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scgate

marker-based purification of cell types from single-cell RNA-seq datasets

https://github.com/carmonalab/scgate

Science Score: 39.0%

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Keywords

filtering marker-genes scgate signatures single-cell
Last synced: 6 months ago · JSON representation

Repository

marker-based purification of cell types from single-cell RNA-seq datasets

Basic Info
  • Host: GitHub
  • Owner: carmonalab
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 1.99 MB
Statistics
  • Stars: 121
  • Watchers: 5
  • Forks: 13
  • Open Issues: 5
  • Releases: 7
Topics
filtering marker-genes scgate signatures single-cell
Created over 4 years ago · Last pushed 7 months ago
Metadata Files
Readme

README.md

scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets

scGate is an R package that automatizes the typical manual marker-based approach to cell type annotation, to enable accurate and intuitive purification of a cell population of interest from a complex scRNA-seq dataset, without requiring reference gene expression profiles or training data. scGate works with any scRNA-seq technology and with other single-cell modalities.

scGate builds upon UCell for robust single-cell signature scoring and Seurat, a comprehensive and powerful framework for single-cell omics analysis.

Briefly, scGate takes as input: i) a gene expression matrix stored in a Seurat object and ii) a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry.

scGate evaluates the strength of signature marker expression in each cell using the rank-based method UCell, and then performs k-nearest neighbor (kNN) smoothing by calculating the mean UCell score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest.

scGate_examples

Installation

Install scGate from CRAN r install.packages("scGate")

For older versions of scGate, download a tagged version from GitHub: r library(remotes) remotes::install_github("carmonalab/scGate", ref="v1.6.2")

Testing the package

Use scGate to purify a cell population of interest using manually defined marker genes

```r library(scGate)

Get a test scRNA-seq dataset (as a list of Seurat objects)

sample.data.seurat.list <- scGate::gettestingdata()

seurat_object <- sample.data.seurat.list$Satija

Manually define a simple scGate gating model to purify eg. Natural Killer (NK) cells, using a positive marker KLRD1 and negative marker CD3D

myscGatemodel <- gating_model(name = "NK", signature = c("KLRD1","CD3D-"))

scGate it!

seuratobject <- scGate(data = seuratobject, model = myscGatemodel)

Use Seurat to visualize "Pure" and "Impure" cells

DimPlot(seurat_object, group.by = "is.pure")

Use Seurat to subset pure cells

seuratobjectpurified <- subset(seurat_object, subset = is.pure == "Pure" ) ```

Demos and tutorials

Check out this scGate demo for a reproducible analysis, construction of hierarchical gating models, tools for performance evaluation and other advanced features. More demos for running scGate on different single-cell modalities are available at scGate.demo repository.

Pre-defined Gating Models

A database of gating models for scGate is available on scGate_models and can be loaded using get_scGateDB() ```r

Get scGate database of pre-defined gating models

scGatemodelsDB <- get_scGateDB()

For example, filter abT cells using one of scGate pre-defined gating models

seuratobject <- scGate(seuratobject, model = scGatemodelsDB$human$generic$Tcell.alphabeta)

DimPlot(seuratobject) `` The first time you rungetscGateDB()` the database will be downloaded from the repository. On successive calls it will load your local version of the DB.

You may manually edit the available models (eg in Excel) or create new models for your cell type of interest. You can then load your custom model into R using: r my_scGate_model <- load_scGate_model("path_to_my.model")

You can use the plot_tree function to visualize the hierarchical structure of one of the models (requires ggparty).

r install.packages("ggparty") scGate::plot_tree(scGate_models_DB$human$generic$Tcell.alphabeta)

scGate as a multi-class classifier

scGate can also be used a cell type classifier, to annotate multiple cell types in a dataset. To annotate a dataset with marker-based cell type definitions, simply provide a list of models to scGate, e.g.:

r models.list <- scGate_models_DB$human$generic[c("Bcell","MoMacDC","CD8T","CD4T","Erythrocyte")] obj <- scGate(obj, model = models.list)

scGate also provides collections of gating models that work nicely with each other to annotate cell types in specific tissues, e.g. for the tumor micro-environment (TME): r models.TME <- scGate_models_DB$human$TME_HiRes obj <- scGate(obj, model=models.TME) table(obj$scGate_multi)

See examples of scGate as a classifier at: scGate multi-class and scGate on integrated objects

Other single-cell modalities

scGate can be applied to modalities other than RNA-seq, such as ATAC-seq (scATAC-seq demo) and antibody-derived tags (ADT) (CITE-seq demo).

References

Massimo Andreatta, Ariel J Berenstein, Santiago J Carmona, scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets, Bioinformatics, Volume 38, Issue 9, 1 May 2022, Pages 2642–2644, https://doi.org/10.1093/bioinformatics/btac141

Owner

  • Name: Cancer Systems Immunology Lab
  • Login: carmonalab
  • Kind: organization
  • Location: Lausanne, Switzerland

At Ludwig Cancer Research Lausanne and Department of Oncology, University of Lausanne & Swiss Institute of Bioinformatics

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Last synced: 6 months ago

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Packages

  • Total packages: 1
  • Total downloads:
    • cran 477 last-month
  • Total docker downloads: 84
  • Total dependent packages: 0
  • Total dependent repositories: 2
  • Total versions: 5
  • Total maintainers: 1
cran.r-project.org: scGate

Marker-Based Cell Type Purification for Single-Cell Sequencing Data

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 2
  • Downloads: 477 Last month
  • Docker Downloads: 84
Rankings
Stargazers count: 4.8%
Forks count: 6.3%
Average: 15.0%
Downloads: 16.2%
Dependent repos count: 19.2%
Dependent packages count: 28.7%
Maintainers (1)
massimo.andreatta@unige.ch
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 4.1.0 depends
  • Seurat >= 4.0.0 imports
  • UCell >= 1.1.0 imports
  • dplyr * imports
  • ggplot2 * imports
  • ggridges * imports
  • patchwork * imports
  • reshape2 * imports
  • stats * imports
  • utils * imports
  • ggparty * suggests