README.md

In keyalone/scDEA: Performing differential expression analysis in single cell RNA sequencing data via ensemble learning

scDEA

R package applies ensemble learning for single-cell differential expression analysis on single-cell RNA-seq dataset. Besides, we also develop a web service (e.g., Shiny Application) for users who do not have a strong programming background. Just load on the website , upload the data and download the results. Enjoy!

However, if you prefer to apply scDEA using R. The first step is to install scDEA in R.

The scDEA package has the following R-pakage dependencies: BPSC, DEsingle, DESeq2, edgeR, MAST, monocle, scDD, limma, Seurat, zingeR, SingleCellExperiment, scater, aggregation. In the dependencies, you can load on most of dependency packages on your R. If the dependencies are not installed correctly, please install them by yourself. In my experience, the scDEA can't install BPSC, DEsingle and zingeR automatically. Hence, we advise, users should install BPSC, DEsingle and zingeR firstly by the following instruction. Then, install scDEA and run scDEA. Enjoy!

BPSC (https://github.com/nghiavtr/BPSC)

library(devtools)

install_github("nghiavtr/BPSC")

DEsingle (http://www.bioconductor.org/packages/release/bioc/html/DEsingle.html)

if (!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

BiocManager::install("DEsingle")

DESeq2 (http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html)

if (!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

BiocManager::install("DESeq2")

edgeR (http://www.bioconductor.org/packages/release/bioc/html/edgeR.html)

if (!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

BiocManager::install("edgeR")

MAST (http://www.bioconductor.org/packages/release/bioc/html/MAST.html)

if (!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

BiocManager::install("MAST")

monocle (http://www.bioconductor.org/packages/release/bioc/html/monocle.html)

if (!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

BiocManager::install("monocle")

scDD (http://www.bioconductor.org/packages/release/bioc/html/scDD.html)

if (!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

BiocManager::install("scDD")

limma (http://www.bioconductor.org/packages/release/bioc/html/limma.html)

if (!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

BiocManager::install("limma")

Seurat (http://www.bioconductor.org/packages/release/bioc/html/Seurat.html)

if (!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

BiocManager::install("Seurat")

zingeR (https://github.com/statOmics/zingeR/)

library(devtools)

install_github("statOmics/zingeR")

SingleCellExperiment (http://www.bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html)

if (!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

BiocManager::install("SingleCellExperiment")

scater (http://www.bioconductor.org/packages/release/bioc/html/scater.html)

if (!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

BiocManager::install("scater")

aggregation (https://cran.r-project.org/web/packages/aggregation/)

install.packages("aggregation")

After upper step, you can use the following commands to install scDEA from Github.

# Step 1. Install the devtools package. Invoke R and then type

install.packages("devtools")

# Step 2. Load the devtools package.

library("devtools")

# Step 3. Install the scDEA package from Github.

devtools::install_github("Zhangxf-ccnu/scDEA")

Step 4. Useage

Load the library scDEA in the R console, by running

library("scDEA")

# Step 5. Simply run the scDEA on the Grun datasets.

data("Grun.counts.hvg")

data("Grun.group.information")

Pvals <- scDEA_individual_methods(raw.count = Grun.counts.hvg,

cell.label = Grun.group.information, DESeq2.parallel = FALSE, scDD = FALSE, monocle.parallel = FALSE)

combination.Pvals <- lancaster.combination(Pvals, weight = TRUE, trimmed = 0.2)

adjusted.Pvals <- p.adjust(combination.Pvals, adjusted.method = "bonferroni")

Please do not hesitate to contact Dr. Xiao-Fei Zhang at [e-mail] to seek any clarifications regarding any contents or operation of the archive.

keyalone/scDEA documentation built on Dec. 21, 2021, 6:36 a.m.