Zhixiang Lin
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View article: MultiGATE: integrative analysis and regulatory inference in spatial multi-omics data via graph representation learning
MultiGATE: integrative analysis and regulatory inference in spatial multi-omics data via graph representation learning Open
View article: Interpretable spatial multi-omics data integration and dimension reduction with SpaMV
Interpretable spatial multi-omics data integration and dimension reduction with SpaMV Open
Spatial multi-omics technologies have revolutionized our understanding of biological systems by providing spatially resolved molecular profiles from multiple perspectives. Existing spatial multi-omics integration methods often assume that …
View article: InterVelo: a mutually enhancing model for estimating pseudotime and RNA velocity in multi-omic single-cell data
InterVelo: a mutually enhancing model for estimating pseudotime and RNA velocity in multi-omic single-cell data Open
Motivation RNA velocity has become a powerful tool for uncovering transcriptional dynamics in snapshot single-cell data. However, current RNA velocity approaches often assume constant transcriptional rates and treat genes independently wit…
View article: TIVelo: RNA velocity estimation leveraging cluster-level trajectory inference
TIVelo: RNA velocity estimation leveraging cluster-level trajectory inference Open
View article: Precise gene expression deconvolution in spatial transcriptomics with STged
Precise gene expression deconvolution in spatial transcriptomics with STged Open
Spatially resolved transcriptomics (SRT) has transformed tissue biology by linking gene expression profiles with spatial information. However, sequencing-based SRT methods aggregate signals from multiple cell types within capture locations…
View article: TIVelo: RNA velocity estimation leveraging cluster-level trajectory inference
TIVelo: RNA velocity estimation leveraging cluster-level trajectory inference Open
RNA velocity inference is a valuable tool for understanding cell development, differentiation, and disease progression. However, existing RNA velocity inference methods typically rely on explicit assumptions of ordinary differential equati…
View article: CAESAR: a cross-technology and cross-resolution framework for spatial omics annotation
CAESAR: a cross-technology and cross-resolution framework for spatial omics annotation Open
The biotechnology of spatial omics has advanced rapidly over the past few years, with enhancements in both throughput and resolution. However, existing annotation pipelines in spatial omics predominantly rely on clustering methods and lack…
View article: Publisher Correction: scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis
Publisher Correction: scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis Open
View article: scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis
scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis Open
View article: ABCD1 as a Novel Diagnostic Marker for Solid Pseudopapillary Neoplasm of the Pancreas
ABCD1 as a Novel Diagnostic Marker for Solid Pseudopapillary Neoplasm of the Pancreas Open
The diagnosis of solid pseudopapillary neoplasm of the pancreas (SPN) can be challenging due to potential confusion with other pancreatic neoplasms, particularly pancreatic neuroendocrine tumors (NETs), using current pathological diagnosti…
View article: INSIDER: Interpretable sparse matrix decomposition for RNA expression data analysis
INSIDER: Interpretable sparse matrix decomposition for RNA expression data analysis Open
RNA sequencing (RNA-Seq) is widely used to capture transcriptome dynamics across tissues, biological entities, and conditions. Currently, few or no methods can handle multiple biological variables (e.g., tissues/ phenotypes) and their inte…
View article: Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope
Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope Open
The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approache…
View article: SGCAST: symmetric graph convolutional auto-encoder for scalable and accurate study of spatial transcriptomics
SGCAST: symmetric graph convolutional auto-encoder for scalable and accurate study of spatial transcriptomics Open
Recent advances in spatial transcriptomics (ST) have enabled comprehensive profiling of gene expression with spatial information in the context of the tissue microenvironment. However, with the improvements in the resolution and scale of S…
View article: stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics
stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics Open
Motivation Recent rapid developments in spatial transcriptomic techniques at cellular resolution have gained increasing attention. However, the unique characteristics of large-scale cellular resolution spatial transcriptomic datasets, such…
View article: SR2: Sparse Representation Learning for Scalable Single-cell RNA Sequencing Data Analysis
SR2: Sparse Representation Learning for Scalable Single-cell RNA Sequencing Data Analysis Open
Single-cell RNA-sequencing (scRNA-seq) technology has been widely used to measure the transcriptome of cells in complex and heterogeneous systems. Integrative analysis of multiple scRNA-seq data can transform our understanding of various a…
View article: SpatialScope: A unified approach for integrating spatial and single-cell transcriptomics data using deep generative models
SpatialScope: A unified approach for integrating spatial and single-cell transcriptomics data using deep generative models Open
The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and their biology. Current ST technologies based on either next generation sequencing (seq-based approache…
View article: SpatialScope: A unified approach for integrating spatial and single-cell transcriptomics data using deep generative models
SpatialScope: A unified approach for integrating spatial and single-cell transcriptomics data using deep generative models Open
The rapid emergence of spatial transcriptomics (ST) technologies are revolutionizing our under-standing of tissue spatial architecture and their biology. Current ST technologies based on either next generation sequencing (seq-based approac…
View article: iPoLNG—An unsupervised model for the integrative analysis of single-cell multiomics data
iPoLNG—An unsupervised model for the integrative analysis of single-cell multiomics data Open
Single-cell multiomics technologies, where the transcriptomic and epigenomic profiles are simultaneously measured in the same set of single cells, pose significant challenges for effective integrative analysis. Here, we propose an unsuperv…
View article: scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data
scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data Open
Motivation Technological advances have enabled us to profile single-cell multi-omics data from the same cells, providing us with an unprecedented opportunity to understand the cellular phenotype and links to its genotype. The available pro…
View article: INSIDER: Interpretable Sparse Matrix Decomposition for Bulk RNA Expression Data Analysis
INSIDER: Interpretable Sparse Matrix Decomposition for Bulk RNA Expression Data Analysis Open
RNA-Seq is widely used to capture transcriptome dynamics across tissues from different biological entities even across biological conditions, with the aim of understanding the contribution of gene activities to phenotypes of biosamples. Ho…
View article: Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics
Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics Open
Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong a…
View article: FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets
FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets Open
Single-cell RNA-sequencing (scRNA-seq) is being used extensively to measure the mRNA expression of individual cells from deconstructed tissues, organs and even entire organisms to generate cell atlas references, leading to discoveries of n…
View article: Adversarial domain translation networks for fast and accurate integration of large-scale atlas-level single-cell datasets
Adversarial domain translation networks for fast and accurate integration of large-scale atlas-level single-cell datasets Open
The rapid emergence of large-scale atlas-level single-cell RNA-seq datasets presents remarkable opportunities for broad and deep biological investigations through integrative analyses. However, harmonizing such datasets requires integratio…
View article: coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data
coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data Open
Technological advances have enabled us to profile multiple molecular layers at unprecedented single-cell resolution and the available datasets from multiple samples or domains are growing. These datasets, including scRNA-seq data, scATAC-s…
View article: RA3 is a reference-guided approach for epigenetic characterization of single cells
RA3 is a reference-guided approach for epigenetic characterization of single cells Open
View article: scAMACE: Model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation
scAMACE: Model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation Open
The advancement in technologies and the growth of available single-cell datasets motivate integrative analysis of multiple single-cell genomic datasets. Integrative analysis of multimodal single-cell datasets combines complementary informa…
View article: Mendelian Randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics
Mendelian Randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics Open
Mendelian Randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong a…
View article: <i>couple</i>CoC+: an information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data
<i>couple</i>CoC+: an information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data Open
Technological advances have enabled us to profile multiple molecular layers at unprecedented single-cell resolution and the available datasets from multiple samples or domains are growing. These datasets, including scRNA-seq data, scATAC-s…
View article: Dimensionality reduction for single cell RNA sequencing data using constrained robust non-negative matrix factorization
Dimensionality reduction for single cell RNA sequencing data using constrained robust non-negative matrix factorization Open
Single cell RNA-sequencing (scRNA-seq) technology, a powerful tool for analyzing the entire transcriptome at single cell level, is receiving increasing research attention. The presence of dropouts is an important characteristic of scRNA-se…
View article: A reference-guided approach for epigenetic characterization of single cells
A reference-guided approach for epigenetic characterization of single cells Open
The recent advancements in single-cell technologies, including single-cell chromatin accessibility sequencing (scCAS), have enabled profiling the epigenetic landscapes for thousands of individual cells. However, the characteristics of scCA…