Li-Xuan Qin, PhD

Attending Biostatistician

Li-Xuan Qin, PhD

Attending Biostatistician
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Li-Xuan Qin

Office Phone

646-888-8251

Education

University of Washington

Current Research Interests

Dr. Qin develops benchmark data, analytic methods, and computational tools to facilitate the reproducible statistical translation of high-dimensional biomedical data. In collaboration with wet-lab colleagues, she has orchestrated the design and collection of unique benchmark datasets, focusing on the important challenge of data harmonization for microarrays and sequencing. She has led the creation of novel simulation algorithms and software tools for unraveling the interplay between data harmonization and subsequent analyses, such as sample classification and survival prediction. She has also developed new statistical methodologies for promoting evidence-based practice of data harmonization. Her research program in reproducible statistical learning for cancer omics data has been supported by multiple completed and ongoing grants awarded by the National Institutes of Health (NIH).

Dr. Qin collaborates with a team of multidisciplinary investigators engaged in research on soft tissue sarcoma and colorectal cancer at MSK, contributing to the design and analysis of both retrospective and prospective studies. Notably, she serves as the Director of the Biostatistics and Bioinformatics Core for the MSK Soft Tissue Sarcoma SPORE (Specialized Program of Research Excellence). Over the years, Dr. Qin has also collaborated with researchers studying the molecular basis of various other cancer types, including breast, ovarian, pancreatic, and pediatric cancer.

Dr. Qin serves as a member of the Research Council at MSK, reviewing investigator-initiated and industry-sponsored protocols for scientific merit and priority. On a national level, she contributes her expertise as the Council of Sections Representative for the American Statistical Association’s Statistics in Genomics and Genetics Section and a Regular Member for the NIH’s ASPA Study Section.

Publications

Selected peer-reviewed publications:

  1. Qin LX, Huang HC, Begg CB. Cautionary note on using cross-validation for molecular classification. Journal of Clinical Oncology 2016, 34:3931-3938.
  2. Ni A, Qin LX. Performance evaluation of transcriptomics data normalization for survival outcome prediction. Briefings in Bioinformatics 2021, 22:bbab257.
  3. Düren Y, Lederer J, Qin LX. Depth normalization of small RNA sequencing: using data and biology to select a suitable method. Nucleic Acids Research 2022, 50:e56.
  4. Wu Y, Yuen BW, Wei Y, Qin LX. On data normalization and batch-effect correction for tumor subtyping with microRNA data. NAR Genomics and  Bioinformatics 2023, 5:lqac100.
  5. Ni A, Liu M, Qin LX. BatMan: Mitigating batch effects via stratification for survival outcome prediction. JCO Clinical Cancer Informatics 2023, 7:e2200138.

View a full listing of Li-Xuan Qin’s journal articles.

Disclosures

Doctors and faculty members often work with pharmaceutical, device, biotechnology, and life sciences companies, and other organizations outside of MSK, to find safe and effective cancer treatments, to improve patient care, and to educate the health care community.

MSK requires doctors and faculty members to report (“disclose”) the relationships and financial interests they have with external entities. As a commitment to transparency with our community, we make that information available to the public.

Li-Xuan Qin discloses the following relationships and financial interests:

No disclosures meeting criteria for time period


The information published here is a complement to other publicly reported data and is for a specific annual disclosure period. There may be differences between information on this and other public sites as a result of different reporting periods and/or the various ways relationships and financial interests are categorized by organizations that publish such data.


This page and data include information for a specific MSK annual disclosure period (January 1, 2023 through disclosure submission in spring 2024). This data reflects interests that may or may not still exist. This data is updated annually.

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