TREG

Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data

https://github.com/lieberinstitute/treg

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Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    fig.path = "man/figures/README-",
    out.width = "100%"
)
```

# TREG 


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The goal of `TREG` is to help find candidate **Total RNA Expression Genes (TREGs)**
in single nucleus (or single cell) RNA-seq data.

_**Note**: TREG is pronounced as a single word and fully capitalized, unlike [Regulatory T cells](https://en.wikipedia.org/wiki/Regulatory_T_cell), which are known as "Tregs" (pronounced "T-regs"). The work described here is unrelated to regulatory T cells._

### Why are TREGs useful?
The expression of a TREG is proportional to the the overall RNA expression in a
cell. This relationship can be used to estimate total RNA content in cells in 
assays where only a few genes can be measured, such as single-molecule 
fluorescent in situ hybridization (smFISH). 

In a smFISH experiment the number of TREG puncta can be used to infer the total
RNA expression of the cell.

The motivation of this work is to collect data via smFISH in to help build better 
deconvolution algorithms. But may be many other application for TREGs in 
experimental design!


![The Expression of a TREG can inform total RNA content of a cell](man/figures/TREG_cartoon.png){width=50%}


### What makes a gene a good TREG?  
1. The gene must have **non-zero expression in most cells** across different tissue 
and cell types.

2. A TREG should also be expressed at a constant level in respect to other genes
across different cell types or have **high rank invariance**. 

3. Be **measurable as a continuous metric** in the experimental assay, for example
have a dynamic range of puncta when observed in RNAscope. This will need to be
considered for the candidate TREGs, and may need to be validated experimentally. 


![Distribution of ranks of a gene of High and Low Invariance](man/figures/fig1_rank_violin_demo.png){width=30%}


### How to find candidate TREGs with `TREG`


![Overview of the Rank Invariance Process](man/figures/RI_flow.png){width=100%}


1. **Filter for low Proportion Zero genes snRNA-seq dataset:** This is 
facilitated with the functions `get_prop_zero()` and `filter_prop_zero()`.
snRNA-seq data is notoriously sparse, these functions enrich for genes with more
universal expression.

2.  **Evaluate genes for Rank Invariance** The nuclei are grouped only
by cell type. Within each cell type, the mean expression for each
gene is ranked, the result is a vector (length is the number of
genes), using the function `rank_group()`. Then the expression of each gene is 
ranked for each nucleus,the result is a matrix (the number of nuclei x number
of genes), using the function `rank_cells()`.Then the absolute difference 
between the rank of each nucleus and the mean expression is found, from here 
the mean of the differences for each gene is calculated, then ranked. 
These steps are repeated for each group, the result is a matrix of ranks, (number of cell
types x number of genes). From here the sum of the ranks for each
gene are reversed ranked, so there is one final value for each gene,
the “Rank Invariance” The genes with the highest rank-invariance are
considered good candidates as TREGs. This is calculated with `rank_invariance_express()`.
**This full process is implemented by: `rank_invariance_express()`.**

## Installation instructions

Get the latest stable `R` release from [CRAN](http://cran.r-project.org/). Then install `TREG` using from [Bioconductor](http://bioconductor.org/) the following code:

```{r 'install', eval = FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("TREG")
```

And the development version from [GitHub](https://github.com/LieberInstitute/TREG) with:

```{r 'install_dev', eval = FALSE}
BiocManager::install("LieberInstitute/TREG")
```
## Example

```{r `libraries`, message = FALSE, warning=FALSE}
## Load packages
library("TREG")
```


### Proportion Zero Filtering  
A  TREG gene should be expressed in almost every cell. The set of
genes should be filtered by maximum Proportion Zero within a groups of cells.

```{r calc_prop_zero, eval = requireNamespace('TREG')}
## Calculate Proportion Zero in groups defined by a column in colData
(prop_zero <- get_prop_zero(sce = sce_zero_test, group_col = "cellType"))

## Get list of genes that pass the max Proportion Zero filter
(filtered_genes <- filter_prop_zero(prop_zero, cutoff = 0.9))

## Filter sce object to this list of genes
sce_filter <- sce_zero_test[filtered_genes, ]
```

### Evaluate RI for Filtered SCE Data
The genes with the highest Rank Invariance are considered good candidates as TREGs.
In this example the gene *g0* would be the strongest candidate TREG.

```{r run_RI, eval = requireNamespace('TREG')}
## Get the Rank Invariance value for each gene
## The highest values are the best TREG candidates
ri <- rank_invariance_express(sce_filter)
sort(ri, decreasing = TRUE)
```


## Citation

Below is the citation output from using `citation('TREG')` in R. Please
run this yourself to check for any updates on how to cite __TREG__.

```{r 'citation', eval = requireNamespace('TREG')}
print(citation("TREG"), bibtex = TRUE)
```

Please note that the `TREG` was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing this package.

## Code of Conduct

Please note that the `TREG` project is released with a [Contributor Code of Conduct](http://bioconductor.org/about/code-of-conduct/). By contributing to this project, you agree to abide by its terms.

## Development tools

* Continuous code testing is possible thanks to [GitHub actions](https://www.tidyverse.org/blog/2020/04/usethis-1-6-0/)  through `r BiocStyle::CRANpkg('usethis')`, `r BiocStyle::CRANpkg('remotes')`, and `r BiocStyle::CRANpkg('rcmdcheck')` customized to use [Bioconductor's docker containers](https://www.bioconductor.org/help/docker/) and `r BiocStyle::Biocpkg('BiocCheck')`.
* Code coverage assessment is possible thanks to [codecov](https://codecov.io/gh) and `r BiocStyle::CRANpkg('covr')`.
* The [documentation website](http://LieberInstitute.github.io/TREG) is automatically updated thanks to `r BiocStyle::CRANpkg('pkgdown')`.
* The code is styled automatically thanks to `r BiocStyle::CRANpkg('styler')`.
* The documentation is formatted thanks to `r BiocStyle::CRANpkg('devtools')` and `r BiocStyle::CRANpkg('roxygen2')`.

For more details, check the `dev` directory.

This package was developed using `r BiocStyle::Biocpkg('biocthis')`.

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