The SCP package provides a comprehensive set of tools for single cell data processing and downstream analysis.
The package includes facilities for:
- 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 data integration methods, including Uncorrected, Seurat, scVI, MNN, fastMNN, Harmony, Scanorama, BBKNN, CSS, LIGER, Conos.
- 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 compatible with other Seurat functions.
You can install the development version of SCP from GitHub with:
if (!require("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_github("zhanghao-njmu/SCP")To run functions such as RunSCVELO or RunPAGA, SCP requires python
3.7-3.9 to be installed in the environment.
Check the version of python in the terminal:
python3 --versionor in the R environment:
if (!require("reticulate", quietly = TRUE)) {
install.packages("reticulate")
}
py <- Sys.which("python3")
reticulate:::python_version(py)Then run PrepareVirtualEnv to create a standalone python virtual
environment for SCP and install the necessary packages.
SCP::PrepareVirtualEnv(python = py, pypi_mirror = "https://pypi.tuna.tsinghua.edu.cn/simple", remove_old = TRUE)
reticulate::virtualenv_python("SCP")The analysis is based on a subsetted version of mouse pancreas data.
library(SCP)
data("pancreas_sub")
ClassDimPlot(
srt = pancreas_sub, group.by = c("CellType", "SubCellType"),
reduction = "UMAP", theme_use = "theme_blank"
)
ClassDimPlot(
srt = pancreas_sub, group.by = "SubCellType", stat.by = "Phase",
reduction = "UMAP", theme_use = "theme_blank"
)
ExpDimPlot(
srt = pancreas_sub, features = c("Sox9", "Neurog3", "Fev", "Rbp4"),
reduction = "UMAP", theme_use = "theme_blank"
)
ExpDimPlot(
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"),
cell_annotation = c("Phase", "G2M_score", "Neurod2"),
cell_palette = c("Dark2", "Paired", "Paired"),
add_dot = TRUE, add_reticle = TRUE
)
print(ht$plot)
pancreas_sub <- RunCellQC(srt = pancreas_sub)
ClassDimPlot(srt = pancreas_sub, group.by = "CellQC", reduction = "UMAP")
ClassStatPlot(srt = pancreas_sub, stat.by = "CellQC", group.by = "CellType", label = TRUE)
ClassStatPlot(
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"
)
pancreas_sub <- Standard_SCP(srt = pancreas_sub)
ClassDimPlot(
srt = pancreas_sub, group.by = c("CellType", "SubCellType"),
reduction = "StandardUMAP2D", theme_use = "theme_blank"
)
ClassDimPlot3D(srt = pancreas_sub, group.by = "SubCellType")
ExpDimPlot3D(srt = pancreas_sub, features = c("Sox9", "Neurog3", "Fev", "Rbp4"))
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")
panc8_sub <- Integration_SCP(srtMerge = panc8_sub, batch = "tech", integration_method = "Harmony")
ClassDimPlot(
srt = panc8_sub, group.by = c("celltype", "tech"), reduction = "SeuratUMAP2D",
title = "Seurat", theme_use = "theme_blank"
)
ClassDimPlot(
srt = panc8_sub, group.by = c("celltype", "tech"), reduction = "HarmonyUMAP2D",
title = "Harmony", theme_use = "theme_blank"
)
panc8_rename <- RenameFeatures(srt = panc8_sub, newnames = make.unique(stringr::str_to_title(rownames(panc8_sub))), assays = "RNA")
pancreas_sub <- RunKNNMap(srt_query = pancreas_sub, srt_ref = panc8_rename, ref_umap = "SeuratUMAP2D")
ProjectionPlot(
srt_query = pancreas_sub, srt_ref = panc8_rename,
query_group = "SubCellType", ref_group = "celltype"
)
data("ref_scMCA")
pancreas_sub <- RunKNNPredict(srt_query = pancreas_sub, bulk_ref = ref_scMCA, filter_lowfreq = 20)
ClassDimPlot(srt = pancreas_sub, group.by = "knnpredict_classification", reduction = "UMAP", label = TRUE)
pancreas_sub <- RunKNNPredict(
srt_query = pancreas_sub, srt_ref = panc8_rename,
ref_group = "celltype", filter_lowfreq = 20
)
ClassDimPlot(srt = pancreas_sub, group.by = "knnpredict_classification", reduction = "UMAP", label = TRUE)
pancreas_sub <- RunPAGA(
srt = pancreas_sub, group_by = "SubCellType",
linear_reduction = "PCA", nonlinear_reduction = "UMAP", return_seurat = TRUE
)
PAGAPlot(srt = pancreas_sub, reduction = "UMAP", label = TRUE, label_insitu = TRUE, label_repel = TRUE)
pancreas_sub <- RunSCVELO(
srt = pancreas_sub, group_by = "SubCellType",
linear_reduction = "PCA", nonlinear_reduction = "UMAP", return_seurat = TRUE
)
VelocityPlot(srt = pancreas_sub, reduction = "UMAP", group_by = "SubCellType")
VelocityPlot(srt = pancreas_sub, reduction = "UMAP", plot_type = "stream")
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$AllMarkers_wilcox
DEGs <- DEGs[with(DEGs, avg_log2FC > 1 & p_val_adj < 0.05), ]
pancreas_sub <- AnnotateFeatures(pancreas_sub, species = "Mus_musculus", db = c("TF", "SP"))
ht <- ExpHeatmap(
srt = pancreas_sub, group.by = "CellType", features = DEGs$gene, feature_split = DEGs$group1,
species = "Mus_musculus", anno_terms = TRUE, anno_keys = TRUE, anno_features = TRUE,
feature_annotation = c("TF", "SP"), feature_palcolor = list(c("gold", "steelblue"), c("forestgreen")),
height = 6, width = 5
)
print(ht$plot)
pancreas_sub <- RunEnrichment(
srt = pancreas_sub, group_by = "CellType", db = "GO_BP", species = "Mus_musculus",
DE_threshold = "avg_log2FC > 1 & 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"
)
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")
GSEAPlot(srt = pancreas_sub, group_by = "CellType", group_use = "Endocrine", geneSetID = "GO:0007186")
pancreas_sub <- RunSlingshot(srt = pancreas_sub, group.by = "SubCellType", reduction = "UMAP")
ExpDimPlot(pancreas_sub, features = paste0("Lineage", 1:3), reduction = "UMAP", theme_use = "theme_blank")
ClassDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", lineages = paste0("Lineage", 1:3), lineages_span = 0.1)
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", anno_terms = TRUE, anno_keys = TRUE, anno_features = TRUE,
heatmap_palette = "viridis", cell_annotation = "SubCellType",
separate_annotation = list("SubCellType", c("Nnat", "Irx1")),
separate_annotation_params = list(height = grid::unit(1, "in")),
feature_annotation = c("TF", "SP"), feature_palcolor = list(c("gold", "steelblue"), c("forestgreen")),
pseudotime_label = 25, pseudotime_label_color = "red",
height = 6, width = 5
)
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
)
ExpStatPlot(
srt = pancreas_sub, group.by = "SubCellType", bg.by = "CellType",
features = c("Sox9", "Neurod2", "Isl1", "Rbp4"),
comparisons = list(
c("Ductal", "Ngn3 low EP"),
c("Ngn3 high EP", "Pre-endocrine"),
c("Alpha", "Beta")
),
multiplegroup_comparisons = TRUE
)
More examples of SCP can be found in the documentation of the individual functions, such as Integration_SCP, RunKNNMap, RunMonocle3, ClassDimPlot, ExpHeatmap, RunSCExplorer, etc.
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