Xiting Yan, PhD
Associate Professor of Medicine (Pulmonary, Critical Care and Sleep Medicine)Cards
About
Research
Overview
Understanding the pathogenesis and progression of chronic lung diseases is critical for therapeutic development. Different types of OMICs data, including genetic, genomic, transcriptomic, epigenetic data and so on, provide rich, reproducible and mechanism indicating information for understanding disease pathogenesis and progression. However, OMICs data usually have high dimension, complicated data structure, high noise level, and complex interactions between features (genes, proteins, metabolites, etc.). The corresponding data analysis is challenging but critical to obtain biologically meaningful and reproducible discoveries.
My current research interest focus on two parts: (1) developing novel statistical and computational models to analyze large scale omics and drug perturbation data to better understand disease pathogenesis and precision medicine, and (2) understanding the heterogeneity, pathogenesis and progression of pulmonary diseases, such as asthma, idiopathic pulmonary fibrosis (IPF), sarcoidosis, pediatric cystic fibrosis and so on, by tailoring statistical and computational methods based on existing biological knowledge of the diseases.
My team has been involved in multiple transcriptomic studies of asthma, IPF, sarcoidosis, cystic fibrosis and lung injuries in pediatric patients undertaking cardio bypass procedure. These studies generated various types of large-scale transcriptomic data including microarray gene expression data, bulk RNA sequencing data, single cell RNA sequencing data, T cell receptor repertoire data, 16s rRNA sequencing data, spatial transcriptomic data and single-cell chromotin structural data. For each study, we tailed our computational and statistical analysis of the data based on existing biological knowledge of the corresponding disease or condition. These analyses have made various discoveries in asthma pathogenesis heterogeneity, cell type specific changes in asthma patients, heterogeneity and molecular biomarker of sarcoidosis, cell populations specific to IPF and COPD, potential antigen specific T cell clones for SARS-CoV-2 infection (COVID19) in adults and so on. My team is currently closely working with physicians and basic scientists to make further and more translational discoveries for the aforementioned and other pulmonary diseases.
Through the extensive analyses of various types of omics data generated by our collaborators, my team also identifies computational and statistical needs and develops novel methods to address these needs. Topics of computational tools we have developed include imputation of single-cell RNA sequencing data (G2S3), identifying differentially expressed genes from scRNA-seq data with mutliple subjects (iDESC), cell type deconvolution of spatial transcriptomic data (SDePER), identifying spatial domains from spatial transcriptomic data using large language model (LLMiniST) and so on. The development of these computational tools further boosted our capacity and ability to analyze different types of OMICS data to better understand disease heterogeneity, pathogenesis and progression.
Medical Research Interests
Public Health Interests
Academic Achievements & Community Involvement
Teaching & Mentoring
Mentoring
Siming Zheng
Postdoc2024 - PresentHuanhuan Wei
Postdoc2023 - 2026Yuening Zhang
Postdoc2023 - Present
News & Links
Media
- B). Heatmap showing the clustering results by KEGG pathways using MCLUST. The color represents the clustering assignment of each sample by the KEGG pathways. C). Pathway based distance matrix among the clusters. The color of entry represents the pathway based distance between the corresponding two samples. Red represents a small distance (samples are strongly related) and white represents longer distance showing the strength of the clusters (samples are weakly related). Samples within TEA cluster 3 are the most strongly related and most homogeneous, followed by cluster 1 and 2, respectively.
News
- September 03, 2025
Yale Researchers Uncover Biological Differences in Asthma Between Males and Females
- August 21, 2024
Unique Immune Profile Identified in Fibrotic Hypersensitivity Pneumonitis
- March 21, 2023
Department of Internal Medicine Promotions and Reappointments
- June 23, 2021
Despite the challenges of COVID-19, Yale-PCCSM section members continued their work on scientific papers