Single-cell RNA-sequencing (scRNA-seq) technology is a powerful tool to study organism from a single cell perspective and explore the heterogeneity between cells. Clustering is a fundamental step in scRNA-seq data analysis and it is the key to understand...
Read More »GeTallele – a method for analysis of DNA and RNA allele frequency distributions
Variant allele frequencies (VAF) are an important measure of genetic variation that can be estimated at single-nucleotide variant (SNV) sites. RNA and...
Read More »Hypercluster – a flexible tool for parallelized unsupervised clustering optimization
Unsupervised clustering is a common and exceptionally useful tool for large biological datasets. However, clustering requires upfront algorithm and hyperparameter selection, which can introduce bias into the final clustering labels. It is therefore...
Read More »Modeling RNA dynamics without metabolic labeling
Transcript’s abundance is typically considered a proxy of the corresponding genes transcriptional activity. Yet, a poorly transcribed gene could see many of its RNA molecules accumulate just because they are highly stable. Conversely, if a gene is very actively transcribed ...
Read More »A sparse Bayesian factor model for the construction of gene co-expression networks from single-cell RNA sequencing count data
Gene co-expression networks (GCNs) are powerful tools that enable biologists to examine associations between genes during different biological processes. With the advancement of new technologies, such as...
Read More »tGPLVM – a robust nonlinear low-dimensional manifold for single cell RNA-seq data
Modern developments in single-cell sequencing technologies enable broad insights into cellular state. Single-cell RNA sequencing (scRNA-seq) can be used to explore cell types, states, and developmental trajectories to broaden our understanding of cellular...
Read More »Machine learning and single-cell RNA sequencing identify the neurons required to restore mobility
Partial mobility can be restored in rodents with impaired spinal cords. Using AI, scientists can now determine the cellular mechanisms responsible – a technique that may be applicable to many biomedical...
Read More »LinTIMaT – a novel statistical learning approach for tracing cell lineage
Our body consists of numerous cells that are functioning continuously to keep us alive. Even though the cells in different organs contain almost similar DNA, they can perform different functions. While the cells perform different functions, their...
Read More »Souporcell – robust clustering of single-cell RNA-seq data by genotype without reference genotypes
Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing...
Read More »PRIME – a probabilistic imputation method to reduce dropout effects in single cell RNA sequencing
Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable...
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