Pathway Enrichment Analysis and Visualization for Single-cell Data
CellEnrich manual is available.
NOTE: on a fresh installation, users may need to install some required interpreter compilers for the system to install other R packages:
Install Dependent Packages:
- Some packages must be installed from sources that need compilation and a proper version of RTools.
- It is recommended to install devtools, and BiocManager packages first before installing the following packages in Github/Bioconductor (not in CRAN).
# install required packages
install.packages('Seurat') # RStudio may need a RESTART for Seurat v5.0.1 to be activated
install.packages('remotes')
install.packages('waiter')
install.packages('farver')
remotes::install_github('vqv/ggbiplot')
# install CellEnrich
remotes::install_github('noobCoding/CellEnrich')# Download data, if not downloaded
download.file('https://github.com/noobcoding/CellEnrich/raw/master/data/pbmcData.RData','pbmcData.RData', mode = 'wb')
download.file('https://github.com/noobcoding/CellEnrich/raw/master/data/pbmcClustInfo.RData','pbmcClustInfo.RData', mode = 'wb')
download.file('https://github.com/noobcoding/CellEnrich/raw/master/data/Human_Reactome.RData', 'Human_Reactome.RData', mode = 'wb')
# Load library and data
library(CellEnrich)
library(Seurat)
load("pbmcData.RData")
load("pbmcClustInfo.RData")
CountData <- pbmcData
GroupInfo <- pbmcClustInfo
# CellEnrich uses normalized count data as input
CountData <- NormalizeData(CountData)
# This will run CellEnrich
CellEnrich(CountData, GroupInfo)Two datasets for testing CellEnrich are too big for hosting on GitHub so you can directly download them at Zenodo link including:
- 'GBM_sub' is the glioblastoma data from link, which contains human HFC(Highly functionally connected) and LFC(Low functionally connected) Glioblastoma cells with added information of GRIA2 expression.
# Download data directly at the Zenodo link above!
gbm_sub <- readRDS(file = "GBM_sub.rds")
gbm_sub$count<-NormalizeData(gbm_sub$count)
CellEnrich(gbm_sub$count,gbm_sub$class)- 'PD_dat' is the Parkinson's disease data from link, which contains human dopamine cell cluster information about Parkinson's disease vs. Control.
PD_dat <- readRDS("PD_dat.rds")
PD_dat$count <- NormalizeData(PD_dat$count)
CellEnrich(PD_dat$count,PD_dat$type)# Download Alzheimer's data to the working directory
download.file('https://github.com/noobcoding/CellEnrich/raw/master/data/Alzheimer_Counts_sampled.RDS','Alzheimer_Counts_sampled.RDS', mode = 'wb')
download.file('https://github.com/noobcoding/CellEnrich/raw/master/data/Alzheimer_CellType_sampled.RDS','Alzheimer_CellType_sampled.RDS', mode = 'wb')
download.file('https://github.com/noobcoding/CellEnrich/raw/master/data/Human_Reactome.RData', 'Human_Reactome.RData', mode = 'wb')
GroupInfo <- readRDS("Alzheimer_CellType_sampled.RDS")
CountData <- readRDS("Alzheimer_Counts_sampled.RDS")
CountData <- NormalizeData(CountData)
CellEnrich(CountData, GroupInfo)- R - >= 4.2.0
- dplyr - 0.8.5
- DT - 0.13
- farver - 2.0.3
- ggplot2 - 3.3.0
- ggrepel - 0.8.2
- highcharter - 0.7.0.9001
- htmltools - 0.4.0
- magrittr - 1.5
- Rtsne - 0.15
- scales - 1.1.0
- scMerge - 1.5.0
- scran - 1.14.6
- Seurat - 5.0.1
- shiny - 1.4.0.2
- shinyFeedback - 0.2.0
- shinyjs - 1.1
- shinymaterial - 1.0.1
- SingleCellExperiment - 1.8.0
- sortable - 0.4.2
- uwot - 0.1.8
- waiter - 0.1.1.9000*
- Hai Nguyen [email protected] -- @noobCoding
- Jinhwan Kim @jhk0530
- Prof. Dougu Nam [email protected]
This project is MIT licensed