DeMixSC for bulk RNA-seq deconvolution

DeMixSC is a generalizable framework designed to leverage single-cell sequencing and a small benchmark dataset for bulk RNA-seq deconvolution. Please refer to our preprint for details of DeMixSC.

Framework overview

DeMixSC offers accurate cell-type deconvolution for large bulk RNA-seq datasets through a two-tier procedure.

First, DeMixSC utilizes a specifically designed benchmark dataset to identify and adjust genes with high inter-platform discrepancies (Fig.1A).

Second, to deconvolve a large unmatched bulk RNA-seq dataset, DeMixSC aligns it with the benchmark dataset (Fig.1B) and then employs a refined weighted non-negative least square (wNNLS) framework for deconvolution (Fig.1C).

Note: DeMixSC requires a matched tissue type between the small benchmark dataset and the large targeted cohort.

Rescource: Figure 1, Workflow of DeMixSC (Guo et al. 2023, doi: https://doi.org/10.1101/2023.10.10.561733).

Usage

DeMixSC is implemented in R. To use DeMixSC, please install the required packages as detailed below:

# Install BiocManager if necessary
  if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")     
      
# Install required pacakges
  BiocManager::install("sva")
  BiocManager::install("preprocessCore")
  install.packages("doParallel")
  install.packages("nnls")
  install.packages('Seurat')

Next, please install the latest version of DeMixSC from our GitHub repository:

# install devtools if necessary
  if (!"devtools" %in% rownames(installed.packages())) {
    install.packages('devtools')
  }

# Install our DeMixSC package with the following command
  devtools::install_github('wwylab/DeMixSC')

You can then use the provided functions to conduct deconvolution analyses. We provide a tutorial on using DeMixSC for deconvolving the benchmarking dataset.

License

This package is licensed under GNU Affero General Public License v3.0. See the LICENSE file for details.

Contact

For questions or comments about DeMixSC, please contact Shuai Guo SGuo3@mdanderson.org, Xiaoqian Liu XLiu31@mdanderson.org and Wenyi Wang wwang7@mdanderson.org. If you find a bug or have a feature request, please submit an issue on the GitHub repository.