Shixiang Wang
- Add: 233, Haike Rd., Pudong District, Shanghai 201210, China
- Working email: wangshx@shanghaitech.edu.cn
- Personal email: w_shixiang@163.com or shixiang1994wang@gmail.com
- Personal website: https://shixiangwang.github.io
Education
2016.09 ~ Present, Ph.D Student, Cancer Biology (focusing on cancer informatics),
ShanghaiTech. University, Shanghai, China2012.09 ~ 2016.07, B.E. Biomedical Engineering,
University of Electronic Science and Technology of China, Chengdu, China
Professional skills
- Programming levels:
- R \(\star\star\star\star\star\)
- Shell \(\star\star\star\)
- Python \(\star\star\star\)
- Golang \(\star\star\)
- Data analysis. I have advanced experience in using R and Shell for data preprocessing, data cleaning and data interpretation.
- Statistics. I have moderate experience in using R for statistical modeling and data visualization.
- Package/pipeline development. I master developing pure R packages and have a little experience in Python package and R Shiny development. I can combine multiple languages to create analysis pipeline.
- Genomic analysis. I can process raw genomic data and analyze them. I have moderate experience in somatic variant calling (including SNV, INDEL and CNV), differential expression analysis and enrichment analysis.
- Clinical analysis. I know how to construct survival models and interpret results.
- Machine learning. I know how to do machine learning (including deep learning) and have applied some technologies to my projects.
- Writing. I like to write with R Markdown (including Markdown) and share my knowledge to others in many ways (e.g. GitHub Issue, Jianshu, Wechat, and etc.).
Developments
- sigminer (https://cran.r-project.org/package=sigminer): mutational signature analysis and visualization in R.
- ezcox (https://cran.r-project.org/package=ezcox): operate a batch of univariate or multivariate Cox models and return tidy result.
- DoAbsolute (https://github.com/ShixiangWang/DoAbsolute): automate ABSOLUTE calling for multiple samples in parallel way.
- metawho (https://cran.r-project.org/package=metawho): simple R implementation of “Meta-analytical method to Identify Who Benefits Most from Treatments”.
- UCSCXenaTools (https://cran.r-project.org/package=UCSCXenaTools): an R package for downloading and exploring data from UCSC Xena data hubs.
- UCSCXenaShiny (https://cran.r-project.org/package=UCSCXenaShiny): a Shiny based on UCSCXenaTools.
- contribution (https://cran.r-project.org/package=contribution): generate contribution table for credit assignment in a project.
- loon (https://pypi.org/project/loon/): a Python toolkit for operating remote host based on SSH.
- sync-deploy (https://github.com/ShixiangWang/sync-deploy): a Shell toolkit for deploying script/command task on a remote host, including up/download files, run script and more.
More activities about my development and contribution can be viewed at github.com/ShixiangWang.
Publications
Total citations: 106. (Data source: Google Scholar. Update time: 2020-08-02)
- Wang, S., He, Z., Wang, X., Li, H., & Liu, X. S. (2019). Antigen presentation and tumor immunogenicity in cancer immunotherapy response prediction. eLife, 8, e49020. https://doi.org/10.7554/eLife.49020 (PDF)
- Wang, S., He, Z., Wang, X., Li, H., Wu, T., Sun, X., … & Liu, X. S. (2019). Can tumor mutational burden determine the most effective treatment for lung cancer patients?. Lung Cancer Management. https://doi.org/10.2217/lmt-2019-0013 (PDF)
- Wang, S., Cowley, L. A., & Liu, X. S. (2019). Sex differences in Cancer immunotherapy efficacy, biomarkers, and therapeutic strategy. Molecules, 24(18), 3214. (PDF)
- Wang, S. & Liu, X. S. (2019). The UCSCXenaTools R package: a toolkit for accessing genomics data from UCSC Xena platform, from cancer multi-omics to single-cell RNA-seq. Journal of Open Source Software, 4(40), 1627, https://doi.org/10.21105/joss.01627 (PDF)
- He, Z., Wang, S., Shao, Y., Zhang, J., Wu, X., Chen, Y., … & Liu, X. S. (2019). Ras downstream effector GGCT alleviates oncogenic stress. iScience. (PDF)
- Wang, S., Zhang, J., He, Z., Wu, K., & Liu, X. S. (2019). The predictive power of tumor mutational burden in lung cancer immunotherapy response is influenced by patients’ sex. International journal of cancer, 145(10), 2840-2849. (PDF)
- Wang, S., Jia, M., He, Z., & Liu, X. S. (2018). APOBEC3B and APOBEC mutational signature as potential predictive markers for immunotherapy response in non-small cell lung cancer. Oncogene, 37(29), 3924-3936. (PDF)