Qi Liu

Professor, Bioinformatics & System Biology
Technical Director of VANGARD
Director of the Omics Coordinating Center at CQS

Dr. Liu’s research focuses on integrating multi-omics data to address fundamental biological questions. Her research interests include

• Multiomics integration to study complex disease
• Methodology development in high-dimensional data, such as single-cell and spatial omics data
• Omics resource portal development

E-mail: qi.liu@vumc.org

Software and Resource Portal

Microenvironment-aware spatial modeling for accurate inference of cell identity

Abstract Spatial omics technologies enable the simultaneous measurement of molecular features within their spatial context, providing unprecedented insights into cellular organization and tissue architecture. The advent of single-cell resolution platforms has further enhanced our ability to uncover microenvironment-dependent cell states. While numerous computational methods have been developed for spatial omics analysis, most focus on spatial domain detection rather than on resolving cell identities. Traditional single-cell clustering methods rely solely on intrinsic molecular features to determine cell identities, missing the impact of the local microenvironment on cell states. To address this gap, we present MEcell, a parameter-free method that explicitly incorporates spatial context and automatically adjusts its contribution to improve cell identity modeling. Applied to 90 simulated and 7 real datasets spanning multiple spatial transcriptomics platforms and tissue types, including MERFISH/Vizgen, Xenium, CosMx, Visium HD, Slide-seqV2, and open-ST, MEcell consistently outperformed existing methods in accurately inferring cell identities. These results highlight the critical role of microenvironment in defining cell identity and demonstrate the power of MEcell for capturing spatially informed cellular heterogeneity.

Jan 7, 2026

stImage: a versatile framework for optimizing spatial transcriptomic analysis through customizable deep histology and location informed integration

Abstract Spatial transcriptomics (ST) integrates gene expression data with the spatial organization of cells and their associated histology, offering unprecedented insights into tissue biology. While existing methods incorporate either location-based or histology-informed information, none fully synergize gene expression, histological features, and precise spatial coordinates within a unified framework. Moreover, these methods often exhibit inconsistent performance across diverse datasets and conditions. Here, we introduce stImage, an open-source R package that provides a comprehensive and flexible solution for ST analysis. By generating deep learning-derived histology features and offering 54 integrative strategies, stImage seamlessly combines transcriptional profiles, histology images, and spatial information. We demonstrate stImage’s effectiveness across multiple datasets, underscoring its ability to guide users toward the most suitable integration strategy using diagnostic graph. Our results highlight how stImage can optimize ST, consistently improving biological insights and advancing our understanding of tissue architecture. stImage is freely available at https://github.com/YuWang-VUMC/stImage.

Sep 4, 2025

eNRSA: a faster and more powerful approach for nascent transcriptome analysis

Abstract Nascent RNA sequencing tracks primary transcriptional events, making it crucial for studying the immediate regulatory changes of genes and enhancers in response to both endogenous and exogenous stimuli. NRSA is a widely used tool for analyzing nascent transcriptomic data, enabling quantification of transcriptional changes at proximal promoters and gene bodies, estimation of pausing indices, identifying active enhancers, and establishing enhancer-target gene relationships. To improve its functionality and broaden its applicability to diverse organisms and complex study designs, we have developed an enhanced version, eNRSA. Key advancements include adaptive selection of major transcripts, support for any organism with known gene structures, compatibility with complex study designs, and identification of alternative transcription start and termination sites, as well as transcription readthrough events. Additionally, eNRSA achieves a approximately 20-fold increase in analysis speed while significantly reducing memory usage. These enhancements make eNRSA a faster, more versatile, and more powerful tool for nascent transcriptome analysis. eNRSA is freely available at https://bioinfo.vanderbilt.edu/eNRSA/.

Jul 4, 2025

VICTOR: Validation and inspection of cell type annotation through optimal regression

Abstract Single-cell RNA sequencing provides unprecedent opportunities to explore the heterogeneity and dynamics inherent in cellular biology. An essential step in the data analysis involves the automatic annotation of cells. Despite development of numerous tools for automated cell annotation, assessing the reliability of predicted annotations remains challenging, particularly for rare and unknown cell types. Here, we introduce VICTOR: Validation and inspection of cell type annotation through optimal regression. VICTOR aims to gauge the confidence of cell annotations by an elastic-net regularized regression with optimal thresholds. We demonstrated that VICTOR performed well in identifying inaccurate annotations, surpassing existing methods in diagnostic ability across various single-cell datasets, including within-platform, cross-platform, cross-studies, and cross-omics settings.

Sep 19, 2024

A distribution-free and analytic method for power and sample size calculation in single-cell differential expression

Abstract MOTIVATION: Differential expression analysis in single-cell transcriptomics unveils cell type-specific responses to various treatments or biological conditions. To ensure the robustness and reliability of the analysis, it is essential to have a solid experimental design with ample statistical power and sample size. However, existing methods for power and sample size calculation often assume a specific distribution for single-cell transcriptomics data, potentially deviating from the true data distribution. Moreover, they commonly overlook cell-cell correlations within individual samples, posing challenges in accurately representing biological phenomena. Additionally, due to the complexity of deriving an analytic formula, most methods employ time-consuming simulation-based strategies. RESULTS: We propose an analytic-based method named scPS for calculating power and sample sizes based on generalized estimating equations. scPS stands out by making no assumptions about the data distribution and considering cell-cell correlations within individual samples. scPS is a rapid and powerful approach for designing experiments in single-cell differential expression analysis. AVAILABILITY AND IMPLEMENTATION: scPS is freely available at https://github.com/cyhsuTN/scPS and Zenodo https://zenodo.org/records/13375996.

Sep 4, 2024

aKNNO: single-cell and spatial transcriptomics clustering with an optimized adaptive k-nearest neighbor graph

aKNNO

Aug 2, 2024

EVPsort: An Atlas of Small ncRNA Profiling and Sorting in Extracellular Vesicles and Particles

Abstract Extracellular vesicles and particles (EVPs) play a crucial role in mediating cell-to-cell communication by transporting various molecular cargos, with small non-coding RNAs (ncRNAs) holding particular significance. A thorough investigation into the abundance and sorting mechanisms of ncRNA within EVPs is imperative for advancing their clinical applications. We have developed EVPsort, which not only provides an extensive overview of ncRNA profiling in 3,162 samples across various biofluids, cell lines, and disease contexts but also seamlessly integrates 19 external databases and tools. This integration encompasses information on associations between ncRNAs and RNA-binding proteins (RBPs), motifs, targets, pathways, diseases, and drugs. With its rich resources and powerful analysis tools, EVPsort extends its profiling capabilities to investigate ncRNA sorting, identify relevant RBPs and motifs, and assess functional implications. EVPsort stands as a pioneering database dedicated to comprehensively addressing both the abundance and sorting of ncRNA within EVPs. It is freely accessible at https://bioinfo.vanderbilt.edu/evpsort/.

Apr 12, 2024

scKWARN: Kernel-weighted-average robust normalization for single-cell RNA-seq data

Abstract MOTIVATION: Single-cell RNA-seq normalization is an essential step to correct unwanted biases caused by sequencing depth, capture efficiency, dropout, and other technical factors. Existing normalization methods primarily reduce biases arising from sequencing depth by modeling count-depth relationship and/or assuming a specific distribution for read counts. However, these methods may lead to over or under-correction due to presence of technical biases beyond sequencing depth and the restrictive assumption on models and distributions. RESULTS: We present scKWARN, a Kernel Weighted Average Robust Normalization designed to correct known or hidden technical confounders without assuming specific data distributions or count-depth relationships. scKWARN generates a pseudo expression profile for each cell by borrowing information from its fuzzy technical neighbors through a kernel smoother. It then compares this profile against the reference derived from cells with the same bimodality patterns to determine the normalization factor. As demonstrated in both simulated and real datasets, scKWARN outperforms existing methods in removing a variety of technical biases while preserving true biological heterogeneity. AVAILABILITY AND IMPLEMENTATION: scKWARN is freely available at https://github.com/cyhsuTN/scKWARN.

Jan 19, 2024

Scalable and model-free detection of spatial patterns and colocalization

Abstract The expeditious growth in spatial omics technologies enables the profiling of genome-wide molecular events at molecular and single-cell resolution, highlighting a need for fast and reliable methods to characterize spatial patterns. We developed SpaGene, a model-free method to discover spatial patterns rapidly in large-scale spatial omics studies. Analyzing simulation and a variety of spatially resolved transcriptomics data showed that SpaGene is more powerful and scalable than existing methods. Spatial expression patterns identified by SpaGene reconstruct unobserved tissue structures. SpaGene also successfully discovers ligand-receptor interactions through their colocalization.

Oct 13, 2022

Dysregulated ligand-receptor interactions from single-cell transcriptomics

Abstract MOTIVATION: Intracellular communication is crucial to many biological processes, such as differentiation, development, homeostasis and inflammation. Single-cell transcriptomics provides an unprecedented opportunity for studying cell-cell communications mediated by ligand-receptor interactions. Although computational methods have been developed to infer cell type-specific ligand-receptor interactions from one single-cell transcriptomics profile, there is lack of approaches considering ligand and receptor simultaneously to identifying dysregulated interactions across conditions from multiple single-cell profiles. RESULTS: We developed scLR, a statistical method for examining dysregulated ligand-receptor interactions between two conditions. scLR models the distribution of the product of ligands and receptors expressions and accounts for inter-sample variances and small sample sizes. scLR achieved high sensitivity and specificity in simulation studies. scLR revealed important cytokine signaling between macrophages and proliferating T cells during severe acute COVID-19 infection, and activated TGF-beta signaling from alveolar type II cells in the pathogenesis of pulmonary fibrosis. AVAILABILITY AND IMPLEMENTATION: scLR is freely available at https://github.com/cyhsuTN/scLR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Apr 29, 2022

A pan-cancer immunogenomic atlas for immune checkpoint blockade immunotherapy

Abstract The ability to identify robust genomic signatures that predict response to immune checkpoint blockade is restricted by limited sample sizes and ungeneralizable performance across cohorts. To address these challenges, we established Cancer-Immu (http://bioinfo.vanderbilt.edu/database/Cancer-Immu/) a comprehensive platform that integrates large-scale multidimensional omics data, including genetic, bulk, and single-cell transcriptomic, proteomic, and dynamic genomic profiles, with clinical phenotypes to explore consistent and rare immunogenomic connections. Currently Cancer-Immu has incorporated data for 3,652 samples for 16 cancer types. It provides easy access to immunogenomic data and empowers researchers to translate omics datasets into biological insights and clinical applications.

Dec 15, 2021

scMRMA: single cell multiresolution marker-based annotation

Abstract Single-cell RNA sequencing has become a powerful tool for identifying and characterizing cellular heterogeneity. One essential step to understanding cellular heterogeneity is determining cell identities. The widely used strategy predicts identities by projecting cells or cell clusters unidirectionally against a reference to find the best match. Here, we develop a bidirectional method, scMRMA, where a hierarchical reference guides iterative clustering and deep annotation with enhanced resolutions. Taking full advantage of the reference, scMRMA greatly improves the annotation accuracy. scMRMA achieved better performance than existing methods in four benchmark datasets and successfully revealed the expansion of CD8 T cell populations in squamous cell carcinoma after anti-PD-1 treatment.

Oct 15, 2021

scRNABatchQC: multi-samples quality control for single cell RNA-seq data

Abstract SUMMARY: Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers. AVAILABILITY AND IMPLEMENTATION: scRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Aug 3, 2019

HACER: an atlas of human active enhancers to interpret regulatory variants

Abstract Recent studies have shown that disease-susceptibility variants frequently lie in cell-type-specific enhancer elements. To identify, interpret, and prioritize such risk variants, we must identify the enhancers active in disease-relevant cell types, their upstream transcription factor (TF) binding, and their downstream target genes. To address this need, we built HACER (http://bioinfo.vanderbilt.edu/AE/HACER/), an atlas of Human ACtive Enhancers to interpret Regulatory variants. The HACER atlas catalogues and annotates in-vivo transcribed cell-type-specific enhancers, as well as placing enhancers within transcriptional regulatory networks by integrating ENCODE TF ChIP-Seq and predicted/validated chromatin interaction data. We demonstrate the utility of HACER in (i) offering a mechanistic hypothesis to explain the association of SNP rs614367 with ER-positive breast cancer risk, (ii) exploring tumor-specific enhancers in selective MYC dysregulation and (iii) prioritizing/annotating non-coding regulatory regions targeting CCND1. HACER provides a valuable resource for studies of GWAS, non-coding variants, and enhancer-mediated regulation.

Sep 25, 2018

Selected Publications

Liu, Q., Wang, Y., Hsu, C. Y., Wanjalla, C. N., Lau, K. S., Shyr, Y. (2026). Microenvironment-aware spatial modeling for accurate inference of cell identity. Nucleic Acids Res.

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Wang, Y., Yang, H., Deng, R., Huo, Y., Liu, Q., Shyr, Y., Zhao, S. (2025). stImage: a versatile framework for optimizing spatial transcriptomic analysis through customizable deep histology and location informed integration. Brief Bioinform.

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Wang, J., Chen, H. C., Hiebert, S. W., Sheng, Q., Tansey, W. P., Shyr, Y., Liu, Q. (2025). eNRSA: a faster and more powerful approach for nascent transcriptome analysis. Gigascience.

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Chang, C. J., Hsu, C. Y., Liu, Q., Shyr, Y. (2024). VICTOR: Validation and inspection of cell type annotation through optimal regression. Comput Struct Biotechnol J.

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Hsu, C. Y., Liu, Q., Shyr, Y. (2024). A distribution-free and analytic method for power and sample size calculation in single-cell differential expression. Bioinformatics.

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Li, J., Simmons, A. J., Hawkins, C. V., Chiron, S., Ramirez-Solano, M. A., Tasneem, N., Kaur, H., Xu, Y., Revetta, F., Vega, P. N., Bao, S., Cui, C., Tyree, R. N., Raber, L. W., Conner, A. N., Pilat, J. M., Jacobse, J., McNamara, K. M., Allaman, M. M., Raffa, G. A., Gobert, A. P., Asim, M., Goettel, J. A., Choksi, Y. A., Beaulieu, D. B., Dalal, R. L., Horst, S. N., Pabla, B. S., Huo, Y., Landman, B. A., Roland, J. T., Scoville, E. A., Schwartz, D. A., Washington, M. K., Shyr, Y., Wilson, K. T., Coburn, L. A., Lau, K. S., Liu, Q. (2024). Identification and multimodal characterization of a specialized epithelial cell type associated with Crohn's disease. Nat Commun.

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Li, J., Shyr, Y., Liu, Q. (2024). aKNNO: single-cell and spatial transcriptomics clustering with an optimized adaptive k-nearest neighbor graph. Genome Biol.

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Chen, H. C., Wang, J., Coffey, R. J., Patton, J. G., Weaver, A. M., Shyr, Y., Liu, Q. (2024). EVPsort: An Atlas of Small ncRNA Profiling and Sorting in Extracellular Vesicles and Particles. J Mol Biol.

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Hsu, C. Y., Chang, C. J., Liu, Q., Shyr, Y. (2024). scKWARN: Kernel-weighted-average robust normalization for single-cell RNA-seq data. Bioinformatics.

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Yang, J., Liu, Q., Shyr, Y. (2023). A Large-Scale Meta-Analysis Reveals Positive Feedback between Macrophages and T Cells That Sensitizes Tumors to Immunotherapy. Cancer Res.

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Liu, Q., Zhou, Y., Cogan, J. D., Mitchell, D. B., Sheng, Q., Zhao, S., Bai, Y., Ciombor, K. K., Sabusap, C. M., Malabanan, M. M., Markin, C. R., Douglas, K., Ding, G., Banovich, N. E., Nickerson, D. A., Blue, E. E., Bamshad, M. J., Brown, K. K., Schwartz, D. A., Phillips, J. A., 3rd, Martinez-Barricarte, R., Salisbury, M. L., Shyr, Y., Loyd, J. E., Kropski, J. A., Blackwell, T. S. (2023). The Genetic Landscape of Familial Pulmonary Fibrosis. Am J Respir Crit Care Med.

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Liu, Q., Hsu, C. Y., Shyr, Y. (2022). Scalable and model-free detection of spatial patterns and colocalization. Genome Res.

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Liu, Q., Hsu, C. Y., Li, J., Shyr, Y. (2022). Dysregulated ligand-receptor interactions from single-cell transcriptomics. Bioinformatics.

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Chu, S. K., Zhao, S., Shyr, Y., Liu, Q. (2022). Comprehensive evaluation of noise reduction methods for single-cell RNA sequencing data. Brief Bioinform.

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Yang, J., Zhao, S., Wang, J., Sheng, Q., Liu, Q., Shyr, Y. (2021). A pan-cancer immunogenomic atlas for immune checkpoint blockade immunotherapy. Cancer Res.

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Li, J., Sheng, Q., Shyr, Y., Liu, Q. (2021). scMRMA: single cell multiresolution marker-based annotation. Nucleic Acids Res.

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Liu, Q., Sheng, Q., Ping, J., Ramirez, M. A., Lau, K. S., Coffey, R. J., Shyr, Y. (2019). scRNABatchQC: multi-samples quality control for single cell RNA-seq data. Bioinformatics.

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Liu, Q., Herring, C. A., Sheng, Q., Ping, J., Simmons, A. J., Chen, B., Banerjee, A., Li, W., Gu, G., Coffey, R. J., Shyr, Y., Lau, K. S. (2018). Quantitative assessment of cell population diversity in single-cell landscapes. PLoS Biol.

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Wang, J., Dai, X., Berry, L. D., Cogan, J. D., Liu, Q., Shyr, Y. (2018). HACER: an atlas of human active enhancers to interpret regulatory variants. Nucleic Acids Res.

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