510, nfeatures = 500, verbose = F)
seu@assays$RNA@var.features[1:10]

Visualization

spatialPlotClusters(seu)

Show the tSNE plot based on the extracted features from DR-SC.

drscPlot(seu)

Show the UMAP plot based on the extracted features from DR-SC.

drscPlot(seu, visu.method = 'UMAP')

Visualization

Next, we show the application of DR-SC in visualization. First, we can visualize the clusters from DR-SC on the spatial coordinates.

spatialPlotClusters(seus)
mclust::adjustedRandIndex(seus$spatial.drsc.cluster, seus$annotation)

We can also visualize the clusters from DR-SC on the two-dimensional tSNE based on the extracted features from DR-SC.

drscPlot(seus)

Similarly, can also visualize the clusters from DR-SC on the two-dimensional UMAP based on the extracted features from DR-SC.

drscPlot(seus, visu.method = 'UMAP')

Since DR.SC uses the Seurat object to save results, all visualization functions in Seurat package can used to visualize the results of DR-SC, such as ridge plot, feature plot, dot plot and so on. ### Ridge plots we show the application of DR-SC in differential expression analysis. Find the marker genes in SVGs for each clusters.

SVGs <- topSVGs(seus, ntop = 400)
dat <- FindAllMarkers(seus, features = SVGs)
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.

genes <- top2$gene[seq(1, 12, by=2)]
RidgePlot(seus, features = genes, ncol = 2)

Violin plot

Visualize single cell expression distributions in each cluster

VlnPlot(seus, features = genes, ncol=2)

Feature plot

We extract tSNE based on the features from DR-SC and then visualize feature expression in the low-dimensional space

seus <- RunTSNE(seus, reduction="dr-sc", reduction.key='drsc_tSNE_')
FeaturePlot(seus, features = genes, reduction = 'tsne' ,ncol=2)

Dot plots

The size of the dot corresponds to the percentage of cells expressing the feature in each cluster. The color represents the average expression level

DotPlot(seus, features = genes)

Heatmap plot

Single cell heatmap of feature expression

top20 <-  dat %>%
  group_by(cluster) %>%
  top_n(n = 20, wt = avg_log2FC)
genes <- top20$gene
# standard scaling (no regression)
seus <- ScaleData(seus)
DoHeatmap(subset(seus, downsample = 500), features = genes, size = 5)