The following is a full example of usage of the widget with a Seurat object.
First, install the R dependencies:
install.packages("seurat")
install.packages("devtools")
devtools::install_github("mojaveazure/seurat-disk")
devtools::install_github("vitessce/vitessceAnalysisR")Download the dataset, load and preprocess the Seurat object, and configure the Vitessce widget:
library(vitessceR)
library(vitessceAnalysisR)
library(Seurat)
# Download example dataset
url <- "https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz"
save_dir <- file.path("data", "seurat")
dir.create(save_dir)
download.file(url, destfile = file.path(save_dir, "filtered_gene_bc_matrices.tar.gz"))
untar(file.path(save_dir, "filtered_gene_bc_matrices.tar.gz"), exdir = save_dir)
# Load example dataset
pbmc.data <- Read10X(data.dir = file.path(save_dir, "filtered_gene_bc_matrices", "hg19"))
# Process example dataset (run PCA and cluster)
pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
pbmc <- FindNeighbors(pbmc, dims = 1:10)
pbmc <- FindClusters(pbmc, resolution = 0.5)
pbmc <- ScaleData(pbmc, features = all.genes, do.center = FALSE)
adata_path <- file.path("data", "seurat", "pbmc3k.h5ad.zarr")
vitessceAnalysisR::seurat_to_anndata_zarr(pbmc, adata_path)
# Create Vitessce view config
vc <- VitessceConfig$new(schema_version = "1.0.16", name = "My config")
dataset <- vc$add_dataset("My dataset")$add_object(AnnDataWrapper$new(
adata_path=adata_path,
obs_set_paths = c("obs/seurat_clusters"),
obs_embedding_paths = c("obsm/X_pca"),
obs_embedding_names = c("PCA"),
obs_feature_matrix_path = "X"
))
scatterplot <- vc$add_view(dataset, Component$SCATTERPLOT, mapping = "PCA")
status <- vc$add_view(dataset, Component$STATUS)
desc <- vc$add_view(dataset, Component$DESCRIPTION)
desc <- desc$set_props(description = "Visualization of a Seurat object containing the PBMC 3K dataset.")
genes <- vc$add_view(dataset, Component$FEATURE_LIST)
heatmap <- vc$add_view(dataset, Component$HEATMAP)
vc$link_views(
list(scatterplot, heatmap),
list(CoordinationType$FEATURE_VALUE_COLORMAP_RANGE),
list(c(0.0, 0.05))
)
vc$layout(hconcat(
vconcat(scatterplot, heatmap),
vconcat(genes, vconcat(desc, status))
))
# Render the Vitessce widget
vc$widget(theme = "light")