SCP: Single-Cell Pipeline

SCP provides a comprehensive set of tools for single-cell data processing and downstream analysis.

The package includes the following facilities:

Integrated single-cell quality control methods.
Pipelines embedded with multiple methods for normalization, feature reduction, and cell population identification (standard Seurat workflow).
Pipelines embedded with multiple integration methods for scRNA-seq or scATAC-seq data, including Uncorrected, Seurat, scVI, MNN, fastMNN, Harmony, Scanorama, BBKNN, CSS, LIGER, Conos, ComBat.
Multiple single-cell downstream analyses such as identification of differential features, enrichment analysis, GSEA analysis, identification of dynamic features, PAGA, RNA velocity, Palantir, Monocle2, Monocle3, etc.
Multiple methods for automatic annotation of single-cell data and methods for projection between single-cell datasets.
High-quality data visualization methods.
Fast deployment of single-cell data into SCExplorer, a shiny app that provides an interactive visualization interface.

The functions in the SCP package are all developed around the Seurat object and are compatible with other Seurat functions.

R version requirement

R >= 4.1.0

Installation in the global R environment

You can install the latest version of SCP from GitHub with:

if (!require("devtools", quietly = TRUE)) {
  install.packages("devtools")
}
devtools::install_github("zhanghao-njmu/SCP")

Create SCP python environment

To run functions such as RunPAGA or RunSCVELO, SCP requires conda to create a separate python environment. The default environment name is "SCP_env". You can specify the environment name for SCP by setting options(SCP_env_name="new_name")

Now, you can run PrepareEnv() to create the python environment for SCP. If the conda binary is not found, it will automatically download and install miniconda.

SCP::PrepareEnv()

To force SCP to use a specific conda binary, it is recommended to set reticulate.conda_binary R option:

options(reticulate.conda_binary = "/path/to/conda")
SCP::PrepareEnv()

If the download of miniconda or pip packages is slow, you can specify the miniconda repo and PyPI mirror according to your network region.

SCP::PrepareEnv(
  miniconda_repo = "https://mirrors.bfsu.edu.cn/anaconda/miniconda",
  pip_options = "-i https://pypi.tuna.tsinghua.edu.cn/simple"
)

Available miniconda repositories:

Available PyPI mirrors:

Installation in an isolated R environment using renv

If you do not want to change your current R environment or require reproducibility, you can use the renv package to install SCP into an isolated R environment.

Create an isolated R environment

if (!require("renv", quietly = TRUE)) {
  install.packages("renv")
}
dir.create("~/SCP_env", recursive = TRUE) # It cannot be the home directory "~" !
renv::init(project = "~/SCP_env", bare = TRUE, restart = TRUE)

Option 1: Install SCP from GitHub and create SCP python environment

renv::activate(project = "~/SCP_env")
renv::install("BiocManager")
renv::install("zhanghao-njmu/SCP", repos = BiocManager::repositories())
SCP::PrepareEnv()

Option 2: If SCP is already installed in the global environment, copy SCP from the local library

renv::activate(project = "~/SCP_env")
renv::hydrate("SCP")
SCP::PrepareEnv()

Activate SCP environment first before use

renv::activate(project = "~/SCP_env")

library(SCP)
data("pancreas_sub")
pancreas_sub <- RunPAGA(srt = pancreas_sub, group_by = "SubCellType", linear_reduction = "PCA", nonlinear_reduction = "UMAP")
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "draw_graph_fr")

Save and restore the state of SCP environment

renv::snapshot(project = "~/SCP_env")
renv::restore(project = "~/SCP_env")

Quick Start

Data exploration
CellQC
Standard pipeline
Integration pipeline
Cell projection between single-cell datasets
Cell annotation using bulk RNA-seq datasets
Cell annotation using single-cell datasets
PAGA analysis
Velocity analysis
Differential expression analysis
Enrichment analysis(over-representation)
Enrichment analysis(GSEA)
Trajectory inference
Dynamic features
Interactive data visualization with SCExplorer
Other visualization examples

Data exploration

The analysis is based on a subsetted version of mouse pancreas data.

library(SCP)
library(BiocParallel)
register(MulticoreParam(workers = 8, progressbar = TRUE))

data("pancreas_sub")
print(pancreas_sub)
#> An object of class Seurat 
#> 47874 features across 1000 samples within 3 assays 
#> Active assay: RNA (15958 features, 3467 variable features)
#>  2 other assays present: spliced, unspliced
#>  2 dimensional reductions calculated: PCA, UMAP

CellDimPlot(
  srt = pancreas_sub, group.by = c("CellType", "SubCellType"),
  reduction = "UMAP", theme_use = "theme_blank"
)

CellDimPlot(
  srt = pancreas_sub, group.by = "SubCellType", stat.by = "Phase",
  reduction = "UMAP", theme_use = "theme_blank"
)

FeatureDimPlot(
  srt = pancreas_sub, features = c("Sox9", "Neurog3", "Fev", "Rbp4"),
  reduction = "UMAP", theme_use = "theme_blank"
)

FeatureDimPlot(
  srt = pancreas_sub, features = c("Ins1", "Gcg", "Sst", "Ghrl"),
  compare_features = TRUE, label = TRUE, label_insitu = TRUE,
  reduction = "UMAP", theme_use = "theme_blank"
)

ht <- GroupHeatmap(
  srt = pancreas_sub,
  features = c(
    "Sox9", "Anxa2", # Ductal
    "Neurog3", "Hes6", # EPs
    "Fev", "Neurod1", # Pre-endocrine
    "Rbp4", "Pyy", # Endocrine
    "Ins1", "Gcg", "Sst", "Ghrl" # Beta, Alpha, Delta, Epsilon
  ),
  group.by = c("CellType", "SubCellType"),
  heatmap_palette = "YlOrRd",
  cell_annotation = c("Phase", "G2M_score", "Cdh2"),
  cell_annotation_palette = c("Dark2", "Paired", "Paired"),
  show_row_names = TRUE, row_names_side = "left",
  add_dot = TRUE, add_reticle = TRUE
)
print(ht$plot)

CellQC

pancreas_sub <- RunCellQC(srt = pancreas_sub)
CellDimPlot(srt = pancreas_sub, group.by = "CellQC", reduction = "UMAP")

CellStatPlot(srt = pancreas_sub, stat.by = "CellQC", group.by = "CellType", label = TRUE)

CellStatPlot(
  srt = pancreas_sub,
  stat.by = c(
    "db_qc", "outlier_qc", "umi_qc", "gene_qc",
    "mito_qc", "ribo_qc", "ribo_mito_ratio_qc", "species_qc"
  ),
  plot_type = "upset", stat_level = "Fail"
)

Standard pipeline

pancreas_sub <- Standard_SCP(srt = pancreas_sub)
CellDimPlot(
  srt = pancreas_sub, group.by = c("CellType", "SubCellType"),
  reduction = "StandardUMAP2D", theme_use = "theme_blank"
)

CellDimPlot3D(srt = pancreas_sub, group.by = "SubCellType")

FeatureDimPlot3D(srt = pancreas_sub, features = c("Sox9", "Neurog3", "Fev", "Rbp4"))

Integration pipeline

Example data for integration is a subsetted version of panc8(eight human pancreas datasets)

data("panc8_sub")
panc8_sub <- Integration_SCP(srtMerge = panc8_sub, batch = "tech", integration_method = "Seurat")
CellDimPlot(
  srt = panc8_sub, group.by = c("celltype", "tech"), reduction = "SeuratUMAP2D",
  title = "Seurat", theme_use = "theme_blank"
)

UMAP embeddings based on different integration methods in SCP:

Cell projection between single-cell datasets

panc8_rename <- RenameFeatures(srt = panc8_sub, newnames = make.unique(capitalize(rownames(panc8_sub), force_tolower = TRUE)), assays = "RNA")
srt_query <- RunKNNMap(srt_query = pancreas_sub, srt_ref = panc8_rename, ref_umap = "SeuratUMAP2D")
ProjectionPlot(
  srt_query = srt_query, srt_ref = panc8_rename,
  query_group = "SubCellType", ref_group = "celltype"
)

Cell annotation using bulk RNA-seq datasets

data("ref_scMCA")
pancreas_sub <- RunKNNPredict(srt_query = pancreas_sub, bulk_ref = ref_scMCA, filter_lowfreq = 20)
CellDimPlot(srt = pancreas_sub, group.by = "KNNPredict_classification", reduction = "UMAP", label = TRUE)

Cell annotation using single-cell datasets

pancreas_sub <- RunKNNPredict(
  srt_query = pancreas_sub, srt_ref = panc8_rename,
  ref_group = "celltype", filter_lowfreq = 20
)
CellDimPlot(srt = pancreas_sub, group.by = "KNNPredict_classification", reduction = "UMAP", label = TRUE)

pancreas_sub <- RunKNNPredict(
  srt_query = pancreas_sub, srt_ref = panc8_rename,
  query_group = "SubCellType", ref_group = "celltype",
  return_full_distance_matrix = TRUE
)
CellDimPlot(srt = pancreas_sub, group.by = "KNNPredict_classification", reduction = "UMAP", label = TRUE)

ht <- CellCorHeatmap(
  srt_query = pancreas_sub, srt_ref = panc8_rename,
  query_group = "SubCellType", ref_group = "celltype",
  nlabel = 3, label_by = "row",
  show_row_names = TRUE, show_column_names = TRUE
)
print(ht$plot)

PAGA analysis

pancreas_sub <- RunPAGA(
  srt = pancreas_sub, group_by = "SubCellType",
  linear_reduction = "PCA", nonlinear_reduction = "UMAP"
)
PAGAPlot(srt = pancreas_sub, reduction = "UMAP", label = TRUE, label_insitu = TRUE, label_repel = TRUE)

Velocity analysis

To estimate RNA velocity, you need to have both “spliced” and “unspliced” assays in your Seurat object. You can generate these matrices using velocyto, bustools, or alevin.

pancreas_sub <- RunSCVELO(
  srt = pancreas_sub, group_by = "SubCellType",
  linear_reduction = "PCA", nonlinear_reduction = "UMAP"
)
VelocityPlot(srt = pancreas_sub, reduction = "UMAP", group_by = "SubCellType")

VelocityPlot(srt = pancreas_sub, reduction = "UMAP", plot_type = "stream")

Differential expression analysis

pancreas_sub <- RunDEtest(srt = pancreas_sub, group_by = "CellType", fc.threshold = 1, only.pos = FALSE)
VolcanoPlot(srt = pancreas_sub, group_by = "CellType")

DEGs <- pancreas_sub@tools$DEtest_CellType$AllMarkers_wilcox
DEGs <- DEGs[with(DEGs, avg_log2FC > 1 & p_val_adj < 0.05), ]
# Annotate features with transcription factors and surface proteins
pancreas_sub <- AnnotateFeatures(pancreas_sub, species = "Mus_musculus", db = c("TF", "CSPA"))
ht <- FeatureHeatmap(
  srt = pancreas_sub, group.by = "CellType", features = DEGs$gene, feature_split = DEGs$group1,
  species = "Mus_musculus", db = c("GO_BP", "KEGG", "WikiPathway"), anno_terms = TRUE,
  feature_annotation = c("TF", "CSPA"), feature_annotation_palcolor = list(c("gold", "steelblue"), c("forestgreen")),
  height = 5, width = 4
)
print(ht$plot)

Enrichment analysis(over-representation)

pancreas_sub <- RunEnrichment(
  srt = pancreas_sub, group_by = "CellType", db = "GO_BP", species = "Mus_musculus",
  DE_threshold = "avg_log2FC > log2(1.5) & p_val_adj < 0.05"
)
EnrichmentPlot(
  srt = pancreas_sub, group_by = "CellType", group_use = c("Ductal", "Endocrine"),
  plot_type = "bar"
)

EnrichmentPlot(
  srt = pancreas_sub, group_by = "CellType", group_use = c("Ductal", "Endocrine"),
  plot_type = "wordcloud"
)

EnrichmentPlot(
  srt = pancreas_sub, group_by = "CellType", group_use = c("Ductal", "Endocrine"),
  plot_type = "wordcloud", word_type = "feature"
)

EnrichmentPlot(
  srt = pancreas_sub, group_by = "CellType", group_use = "Ductal",
  plot_type = "network"
)

To ensure that labels are visible, you can adjust the size of the viewer panel on Rstudio IDE.

EnrichmentPlot(
  srt = pancreas_sub, group_by = "CellType", group_use = "Ductal",
  plot_type = "enrichmap"
)

EnrichmentPlot(srt = pancreas_sub, group_by = "CellType", plot_type = "comparison")

Enrichment analysis(GSEA)

pancreas_sub <- RunGSEA(
  srt = pancreas_sub, group_by = "CellType", db = "GO_BP", species = "Mus_musculus",
  DE_threshold = "p_val_adj < 0.05"
)
GSEAPlot(srt = pancreas_sub, group_by = "CellType", group_use = "Endocrine", id_use = "GO:0007186")

GSEAPlot(srt = pancreas_sub, group_by = "CellType", plot_type = "comparison")

Trajectory inference

pancreas_sub <- RunSlingshot(srt = pancreas_sub, group.by = "SubCellType", reduction = "UMAP")

FeatureDimPlot(pancreas_sub, features = paste0("Lineage", 1:3), reduction = "UMAP", theme_use = "theme_blank")

CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", lineages = paste0("Lineage", 1:3), lineages_span = 0.1)

Dynamic features

pancreas_sub <- RunDynamicFeatures(srt = pancreas_sub, lineages = c("Lineage1", "Lineage2"), n_candidates = 200)
ht <- DynamicHeatmap(
  srt = pancreas_sub, lineages = c("Lineage1", "Lineage2"),
  use_fitted = TRUE, n_split = 6, reverse_ht = "Lineage1",
  species = "Mus_musculus", db = "GO_BP", anno_terms = TRUE, anno_keys = TRUE, anno_features = TRUE,
  heatmap_palette = "viridis", cell_annotation = "SubCellType",
  separate_annotation = list("SubCellType", c("Nnat", "Irx1")), separate_annotation_palette = c("Paired", "Set1"),
  feature_annotation = c("TF", "CSPA"), feature_annotation_palcolor = list(c("gold", "steelblue"), c("forestgreen")),
  pseudotime_label = 25, pseudotime_label_color = "red",
  height = 5, width = 2
)
print(ht$plot)

DynamicPlot(
  srt = pancreas_sub, lineages = c("Lineage1", "Lineage2"), group.by = "SubCellType",
  features = c("Plk1", "Hes1", "Neurod2", "Ghrl", "Gcg", "Ins2"),
  compare_lineages = TRUE, compare_features = FALSE
)

FeatureStatPlot(
  srt = pancreas_sub, group.by = "SubCellType", bg.by = "CellType",
  stat.by = c("Sox9", "Neurod2", "Isl1", "Rbp4"), add_box = TRUE,
  comparisons = list(
    c("Ductal", "Ngn3 low EP"),
    c("Ngn3 high EP", "Pre-endocrine"),
    c("Alpha", "Beta")
  )
)

Interactive data visualization with SCExplorer

PrepareSCExplorer(list(mouse_pancreas = pancreas_sub, human_pancreas = panc8_sub), base_dir = "./SCExplorer")
app <- RunSCExplorer(base_dir = "./SCExplorer")
list.files("./SCExplorer") # This directory can be used as site directory for Shiny Server.

if (interactive()) {
  shiny::runApp(app)
}