Robust and Accurate Deconvolution (RAD)

Introduction

RAD is a toolkit that unmixes bulk tumor samples. Given a non-negative bulk RNA expression matrix B \in R_+^{m x n}, where each row i is a gene, each column j is a tumor sample, our goal is to infer an expression profile matrix C \in R_+^{m x k}, where each column l is a cell community, and a fraction matrix F \in R_+^{k x n}, such that:

  B ~= C F. 

To be more specific, RAD solves the following problem:

min_{C, F} || B - C F ||_{Fr}^2, 

      s.t. C_{il} >= 0,              i=1,...,m, l=1,...,k,

           F_{lj} >= 0,              l=1,...,k, j=1,...,n,

           \sum_{l=1}^{k} F_{lj} = 1,           j=1,...,n.

RAD has the following features and advantages:

• compress_module: Integrate gene module knowledge to reduce noise.
• estimate_number: Estimate the number of cell populations automatically.
• estimate_clones: Utilize core RAD algorithm to unmix the cell populations accurately and robustly.
• estimate_marker: Estimate other biomarkers of cell populations given bulk marker data.

Prerequisites

The code runs on Python 3. You will need to install the additional Python package cvxopt. Most other packages are available in the Anaconda.

Tutorial

You can find a brief tutorial with code and output in the jupyter notebook tutorial.ipynb.

Citation

If you find RAD helpful, please cite the following paper: Yifeng Tao, Haoyun Lei, Xuecong Fu, Adrian V. Lee, Jian Ma, and Russell Schwartz. Robust and accurate deconvolution of tumor populations uncovers evolutionary mechanisms of breast cancer metastasis. Bioinformatics, 36(Supplement_1):i407-i416. jul 2020.

@article{tao2020rad,
  title = {Robust and Accurate Deconvolution of Tumor Populations Uncovers Evolutionary Mechanisms of Breast Cancer Metastasis},
  author = {Tao, Yifeng and Lei, Haoyun and Fu, Xuecong and Lee, Adrian V and Ma, Jian and Schwartz, Russell},
  journal = {Bioinformatics},
  volume = {36},
  number = {Supplement_1},
  pages = {i407-i416},
  year = {2020},
  month = {jul},
  issn = {1367-4803},
  doi = {10.1093/bioinformatics/btaa396},
  url = {https://doi.org/10.1093/bioinformatics/btaa396},
  eprint = {https://academic.oup.com/bioinformatics/article-pdf/36/Supplement\_1/i407/33488922/btaa396.pdf},
}

We compared RAD with a few other methods in the paper, you can find the links to these algorithms below:

• Geometric Unmixing: Paper, Code
• LinSeed: Paper, Code
• NND: Paper, Code