arXiv subjects

Lana X. Garmire

Publications and source records attributed to Lana X. Garmire.

6 recordsLinked to original sources

arXiv2023

Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies

Metadata, often termed "data about data," is crucial for organizing, understanding, and managing vast omics datasets. It aids in efficient data discovery, integration, and interpretation, enabling users to access, comprehend, and utilize data effectively. Its significance spans the domains of scientific research, facilitating data reproducibility, reusability, and secondary analysis. However, numerous perceptual and technical barriers hinder the sharing of metadata among researchers. These barriers compromise the reliability of research results and hinder integrative meta-analyses of omics studies . This study highlights the key barriers to metadata sharing, including the lack of uniform standards, privacy and legal concerns, limitations in study design, limited incentives, inadequate infrastructure, and the dearth of well-trained personnel for metadata management and reuse. Proposed solutions include emphasizing the promotion of standardization, educational efforts, the role of journals and funding agencies, incentives and rewards, and the improvement of infrastructure. More accurate, reliable, and impactful research outcomes are achievable if the scientific community addresses these barriers, facilitating more accurate, reliable, and impactful research outcomes.

cs.DL↗

arXiv2023

Challenges and perspectives in computational deconvolution of genomics data

Deciphering cell type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach estimating cell type abundances from a variety of omics data. Despite significant methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four significant challenges related to computational deconvolution, from the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies and strategies to promote rigorous benchmarking.

q-bio.OT↗

arXiv2022

ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs

Intercellular heterogeneity is a major obstacle to successful precision medicine. Single-cell RNA sequencing (scRNA-seq) technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose a new drug recommendation system called: A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD). ASGARD defines a novel drug score predicting drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. We tested ASGARD on multiple diseases, including breast cancer, acute lymphoblastic leukemia, and coronavirus disease 2019 (COVID-19). On single-drug therapy, ASGARD shows significantly better average accuracy (AUC of 0.92) compared to two other bulk-cell-based drug repurposing methods (AUC of 0.80 and 0.76). It is also considerably better (AUC of 0.82) than other cell cluster level predicting methods (AUC of 0.67 and 0.55). In addition, ASGARD is also validated by the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. Many top-ranked drugs are either approved by FDA or in clinical trials treating corresponding diseases. In silico cell-type specific drop-out experiments using triple-negative breast cancers show the importance of T cells in the tumor microenvironment in affecting drug predictions. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD.

q-bio.QM↗

arXiv2022

Emerging Artificial Intelligence Applications in Spatial Transcriptomics Analysis

Spatial transcriptomics (ST) has advanced significantly in the last few years. Such advancement comes with the urgent need for novel computational methods to handle the unique challenges of ST data analysis. Many artificial intelligence (AI) methods have been developed to utilize various machine learning and deep learning techniques for computational ST analysis. This review provides a comprehensive and up-to-date survey of current AI methods for ST analysis.

cs.LG↗

arXiv2022

Computational Methods for Single-Cell Multi-Omics Integration and Alignment

Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology. Multi-omics assays offer even greater opportunities to understand cellular states and biological processes. However, the problem of integrating different -omics data with very different dimensionality and statistical properties remains quite challenging. A growing body of computational tools are being developed for this task, leveraging ideas ranging from machine translation to the theory of networks and representing a new frontier on the interface of biology and data science. Our goal in this review paper is to provide a comprehensive, up-to-date survey of computational techniques for the integration of multi-omics and alignment of multiple modalities of genomics data in the single cell research field.

q-bio.GN↗

arXiv2020

Recommendations to enhance rigor and reproducibility in biomedical research

Computational methods have reshaped the landscape of modern biology. While the biomedical community is increasingly dependent on computational tools, the mechanisms ensuring open data, open software, and reproducibility are variably enforced by academic institutions, funders, and publishers. Publications may present academic software for which essential materials are or become unavailable, such as source code and documentation. Publications that lack such information compromise the role of peer review in evaluating technical strength and scientific contribution. Incomplete ancillary information for an academic software package may bias or limit any subsequent work produced with the tool. We provide eight recommendations across four different domains to improve reproducibility, transparency, and rigor in computational biology - precisely on the main values which should be emphasized in life science curricula. Our recommendations for improving software availability, usability, and archival stability aim to foster a sustainable data science ecosystem in biomedicine and life science research.

q-bio.OT↗