# SCPA

**Repository Path**: joyeric_admin_admin/SCPA

## Basic Information

- **Project Name**: SCPA
- **Description**: nonparametric graph-based statistical framework
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-02-21
- **Last Updated**: 2024-05-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


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# Single Cell Pathway Analysis <img src="man/figures/logo.png" align="right" width=80px/>

![](man/figures/scpa_outline.png)

### On this page

1.  Recent updates to SCPA
2.  A brief [overview](https://jackbibby1.github.io/SCPA/#about-scpa) of
    SCPA
3.  Package
    [installation](https://jackbibby1.github.io/SCPA/#installation)
4.  Links to [tutorials](https://jackbibby1.github.io/SCPA/#tutorials)
5.  Submitting
    [issues/comments](https://jackbibby1.github.io/SCPA/#issues)
6.  [Improvements/changes/updates](https://jackbibby1.github.io/SCPA/news/index.html)
    to SCPA

### Recent updates

- Compatability with Seurat v5 objects to use the layer system in
  `seurat_extract()` and `compare_seurat()` (v1.6). SCPA automatically
  detects Seurat object version
- Ability to specify [multiple gmt
  files](https://jackbibby1.github.io/SCPA/articles/using_gene_sets.html#using-a-gmt-file)
  as your pathway input (v1.5.2)
- [Parallel
  processing](https://jackbibby1.github.io/SCPA/articles/parallel_implementation.html)
  implemented to speed up analyses (v1.5.0)
- Much more efficient usage of memory, so this shouldn’t be limiting
  (v1.3.0)

### About SCPA

SCPA is a method for pathway analysis in single cell RNA-seq data. It’s
a different approach to pathway analysis that defines pathway activity
as a change in multivariate distribution of a given pathway across
conditions, rather than enrichment or over representation of genes.

This approach allows for a number of benefits over current methods:

1.  Multivariate distribution testing allows for the identification of
    pathways that show enrichment in a given population AND also
    pathways that show transcriptional change independent of enrichment.
    You essentially get the best of both worlds, as pathways with
    changes in multivariate distribution (high qval) but no overall
    enrichment (low fold change) are still interestingly different
    pathways, as we show in [our
    paper](https://www.cell.com/cell-reports/fulltext/S2211-1247(22)01571-6).
    For more on this, see our [SCPA interpretation
    page](https://jackbibby1.github.io/SCPA/articles/interpreting_scpa_output.html)

2.  SCPA allows for multisample testing, so you can compare multiple
    conditions simultaneously e.g. compare across 3 time points, or
    across multiple phases of a [pseuodotime
    trajectory](https://jackbibby1.github.io/SCPA/articles/pseudotime.html).
    This means you can assess pathway activity through multiple
    stimulation phases, or across cell differentiation

SCPA can be applied directly on Seurat and SCE objects, as well as
manually subsetted expression matrices. You just need to supply this
along with your pathways that you want to analyse.

To see the stats behind SCPA, you can see our paper in JASA
[here](https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1791131)

Our paper introducing SCPA and demonstrating its use on a T cell
scRNA-seq dataset is published in Cell Reports
[here](https://www.cell.com/cell-reports/fulltext/S2211-1247(22)01571-6)

### Installation

You can install SCPA by running:

``` r
# install.packages("devtools")
devtools::install_version("crossmatch", version = "1.3.1", repos = "http://cran.us.r-project.org")
devtools::install_version("multicross", version = "2.1.0", repos = "http://cran.us.r-project.org")
devtools::install_github("jackbibby1/SCPA")
```

### Installation problems

If you’re running into installation errors, you’ll likely need to
manually install the dependencies that are mentioned in the error. For
example, see these issues
[here](https://github.com/jackbibby1/SCPA/issues/54) and
[here](https://github.com/jackbibby1/SCPA/issues/56)

### Tutorials

If you’re viewing this page on GitHub, the SCPA webpage with all the
documentation and tutorials is
[here](https://jackbibby1.github.io/SCPA/)

We have various examples and walkthroughs, including:

- A generic quick start
  [tutorial](https://jackbibby1.github.io/SCPA/articles/quick_start.html)
  on a basic scRNA-seq dataset
- A
  [tutorial](https://jackbibby1.github.io/SCPA/articles/using_gene_sets.html)
  on how to get and use gene sets with SCPA
- [An
  outline](https://jackbibby1.github.io/SCPA/articles/parallel_implementation.html)
  for speeding up SCPA with parallel processing
- A
  [tutorial](https://jackbibby1.github.io/SCPA/articles/comparing_two_populations.html)
  for more detailed two group comparison with a specific scRNA-seq data
  set
- A
  [tutorial](https://jackbibby1.github.io/SCPA/articles/seurat_comparison.html)
  on how to use SCPA directly within a Seurat or SingleCellExperiment
  object
- A
  [tutorial](https://jackbibby1.github.io/SCPA/articles/visualisation.html)
  on visualising SCPA output
- A
  [tutorial](https://jackbibby1.github.io/SCPA/articles/pseudotime.html)
  for multisample SCPA, comparing pathways across a scRNA-seq T cell
  pseudotime trajectory
- A
  [tutorial](https://jackbibby1.github.io/SCPA/articles/systematic_tissue_comparison.html)
  of a systematic analysis of many cell types across multiple tissues
- A
  [tutorial](https://jackbibby1.github.io/SCPA/articles/disease_comparison.html)
  for a systems level analysis of many cells types in disease (COVID-19)

### Issues

To report any issues or submit comments please use:
<https://github.com/jackbibby1/SCPA/issues>

### Changelog

Any new features or alterations to SCPA can be found
[here](https://jackbibby1.github.io/SCPA/news/index.html)