DR-SC: HCC1 Data Analysis

Wei Liu

2021-11-19

The package can be loaded with the command:

library("DR.SC")

Fit DR-SC using real data CRC

load the data HCC1 in package DR.SC

Data preprocessing

Fit DR-SC model using 2000 highly variable features

We set the argument variable.type=‘HVGs’ (default option) to use the highly variable genes.

### Given K
seu <- DR.SC(seu, K=6, platform = 'Visium', variable.type = 'HVGs',verbose=F)

Visualization

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

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

Fit DR-SC model using 2000 spatially variable features

# choose 2000 spatially variable features
seus <- FindSVGs(seu, nfeatures = 1000)

We set the argument variable.type=‘SVGs’ (default option) to use the spatially variable genes.

### Given K
seus <- DR.SC(seus, K=6, platform = 'Visium', variable.type='SVGs', verbose=T)

Visualization

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.

Ridge plots

Find the marker genes for each clusters

Visualize single cell expression distributions in each cluster from Seruat.

Violin plot

Visualize single cell expression distributions in each cluster

Feature plot

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

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

Heatmap plot

Single cell heatmap of feature expression

Fit DR-SC model using 2000 spatially variable features and using MBIC to determine clusters

# choose 2000 spatially variable features
seus <- FindSVGs(seu, nfeatures = 2000, verbose = F)

We set the argument variable.type=‘SVGs’ (default option) to use the spatially variable genes.

### Given K
seus <- DR.SC(seus, K_set=3:8, platform = 'Visium', variable.type='SVGs', verbose=F)

Plot the MBIC curve

mbicPlot(seus)

Show the spatial scatter plot for clusters

spatialPlotClusters(seus)

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

drscPlot(seus, dims=1:10)

Session information

sessionInfo()