Biology subjects

Garmire, L. X.

Publications and source records attributed to Garmire, L. X..

6 recordsLinked to original sources

bioRxiv2026

STDrug enables spatially informed personalized drug repurposing from spatial transcriptomics

Drug repurposing offers a scalable route to accelerate therapeutic discovery, yet existing approaches based on single-cell RNA sequencing (scRNA-seq) often overlook spatial tissue context, limiting their ability to capture microenvironment-dependent drug responses. Here we present STDrug, a spatially informed computational framework that integrates spatial transcriptomics, graph-based modeling, and multimodal learning to enable patient-specific therapeutic prioritization. STDrug identifies and aligns disease and control spatial domains using graph convolutional networks and coherent point drift, and prioritizes candidate drugs through an integrative scoring scheme combining tumor-reversible gene signatures, perturbation-based reversal scores, and knowledge-guided gene weighting within a machine learning framework. By modeling spatial domain interactions alongside predicted drug efficacy and toxicity, STDrug generates robust patient-level drug scores. Across hepatocellular carcinoma and prostate cancer datasets, STDrug outperforms existing single-cell and spatial transcriptomics-based drug repurposing methods, achieving signficantly improved predictive accuracy (AUCs=0.81-0.82) across patients. Validation using large-scale electronic health records and in vitro assays further supports the translational relevance of top-ranked candidates. Taking together, STDrug establishes a generalizable framework for incorporating spatial omics into therapeutic discovery, advancing spatially informed and personalized drug repurposing.

bioinformatics↗

bioRxiv2024

Originator: Computational Framework Separating Single-Cell RNA-Seq by Genetic and Contextual Information

Single-cell RNA sequencing (scRNA-Seq) data from complex human tissues have prevalent blood cell contamination during the sample preparation process. They may also comprise cells of different genetic makeups. These issues demand rigorous preprocessing and filtering prior to the downstream functional analysis, to avoid biased conclusions due to cell types not of interest. Towards this, we propose a new computational framework, Originator, which deciphers single cells by the genetic origin and separates immune cells of blood contamination from those of expected tissue-resident cells. We demonstrate the accuracy of Originator at separating immune cells from the blood and tissue as well as cells of different genetic origins, using a variety of artificially mixed and real datasets. We show the prevalence of blood contamination in scRNA-Seq data of many tissue types. We alert the significant consequences if failing to adjust for these confounders, using scRNA-Seq data of pancreatic cancer and placentas as examples.

bioinformatics↗

bioRxiv2022

Unlocking the microblogging potential for science and medicine

Microblogging platform Twitter allows researchers to showcase their work, receive constructive feedback, find jobs, and build scientific collaborations. While existing literature has analyzed the benefits of Twitter in the development and distribution of scientific knowledge, most of the studies only took into account a limited number of researchers, which affected the generalizability of derived results. Our study analyzed the activity of 6,000 biomedical scientists on Twitter using data-driven approaches, a third of whom were female. Furthermore, we estimated that up to a quarter of the members of the scientific community are engaged on Twitter. While the number of male scientists joining the microblogging platform every year has decreased, the number of female scientists has remained roughly the same. Scientists are very selective in who they are following as compared to the general public. We also found that the type of tweets and retweets one posts may affect the number of followers, specifically, that a moderate to high level of professionalism and a high level of positivity is correlated with followers count. Moreover, female scientists send fewer negative tweets as compared to male scientists (31.1% for females and 34.7% for males). Our analysis could provide insights and launch a conversation on the advantages and limitations of using Twitter for disseminating scientific information and engaging in constructive discussion and collaborations within the scientific community.

scientific communication and education↗

bioRxiv2020

Domino: reconstructing intercellular signaling dynamics with transcription factor activation in model biomaterial environments

Biomaterials serve as the basis of implants, tissue engineering scaffolds, and multiple other biomedical therapeutics. New technologies, such as single cell RNA sequencing (scRNAseq), are enabling characterization of the biomaterial response to an unprecedented level of detail, facilitating new discoveries in the complex cellular environment surrounding materials. We performed scRNAseq and integrated data sets from multiple experiments to create a single cell atlas of the biomaterials response that contains 42,156 cells from biological extracellular matrix (ECM)-derived and synthetic polyester (polycaprolactone, PCL) scaffold biomaterials implanted in murine muscle wounds. We identified 18 clusters of cells, including natural killer (NK) cells, multiple subsets of fibroblasts, and myeloid cells, many of which were previously unknown in the biomaterial response. To determine intra and intercellular signaling occurring between the numerous cell subsets, including immune-stromal interactions in the biomaterial response, we developed Domino (github.com/chris-cherry/domino), a computational tool which allows for identification of condition specific intercellular signaling patterns connected to transcription factor activation from single cell data. The Domino networks self-assembled into signaling modules and cellular subsets involved in signaling independent of clustering, defining interactions between immune, fibroblast, and tissue-specific modules with biomaterials-specific communication patterns. Further compilation and integration of biomaterials single cell data sets will delineate the impact of materials chemical and physical properties and biological factors, such as anatomical placement, age, or systemic disease, that will direct biomaterials design.

bioinformatics↗

bioRxiv2017

Phenotypic associations of consensus driver genes in hepatocellular carcinoma

Although driver genes in hepatocellular carcinoma (HCC) have been investigated in various previous genetic studies, prevalence of key driver genes among heterogeneous populations is unknown. Moreover, the phenotypic associations of these driver genes are poorly understood. This report aims to reveal the phenotypic impacts of a group of consensus driver genes in HCC. We used MutSigCV and OncodriveFM modules implemented in the IntOGen pipeline to identify consensus driver genes across six HCC cohorts comprising 1,494 samples in total. To access their global impacts, we used TCGA mutations and copy number variations to predict the transcriptomics data, under generalized linear models. We further investigated the associations of the consensus driver genes to patient survival, age, gender, race and risk factors. We identify 10 consensus driver genes across six HCC cohorts in total. Integrative analysis of driver mutations, copy number variations and transcriptomic data reveals that these consensus driver mutations and their copy number variations are associated with majority (62.5%) of the mRNA transcriptome, but only a small fraction (8.9%) of miRNAs. Genes associated with TP53, CTNNB1, and ARID1A mutations contribute to the tripod of most densely connected pathway clusters. These driver genes are significantly associated with patients overall survival. Some driver genes are significantly linked to HCC gender (CTNNB1, ALB, TP53 and AXIN1), race (TP53 and CDKN2A), and age (RB1) disparities. This study prioritizes a group of consensus drivers in HCC, which collectively show vast impacts on the phenotypes. These driver genes may warrant as valuable therapeutic targets of HCC.

bioinformatics↗

bioRxiv2016

Using Single Nucleotide Variations in Cancer Single-Cell RNA-Seq Data for Subpopulation Identification and Genotype-phenotype Linkage Analysis

Despite its popularity, characterization of subpopulations with transcript abundance is subject to a significant amount of noise. We propose to use effective and expressed nucleotide variations (eeSNVs) from scRNA-seq as alternative features for tumor subpopulation identification. We developed a linear modeling framework, SSrGE, to link eeSNVs associated with gene expression. In all the datasets tested, eeSNVs achieve better accuracies than gene expression for identifying subpopulations. Previously validated cancer-relevant genes are also highly ranked, confirming the significance of the method. Moreover, SSrGE is capable of analyzing coupled DNA-seq and RNA-seq data from the same single cells, demonstrating its value in integrating multi-omics single cell techniques. In summary, SNV features from scRNA-seq data have merits for both subpopulation identification and linkage of genotype-phenotype relationship. The method SSrGE is available at https://github.com/lanagarmire/SSrGE.

bioinformatics↗