About

Lingling An is an Associate Professor in the Department of Biosystems Engineering at the University of Arizona. She is associated with the Biosystems Analytics & Technology and Biosystems Informatics programs. Her office is located in Shantz 501, and she can be reached via phone at 520-621-1248 or email at anling@arizona.edu. Her research focuses on biosystems engineering, with an emphasis on biosystems analytics and informatics, contributing to advancements in these fields through her academic and research activities.

Research topics

• Data Mining
• Artificial Intelligence
• Computer Science
• Biology
• Machine Learning
• Database
• Mathematics
• Internal medicine
• Medicine
• Data science
• Genetics
• Endocrinology
• Computational biology
• Bioinformatics

Selected publications

• Aging Associated Decrease of Nrf2, Antioxidant and Detoxification Genes in the Myocardium of Human, Monkey and Rodents

  Physiology · 2025-05-01

  article

  Aging is a major contributing risk factor for oxidative stress and cardiovascular diseases. Cardiac aging leads to changes in the structure, function, and oxidative environment of the heart. The geriatric population shows a higher incidence of myocardial infarction (MI), contributing to increased mortality and morbidity rates. Nuclear Factor (Erythroid-derived 2)-Like 2 (NFE2L2 or Nrf2) is a transcription factor that regulates the expression of antioxidant and detoxification genes. Aging has been associated with decreased Nrf2 expression in various tissues. Our study investigates Nrf2 signaling in aged humans and species closely related to humans, such as monkeys and rodent models. Using RNA-seq datasets from the Genotype-Tissue Expression (GTEx) project, which contains 980 participants, we analyzed Nrf2 and its downstream gene expression using effective statistical and bioinformatic methods. Violin plots revealed a downward trend in Nrf2 mRNA levels and its target genes SOD1, SOD2, CAT, GCLM, GCLC in the myocardium with aging. Similarly, Rhesus monkey, rat and mice myocardium also showed decreased Nrf2 expression with aging. Motor activity, myocardial structure and function were accessed in aged wildtype and Nrf2KO mice to explore the role of Nrf2 in aging using nesting test and echocardiography (ECHO). Aged Nrf2KO mice demonstrated impaired nest-building behavior compared to the aged wildtype mice. At 19 months, both aged wildtype and Nrf2KO mice developed cardiac hypertrophy, with wildtype mice showing concentric hypertrophy and Nrf2KO displaying eccentric hypertrophy. These findings suggest age-associated hypertrophic cardiac remodeling in both wildtype and Nrf2KO mice, with an exacerbated progression toward heart failure in Nrf2KO mice. This highlights the importance of Nrf2-targeted therapeutics in cardiac aging. NIHR01 GM125212, R01 GM126165, NIHR56 HL166330, HolsclawEndowmentandtheUniversityofArizonaCollegeofPharmacystart-up fund. This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

  PublisherDOI

• Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach

  BMC Bioinformatics · 2025-01-31 · 5 citations

  articleOpen access

  MOTIVATION: Spatial transcriptomics is a state-of-art technique that allows researchers to study gene expression patterns in tissues over the spatial domain. As a result of technical limitations, the majority of spatial transcriptomics techniques provide bulk data for each sequencing spot. Consequently, in order to obtain high-resolution spatial transcriptomics data, performing deconvolution becomes essential. Most existing deconvolution methods rely on reference data (e.g., single-cell data), which may not be available in real applications. Current reference-free methods encounter limitations due to their dependence on distribution assumptions, reliance on marker genes, or the absence of leveraging histology and spatial information. Consequently, there is a critical need for the development of highly flexible, robust, and user-friendly reference-free deconvolution methods capable of unifying or leveraging case-specific information in the analysis of spatial transcriptomics data. RESULTS: We propose a novel reference-free method based on regularized non-negative matrix factorization (NMF), named Flexible Analysis of Spatial Transcriptomics (FAST), that can effectively incorporate gene expression data, spatial, and histology information into a unified deconvolution framework. Compared to existing methods, FAST imposes fewer distribution assumptions, utilizes the spatial structure information of tissues, and encourages interpretable factorization results. These features enable greater flexibility and accuracy, making FAST an effective tool for deciphering the complex cell-type composition of tissues and advancing our understanding of various biological processes and diseases. Extensive simulation studies have shown that FAST outperforms other existing reference-free methods. In real data applications, FAST is able to uncover the underlying tissue structures and identify the corresponding marker genes.

  PublisherOA PDFDOI

• PhyImpute and UniFracImpute: two imputation approaches incorporating phylogeny information for microbial count data

  Briefings in Bioinformatics · 2024-11-22 · 1 citations

  articleOpen accessSenior author

  Sequencing-based microbial count data analysis is a challenging task due to the presence of numerous non-biological zeros, which can impede downstream analysis. To tackle this issue, we introduce two novel approaches, PhyImpute and UniFracImpute, which leverage similar microbial samples to identify and impute non-biological zeros in microbial count data. Our proposed methods utilize the probability of non-biological zeros and phylogenetic trees to estimate sample-to-sample similarity, thus addressing this challenge. To evaluate the performance of our proposed methods, we conduct experiments using both simulated and real microbial data. The results demonstrate that PhyImpute and UniFracImpute outperform existing methods in recovering the zeros and empowering downstream analyses such as differential abundance analysis, and disease status classification.

  PublisherOA PDFDOI

• Enhanced positronium lifetime imaging through two-component reconstruction in time-of-flight positron emission tomography

  Frontiers in Physics · 2024-07-15 · 14 citations

  articleOpen accessSenior authorCorresponding

  Positronium lifetime imaging (PLI) is a newly demonstrated technique possible with time-of-flight (TOF) positron emission tomography (PET), capable of producing an image reflecting the lifetime of the positron, more precisely ortho-positronium (o-Ps), before annihilation, in addition to the traditional uptake image of the PET tracer. Due to the limited time resolution of TOF-PET systems and the added complexities in physics and statistics, lifetime image reconstruction presents a challenge. Recently, we described a maximum-likelihood approach for PLI by considering only o-Ps. In real-world scenarios, other populations of positrons that exhibit different lifetimes also exist. This paper introduces a novel two-component model aimed at enhancing the accuracy of o-Ps lifetime images. Through simulation studies, we compare this new model with the existing single-component model and demonstrate its superior performance in accurately capturing complex lifetime distributions.

  PublisherOA PDFDOI

• Comparing Microbial Source Tracking Methods for Precision and Reliability

  International Journal of Forensic Sciences · 2024-01-01 · 1 citations

  articleOpen access1st authorCorresponding

  Microbial source tracking is a valuable tool in forensic science, specifically in the analysis of trace evidence. Numerous tools have been developed to estimate the proportion of different contamination sources within a mixture. In this study, we evaluate the accuracy of various source tracking methods using datasets from microbiome studies. In addition to assessing source tracking methods, we also incorporate two widely used cell type deconvolution methods, namely EPIC and PREDE, which are designed to identify missing cell types in a given dataset. Furthermore, we investigate the effectiveness of combined methods by integrating RAD, a source tracking method aimed at filtering out unimportant sources, with either EPIC or PREDE for enhanced accuracy in both source tracking and cell type deconvolution. This research represents a pioneering effort to examine the application of cell type deconvolution methods in source tracking and vice versa. Particularly noteworthy is our focus on scenarios involving missing sources or cell types in the reference data, shedding light on the intricate interplay between these two analytical domains.

  PublisherDOI

• Enhancing Positronium Lifetime Imaging through Two-Component Reconstruction in Time-of-Flight Positron Emission Tomography

  arXiv (Cornell University) · 2024-03-22 · 2 citations

  preprintOpen accessSenior author

  Positron Emission Tomography (PET) is a crucial tool in medical imaging, particularly for diagnosing diseases like cancer and Alzheimer's. The advent of Positronium Lifetime Imaging (PLI) has opened new avenues for assessing the tissue micro-environment, which is vital for early-stage disease detection. In this study, we introduce a two-component reconstruction model for PLI in Time-of-Flight (TOF) PET, incorporating both ortho-positronium and para-positronium decays. Our model enhances the accuracy of positronium imaging by providing a more detailed representation of the tissue environment. We conducted simulation studies to evaluate the performance of our model and compared it with existing single-component models. The results demonstrate the superiority of the two-component model in capturing the intricacies of the tissue micro-environment, thus paving the way for more precise and informative PET diagnostics.

  PublisherOA PDFDOI

• SPADE: spatial deconvolution for domain specific cell-type estimation

  Communications Biology · 2024-04-17 · 16 citations

  articleOpen accessSenior author

  Understanding gene expression in different cell types within their spatial context is a key goal in genomics research. SPADE (SPAtial DEconvolution), our proposed method, addresses this by integrating spatial patterns into the analysis of cell type composition. This approach uses a combination of single-cell RNA sequencing, spatial transcriptomics, and histological data to accurately estimate the proportions of cell types in various locations. Our analyses of synthetic data have demonstrated SPADE's capability to discern cell type-specific spatial patterns effectively. When applied to real-life datasets, SPADE provides insights into cellular dynamics and the composition of tumor tissues. This enhances our comprehension of complex biological systems and aids in exploring cellular diversity. SPADE represents a significant advancement in deciphering spatial gene expression patterns, offering a powerful tool for the detailed investigation of cell types in spatial transcriptomics.

  PublisherOA PDFDOI

• Accurate Prediction of Death Time via Integrating Microbial Community Dynamics

  International Journal of Forensic Sciences · 2024-01-01 · 1 citations

  articleOpen access1st authorCorresponding

  This study addresses the challenge of accurately estimating Postmortem Interval (PMI), the time since death, employing a data-driven approach. PMI determination is crucial in forensic investigations, and traditional methods often lack precision. We focus on utilizing a data mining approach Regularized Random Forest with cross-validation to enhance PMI prediction accuracy. Unlike conventional methods, our approach incorporates external information about the deceased, recognizing the impact of contextual factors on PMI estimation. Recent advancements have seen statistical methods leveraging dynamic changes in microbial communities to predict PMI. However, accuracy has been hindered by various sources of noise. To overcome this limitation, we propose a novel data mining approach, integrating cross-validation techniques and external information to refine PMI predictions. Through an empirical demonstration, we establish that our approach surpasses existing procedures in terms of accuracy, as validated against published datasets. This research contributes to the advancement of PMI estimation methodologies, emphasizing the importance of incorporating comprehensive data mining techniques and contextual information for more precise forensic applications.

  PublisherDOI

• TimeNorm: a novel normalization method for time course microbiome data

  Frontiers in Genetics · 2024-09-24

  articleOpen accessSenior authorCorresponding

  Metagenomic time-course studies provide valuable insights into the dynamics of microbial systems and have become increasingly popular alongside the reduction in costs of next-generation sequencing technologies. Normalization is a common but critical preprocessing step before proceeding with downstream analysis. To the best of our knowledge, currently there is no reported method to appropriately normalize microbial time-series data. We propose TimeNorm, a novel normalization method that considers the compositional property and time dependency in time-course microbiome data. It is the first method designed for normalizing time-series data within the same time point (intra-time normalization) and across time points (bridge normalization), separately. Intra-time normalization normalizes microbial samples under the same condition based on common dominant features. Bridge normalization detects and utilizes a group of most stable features across two adjacent time points for normalization. Through comprehensive simulation studies and application to a real study, we demonstrate that TimeNorm outperforms existing normalization methods and boosts the power of downstream differential abundance analysis.

  PublisherOA PDFDOI

• SPADE: Spatial Deconvolution for Domain Specific Cell-type Estimation

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-04-17 · 1 citations

  preprintOpen accessSenior authorCorresponding

  The advent of spatial transcriptomics technology has allowed for the acquisition of gene expression profiles with multi-cellular resolution in a spatially resolved manner, presenting a new milestone in the field of genomics. However, the aggregate gene expression from heterogeneous cell types obtained by these technologies poses a significant challenge for a comprehensive delineation of cell type-specific spatial patterns. Here, we propose SPADE (SPAtial DEconvolution), an in-silico method designed to address this challenge by incorporating spatial patterns during cell type decomposition. SPADE utilizes a combination of single-cell RNA sequencing data, spatial location information, and histological information to computationally estimate the proportion of cell types present at each spatial location. In our study, we showcased the effectiveness of SPADE by conducting analyses on synthetic data. Our results indicated that SPADE was able to successfully identify cell type-specific spatial patterns that were not previously identified by existing deconvolution methods. Furthermore, we applied SPADE to a real-world dataset analyzing the developmental chicken heart, where we observed that SPADE was able to accurately capture the intricate processes of cellular differentiation and morphogenesis within the heart. Specifically, we were able to reliably estimate changes in cell type compositions over time, which is a critical aspect of understanding the underlying mechanisms of complex biological systems. These findings underscore the potential of SPADE as a valuable tool for analyzing complex biological systems and shedding light on their underlying mechanisms. Taken together, our results suggest that SPADE represents a significant advancement in the field of spatial transcriptomics, providing a powerful tool for characterizing complex spatial gene expression patterns in heterogeneous tissues.

  PublisherOA PDFDOI

Recent grants

• ATD: Statistical methods for functional metagenomics in biothreat detection

  NSF · $723k · 2012–2016

Frequent coauthors

• Shu Wang

  Chinese Academy of Sciences

  69 shared

• Fude Feng

  Ningbo Institute of Industrial Technology

  44 shared

• Daoben Zhu

  Institute of Semiconductors

  27 shared

• Yuliang Li

  University of Chinese Academy of Sciences

  26 shared

• Yanli Tang

  Beijing Friendship Hospital

  26 shared

• Fang He

  22 shared

• Libing Liu

  Southwest Petroleum University

  20 shared

• Minghui Yu

  Chinese Academy of Sciences

  18 shared

Education

• PhD, Statistics

  Purdue University

  2008