# SCP

**Repository Path**: feigeliudan01/SCP

## Basic Information

- **Project Name**: SCP
- **Description**: An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2024-08-11
- **Last Updated**: 2024-08-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# SCP: Single-Cell Pipeline

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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](https://github.com/satijalab/seurat),
  [scVI](https://github.com/scverse/scvi-tools),
  [MNN](http://www.bioconductor.org/packages/release/bioc/html/batchelor.html),
  [fastMNN](http://www.bioconductor.org/packages/release/bioc/html/batchelor.html),
  [Harmony](https://github.com/immunogenomics/harmony),
  [Scanorama](https://github.com/brianhie/scanorama),
  [BBKNN](https://github.com/Teichlab/bbknn),
  [CSS](https://github.com/quadbiolab/simspec),
  [LIGER](https://github.com/welch-lab/liger),
  [Conos](https://github.com/kharchenkolab/conos),
  [ComBat](https://bioconductor.org/packages/release/bioc/html/sva.html).
- Multiple single-cell downstream analyses such as identification of
  differential features, enrichment analysis, GSEA analysis,
  identification of dynamic features,
  [PAGA](https://github.com/theislab/paga), [RNA
  velocity](https://github.com/theislab/scvelo),
  [Palantir](https://github.com/dpeerlab/Palantir),
  [Monocle2](http://cole-trapnell-lab.github.io/monocle-release),
  [Monocle3](https://cole-trapnell-lab.github.io/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](https://shiny.rstudio.com/) that provides an interactive
  visualization interface.

The functions in the SCP package are all developed around the [Seurat
object](https://github.com/mojaveazure/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](https://github.com/zhanghao-njmu/SCP) with:

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

#### Create a python environment for SCP

To run functions such as `RunPAGA` or `RunSCVELO`, SCP requires
[conda](https://docs.conda.io/en/latest/miniconda.html) 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.

``` r
SCP::PrepareEnv()
```

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

``` r
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.

``` r
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)

- <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>

## 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](https://rstudio.github.io/renv/)
package to install SCP into an isolated R environment.

#### Create an isolated R environment

``` r
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

``` r
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

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

#### Activate SCP environment first before use

``` r
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

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

## Quick Start

- [Data exploration](#data-exploration)

- [CellQC](#cellqc)

- [Standard pipeline](#standard-pipeline)

- [Integration pipeline](#integration-pipeline)

- [Cell projection between single-cell
  datasets](#cell-projection-between-single-cell-datasets)

- [Cell annotation using bulk RNA-seq
  datasets](#cell-annotation-using-bulk-rna-seq-datasets)

- [Cell annotation using single-cell
  datasets](#cell-annotation-using-single-cell-datasets)

- [PAGA analysis](#paga-analysis)

- [Velocity analysis](#velocity-analysis)

- [Differential expression analysis](#differential-expression-analysis)

- [Enrichment
  analysis(over-representation)](#enrichment-analysisover-representation)

- [Enrichment analysis(GSEA)](#enrichment-analysisgsea)

- [Trajectory inference](#trajectory-inference)

- [Dynamic features](#dynamic-features)

- [Interactive data visualization with
  SCExplorer](#interactive-data-visualization-with-scexplorer)

- [Other visualization examples](#other-visualization-examples)

### Data exploration

The analysis is based on a subsetted version of [mouse pancreas
data](https://doi.org/10.1242/dev.173849).

``` r
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
```

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

<img src="man/figures/EDA-1.png" width="100%" style="display: block; margin: auto;" />

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

<img src="man/figures/EDA-2.png" width="100%" style="display: block; margin: auto;" />

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

<img src="man/figures/EDA-3.png" width="100%" style="display: block; margin: auto;" />

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

<img src="man/figures/EDA-4.png" width="100%" style="display: block; margin: auto;" />

``` r
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)
```

<img src="man/figures/EDA-5.png" width="100%" style="display: block; margin: auto;" />

### CellQC

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

<img src="man/figures/RunCellQC-1.png" width="100%" style="display: block; margin: auto;" />

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

<img src="man/figures/RunCellQC-2.png" width="100%" style="display: block; margin: auto;" />

``` r
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"
)
```

<img src="man/figures/RunCellQC-3.png" width="100%" style="display: block; margin: auto;" />

### Standard pipeline

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

<img src="man/figures/Standard_SCP-1.png" width="100%" style="display: block; margin: auto;" />

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

![CellDimPlot3D](man/figures/CellDimPlot3D-1.png)

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

![FeatureDimPlot3D](man/figures/FeatureDimPlot3D-1.png)

### Integration pipeline

Example data for integration is a subsetted version of [panc8(eight
human pancreas datasets)](https://github.com/satijalab/seurat-data)

``` r
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"
)
```

<img src="man/figures/Integration_SCP-1.png" width="100%" style="display: block; margin: auto;" />

UMAP embeddings based on different integration methods in SCP:

![Integration-all](man/figures/Integration-all.png)

### Cell projection between single-cell datasets

``` r
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"
)
```

<img src="man/figures/RunKNNMap-1.png" width="100%" style="display: block; margin: auto;" />

### Cell annotation using bulk RNA-seq datasets

``` r
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)
```

<img src="man/figures/RunKNNPredict-bulk-1.png" width="100%" style="display: block; margin: auto;" />

### Cell annotation using single-cell datasets

``` r
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)
```

<img src="man/figures/RunKNNPredict-scrna-1.png" width="100%" style="display: block; margin: auto;" />

``` r

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)
```

<img src="man/figures/RunKNNPredict-scrna-2.png" width="100%" style="display: block; margin: auto;" />

``` r

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)
```

<img src="man/figures/RunKNNPredict-scrna-3.png" width="100%" style="display: block; margin: auto;" />

### PAGA analysis

``` r
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)
```

<img src="man/figures/RunPAGA-1.png" width="100%" style="display: block; margin: auto;" />

### 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](http://velocyto.org/velocyto.py/index.html),
> [bustools](https://bustools.github.io/BUS_notebooks_R/velocity.html),
> or
> [alevin](https://combine-lab.github.io/alevin-fry-tutorials/2021/alevin-fry-velocity/).

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

<img src="man/figures/RunSCVELO-1.png" width="100%" style="display: block; margin: auto;" />

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

<img src="man/figures/RunSCVELO-2.png" width="100%" style="display: block; margin: auto;" />

### Differential expression analysis

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

<img src="man/figures/RunDEtest-1.png" width="100%" style="display: block; margin: auto;" />

``` r
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)
```

<img src="man/figures/FeatureHeatmap-1.png" width="100%" style="display: block; margin: auto;" />

### Enrichment analysis(over-representation)

``` r
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"
)
```

<img src="man/figures/RunEnrichment-1.png" width="100%" style="display: block; margin: auto;" />

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

<img src="man/figures/RunEnrichment-2.png" width="100%" style="display: block; margin: auto;" />

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

<img src="man/figures/RunEnrichment-3.png" width="100%" style="display: block; margin: auto;" />

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

<img src="man/figures/RunEnrichment-4.png" width="100%" style="display: block; margin: auto;" />

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

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

<img src="man/figures/Enrichment_enrichmap-1.png" width="100%" style="display: block; margin: auto;" />

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

<img src="man/figures/Enrichment_comparison-1.png" width="100%" style="display: block; margin: auto;" />

### Enrichment analysis(GSEA)

``` r
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")
```

<img src="man/figures/RunGSEA-1.png" width="100%" style="display: block; margin: auto;" />

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

<img src="man/figures/GSEA_bar-1.png" width="100%" style="display: block; margin: auto;" />

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

<img src="man/figures/GSEA_comparison-1.png" width="100%" style="display: block; margin: auto;" />

### Trajectory inference

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

<img src="man/figures/RunSlingshot-1.png" width="100%" style="display: block; margin: auto;" />

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

<img src="man/figures/RunSlingshot-2.png" width="100%" style="display: block; margin: auto;" />

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

<img src="man/figures/RunSlingshot-3.png" width="100%" style="display: block; margin: auto;" />

### Dynamic features

``` r
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)
```

<img src="man/figures/DynamicHeatmap-1.png" width="100%" style="display: block; margin: auto;" />

``` r
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
)
```

<img src="man/figures/DynamicPlot-1.png" width="100%" style="display: block; margin: auto;" />

``` r
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")
  )
)
```

<img src="man/figures/FeatureStatPlot-1.png" width="100%" style="display: block; margin: auto;" />

### Interactive data visualization with SCExplorer

``` r
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](man/figures/SCExplorer-1.png)
![SCExplorer2](man/figures/SCExplorer-2.png)

### Other visualization examples

[**CellDimPlot**](https://zhanghao-njmu.github.io/SCP/reference/CellDimPlot.html)![Example1](man/figures/Example-1.jpg)
[**CellStatPlot**](https://zhanghao-njmu.github.io/SCP/reference/CellStatPlot.html)![Example2](man/figures/Example-2.jpg)
[**FeatureStatPlot**](https://zhanghao-njmu.github.io/SCP/reference/FeatureStatPlot.html)![Example3](man/figures/Example-3.jpg)
[**GroupHeatmap**](https://zhanghao-njmu.github.io/SCP/reference/GroupHeatmap.html)![Example3](man/figures/Example-4.jpg)

You can also find more examples in the documentation of the function:
[Integration_SCP](https://zhanghao-njmu.github.io/SCP/reference/Integration_SCP.html),
[RunKNNMap](https://zhanghao-njmu.github.io/SCP/reference/RunKNNMap.html),
[RunMonocle3](https://zhanghao-njmu.github.io/SCP/reference/RunMonocle3.html),
[RunPalantir](https://zhanghao-njmu.github.io/SCP/reference/RunPalantir.html),
etc.