Biology subjects

Yang, J. Y. H.

Publications and source records attributed to Yang, J. Y. H..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Covidscope: An atlas-scale COVID-19 resource for single-cell meta analysis at sample and cell levels

With the recent advancement in single-cell technologies and the increased availability of integrative tools, challenges arise in easy and fast access to large collections of cell atlas. Existing cell atlas portals rarely are open sourced and adaptable, and do not support meta-analysis at cell level. Here, we present an open source, highly optimised and scalable architecture, named Scope+, to allow quick access, meta-analysis and cell-level selection of the atlas data. We applied this architecture to our well-curated 5 million Covid-19 blood and immune cells, as a portal, Covidscope (https://covidsc.d24h.hk/). We achieved efficient access to atlas-scale data via three strategies, such as server-side rendering, novel database optimization strategies and an innovative architectural design. Scope+ serves as an open source architecture for researchers to build on with their own atlas, and demonstrated its capability in the Covidscope portal for an effective meta-analysis to atlas data at cellular resolution for reproducible research.

bioinformatics↗

bioRxiv2022

scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction

Recent advances in single-cell technologies enable scientists to measure molecular data at high-resolutions and hold the promise to substantially improve clinical outcomes through personalised medicine. However, due to a lack of tools specifically designed to represent each sample (e.g. patient) from the collection of cells sequenced, disease outcome prediction on the sample level remains a challenging task. Here, we present scFeatures, a tool that creates interpretable molecular representation of single-cell and spatial data using 17 types of features motivated by current literature. The feature types span across six distinct categories including cell type proportions, cell type specific gene expressions, cell type specific pathway scores, cell type specific cell-cell interaction scores, overall aggregated gene expressions and spatial metrics. By generating molecular representation using scFeatures for single-cell RNA-seq, spatial proteomic and spatial transcriptomic data, we demonstrate that different types of features are important for predicting different disease outcomes in different datasets and the downstream analysis of features uncover novel biological discoveries.

bioinformatics↗

bioRxiv2021

scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model

Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished through trial and error, a time-consuming and costly method. A computational model for cell reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time and resources. Current methods often cannot account for the heterogeneity observed in cell reprogramming, or they only make short-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel computational model for cell reprogramming that leverages single cell multiomics data, enabling a more holistic view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential of each transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate the effect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect of overexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations. Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate model for cell reprogramming with significant potential to accelerate research in regenerative medicine.

bioinformatics↗

bioRxiv2021

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

Survival analysis is a branch of statistics that deals with both, the tracking of time and of the survival status simultaneously as the dependent response. Current comparisons of survival model performance mostly center on clinical data with classic statistical survival models, with prediction accuracy often serving as the sole metric of model performance. Moreover, survival analysis approaches for censored omics data have not been thoroughly investigated. The common approach is to binarise the survival time and perform a classification analysis. Here, we develop a benchmarking framework, SurvBenchmark, that evaluates a diverse collection of survival models for both clinical and omics datasets. SurvBenchmark not only focuses on classical approaches such as the Cox model, but it also evaluates state-of-art machine learning survival models. All approaches were assessed using multiple performance metrics, these include model predictability, stability, flexibility and computational issues. Our systematic comparison framework with over 320 comparisons (20 methods over 16 datasets) shows that the performances of survival models vary in practice over real-world datasets and over the choice of the evaluation metric. In particular, we highlight that using multiple performance metrics is critical in providing a balanced assessment of various models. The results in our study will provide practical guidelines for translational scientists and clinicians, as well as define possible areas of investigation in both survival technique and benchmarking strategies. Contactjean.yang@sydney.edu.au

bioinformatics↗

bioRxiv2021

Identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data

High-throughput single cell technologies hold the promise of discovering novel cellular relationships with disease. However, analytical workflows constructed for these technologies to associate cell proportions with disease often employ unsupervised clustering techniques that overlook the valuable hierarchical structures that have been used to define cell types. We present treekoR, a framework that empirically recapitulates these structures, facilitating multiple quantifications and comparisons of cell type proportions. Our results from twelve case studies reinforce the importance of quantifying proportions relative to parent populations in the analyses of cytometry data -- as failing to do so can lead to missing important biological insights.

bioinformatics↗

bioRxiv2021

A benchmark study of simulation methods for single-cell RNA sequencing data

Single-cell RNA-seq (scRNA-seq) data simulation is critical for evaluating computational methods for analysing scRNA-seq data especially when ground truth is experimentally unattainable. The reliability of evaluation depends on the ability of simulation methods to capture properties of experimental data. However, while many scRNA-seq data simulation methods have been proposed, a systematic evaluation of these methods is lacking. We developed a comprehensive evaluation framework, SimBench, including a novel kernel density estimation measure to benchmark 12 simulation methods through 35 scRNA-seq experimental datasets. We evaluated the simulation methods on a panel of data properties, ability to maintain biological signals, scalability and applicability. Our benchmark uncovered performance differences among the methods and highlighted the varying difficulties in simulating data characteristics. Furthermore, we identified several limitations including maintaining heterogeneity of distribution. These results, together with the framework and datasets made publicly available as R packages, will guide simulation methods selection and their future development.

bioinformatics↗

bioRxiv2021

Cepo uncovers cell identity through differential stability

We present Cepo, a method to generate cell-type-specific gene statistics of differentially stable genes from single-cell RNA-sequencing (scRNA-seq) data to define cell identity. Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells, and lineage inference of single cells.

systems biology↗

bioRxiv2021

scJoint: transfer learning for data integration of single-cell RNA-seq and ATAC-seq

Single-cell multi-omics data continues to grow at an unprecedented pace, and effectively integrating different modalities holds the promise for better characterization of cell identities. Although a number of methods have demonstrated promising results in integrating multiple modalities from the same tissue, the complexity and scale of data compositions typically present in cell atlases still pose a significant challenge for existing methods. Here we present scJoint, a transfer learning method to integrate atlas-scale, heterogeneous collections of scRNA-seq and scATAC-seq data. scJoint leverages information from annotated scRNA-seq data in a semi-supervised framework and uses a neural network to simultaneously train labeled and unlabeled data, enabling label transfer and joint visualization in an integrative framework. Using multiple atlas data and a biologically varying multi-modal data, we demonstrate scJoint is computationally efficient and consistently achieves significantly higher cell type label accuracy than existing methods while providing meaningful joint visualizations. This suggests scJoint is effective in overcoming the heterogeneity in different modalities towards a more comprehensive understanding of cellular phenotypes.

bioinformatics↗

bioRxiv2021

Single-cell and single-variant resolution analysis of clonal evolution in human liver cancer

Genetic heterogeneity of tumor is closely related to clonal evolution, phenotypic diversity and treatment resistance. Such heterogeneity has been characterized in liver cancer at single-cell sub-chromosomal scale, and a more precise single-variant resolution analysis is lacking. Here we employed a strategy to analyze both the single-cell genomic mutations and transcriptomic changes in 5 patients with liver cancer. Target sequencing was done for a total of 480 single cells in a patient-specific manner. DNA copy number status of point mutations was obtained from single-cell mutational profiling. The clonal structures of liver cancers were then uncovered at single-variant resolution, and mutation combinations in single cells enabled reconstruction of their evolutionary history. A common origin but independent evolutionary fate was revealed for primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. The mutational signature suggested early evolutionary process may be related to specific etiology like aristolochic acids. By parallel single-cell RNA-Seq, the transcriptomic phenotype was found to be related with genetic heterogeneity in liver cancer. We reconstructed the single-cell and single-variant resolution clonal evolutionary history of liver cancer, and dissection of both genetic and phenotypic heterogeneity provides knowledge for mechanistic understanding of liver cancer initiation and progression.

genomics↗

bioRxiv2020

Characterization of cell-cell communication in COVID-19 patients

COVID-19 patients display a wide range of disease severity, ranging from asymptomatic to critical symptoms with high mortality risk. Our ability to understand the interaction of SARS-CoV-2 infected cells within the lung, and of protective or dysfunctional immune responses to the virus, is critical to effectively treat these patients. Currently, our understanding of cell-cell interactions across different disease states, and how such interactions may drive pathogenic outcomes, is incomplete. Here, we developed a generalizable workflow for identifying cells that are differentially interacting across COVID-19 patients with distinct disease outcomes and use it to examine five public single-cell RNA-seq datasets with a total of 85 individual samples. By characterizing the cell-cell interaction patterns across epithelial and immune cells in lung tissues for patients with varying disease severity, we illustrate diverse communication patterns across individuals, and discover heterogeneous communication patterns among moderate and severe patients. We further illustrate patterns derived from cell-cell interactions are potential signatures for discriminating between moderate and severe patients.

systems biology↗

bioRxiv2020

hRUV: Hierarchical approach to removal of unwanted variation for large-scale metabolomics data

Liquid chromatography-mass spectrometry based metabolomics studies are increasingly applied to large population cohorts, running for several weeks to months, even extending to years of data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalization approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we designed an experiment with an arrangement to embed biological sample replicates to measure the variance within and between batches for over 1,000 human plasma samples run over 44 days. We integrate these replicates in a novel workflow to remove unwanted variation in a hierarchical structure (hRUV) by progressively merging the adjustments in neighbouring batches. We demonstrate significant improvement of hRUV over existing methods in maintaining biological signals whilst removing unwanted variation for large scale metabolomics studies.

bioinformatics↗

bioRxiv2020

Guiding and interpreting brain network classification with transcriptional data

The investigation of brain networks has yielded many insights that have helped to characterise many neurological and psychiatric disorders. In particular, network classification of functional magnetic resonance imaging (fMRI) data is an important tool for the identification of prognostic and diagnostic biomarkers of brain connectivity disorders such as schizophrenia and depression. However, existing generic network classification methods provide no direct information on the underlying molecular mechanisms of the selected functional connectivity features when applied to fMRI data. To address this, we propose a novel fMRI network classification method that incor-porates brain transcriptional data using a user-specified gene set collection (GSC) to construct feature groups for use in classification of brain connectivity data. The use of GSCs are an opportunity to incorporate knowledge of potential molecular mechanisms which may be associated with a disease. The inclusion of transcriptional data yields improved prediction accuracy on publicly available schizophrenia fMRI data for several of the GSCs we consider. We also introduce a post-hoc interpretation framework to provide transcriptional-data-guided biological interpretations for discriminative functional connectivity features identified by existing fMRI network classification methods.

bioinformatics↗

bioRxiv2019

CiteFuse enables multi-modal analysis of CITE-seq data

Multi-modal profiling of single cells represents one of the latest technological advancements in molecular biology. Among various single-cell multi-modal strategies, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) allows simultaneous quantification of two distinct species: RNA and surface marker proteins (ADT). Here, we introduce CiteFuse, a streamlined package consisting of a suite of tools for pre-processing, modality integration, clustering, differential RNA and ADT expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualization of CITE-seq data. We show the capacity of CiteFuse to integrate the two data modalities and its relative advantage against data generated from single modality profiling. Furthermore, we illustrate the pre-processing steps in CiteFuse and in particular a novel doublet detection method based on a combined index of cell hashing and transcriptome data. Collectively, we demonstrate the utility and effectiveness of CiteFuse for the integrative analysis of transcriptome and epitope profiles from CITE-seq data.

bioinformatics↗

bioRxiv2019

Investigating higher order interactions in single cell data with scHOT

Single-cell RNA-sequencing has transformed our ability to examine cell fate choice. For example, in the context of development and differentiation, computational ordering of cells along pseudotime enables the expression profiles of individual genes, including key transcription factors, to be examined at fine scale temporal resolution. However, while cell fate decisions are typically marked by profound changes in expression, many such changes are observed in genes downstream of the initial cell fate decision. By contrast, the genes directly involved in the cell fate decision process are likely to interact in subtle ways, potentially resulting in observed changes in patterns of correlation and variation rather than mean expression prior to cell fate commitment. Herein, we describe a novel approach, scHOT - single cell Higher Order Testing - which provides a flexible and statistically robust framework for identifying changes in higher order interactions among genes. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. We demonstrate the utility of scHOT by studying embryonic development of the liver, where we find coordinated changes in higher order interactions of programs related to differentiation and liver function. We also demonstrate its ability to find subtle changes in gene-gene correlation patterns across space using spatially-resolved expression data from the mouse olfactory bulb. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data.

bioinformatics↗

bioRxiv2019

Single-cell dissection of a rare human prostate basal cell carcinoma

As a rare subtype of prostate carcinoma, basal cell carcinoma (BCC) has not been studied extensively and thus lacks systematic molecular characterization. Here we applied single-cell genomic amplification and RNA-Seq to a specimen of human prostate BCC (CK34{beta}E12+/P63+/PAP-/PSA-). The mutational landscape was obtained via whole exome sequencing of the amplification mixture of 49 single cells, and the 5 putative driver genes mutated are CASC5, NUTM1, PTPRC, KMT2C and TBX3. The top 3 nucleotide substitutions are C>T, T>C and C>A, similar to common prostate cancer. The distribution of the variant allele frequency values indicated these single cells are from the same tumor clone. The transcriptomes of 69 single cells were obtained, and they were clustered into tumor, stromal and immune cells based on their global transcriptomic profiles. The tumor cells specifically express basal cell markers like KRT5, KRT14 and KRT23, and epithelial markers EPCAM, CDH1 and CD24. The transcription factor (TF) co-variance network analysis showed that the BCC tumor cells have distinct regulatory networks. By comparison with current prostate cancer datasets, we found that some of the bulk samples exhibit basal-cell signatures. Interestingly, at single-cell resolution the gene expression patterns of prostate BCC tumor cells show uniqueness compared with that of common prostate cancer-derived circulating tumor cells. This study, for the first time, discloses the comprehensive mutational and transcriptomic landscapes of prostate BCC, which lays a foundation for the understanding of its tumorigenesis mechanism and provides new insights into prostate cancers in general.

cancer biology↗

bioRxiv2019

scClassify: hierarchical classification of cells

Cell type identification is a key computational challenge in single-cell RNA-sequencing (scRNA-seq) data. To capitalize on the large collections of well-annotated scRNA-seq datasets, we present scClassify, a hierarchical classification framework based on ensemble learning. scClassify can identify cells from published scRNA-seq datasets more accurately and more finely than in the original publications. We also estimate the cell number needed for accurate classification anywhere in a cell type hierarchy.

bioinformatics↗

bioRxiv2019

Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis

BackgroundSingle-cell RNA-sequencing (scRNA-seq) is a transformative technology, allowing global transcriptomes of individual cells to be profiled with high accuracy. An essential task in scRNA-seq data analysis is the identification of cell types from complex samples or tissues profiled in an experiment. To this end, clustering has become a key computational technique for grouping cells based on their transcriptome profiles, enabling subsequent cell type identification from each cluster of cells. Due to the high feature-dimensionality of the transcriptome (i.e. the large number of measured genes in each cell) and because only a small fraction of genes are cell type-specific and therefore informative for generating cell type-specific clusters, clustering directly on the original feature/gene dimension may lead to uninformative clusters and hinder correct cell type identification.\n\nResultsHere, we propose an autoencoder-based cluster ensemble framework in which we first take random subspace projections from the data, then compress each random projection to a low-dimensional space using an autoencoder artificial neural network, and finally apply ensemble clustering across all encoded datasets for generating clusters of cells. We employ four evaluation metrics to benchmark clustering performance and our experiments demonstrate that the proposed autoencoder-based cluster ensemble can lead to substantially improved cell type-specific clusters when applied with both the standard k-means clustering algorithm and a state-of-the-art kernel-based clustering algorithm (SIMLR) designed specifically for scRNA-seq data. Compared to directly using these clustering algorithms on the original datasets, the performance improvement in some cases is up to 100%, depending on the evaluation metrics used.\n\nConclusionsOur results suggest that the proposed framework can facilitate more accurate cell type identification as well as other downstream analyses. The code for creating the proposed autoencoder-based cluster ensemble framework is freely available from https://github.com/gedcom/autoencoder_cluster_ensemble

bioinformatics↗

bioRxiv2019

Single-Cell RNA-Seq Reveals Naïve B cells Associated with Better Prognosis of HCC

Hepatocellular Carcinoma (HCC) is a type of malignant solid tumor, causing high morbidity and mortality around the world and the major portion of HCC patients is from China. Cancer immunotherapies have shown some clinical responses in treating some types of cancer but did not shown significant efficiency in HCC treatment. This in part due to the impact of immune cells in the tumor microenvironment. It is commonly believed that HCC is a heterogeneous solid tumor and the microenvironment of HCC plays an important role in tumorgenesis and development. Currently, the residents of the microenvironment of HCC is not well-defined and clarification, especially the immune cells, which we believe that paly pivotal roles in tumorgenesis and development. To depict the landscape of the composition, lineage and functional states of the immune cells in HCC, we performed single-cell RNA sequencing on Diethylnitrosamine (DEN)-induced mouse HCC model. We observed heterogeneity within the immune and hepatocytes both in the precancerous condition of tumorigenesis and cancerous condition of HCC. In this study we found that the disease-associated changes appeared early in pathological progression and were highly cell-type specific. Specific subsets of T and B cells preferentially enriched in HCC, and we identified signature genes for each subset. Additionally, we mapped this group of specific cells to the human TCGA database. We found a cluster of naive B cells characterized by high expression of CD38 associated with better prognosis of human HCC. Our study demonstrates signaling interaction map based on receptor-ligand bonding on the single-cell level could broaden our comprehending of cellular networks in varies status. Our finding provides a new approach for patient stratification and will help further understand the functional states, dynamics and signaling interaction of B cells in hepatocellular carcinoma, and may provide a novel insight and therapeutics for the HCC.

cancer biology↗