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Single-cell Case Control Analysis

cacoa 최신버젼 다운로드

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# cacoa

Case-Control Analysis of scRNA-seq experiments

The package implements methods described in [this pre-print](https://doi.org/10.1101/2022.03.15.484475). 
To reproduce results from the paper, please see [this repository](https://github.com/kharchenkolab/cacoaAnalysis).

## Installation

To install the latest version, use:

```r
install.packages('devtools')
devtools::install_github('kharchenkolab/cacoa')
```

Prior to installing the package, dependencies have to be installed:

```r
BiocManager::install(c("clusterProfiler", "DESeq2", "DOSE", "EnhancedVolcano", "enrichplot", "fabia", "GOfuncR", "Rgraphviz"))
```

## Initialization

Cacoa currently supports inputs in several formats (see below). Most of them require the following metadata:

- `sample.groups`: vector with condition labels per sample named with sample ids
- `cell.groups`: cell type annotation vector named by cell ids
- `sample.per.cell`: vector with sample labels per cell named with cell ids
- `ref.level`: id of the condition, corresponding to the reference (i.e. control)
- `target.level`: id of the condition, corresponding to the target (i.e. case)

Additionally, the `embedding` parameter containing a matrix or data.frame with a cell embedding can be provided. Rownames should match to the cell ids. 
It is used for visualization and some cluster-free analysis.

### No expression data

Cacoa can be ran without any expression data by passing `NULL` instead of a data object:

```r
cao <- Cacoa$new(
    NULL, sample.groups=sample.groups, cell.groups=cell.groups, sample.per.cell=sample.per.cell, 
    ref.level=ref.level, target.level=target.level, embedding=embedding
)
```

In this case, only compositional analyses will be available.

### Raw or normalized joined count matrix `cm`

```r
cao <- Cacoa$new(
    cm, sample.groups=sample.groups, cell.groups=cell.groups, sample.per.cell=sample.per.cell, 
    ref.level=ref.level, target.level=target.level, embedding=embedding
)
```

### Seurat object `so`

```r
cao <- Cacoa$new(
    so, sample.groups=sample.groups, cell.groups=cell.groups, sample.per.cell=sample.per.cell, 
    ref.level=ref.level, target.level=target.level, graph.name=graph.name, data.slot='data'
)
```

Parameter `graph.name` is required for cluster-free analysis, and must contain a name of joint graph in Seurat object. For that, the Seurat object must have a joint graph estimated (see [FindNeighbors](https://satijalab.org/seurat/reference/findneighbors)). For visualization purposes, Seurat also must have cell embedding estimated or the embedding data frame must be provided in the `embedding` parameter.

### Conos object `co`

```r
cao <- Cacoa$new(
    co, sample.groups=sample.groups, cell.groups=cell.groups, 
    ref.level=ref.level, target.level=target.level
)
```

For visualization purposes, Conos must have cell embedding estimated or the embedding data frame must be provided in the `embedding` parameter. And for cluster-free analysis it should have a joint graph (see the method `Conos$buildGraph()` from [conos](https://CRAN.R-project.org/package=conos) method).

## Usage

Cacoa can estimate and visualize various statistics. Most of them have paired functions `cao$estimateX(...)` and `cao$plotX(...)` (for example, `cao$estimateCellLoadings()` and `cao$plotCellLoadings()`). Results of all estimation are stored in `cao$test.results`, and their exact name can be controlled by `name` parameter passed to `cao$estimateX()`. For example, calling `cao$estimateExpressionShiftMagnitudes(name='es')` would save the results in `cao$test.results$es`.

Please, see the documentation for exact functions inside the package. For a demonstration see [the vignette](http://pklab.med.harvard.edu/viktor/cacoa/walkthrough_short.html) ([code](https://github.com/kharchenkolab/cacoa/blob/main/vignettes/walkthrough_short.Rmd)). Additionally, the [cacoaAnalysis](https://github.com/kharchenkolab/cacoaAnalysis/) repository contains analysis conducted inside the paper, though the Cacoa version there may be out of date.

## Citation

If you find this pipeline useful for your research, please consider citing the pre-print:

Case-control analysis of single-cell RNA-seq studies
Viktor Petukhov, Anna Igolkina, Rasmus Rydbirk, Shenglin Mei, Lars Christoffersen, Konstantin Khodosevich, Peter V. Kharchenko
bioRxiv 2022.03.15.484475; doi: https://doi.org/10.1101/2022.03.15.484475

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