EWCE: Expression Weighted Celltype Enrichment

Alan Murphy, Brian Schilder, and Nathan Skene

Oct-22-2021

Introduction

The EWCE R package is designed to facilitate expression weighted cell type enrichment analysis as described in our Frontiers in Neuroscience paper.1EWCE can be applied to any gene list.

Using EWCE essentially involves two steps:

  1. Prepare a single-cell reference; i.e. CellTypeDataset (CTD). Alternatively, you can use one of the pre-generated CTDs we provide via the package ewceData (which comes with EWCE).
  2. Run cell type enrichment on a user-provided gene list.

Installation

EWCE>=### requires R>=4.1 and Bioconductor>=1.4. To install EWCE on Bioconductor run:

if (!require("BiocManager")){install.packages("BiocManager")}

BiocManager::install(version = "devel")
BiocManager::install("EWCE")

Vignettes

EWCE: Getting started

A minimal example to get started with running EWCE.

EWCE: Extended examples

Example EWCE enrichment tests with in-depth explanations of each step.

EWCE: Creating CellTypeDatasets

Instructions on how to create new make new CellTypeDataset references to use with EWCE.

EWCE: Conditional examples

Examples and explanations of conditional cell type enrichment tests (e.g. controlling for a dominant cell type signal) .

EWCE: Transcriptome examples

Additional applications of EWCE to transcriptomic studies.

Docker container

Docker containers with the latest version of EWCE are regularly pushed to Dockerhub. If you already have Docker installed, you can load up a working copy using the following commands.

Note, that you will need to replace the initial directory path with a location on your computer that you wish to be able to access from within the docker image.

docker pull nathanskene/ewce
docker run --name=ewce -e PASSWORD=ewcedocker -p 8790:8790 -d -v /User/$USER:/var/ewce nathanskene/ewce:latest
docker exec -ti ewce R

Troubleshooting

If you have any problems, please do submit an issue here on GitHub with a reproducible example.

Session Info

utils::sessionInfo()
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] compiler_4.1.1  magrittr_2.0.1  fastmap_1.1.0   tools_4.1.1    
##  [5] htmltools_0.5.2 yaml_2.2.1      stringi_1.7.5   rmarkdown_2.11 
##  [9] knitr_1.36      stringr_1.4.0   xfun_0.27       digest_0.6.28  
## [13] rlang_0.4.12    evaluate_0.14

References

1. Skene, N. & Grant, S. Identification of vulnerable cell types in major brain disorders using single cell transcriptomes and expression weighted cell type enrichment. Frontiers in Neuroscience (2016). doi:[10.3389/fnins.2016.00016](https://doi.org/10.3389/fnins.2016.00016)