Biology subjects

Garmire, L.

Publications and source records attributed to Garmire, L..

17 recordsLinked to original sources

bioRxiv2026

HOMED enables hierarchical and multimodal optimization of DNA methylation deconvolution across tissues

Cellular heterogeneity is a major confounder in bulk DNA methylation data for epigenome-wide association studies. Existing reference-based DNAm deconvolution methods often ignore hierarchies among related cell types and may generalize poorly across datasets due to limited variability in reference profiles. We developed HOMED (Hierarchically Optimized Methylation Deconvolution), a framework that integrates cell-lineage hierarchies, single-cell RNA sequencing-guided deconvolution, and paired bulk RNA-seq/DNAm data for CpG signature optimization. Across simulated and real peripheral blood mononuclear cell, lung, and placental datasets, HOMED consistently yielded the highest PCCs and lowest RMSEs, outperforming existing scRNA-seq-guided DNAm deconvolution methods, improving accuracy, resolution, and cross-tissue generalizability.

bioinformatics↗

bioRxiv2024

Landscape of Differentiation Potentials as a "Hallmark" in Oral-derived MSCs

BackgroundMesenchymal stem cells (MSCs) offer clinical promise for use in cell therapy approaches for regenerative medicine. A therapeutic challenge is that MSCs from different tissues are phenotypically and functionally distinct. Therefore, this study aims to molecularly characterize oral-derived MSCs by defining one of the three hallmarks of MSCs, differentiation potential, to discern their true molecular identities. MethodsThree different populations of oral tissue MSCs (from alveolar bone-aBMSCs; from dental pulp-DPSCs; and from gingiva-GMSCs) from three different patients were isolated and cultured. These MSCs were characterized for their stemness by flow cytometry and multi-differentiation potential, and their RNA was also isolated and analyzed quantitatively with RNA sequencing. Total mRNA-seq was performed and differentially expressed genes (DEGs) were identified in pairwise (DPSCs vs. aBMSCs, GMSCs vs. aBMSCs, and GMSCs vs. DPSCs) and tissue-specific comparisons (aBMSCs vs. Others, DPSCs vs. Others, GMSCs vs. Others) (FDR, p<0.05). Further, these DEGs, either common between MSC populations or unique to a specific MSC population, were evaluated for pathways and biological processes ResultsaBMSCs, DPSCs, and GMSCs were successfully isolated and characterized. The tissue-specific comparison revealed that DEGs were most numerous in DPSCs (693 genes) as compared to aBMSCs (103 genes) or DPSCs (232 genes). Statistically significant DEGs through pairwise comparisons present higher numbers in GMSCs vs. DPSCs (627) as compared to either DPSCs vs aBMSCs (286) or GMSCs vs. aBMSCs (82). Further analysis found that RUNX2, IBSP, SOX6, ACAN, and VCAM1 were significantly upregulated in aBMSCs. In DPSCs, BMP4 and IL6 were significantly downregulated, whereas AXL and NES were significantly upregulated. In GMSCs, AGPT1, SEMA4D, and PGDFA were significantly downregulated. Additionally, MAPK, PI3-AKT, and RAS signaling pathways were significantly regulated in GMSCs. Interestingly, aBMSCs and DPSCs revealed positive regulation of osteoblast differentiation, whereas GMSCs revealed negative regulation of osteoblast differentiation. DPSCs also revealed negative regulation of angiogenesis. ConclusionsOral-derived MSCs have an inherent "landscape" of differentiation defined by their tissue of origin; yet this differentiation potential can be modulated by their microenvironment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC="FIGDIR/small/606413v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@60aeceorg.highwire.dtl.DTLVardef@17480a2org.highwire.dtl.DTLVardef@1a9315aorg.highwire.dtl.DTLVardef@e74d30_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2022

Single-cell imaging based prognosis prediction identifies new breast cancer survival subtypes

Quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at population scale are currently missing. Here, we computationally extracted hundreds of features describing single-cell based cell-cell interactions and cellular phenotypes from a large, published cohort of cyto-images of breast cancer patients. We applied these features to a neural-network based Cox-nnet survival model and obtained high accuracy in predicting patient survival in test data (Concordance Index > 0.8). We identified seven survival subtypes using the top survival features, which present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. We identified atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. This work provides important guidelines on bridging single-cell level information towards population-level survival prediction. STATEMENT OF TRANSLATIONAL RELEVANCEOur findings from a breast cancer population cohort demonstrate the clinical utility of using the single-cell level imaging mass cytometry (IMC) data as a new type of patient prognosis prediction marker. Not only did the prognosis prediction achieve high accuracy with a Concordance index score greater than 0.8, it also enabled the discovery of seven survival subtypes that are more distinguishable than the molecular subtypes. These new subtypes present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. Most importantly, this study identified and validated atypical subpopulations of TNBC patients with moderate prognosis (GATA3 over-expression) and Luminal A patients with poor prognosis (KRT6 and ACTA2 over-expression and CDH1 under-expression), using multiple large breast cancer cohorts.

bioinformatics↗

bioRxiv2021

ScRNA-Seq study of neutrophils reveals vast heterogeneity and breadth of inflammatory responses in severe COVID-19 patients

Severe cases of COVID-19 are characterized by dysregulated immune responses, but specific mechanisms contributing to the most severe outcomes remain unclear. Neutrophils are the most abundant leukocyte population in human hosts and reach markedly high numbers during severe COVID-19. However, a detailed examination of their responses has been largely overlooked in the COVID-19 literature to date. Here, we report for the first time a dedicated study of neutrophil responses using single-cell RNA sequencing (scRNA-Seq) of fresh leukocytes from 11 hospitalized adult patients with mild and severe COVID-19 disease and 5 healthy controls. We observed that neutrophils display a pronounced inflammatory profile, with dramatic disruption of predicted cell-cell interactions as the severity of the disease increases. We also identified unique mature and immature neutrophil subpopulations based on transcriptomic profiling, including an antiviral phenotype, and changes in the proportion of each population linked to the severity of the disease. Finally, pathway analysis revealed increased markers of oxidative phosphorylation and ribosomal genes, along with downregulation of many antiviral and host defense pathway genes during severe disease compared to mild infections. Collectively, our findings indicate that neutrophils are capable of mounting effective antiviral defenses but adopt a form of immune dysregulation characterized by excess cellular stress, thereby contributing to the pathogenesis of severe COVID-19.

immunology↗

bioRxiv2021

Benchmarking Computational Integration Methods for Spatial Transcriptomics Data

The increasing popularity of spatial transcriptomics has allowed researchers to analyze transcriptome data in its tissue samples spatial context. Various methods have been developed for detecting SV (spatially variable) genes, with distinct spatial expression patterns. However, the accuracy of using these SV genes in clustering has not been thoroughly studied. On the other hand, in single cell resolution sequencing data without spatial context, clustering analysis is usually done on highly variable (HV) genes. Here we investigate if integrating SV genes and HV genes from spatial transcriptomics data can improve clustering performance beyond using SV genes alone. We examined three methods that detect SV genes, including Giotto, spatialDE, and SPARK, and evaluated six methods that integrate different features measured from the same samples including MOFA+, scVI, Seurat v4, CIMLR, SNF, and the straightforward concatenation approach. We applied these methods on 19 real datasets from three different spatial transcriptomics technologies (merFISH, SeqFISH+, and Visium) as well as 20 simulated datasets of varying spatial expression conditions. Our evaluations show that MOFA+ and simple concatenation have good performances in general, despite the variations among datasets and spatial transcriptomics platforms. This work shows that integrating highly variable and spatially variable genes in the spatial transcriptomics data can improve clustering beyond using spatially variable genes only. It also provides practical guides on the choices of computational methods to accomplish this goal.

bioinformatics↗

bioRxiv2021

BML: a versatile web server for bipartite motif discovery

Motif discovery and characterization are important for gene regulation analysis. The lack of intuitive and integrative web servers impedes effective use of motifs. Here we describe Bipartite Motifs Learning (BML), a web server that provides a user-friendly portal for online discovery and analysis of sequence motifs, using high-throughput sequencing data as the input. BML utilizes both position weight matrix (PWM) and dinucleotide weight matrix (DWM), the latter of which enables the expression of the interdependencies of neighboring bases. With input parameters concerning the motifs are given, the BML achieves significantly higher accuracy than other available tools for motif finding. When no parameters are given by non-expert users, unlike other tools BML employs a learning method to identify motifs automatically and achieve accuracy comparable to the scenario where the parameters are set. The BML web server is freely available at http://motif.t-ridership.com/.

bioinformatics↗

bioRxiv2019

Evaluation of Cell Type Deconvolution R Packages on Single Cell RNA-seq Data

Annotating cell types is a critical step in single cell RNA-Seq (scRNA-Seq) data analysis. Some supervised/semi-supervised classification methods have recently emerged to enable automated cell type identification. However, comprehensive evaluations of these methods are lacking to provide practical guidelines. Moreover, it is not clear whether some classification methods originally designed for analyzing other bulk omics data are adaptable to scRNA-Seq analysis. In this study, we evaluated ten cell-type annotation methods publicly available as R packages. Eight of them are popular methods developed specifically for single cell research (Seurat, scmap, SingleR, CHETAH, SingleCellNet, scID, Garnett, SCINA). The other two methods are repurposed from deconvoluting DNA methylation data: Linear Constrained Projection (CP) and Robust Partial Correlations (RPC). We conducted systematic comparisons on a wide variety of public scRNA-seq datasets as well as simulation data. We assessed the accuracy through intra-dataset and inter-dataset predictions, the robustness over practical challenges such as gene filtering, high similarity among cell types, and increased classification labels, as well as the capabilities on rare and unknown cell-type detection. Overall, methods such as Seurat, SingleR, CP, RPC and SingleCellNet performed well, with Seurat being the best at annotating major cell types. Also, Seurat, SingleR, CP and RPC are more robust against down-sampling. However, Seurat does have a major drawback at predicting rare cell populations, and it is suboptimal at differentiating cell types that are highly similar to each other, while SingleR and RPC are much better in these aspects. All the codes and data are available at: https://github.com/qianhuiSenn/scRNA_cell_deconv_benchmark.

bioinformatics↗

bioRxiv2019

Network-Based Matching of Patients and Targeted Therapies for Precision Oncology

The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible - allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitivity prediction, often use cell lines as exemplars of patient tumors and for model training. However, these models are limited in their ability to accurately predict drug sensitivity of individual cancer patients to a large set of drugs, given the paucity of patient drug sensitivity data used for testing and high variability across different drugs. To address these challenges, we developed a multilayer network-based approach to impute individual patients responses to a large set of drugs. This approach considers the triplet of patients, cell lines and drugs as one inter-connected holistic system. We first use the omics profiles to construct a patient-cell line network and determine best matching cell lines for patient tumors based on robust measures of network similarity. Subsequently, these results are used to impute the \"missing link\" between each individual patient and each drug, called Personalized Imputed Drug Sensitivity Score (PIDS-Score), which can be construed as a measure of the therapeutic potential of a drug or therapy. We applied our method to two subtypes of lung cancer patients, matched these patients with cancer cell lines derived from 19 tissue types based on their functional proteomics profiles, and computed their PIDS-Scores to 251 drugs and experimental compounds. We identified the best representative cell lines that conserve lung cancer biology and molecular targets. The PIDS-Score based top sensitive drugs for the entire patient cohort as well as individual patients are highly related to lung cancer in terms of their targets, and their PIDS-Scores are significantly associated with patient clinical outcomes. These findings provide evidence that our method is useful to narrow the scope of possible effective patient-drug matchings for implementing evidence-based personalized medicine strategies.\n\nData and code availabilityhttps://github.com/bayesrx/bayesrx.github.io/tree/master/authors/liu-q./ Shiny app (data and results visualization tool): https://qingzliu.shinyapps.io/psb-app/

bioinformatics↗

bioRxiv2019

Placenta microbiome diversity is associated with maternal pre-pregnancy obesity and placenta biogeography

Recently there has been considerable debate in the scientific community regarding the placenta as the host of a unique microbiome. No studies have addressed the associations of clinical conditions such as maternal obesity, or localizations on the placental microbiome. We examined the placental microbiome in a multi-ethnic maternal pre-pregnant obesity cohort using controls for environmental contaminants and an optimized microbiome protocol to enrich low bacterial biomass samples. We confirmed that a distinct placenta microbiome does exist, as compared to the environmental background. The placenta microbiome consists predominantly of Lactobacillus, Enterococcus and Chryseobacterium. Moreover, the microbiome in the placentas of obese pre-pregnant mothers are less diverse when compared to those of mothers of normal pre-pregnancy weight. Lastly, microbiome richness also decreases from the maternal side to fetal side. In summary, our study reveals associations of placental microbiome with placenta biogeography and with maternal pre-pregnant obesity.

microbiology↗

bioRxiv2018

GranatumX: A community engaging and flexible software environment for single-cell analysis

We present GranatumX, a next-generation software environment for single-cell data analysis. GranatumX is inspired by the interactive web tool Granatum. It enables biologists to access the latest single-cell bioinformatics methods in a web-based graphical environment. It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines. The architecture of GranatumX allows for easy inclusion of plugin modules, named Gboxes, that wrap around bioinformatics tools written in various programming languages and on various platforms. GranatumX can be run on the cloud or private servers and generate reproducible results. It is a community-engaging, flexible, and evolving software ecosystem for scRNA-Seq analysis, connecting developers with bench scientists. GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.

bioinformatics↗

bioRxiv2018

DeepImpute: an accurate, fast and scalable deep neural network method to impute single-cell RNA-Seq data

BackgroundSingle-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. However, a significant problem of current scRNA-seq data is the large fractions of missing values or \"dropouts\" in gene counts. Incorrect handling of dropouts may affect downstream bioinformatics analysis. As the number of scRNA-seq datasets grows drastically, it is crucial to have accurate and efficient imputation methods to handle these dropouts.\n\nMethodsWe present DeepImpute, a deep neural network based imputation algorithm. The architecture of DeepImpute efficiently uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation.\n\nResultsOverall DeepImpute yields better accuracy than other publicly available scRNA-Seq imputation methods on experimental data, as measured by mean squared error or Pearsons correlation coefficient. Moreover, its efficient implementation provides significantly higher performance over the other methods as dataset size increases. Additionally, as a machine learning method, DeepImpute allows to use a subset of data to train the model and save even more computing time, without much sacrifice on the prediction accuracy.\n\nConclusionsDeepImpute is an accurate, fast and scalable imputation tool that is suited to handle the ever increasing volume of scRNA-seq data. The package is freely available at https://github.com/lanagarmire/DeepImpute

bioinformatics↗

bioRxiv2018

Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data

Lilikoi (Hawaiian word for passion fruit) is a new and comprehensive R package for personalized pathway based classification modelling, using metabolomics data. Four basic modules are presented as the backbone of the package: 1) Feature mapping module, which standardizes the metabolite names provided by users, and map them to pathways. 2) Dimension transformation module, which transforms the metabolomic profiles to personalized pathway-based profiles using pathway deregulation scores (PDS). 3) Feature selection module which helps to select the significant pathway features related to the disease phenotypes, and 4) Classification and prediction module which offers various machine-learning classification algorithms. The package is freely available under the GPLv3 license through the github repository at: https://github.com/lanagarmire/lilikoi

bioinformatics↗

bioRxiv2018

Metabolomics profile of umbilical cord blood is associated with maternal pre-pregnant obesity in a prospective multi-ethnic cohort displaying health disparities

Maternal obesity has become a growing global health concern that may predispose the offspring to medical conditions later in life. However, the metabolic link between maternal pre-pregnant obesity and healthy offspring has not yet been fully elucidated. In this study, we conducted a case-control study using coupled untargeted and targeted metabolomics approach, from the newborn cord blood metabolomes associated with a matched maternal pre-pregnant obesity cohort of 28 cases and 29 controls. The subjects were recruited from multi-ethnic populations in Hawaii, including rarely reported Native Hawaiian and other Pacific Islanders (NHPI). We found that maternal obesity was the most important factor contributing to differences in cord blood metabolomics. Using elastic net regularization based logistic regression model, we identified 29 metabolites as potential early-life biomarkers manifesting intrauterine effect of maternal obesity, with accuracy as high as 0.947 after adjusting for clinical confounding (maternal and paternal age and ethnicity, parity and gravidity). We validated the model results in a subsequent set of samples (N=30) with an accuracy of 0.822. Among the metabolites, six metabolites (galactonic acid, butenylcarnitine, 2-hydroxy-3-methylbutyric acid, phosphatidylcholine diacyl C40:3, 1,5-anhydrosorbitol, and phosphatidylcholine acyl-alkyl 40:3) were individually and significantly different between the maternal obese vs. norm-weight groups. Interestingly, Hydroxy-3-methylbutyric acid showed significnatly higher levels in cord blood from the NHPI group, compared to asian and caucasian groups. In summary, significant associations were observed between maternal pre-pregnant obesity and offspring metabolomics alternation at birth, revealing the inter-generational impact of maternal obesity.

bioinformatics↗

bioRxiv2017

Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+) and 67 negative estrogen receptor (ER-), to test the accuracies of autoencoder, a deep learning (DL) framework, as well as six widely used machine learning models, namely Random Forest (RF), Support Vector Machines (SVM), Recursive Partitioning and Regression Trees (RPART), Linear Discriminant Analysis (LDA), Prediction Analysis for Microarrays (PAM), and Generalized Boosted Models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER-patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value<0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion & absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accurcy (AUC=0.93) and better revelation of disease biology. We encourage the adoption of autoencoder based deep learning method in the metabolomics research community for classification.

bioinformatics↗

bioRxiv2017

Deep Learning based multi-omics integration robustly predicts survival in liver cancer

Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill in this gap, we present a deep learning (DL) based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We build the DL based, survival-sensitive model on 360 HCC patients data using RNA-seq, miRNA-seq and methylation data from TCGA, which predicts prognosis as good as an alternative model where genomics and clinical data are both considered. This DL based model provides two optimal subgroups of patients with significant survival differences (P=7.13e-6) and good model fitness (C-index=0.68). More aggressive subtype is associated with frequent TP53 inactivation mutations, higher expression of stemness markers (KRT19, EPCAM) and tumor marker BIRC5, and activated Wnt and Akt signaling pathways. We validated this multi-omics model on five external datasets of various omics types: LIRI-JP cohort (n=230, C-index=0.75), NCI cohort (n=221, C-index=0.67), Chinese cohort (n=166, C-index=0.69), E-TABM-36 cohort (n=40, C-index=0.77), and Hawaiian cohort (n=27, C-index=0.82). This is the first study to employ deep learning to identify multi-omics features linked to the differential survival of HCC patients. Given its robustness over multiple cohorts, we expect this workflow to be useful at predicting HCC prognosis prediction.

bioinformatics↗

bioRxiv2017

Granatum: a graphical single-cell RNA-seq analysis pipeline for genomics scientists

BackgroundSingle-cell RNA sequencing (scRNA-Seq) is an increasingly popular platform to study heterogeneity at the single-cell level.\n\nComputational methods to process scRNA-Seq have limited accessibility to bench scientists as they require significant amounts of bioinformatics skills.\n\nResultsWe have developed Granatum, a web-based scRNA-Seq analysis pipeline to make analysis more broadly accessible to researchers. Without a single line of programming code, users can click through the pipeline, setting parameters and visualizing results via the interactive graphical interface Granatum conveniently walks users through various steps of scRNA-Seq analysis. It has a comprehensive list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene filtering, geneexpression normalization, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein-networ interaction visualization, and pseudo-time cell series construction.\n\nConclusionsGranatum enables broad adoption of scRNA-Seq technology by empowering the bench scientists with an easy-to-use graphical interface for scRNA-Seq data analysis. The package is freely available for research use at http://garmiregroup.org/granatum/app

bioinformatics↗

bioRxiv2016

Cox-nnet: an artificial neural network Cox regression for prognosis prediction

Artificial neural networks (ANN) are computing architectures with massively parallel interconnections of simple neurons and has been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In over 10 TCGA RNA-Seq data sets, Cox-nnet achieves a statistically significant increase in predictive accuracy, compared to the other three methods including Cox-proportional hazards (Cox-PH), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, from both pathway and gene levels. The outputs from the hidden layer node can provide a new approach for survival-sensitive dimension reduction. In summary, we have developed a new method for more accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at github.com/lanagarmire/cox-nnet.

bioinformatics↗