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huayc09 / SeuratExtend

https://huayc09.github.io/SeuratExtend/
GNU General Public License v3.0
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SeuratExtend: An Enhanced Toolkit for scRNA-seq Analysis

Overview

SeuratExtend is an R package designed to provide an improved and easy-to-use toolkit for scRNA-seq analysis and visualization, built upon the Seurat object. While Seurat is a widely-used tool in the R community that offers a foundational framework for scRNA-seq analysis, it has limitations when it comes to more advanced analysis and customized visualization. SeuratExtend expands upon Seurat by offering an array of enhanced visualization tools, an integrated functional and pathway analysis pipeline, seamless integration with popular Python tools, and a suite of utility functions for data manipulation and presentation. Designed to be user-friendly even for beginners, the package retains a level of professionalism that ensures rigorous analysis.

Key Features:

Resources

Installation

Install SeuratExtend directly from GitHub:

if (!requireNamespace("remotes", quietly = TRUE)) {
    install.packages("remotes")
}
remotes::install_github("huayc09/SeuratExtend")

Vignettes and Tutorials

Quick Start-Up Guide

Enhanced Visualization

Geneset Enrichment Analysis (GSEA)

Trajectory and Pseudotime Analysis

SCENIC for Gene Regulatory Networks Analysis

Utility Tools and Functions

FAQ

Quick Start-Up Guide

This quick start-up guide provides an overview of the most frequently used functions in single-cell RNA sequencing (scRNA-seq) analysis. After running the standard Seurat pipeline (refer to this Seurat pbmc3k tutorial), you should have a Seurat object ready for further analysis. Below, we illustrate the use of a subset of the pbmc dataset as an example to demonstrate various functionalities of the SeuratExtend package.

Visualizing Clusters

library(Seurat)
library(SeuratExtend)

# Visualizing cell clusters using DimPlot2
DimPlot2(pbmc)

Analyzing Cluster Distribution

To check the percentage of each cluster within different samples:

# Cluster distribution bar plot
ClusterDistrBar(pbmc$orig.ident, pbmc$cluster)

Marker Gene Analysis with Heatmap

To examine the marker genes of each cluster and visualize them using a heatmap:

# Calculating z-scores for variable features
genes.zscore <- CalcStats(
  pbmc,
  features = VariableFeatures(pbmc),
  group.by = "cluster",
  order = "p",
  n = 4)

# Displaying heatmap
Heatmap(genes.zscore, lab_fill = "zscore")

Enhanced Visualization of Marker Genes

For visualizing specific markers via a violin plot that incorporates box plots, median lines, and performs statistical testing:

# Specifying genes and cells of interest
genes <- c("CD3D", "CD14", "CD79A")
cells <- WhichCells(pbmc, idents = c("B cell", "CD8 T cell", "Mono CD14"))

# Violin plot with statistical analysis
VlnPlot2(
  pbmc,
  features = genes,
  group.by = "cluster",
  cells = cells,
  stat.method = "wilcox.test")

Visualizing Multiple Markers on UMAP

Displaying three markers on a single UMAP, using RYB coloring for each marker:

FeaturePlot3(pbmc, feature.1 = "CD3D", feature.2 = "CD14", feature.3 = "CD79A")

Conducting Geneset Enrichment Analysis (GSEA)

Examining all the pathways of the immune process in the Gene Ontology (GO) database, and visualizing by a heatmap that displays the top pathways of each cluster across multiple cell types:

options(spe = "human")
pbmc <- GeneSetAnalysisGO(pbmc, parent = "immune_system_process", n.min = 5)
matr <- RenameGO(pbmc@misc$AUCell$GO$immune_system_process)
go_zscore <- CalcStats(
  matr,
  f = pbmc$cluster,
  order = "p",
  n = 3)
Heatmap(go_zscore, lab_fill = "zscore")

Detailed Comparison of Two Cell Types

Using a GSEA plot to focus on a specific pathway for deeper comparative analysis:

GSEAplot(
  pbmc,
  ident.1 = "B cell",
  ident.2 = "CD8 T cell",
  title = "GO:0042113 B cell activation (335g)",
  geneset = GO_Data$human$GO2Gene[["GO:0042113"]])

Importing and Visualizing SCENIC Analysis

After conducting Gene Regulatory Networks Analysis using pySCENIC, import the output and visualize various aspects within Seurat:

# Downloading a pre-computed SCENIC loom file
scenic_loom_path <- file.path(tempdir(), "pyscenic_integrated-output.loom")
download.file("https://zenodo.org/records/10944066/files/pbmc3k_small_pyscenic_integrated-output.loom", scenic_loom_path)

# Importing SCENIC Loom Files into Seurat
pbmc <- ImportPyscenicLoom(scenic_loom_path, seu = pbmc)

# Visualizing variables such as cluster, gene expression, and SCENIC regulon activity with customized colors
DimPlot2(
  pbmc,
  features = c("cluster", "orig.ident", "CEBPA", "tf_CEBPA"),
  cols = list("tf_CEBPA" = "D"),
  theme = NoAxes()
)

# Creating a waterfall plot to compare regulon activity between cell types
DefaultAssay(pbmc) <- "TF"
WaterfallPlot(
  pbmc,
  features = rownames(pbmc),
  ident.1 = "Mono CD14",
  ident.2 = "CD8 T cell",
  exp.transform = FALSE,
  top.n = 20)

Trajectory Analysis with Palantir in R

Trajectory analysis helps identify developmental pathways and transitions between different cell states. In this section, we demonstrate how to perform trajectory analysis using the Palantir algorithm on a subset of myeloid cells, integrating everything within the R environment.

Download and Prepare the Data

First, we download a small subset of myeloid cells to illustrate the analysis:

# Download the example Seurat Object with myeloid cells
mye_small <- readRDS(url("https://zenodo.org/records/10944066/files/pbmc10k_mye_small_velocyto.rds", "rb"))

Diffusion Map Calculation

Palantir uses diffusion maps for dimensionality reduction to infer trajectories. Here’s how to compute and visualize them:

# Compute diffusion map
mye_small <- Palantir.RunDM(mye_small)

# Visualize the first two diffusion map dimensions
DimPlot2(mye_small, reduction = "ms")

Pseudotime Calculation

Pseudotime ordering assigns each cell a time point in a trajectory, indicating its progression along a developmental path:

# Calculate pseudotime with a specified start cell
mye_small <- Palantir.Pseudotime(mye_small, start_cell = "sample1_GAGAGGTAGCAGTACG-1")

# Store pseudotime results in meta.data for easy plotting
ps <- mye_small@misc$Palantir$Pseudotime
colnames(ps)[3:4] <- c("fate1", "fate2")
mye_small@meta.data[,colnames(ps)] <- ps

# Visualize pseudotime and cell fates
DimPlot2(
  mye_small,
  features = colnames(ps),
  reduction = "ms",
  cols = list(Entropy = "D"))

Visualization Along Trajectories

Visualizing gene expression or regulon activity along calculated trajectories can provide insights into dynamic changes:

# Create smoothed gene expression curves along trajectory
GeneTrendCurve.Palantir(
  mye_small,
  pseudotime.data = ps,
  features = c("CD14", "FCGR3A")
)

# Create a gene trend heatmap for different fates
GeneTrendHeatmap.Palantir(
  mye_small,
  features = VariableFeatures(mye_small)[1:10],
  pseudotime.data = ps,
  lineage = "fate1"
)

scVelo Analysis

scVelo is a Python tool used for RNA velocity analysis. We demonstrate how to integrate and analyze velocyto-generated data within the Seurat workflow using scVelo.

Preparing for scVelo

First, download the pre-calculated velocyto loom file:

# Download velocyto loom file
loom_path <- file.path(tempdir(), "pbmc10k_mye_small.loom")
download.file("https://zenodo.org/records/10944066/files/pbmc10k_mye_small.loom", loom_path)

# Path for saving the integrated AnnData object
adata_path <- file.path(tempdir(), "mye_small.h5ad")

# Integrate Seurat Object and velocyto loom into an AnnData object
scVelo.SeuratToAnndata(
  mye_small,
  filename = adata_path,
  velocyto.loompath = loom_path,
  prefix = "sample1_",
  postfix = "-1"
)

Plotting scVelo Results

Once the data is processed, visualize the RNA velocity:

# Plot RNA velocity
scVelo.Plot(color = "cluster", basis = "ms_cell_embeddings", figsize = c(5,4))

For detailed usage of the functions and more advanced analysis, please refer to the vignettes and tutorials.

License

GPL (>= 3)

Publications Using SeuratExtend

  1. Hua, Y., Vella, G., Rambow, F., et al. (2022). Cancer immunotherapies transition endothelial cells into HEVs that generate TCF1+ T lymphocyte niches through a feed-forward loop. Cancer Cell 40, 1600-1618. https://doi.org/10.1016/j.ccell.2022.11.002
  2. Hua, Y., Wu, N., Miao, J., Shen, M. (2023). Single-cell transcriptomic analysis in two patients with rare systemic autoinflammatory diseases treated with anti-TNF therapy. Front. Immunol. 14. https://doi.org/10.3389/fimmu.2023.1091336