scop: Single-Cell Omics analysis Pipeline

Introduction

The scop package provides a comprehensive set of tools for single-cell omics data processing and downstream analysis:

• Integrated single-cell quality control methods, including doublet detection methods (scDblFinder, scds, Scrublet, DoubletDetection).
• Pipelines embedded with multiple methods for normalization, feature reduction (PCA, ICA, NMF, MDS, GLMPCA, UMAP, TriMap, LargeVis, PaCMAP, PHATE, DM, FR), and cell population identification.
• Pipelines embedded with multiple integration methods for scRNA-seq, including Uncorrected, Seurat, scVI, MNN, fastMNN, Harmony, Scanorama, BBKNN, CSS, LIGER, Conos, ComBat.
• Multiple methods for automatic annotation of single-cell data (CellTypist, SingleR, Scmap, KNNPredict) and methods for projection between single-cell datasets (CSSMap, PCAMap, SeuratMap, SymphonyMap).
• Multiple single-cell downstream analyses:
  • Differential expression analysis: identification of differential features, expressed marker identification.
  • Enrichment analysis: over-representation analysis, GSEA analysis, dynamic enrichment analysis.
  • Cellular potency: CytoTRACE 2 for predicting cellular differentiation potential.
  • RNA velocity: RNA velocity, PAGA, Palantir, CellRank, WOT.
  • Trajectory inference: Slingshot, Monocle2, Monocle3, identification of dynamic features.
  • Cell-Cell Communication: CellChat for cell-cell communication.
• 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 scop are all developed around the Seurat object and are compatible with other Seurat functions.

Quick Start

• scop: Single-Cell Omics analysis Pipeline
  • Introduction
  • Quick Start
  • Credits
  • Installation
    • R version requirement
    • Prepare python environment
    • 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
    • Cellular potency
      • CytoTRACE 2
    • Velocity analysis
      • SCVELO
    • Trajectory inference
      • PAGA analysis
      • Slingshot
      • Monocle3
    • Dynamic features
    • Differential expression analysis
    • Enrichment analysis(over-representation)
    • Enrichment analysis(GSEA)
    • Interactive data visualization with SCExplorer
    • Other visualization examples

Credits

The scop package is developed based on the SCP package, making it compatible with Seurat V5 and adding support for single-cell omics data.

Installation

R version requirement

• R >= 4.1.0

You can install the latest version of scop with pak from GitHub with:

if (!require("pak", quietly = TRUE)) {
  install.packages("pak")
}
pak::pak("mengxu98/scop")

Prepare python environment

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

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

scop::PrepareEnv()

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

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

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

Available miniconda repositories:

• https://repo.anaconda.com/miniconda (default)

• http://mirrors.aliyun.com/anaconda/miniconda

• https://mirrors.bfsu.edu.cn/anaconda/miniconda

• https://mirrors.pku.edu.cn/anaconda/miniconda

• https://mirror.nju.edu.cn/anaconda/miniconda

• https://mirrors.sustech.edu.cn/anaconda/miniconda

• https://mirrors.xjtu.edu.cn/anaconda/miniconda

• https://mirrors.hit.edu.cn/anaconda/miniconda

Available PyPI mirrors:

• https://pypi.python.org/simple (default)

• https://mirrors.aliyun.com/pypi/simple

• https://pypi.tuna.tsinghua.edu.cn/simple

• https://mirrors.pku.edu.cn/pypi/simple

• https://mirror.nju.edu.cn/pypi/web/simple

• https://mirrors.sustech.edu.cn/pypi/simple

• https://mirrors.xjtu.edu.cn/pypi/simple

• https://mirrors.hit.edu.cn/pypi/web/simple

Data exploration

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

library(scop)
data(pancreas_sub)
print(pancreas_sub)
#> An object of class Seurat
#> 47886 features across 1000 samples within 3 assays
#> Active assay: RNA (15962 features, 2000 variable features)
#>  3 layers present: counts, data, scale.data
#>  2 other assays present: spliced, unspliced
#>  2 dimensional reductions calculated: pca, umap

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

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

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

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

ht <- GroupHeatmap(
  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(pancreas_sub)
CellDimPlot(pancreas_sub, group.by = "CellQC", reduction = "UMAP")

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

CellStatPlot(
  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_scop(pancreas_sub)
CellDimPlot(
  pancreas_sub,
  group.by = c("CellType", "SubCellType"),
  reduction = "StandardUMAP2D",
  theme_use = "theme_blank"
)

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

CellDimPlot3D

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

FeatureDimPlot3D

Integration pipeline

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

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

Cell projection between single-cell datasets

genenames <- make.unique(
  thisutils::capitalize(
    rownames(panc8_sub[["RNA"]]
  ),
  force_tolower = TRUE)
)
names(genenames) <- rownames(panc8_sub)
panc8_rename <- RenameFeatures(
  panc8_sub,
  newnames = genenames,
  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(
  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(
  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)

Cellular potency

CytoTRACE 2

pancreas_sub <- RunCytoTRACE(
  pancreas_sub,
  species = "mouse"
)
CytoTRACEPlot(
  pancreas_sub,
  group.by = "SubCellType"
)

Velocity analysis

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

SCVELO

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

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

Trajectory inference

PAGA analysis

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

Slingshot

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

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

Monocle3

pancreas_sub <- RunMonocle3(
  pancreas_sub,
  group.by = "SubCellType"
)

Dynamic features

pancreas_sub <- RunDynamicFeatures(
  pancreas_sub,
  lineages = c("Lineage1", "Lineage2"),
  n_candidates = 200
)
ht <- DynamicHeatmap(
  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(
  pancreas_sub,
  lineages = c("Lineage1", "Lineage2"),
  group.by = "SubCellType",
  features = c(
    "Plk1", "Hes1", "Neurod2", "Ghrl", "Gcg", "Ins2"
  ),
  compare_lineages = TRUE,
  compare_features = FALSE
)

FeatureStatPlot(
  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")
  )
)

Differential expression analysis

pancreas_sub <- RunDEtest(
  pancreas_sub,
  group_by = "CellType",
  fc.threshold = 1,
  only.pos = FALSE
)
VolcanoPlot(
  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(
  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(
  pancreas_sub,
  group_by = "CellType",
  db = "GO_BP",
  species = "Mus_musculus",
  DE_threshold = "avg_log2FC > log2(1.5) & p_val_adj < 0.05"
)
EnrichmentPlot(
  pancreas_sub,
  group_by = "CellType",
  group_use = c("Ductal", "Endocrine"),
  plot_type = "bar"
)

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

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

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

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

Enrichment analysis(GSEA)

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

GSEAPlot(
  pancreas_sub,
  group_by = "CellType",
  group_use = "Endocrine",
  plot_type = "bar",
  direction = "both",
  topTerm = 20
)

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)
}

SCExplorer1

Other visualization examples

CellDimPlot

CellStatPlotFeatureStatPlotGroupHeatmap

You can also find more examples in the documentation of the function: integration_scop, RunKNNMap, RunPalantir, etc.