Interactively explore and visualize Single Cell RNA seq data
2021-10-26
Contents
1 Introduction
scTreeViz is a package for interactive visualization and exploration of Single Cell RNA sequencing data. scTreeViz provides methods for exploring hierarchical features (eg. clusters in single cell at different resolutions or taxonomic hierarchy in single cell datasets), while supporting other useful data visualization charts like heatmaps for expression and scatter plots for dimensionality reductions like UMAP or TSNE.
1.1 Loading required packages
library(scTreeViz)
library(Seurat)
library(SC3)
library(scran)
library(scater)
library(clustree)
library(igraph)
library(scRNAseq)2 Preparing Datasets
The first step in using the scTreeViz package is to wrap datasets into TreeViz objects. The TreeViz class extends SummarizedExperiment and provides various methods to interactively perform various operations on the underlying hierarchy and count or expression matrices. In this section, we show various ways to generate a TreeViz object either from existing Single Cell packages (SingleCellExperiment or Seurat) or from a raw count matrix and cluster hierarchy.
2.1 From SingleCellExperiment
A number of Single cell datasets are available as SingleCellExperiment objects through the scRNAseq package, for this usecase, we use LunSpikeInData dataset. In addition, we calculate the dimensionality reductions; UMAP, TSNE and PCA from the functions provided in scater package.
# load dataset
sce<- ZeiselBrainData()
# Normalization
sce <- logNormCounts(sce)
# calculate umap and tsne
sce <- runUMAP(sce)
sce<- runTSNE(sce)
sce<- runPCA(sce)We provide createFromSCE function to create a TreeViz object from SingleCellExperiment object. Here, the workflow works in two ways:
- If no cluster information is available in the
colDataof theSingleCellExperimentobject, we create clusters at different resolutions using theWalkTrapalgorithm by calling an internal functiongenerate_walktrap_hierarchyand use this cluster information for visualization.
treeViz <- createFromSCE(sce, reduced_dim = c("UMAP","PCA","TSNE"))
#> [1] "1.cluster1" "2.cluster2" "3.cluster3" "4.cluster4" "5.cluster5"
#> [6] "6.cluster6" "7.cluster7" "8.cluster8" "9.cluster9" "10.cluster10"
#> [11] "11.cluster11" "12.cluster12" "13.cluster13" "14.cluster14" "samples"
plot(treeViz)
- If cluster information is provided in the
colDataof the object, then the user should set the flag parametercheck_coldatatoTRUEand provide prefix for the columns where cluster information is stored.
# Forming clusters
set.seed(1000)
for (i in seq(10)) {
clust.kmeans <- kmeans(reducedDim(sce, "TSNE"), centers = i)
sce[[paste0("clust", i)]] <- factor(clust.kmeans$cluster)
}
treeViz<- createFromSCE(sce, check_coldata = TRUE, col_regex = "clust")
plot(treeViz)Note: In both cases the user needs to provide the name of dimensionality reductions present in the object as a parameter.
2.2 From Seurat
We use the dataset pbmc_small available through Seurat to create a TreeViz object.
data(pbmc_small)
pbmc <- pbmc_smallWe then preprocess the data and find clusters at different resolutions.
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
pbmc <- NormalizeData(pbmc)
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, vars.to.regress = "percent.mt")
pbmc <- FindVariableFeatures(object = pbmc)
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
pbmc <- FindNeighbors(pbmc, dims = 1:10)
pbmc <- FindClusters(pbmc, resolution = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0), print.output = 0, save.SNN = TRUE)
pbmcThe measurements for dimensionality reduction methods we want to visualize are also added to the object via native functions in Seurat. Since PCA is already added, we calculate TSNE and UMAP
# pbmc<- RunTSNE(pbmc)
pbmc<- RunUMAP(pbmc, dims=1:3)
Reductions(pbmc)We use the createFromSeurat function to create a TreeViz object from Seurat object. In addition the object, we pass the name of dimensionality reductions present in the object as a paramter in vector format to indicate these measurements should be added to treeviz for visualization. If the mentioned reduced dimension is not present it would simply be ignored.
treeViz<- createFromSeurat(pbmc, check_metadata = TRUE, reduced_dim = c("umap","pca","tsne"))
#> [1] "6.cluster6" "10.cluster10" "11.cluster11" "samples"
#> [1] "umap" "pca" "tsne"
plot(treeViz)
2.3 Create TreeViz from count matrix and Cluster hierarchy
n=64
# create a hierarchy
df<- data.frame(cluster0=rep(1,n))
for(i in seq(1,5)){
df[[paste0("cluster",i)]]<- rep(seq(1:(2**i)),each=ceiling(n/(2**i)),len=n)
}
# generate a count matrix
counts <- matrix(rpois(6400, lambda = 10), ncol=n, nrow=100)
colnames(counts)<- seq(1:64)
# create a `TreeViz` object
treeViz <- createTreeViz(df, counts)
plot(treeViz)
2.4 Start the TreeViz App (using hosted app)
Start the App from the treeViz object we created. This adds a facetZoom to navigate the cluster hierarchy, a heatmap of the top n most variable genes from the dataset, where ‘n’ is selected by the user and one scatter plot for each of the reduced dimensions.
app <- startTreeviz(treeViz, top_genes = 500)
Cell 416B dataset
pbmc dataset
Users can also use the interface to explore the same dataset using different visualizations available through Epiviz.
2.5 Visualize gene expression across clusters
Users can also add Gene Box plots using either the frontend application, or from R session. In the following example, we visualize the 5th, 50th and 500th most variable gene as Box plots
2.5.1 Adding Gene Box Plots via UI
Users need to select Add Visualization -> Gene Box PLot option from menu and then select the desired gene using the search pane in the appeared dialogue box
Selecting a gene to visualize
2.5.2 Adding Gene Box Plots via R Session
Users can also select the gene from R session by using the plotGene command followed by Gene name.
app$plotGene(gene="AIF1")2.6 Stop App
After exploring the dataset, this command the websocket connection.
app$stop_app()2.7 Session
sessionInfo()
#> R version 4.1.1 (2021-08-10)
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