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...
Read More »Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality
The constant evolving and development of next-generation sequencing techniques lead to high throughput data composed of datasets that include a large number of biological samples. Although a...
Read More »scISR – a novel method for single-cell data imputation using subspace regression
Recent advances in biochemistry and single-cell RNA sequencing (scRNA-seq) have allowed us to monitor the biological systems at the single-cell resolution...
Read More »ResPAN – a powerful batch correction model for scRNA-seq data through residual adversarial networks
With the advancement of technology, we can generate and access large-scale, high dimensional and diverse genomics data, especially through single-cell RNA sequencing (scRNA-seq). However...
Read More »scCAN – single-cell clustering using autoencoder and network fusion
Unsupervised clustering of single-cell RNA sequencing data (scRNA-seq) is important because it allows us to identify putative cell types. However, the large number of cells (up to millions), the high...
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