Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis
Background Feature selection is an essential task in single-cell RNA-seq (scRNA-seq) data
analysis and can be critical for gene dimension reduction and downstream analyses, such …
analysis and can be critical for gene dimension reduction and downstream analyses, such …
scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data
Although single-cell sequencing has provided a powerful tool to deconvolute cellular
heterogeneity of diseases like cancer, extrapolating clinical significance or identifying …
heterogeneity of diseases like cancer, extrapolating clinical significance or identifying …
Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data
The advances of single-cell transcriptomic technologies have led to increasing use of single-
cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting …
cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting …
Integrating spatially-resolved transcriptomics data across tissues and individuals: challenges and opportunities
Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the
development of new computational analysis methods to unlock biological insights. As the …
development of new computational analysis methods to unlock biological insights. As the …
Scope+: An open source generalizable architecture for single-cell RNA-seq atlases at sample and cell levels
With the recent advancement in single-cell RNA-sequencing technologies and the
increased availability of integrative tools, challenges arise in easy and fast access to large …
increased availability of integrative tools, challenges arise in easy and fast access to large …
Spatial gene expression at single-cell resolution from histology using deep learning with GHIST
The increased use of spatially resolved transcriptomics provides new biological insights into
disease mechanisms. However, the high cost and complexity of these methods are barriers …
disease mechanisms. However, the high cost and complexity of these methods are barriers …
pasta: Pattern Analysis for Spatial Omics Data
Spatial omics assays allow for the molecular characterisation of cells in their spatial context.
Notably, the two main technological streams, imaging-based and high-throughput …
Notably, the two main technological streams, imaging-based and high-throughput …
Multi-task benchmarking of spatially resolved gene expression simulation models
X Liang, Y Cao, JYH Yang - bioRxiv, 2024 - biorxiv.org
Computational methods for spatially resolved transcriptomics (SRT) are frequently
developed and assessed through data simulation. The effectiveness of these evaluations …
developed and assessed through data simulation. The effectiveness of these evaluations …
[HTML][HTML] Thinking process templates for constructing data stories with SCDNEY
Background Globally, scientists now have the ability to generate a vast amount of high
throughput biomedical data that carry critical information for important clinical and public …
throughput biomedical data that carry critical information for important clinical and public …
Spatial mapping reveals unique cellular interactions and enhanced tertiary lymphoid structures in responders to anti-PD-1 therapy in mucosal head and neck cancers.
Survival in recurrent/metastatic head and neck mucosal squamous cell carcinoma
(HNmSCC) remains poor. Anti-programmed death (PD)-1 therapies have demonstrated …
(HNmSCC) remains poor. Anti-programmed death (PD)-1 therapies have demonstrated …