Xiaotao Shen

Postdoctoral Research Fellow

Stanford University School of Medicine

About

I am now a postdoctoral research fellow at Stanford University School of Medicine. My overarching research interests are bioinformatics algorithms (R/Python) development for multi-omics data, and their application to precision medicine. Specifically, I am interested in bioinformatics algorithm development, including 1) Comprehensive analysis workflow and deep learning for LC-MS data, 2) Metabolic network analysis, 3) Wearable and multi-omics data integration, and 4) microbiome and metabolome data integration.

I also employed the developed bioinformatics algorithms as a unique systems biology approach to study the potential biomarkers and mechanisms of 1) pregnancy and related diseases, 2) aging and related diseases, and 3) cancer. More tools and projects I developed can be found in the Projects part.

Now I am also the member of Stanford Chinese Postdoctral Association (SCPA). Fell free to let me know if you want to join us and work for the amazing community!

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My favorite song recently: The hall of fame - The Script

Interests

Bioinformatics
Metabolomics
Proteomics
Microbiome
Multi-omics
Systems Biology
Aging

Education

PhD, 2013-2018

Chinese Academy of Sciences
BSc, 2009-2013

Inner Mongolia University

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Research highlights

Last updated on Jan 24, 2023

Multi-omics microsampling for the profiling of lifestyle-associated changes in health

Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors.

Last updated on Sep 20, 2022

Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine

Liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics provides systematic profiling of metabolic. Yet its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite identification, information loss, and low reproducibility. Here, we present the deepPseudoMSI project (https://www.deeppseudomsi.org/), which converts LC-MS raw data to pseudo-MS images and then processes them by deep learning for precision medicine, such as disease diagnosis. Extensive tests based on real data demonstrated the superiority of deepPseudoMSI over traditional approaches and the capacity of our method to achieve an accurate individualized diagnosis.

Last updated on Sep 4, 2022

TidyMass an object-oriented reproducible analysis framework for LC–MS data profiling

Reproducibility, traceability, and transparency have been long-standing issues for metabolomics data analysis. Multiple tools have been developed, but limitations still exist. Here, we present the tidyMass project (https://www.tidymass.org/), a comprehensive R-based computational framework that can achieve the traceable, shareable, and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass is an ecosystem of R packages that share an underlying design philosophy, grammar, and data structure, which provides a comprehensive, reproducible, and object-oriented computational framework.

Last updated on Sep 4, 2022

Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics

Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis.