scop: Single-Cell Omics analysis Pipeline

Introduction

The scop package provides a unified and extensible framework for single-cell omics data processing and downstream analysis in Seurat:

Integrated single-cell quality control methods, including doublet detection methods (scDblFinder, scds, Scrublet, DoubletDetection) and ambient RNA decontamination via decontX.
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, Coralysis, 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, CellphoneDB, NicheNet, and MultiNicheNet.
High-quality data visualization methods.
Fast deployment of single-cell data into SCExplorer, a shiny app that provides an interactive visualization interface.

Credits

The scop package is developed based on the SCP package, with the following major improvements:

Compatibility: full support for Seurat v5.
Stability: a large number of known issues have been fixed, and all functions have passed devtools::check().
Usability: the Python environment setup workflow has been improved, allowing a new complete environment to be deployed within minutes; standardized console messages via thisutils::log_message for consistent, readable function outputs.
Performance: a new parallel framework has been developed based on thisutils::parallelize_fun, providing a consistent experience across Linux, macOS, and Windows.
Functionality: more analysis methods have been added, including CellRank, CellTypist, CytoTRACE 2, CellChat, GSVA, scMetabolism and scProportionTest.

Installation

R 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")

Python environment

To run functions such as RunPAGA(), RunSCVELO(), scop requires conda to create a separate 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"
)

Pipeline

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 
#> 47994 features across 1000 samples within 3 assays 
#> Active assay: RNA (15998 features, 0 variable features)
#>  1 layer present: counts
#>  2 other assays present: spliced, unspliced

pancreas_sub <- standard_scop(pancreas_sub)
print(pancreas_sub)
#>  An object of class Seurat 
#>  47994 features across 1000 samples within 3 assays 
#>  Active assay: RNA (15998 features, 2000 variable features)
#>   3 layers present: counts, data, scale.data
#>   2 other assays present: spliced, unspliced
#>   5 dimensional reductions calculated: Standardpca, StandardpcaUMAP2D, StandardpcaUMAP3D, StandardUMAP2D, StandardUMAP3D

CellDimPlot(
  pancreas_sub,
  group.by = c("CellType", "SubCellType"),
  reduction = "UMAP",
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)

CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  stat.by = "Phase",
  reduction = "UMAP",
  theme_use = "theme_blank",
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)

FeatureDimPlot(
  pancreas_sub,
  features = c("Sox9", "Neurog3", "Fev", "Rbp4"),
  reduction = "UMAP",
  theme_use = "theme_blank",
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)

FeatureDimPlot(
  pancreas_sub,
  features = c("Ins1", "Gcg", "Sst", "Ghrl"),
  compare_features = TRUE,
  label = TRUE,
  label_insitu = TRUE,
  reduction = "UMAP",
  theme_use = "theme_blank",
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)

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 = "Spectral",
  cell_annotation = c("Phase", "G2M_score", "Cdh2"),
  cell_annotation_palette = c("Dark2", "Chinese", "Chinese"),
  show_row_names = TRUE,
  nlabel = 0,
  row_names_side = "left",
  add_dot = TRUE,
  add_reticle = TRUE,
  width = 2.2,
  height = 2.5
)
print(ht$plot)

Quality control

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

CellStatPlot(
  pancreas_sub,
  stat.by = "CellQC",
  group.by = "CellType"
) + ggplot2::theme(aspect.ratio = 1 / 2)

CellStatPlot(
  pancreas_sub,
  stat.by = c(
    "db_qc", "decontX_qc", "outlier_qc",
    "umi_qc", "gene_qc",
    "mito_qc", "ribo_qc",
    "ribo_mito_ratio_qc", "species_qc"
  ),
  plot_type = "upset",
  stat_level = "Fail"
) + ggplot2::theme(aspect.ratio = 1 / 2)

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 = "Harmony"
)
CellDimPlot(
  panc8_sub,
  group.by = c("tech", "celltype"),
  reduction = "HarmonyUMAP",
  theme_use = "theme_blank"
)

Cell annotation

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 = "HarmonyUMAP2D"
)
ProjectionPlot(
  srt_query = srt_query,
  srt_ref = panc8_rename,
  query_group = "SubCellType",
  ref_group = "celltype",
  ref_param = list(
    xlab = "UMAP_1",
    ylab = "UMAP_2"
  )
)

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,
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)

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,
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)

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,
  width = 4,
  height = 2.5
)
print(ht$plot)

Cellular potency

CytoTRACE 2

pancreas_sub <- RunCytoTRACE(
  pancreas_sub,
  species = "Mus_musculus"
)
CytoTRACEPlot(
  pancreas_sub,
  group.by = "CellType",
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)

Trajectory inference

RNA Velocity analysis

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

pancreas_sub <- RunSCVELO(
  pancreas_sub,
  group.by = "SubCellType",
  linear_reduction = "PCA",
  nonlinear_reduction = "UMAP"
)
VelocityPlot(
  pancreas_sub,
  reduction = "UMAP",
  group.by = "SubCellType",
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)

VelocityPlot(
  pancreas_sub,
  reduction = "UMAP",
  plot_type = "stream",
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)

PAGA

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,
  edge_size = c(0.5, 1),
  edge_color = "black",
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)

Slingshot

pancreas_sub <- RunSlingshot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  start = "Ductal",
  end = c("Alpha", "Beta")
)
CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  lineages = paste0("Lineage", 1:2),
  reduction = "UMAP",
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)

FeatureDimPlot(
  pancreas_sub,
  features = paste0("Lineage", 1:2),
  reduction = "UMAP",
  theme_use = "theme_blank",
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)

Monocle3

pancreas_sub <- RunMonocle3(
  pancreas_sub,
  group.by = "SubCellType"
)
trajectory <- pancreas_sub@tools$Monocle3$trajectory
milestones <- pancreas_sub@tools$Monocle3$milestones
CellDimPlot(
  pancreas_sub,
  group.by = "Monocle3_partitions",
  reduction = "UMAP",
  label = TRUE,
  xlab = "UMAP_1",
  ylab = "UMAP_2"
) +
  trajectory +
  milestones +
  CellDimPlot(
    pancreas_sub,
    group.by = "Monocle3_clusters",
    reduction = "UMAP",
    label = TRUE,
    xlab = "UMAP_1",
    ylab = "UMAP_2"
  ) +
  trajectory +
  CellDimPlot(
    pancreas_sub,
    group.by = "SubCellType",
    reduction = "UMAP",
    label = TRUE,
    xlab = "UMAP_1",
    ylab = "UMAP_2"
  ) +
  trajectory +
  FeatureDimPlot(
    pancreas_sub,
    features = "Monocle3_Pseudotime",
    reduction = "UMAP",
    xlab = "UMAP_1",
    ylab = "UMAP_2"
  ) +
  trajectory

Dynamic features

pancreas_sub <- RunDynamicFeatures(
  pancreas_sub,
  lineages = c("Lineage1", "Lineage2"),
  n_candidates = 200
)
# Annotate features with transcription factors and surface proteins
pancreas_sub <- AnnotateFeatures(
  pancreas_sub,
  species = "Mus_musculus",
  db = c("TF", "CSPA")
)
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,
  exp_legend_title = "Z-score",
  heatmap_palette = "viridis",
  cell_annotation = "SubCellType",
  separate_annotation = list(
    "SubCellType", c("Nnat", "Irx1")
  ),
  separate_annotation_palette = c("Chinese", "Set1"),
  feature_annotation = c("TF", "CSPA"),
  feature_annotation_palcolor = list(
    c("gold", "steelblue"), c("forestgreen")
  ),
  pseudotime_label = 25,
  pseudotime_label_color = "red",
  cores = 6,
  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"
  )
)

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")
  )
) + patchwork::plot_layout(
  guides = "collect"
)

Differential expression analysis

pancreas_sub <- RunDEtest(
  pancreas_sub,
  group.by = "CellType",
  fc.threshold = 1,
  only.pos = FALSE
)
DEtestPlot(
  pancreas_sub,
  group.by = "CellType",
  plot_type = "volcano",
  label.size = 2
)

DEtestPlot(
  pancreas_sub,
  group.by = "CellType",
  plot_type = "manhattan",
  label.size = 2
) + ggplot2::theme(aspect.ratio = 1 / 2)

DEtestPlot(
  pancreas_sub,
  group.by = "CellType",
  plot_type = "ring",
  label.size = 2
)

DEGs <- pancreas_sub@tools$DEtest_CellType$AllMarkers_wilcox
DEGs <- DEGs[with(DEGs, avg_log2FC > 1 & p_val_adj < 0.05), ]

ht <- FeatureHeatmap(
  pancreas_sub,
  group.by = "CellType",
  features = DEGs$gene,
  feature_split = DEGs$group1,
  exp_legend_title = "Z-score",
  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")
  ),
  cores = 6,
  height = 5,
  width = 3
)
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",
  cores = 5
)
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 = "Ngn3-low-EP",
  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",
  topTerm = 3
)

EnrichmentPlot(
  pancreas_sub,
  group.by = "CellType",
  group_use = "Ductal",
  plot_type = "lollipop"
)

GSEA

pancreas_sub <- RunGSEA(
  pancreas_sub,
  group.by = "CellType",
  db = "GO_BP",
  species = "Mus_musculus",
  DE_threshold = "p_val_adj < 0.05",
  cores = 5
)
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 = 10
)

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)
}

Other visualization examples

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

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
R		R
data		data
inst/python		inst/python
man		man
renv		renv
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.md		README.md
_pkgdown.yml		_pkgdown.yml
renv.lock		renv.lock
scop.Rproj		scop.Rproj

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scop: Single-Cell Omics analysis Pipeline

Introduction

Table of Contents

Credits

Installation

R requirement

Python environment

Pipeline

Data exploration

Quality control

Integration pipeline

Cell annotation

Cell projection between single-cell datasets

Cell annotation using bulk RNA-seq datasets

Cell annotation using single-cell datasets

Cellular potency

CytoTRACE 2

Trajectory inference

RNA Velocity analysis

PAGA

Slingshot

Monocle3

Dynamic features

Differential expression analysis

Enrichment analysis

Over-representation

GSEA

Interactive data visualization with SCExplorer

Other visualization examples

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Repository files navigation

scop: Single-Cell Omics analysis Pipeline

Introduction

Table of Contents

Credits

Installation

R requirement

Python environment

Pipeline

Data exploration

Quality control

Integration pipeline

Cell annotation

Cell projection between single-cell datasets

Cell annotation using bulk RNA-seq datasets

Cell annotation using single-cell datasets

Cellular potency

CytoTRACE 2

Trajectory inference

RNA Velocity analysis

PAGA

Slingshot

Monocle3

Dynamic features

Differential expression analysis

Enrichment analysis

Over-representation

GSEA

Interactive data visualization with SCExplorer

Other visualization examples

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