Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells...
Read More »Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer
Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients yet are approved only for...
Read More »Gene expression model inference from snapshot RNA data using Bayesian non-parametrics
Gene expression models, which are key towards understanding cellular regulatory response, underlie observations of single-cell transcriptional dynamics. Although RNA expression data encode information on gene expression models, existing computational...
Read More »DGAN – improved downstream functional analysis of single-cell RNA-sequence data
The dramatic increase in the number of single-cell RNA-sequence (scRNA-seq) investigations is indeed an endorsement of the new-fangled proficiencies...
Read More »Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data
Effective dimension reduction is essential for single cell RNA-seq (scRNAseq) analysis. Principal component analysis (PCA) is widely used, but requires continuous, normally-distributed data; therefore...
Read More »A lineage tree-based hidden Markov model quantifies cellular heterogeneity and plasticity
Individual cells can assume a variety of molecular and phenotypic states and recent studies indicate that cells can rapidly adapt in response to...
Read More »sc-linker – identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetic
Genome-wide association studies provide a powerful means of identifying loci and genes contributing to disease, but in many cases, the related cell types...
Read More »Neighboring cell types influence single-cell gene expression variability
Researchers from the University of Tsukuba have designed a statistical framework that identifies regulation of gene expression by neighboring cell...
Read More »CellRegMap – a statistical framework for mapping context-specific regulatory variants using scRNA-seq
Single-cell RNA sequencing (scRNA-seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population-scale scRNA-seq studies in...
Read More »Significance analysis for clustering with single-cell RNA-sequencing data
Unsupervised clustering of single-cell RNA-sequencing data enables the identification and discovery of distinct cell populations. However, the most widely used clustering algorithms are heuristic and do not formally account for statistical uncertainty...
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