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https://github.com/huayc09/SeuratExtend
https://github.com/huayc09/SeuratExtend
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- Host: GitHub
- URL: https://github.com/huayc09/SeuratExtend
- Owner: huayc09
- License: gpl-3.0
- Created: 2020-03-01T16:06:00.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-07-11T14:48:36.000Z (4 months ago)
- Last Synced: 2024-07-12T10:26:05.074Z (4 months ago)
- Language: R
- Size: 163 MB
- Stars: 3
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# 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**:
- **Enhanced Data Visualization**: Includes heatmaps, violin plots, feature plots, waterfall plots, proportion bars, and GSEA plots.
- **Integrated Functional and Pathway Analysis**: Supports GO and Reactome databases, with the option to use custom databases.
- **Python Tool Integration**: Easily apply tools like scVelo, SCENIC, and Palantir within R using the Seurat object.
- **Utility Functions**: Assorted functions for calculations and color selections to streamline your scRNA-seq analysis.
## Resources
- **GitHub Repository**: Access the source code and contribute to SeuratExtend on [GitHub](https://github.com/huayc09/SeuratExtend).
- **Online Tutorial**: For a comprehensive guide on using SeuratExtend, visit our [tutorial website](https://huayc09.github.io/SeuratExtend/).
- **SeuratExtend Chatbot**: Try our AI-powered assistant (beta version, powered by ChatGPT) for help with scRNA-seq analysis: [scRNA-seq Assistant](https://chatgpt.com/g/g-8scQjmzkd-scrna-seq-assistant).
## Installation
Install `SeuratExtend` directly from GitHub:
```R
if (!requireNamespace("remotes", quietly = TRUE)) {
install.packages("remotes")
}
remotes::install_github("huayc09/SeuratExtend")
```
## Vignettes and Tutorials
### [Quick Start-Up Guide](#quick-start-up-guide-1)
### [Enhanced Visualization](vignettes/Visualization.md)
- [Generate a Heatmap Plot](vignettes/Visualization.md#generate-a-heatmap-plot) `Heatmap`
- [Create an Enhanced Dimensional Reduction Plot](vignettes/Visualization.md#create-an-enhanced-dimensional-reduction-plot) `DimPlot2` `FeaturePlot3` `FeaturePlot3.grid`
- [Create an Enhanced Violin Plot](vignettes/Visualization.md#create-an-enhanced-violin-plot) `VlnPlot2`
- [Visualize Cluster Distribution in Samples](vignettes/Visualization.md#visualize-cluster-distribution-in-samples) `ClusterDistrBar`
- [Generate a Waterfall Plot](vignettes/Visualization.md#generate-a-waterfall-plot) `WaterfallPlot`
- [Explore Color Functions](Visualization.md#explore-color-functions) `color_pro` `color_iwh` `ryb2rgb` `save_colors`
### [Geneset Enrichment Analysis (GSEA)](vignettes/GSEA.md)
- [Conduct GSEA using the GO or Reactome database](vignettes/GSEA.md#conduct-gsea-using-the-go-or-reactome-database) `GeneSetAnalysisGO` `GeneSetAnalysisReactome`
- [Perform GSEA using customized genesets](vignettes/GSEA.md#perform-gsea-using-customized-genesets) `GeneSetAnalysis`
- [Find pathways in the GO/Reactome database or customized genesets](vignettes/GSEA.md#find-pathways-in-the-goreactome-database-or-customized-genesets) `SearchDatabase` `SearchPathways`
- [Convert GO/Reactome pathway IDs to pathway names](vignettes/GSEA.md#convert-goreactome-pathway-ids-to-pathway-names) `RenameGO` `RenameReactome`
- [Filter the GO/Reactome pathway list based on certain criteria](vignettes/GSEA.md#filter-the-goreactome-pathway-list-based-on-certain-criteria) `FilterGOTerms` `FilterReactomeTerms`
- [Create a GSEA plot emulating the Broad Institute analysis](vignettes/GSEA.md#create-a-gsea-plot-emulating-the-broad-institute-analysis) `GSEAplot`
### [Trajectory and Pseudotime Analysis](vignettes/Trajectory.md)
- [scVelo Tutorial for Trajectory Analysis](vignettes/Trajectory.md#analyzing-single-cell-trajectories-with-scvelo) `scVelo.SeuratToAnndata` `scVelo.Plot`
- [Palantir Tutorial for Trajectory and Pseudotime Analysis](vignettes/Trajectory.md#palantir-tutorial-for-trajectory-and-pseudotime-analysis) `Palantir.RunDM` `Palantir.Pseudotime`
- [MAGIC for Denoising and Smoothing Gene Expression](vignettes/Trajectory.md#magic-for-denoising-and-smoothing-gene-expression) `Palantir.Magic`
- [CellRank Tutorial for Trajectory Analysis](vignettes/Trajectory.md#cellrank-tutorial-for-trajectory-analysis) `Cellrank.Compute` `Cellrank.Plot`
- [Gene Expression Dynamics Along Differentiation Trajectories](vignettes/Trajectory.md#gene-expression-dynamics-along-differentiation-trajectories) `GeneTrendCurve.Palantir` `GeneTrendHeatmap.Palantir` `GeneTrendCurve.Slingshot` `GeneTrendHeatmap.Slingshot`
- [Slingshot Tutorial for Pseudotime Analysis](vignettes/Trajectory.md#slingshot-tutorial-for-pseudotime-analysis) `RunSlingshot`
- [Integration of Seurat with Python Tools](vignettes/Trajectory.md#integration-of-seurat-with-python-tools) `create_condaenv_seuratextend` `Seu2Adata` `Seu2Loom` `adata.LoadLoom` `adata.AddDR` `adata.AddMetadata` `adata.Save` `adata.Load`
### [SCENIC for Gene Regulatory Networks Analysis](vignettes/SCENIC.md)
- [Importing SCENIC Loom Files into Seurat](vignettes/SCENIC.md#importing-scenic-loom-files-into-seurat) `ImportPyscenicLoom`
- [Visualizing SCENIC Results](vignettes/SCENIC.md#visualizing-scenic-results)
### [Utility Tools and Functions](vignettes/Utilities.md)
- [Facilitate Gene Naming Conversions](vignettes/Utilities.md#facilitate-gene-naming-conversions) `HumanToMouseGenesymbol` `MouseToHumanGenesymbol` `EnsemblToGenesymbol` `GenesymbolToEnsembl` `UniprotToGenesymbol`
- [Compute Statistics Grouped by Clusters](vignettes/Utilities.md#compute-statistics-grouped-by-clusters) `CalcStats`
- [Assess Proportion of Positive Cells in Clusters](vignettes/Utilities.md#assess-proportion-of-positive-cells-in-clusters) `feature_percent`
- [Run Standard Seurat Pipeline](vignettes/Utilities.md#run-standard-seurat-pipeline) `RunBasicSeurat`
### [FAQ](vignettes/FAQ.md)
## 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](https://satijalab.org/seurat/articles/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
```R
library(Seurat)
library(SeuratExtend)
# Visualizing cell clusters using DimPlot2
DimPlot2(pbmc)
```
### Analyzing Cluster Distribution
To check the percentage of each cluster within different samples:
```R
# 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:
```R
# 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:
```R
# 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:
```R
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:
```R
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:
```R
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:
```R
# 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()
)
```
```R
# 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:
```R
# 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:
```{r}
# 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:
```R
# 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")
[email protected][,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:
```R
# Create smoothed gene expression curves along trajectory
GeneTrendCurve.Palantir(
mye_small,
pseudotime.data = ps,
features = c("CD14", "FCGR3A")
)
```
```R
# 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:
```R
# 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:
```R
# 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