# UCell

**Repository Path**: emberwhirl/UCell

## Basic Information

- **Project Name**: UCell
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-03-09
- **Last Updated**: 2022-03-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# UCell: Robust and scalable single-cell gene signature scoring


`UCell` is an R package for scoring gene signatures in single-cell datasets. UCell scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands relatively less computing time and memory than other robust methods, enabling the processing of large datasets (>10^5 cells). UCell can be applied to any cell vs. gene data matrix, and includes functions to directly interact with Seurat and SingleCellExperiment objects. 


![UCell_figure](https://github.com/carmonalab/UCell_demo/blob/master/docs/Figure1.png?raw=true)


Find the installation instructions for the package and usage vignettes below.

### Package Installation

To install `UCell` directly from its GitHub repository, run the following code from within R or RStudio:
```
library(remotes)
remotes::install_github("carmonalab/UCell", ref="v1.3")
```
For previous releases, specify the relevant tag with the 'ref' option.


### Test the package

Load sample data and test your installation:
```
library(UCell)

data(sample.matrix)
gene.sets <- list(Tcell_signature = c("CD2","CD3E","CD3D"),
			Myeloid_signature = c("SPI1","FCER1G","CSF1R"))

scores <- ScoreSignatures_UCell(sample.matrix, features=gene.sets)
head(scores)
```

### Examples and tutorials

Run UCell demos to learn about the functionalities of the package:

* [Single-cell gene signature scoring with UCell](https://carmonalab.github.io/UCell_demo/UCell_matrix_vignette.html)

* [Using UCell with Seurat objects](https://carmonalab.github.io/UCell_demo/UCell_Seurat_vignette.html)

* [Using UCell and Seurat to identify different T cell subtypes/states in human tumors](https://carmonalab.github.io/UCell_demo/UCell_vignette_TILstates.html)

### New in version 1.1.0

You can now specify positive and negative (up- or down-regulated) genes in signatures. For example, build signatures as:

```
markers <- list()
markers$Tcell_gd <- c("TRDC+", "TRGC1+", "TRGC2+", "TRDV1+","TRAC-","TRBC1-","TRBC2-")
markers$Tcell_NK <- c("FGFBP2+", "SPON2+", "KLRF1+", "FCGR3A+", "CD3E-","CD3G-")
markers$Tcell_CD4 <- c("CD4","CD40LG")
markers$Tcell_CD8 <- c("CD8A","CD8B")
markers$Tcell_Treg <- c("FOXP3","IL2RA")
```
If you don't specify +/- for genes, they are assumed to be all as a positive set.

The **UCell score** is calculated as:  U = max(0, U<sup>+</sup> - *w_neg* * U<sup>-</sup>)
 
where U<sup>+</sup> and U<sup>-</sup> are respectively the U scores for the positive and negative set, and *w_neg* is a weight on the negative set.

When no negative set of genes is present, U = U<sup>+</sup>, therefore the behavior is identical to previous UCell versions.   



### Documentation

See a description of the functions implemented in UCell at: [UCell functions](docs/functions.md)


### Citation

UCell: robust and scalable single-cell gene signature scoring. Massimo Andreatta & Santiago J Carmona **(2021)** *CSBJ* https://doi.org/10.1016/j.csbj.2021.06.043