SCP provides a comprehensive set of tools for single-cell data processing and downstream analysis.
The package includes the following facilities:
The functions in the SCP package are all developed around the Seurat object and are compatible with other Seurat functions.
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")
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:
https://repo.anaconda.com/miniconda (default)
Available PyPI mirrors:
https://pypi.python.org/simple (default)
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.
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()
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")
renv::snapshot(project = "~/SCP_env")
renv::restore(project = "~/SCP_env")
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)
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"
)
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"))
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:
panc8_rename <- RenameFeatures(
srt = panc8_sub,
newnames = make.unique(capitalize(rownames(panc8_sub[["RNA"]]), 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"
)
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)
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)
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)
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")
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)
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")
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", group_use = "Endocrine", plot_type = "bar",
direction = "both", topTerm = 20
)
GSEAPlot(srt = pancreas_sub, group_by = "CellType", plot_type = "comparison")
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)
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")
)
)
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)
}
CellDimPlot
CellStatPlot
FeatureStatPlot
GroupHeatmap
You can also find more examples in the documentation of the function: Integration_SCP, RunKNNMap, RunMonocle3, RunPalantir, etc.
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