The use of low quality RNA samples in whole-genome gene expression profiling remains controversial. It is unclear if transcript degradation in low quality RNA samples occurs uniformly, in which case the effects of degradation can be corrected via data normalization, ...
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TWO-SIGMA-G – a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell–cell correlation
Researchers at Harvard T.H. Chan School of Public Health and the University of North Carolina at Cha...
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Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose–response study designs
The application of single-cell RNA sequencing (scRNAseq) for the evaluation of chemicals, drugs, and...
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Representation learning of RNA velocity reveals robust cell transitions
RNA velocity is a promising technique for quantifying cellular transitions from single-cell transcri...
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Isoform variability is an important source of latent information in RNA-seq data that can be used to improve clinical prediction models.
Most predictive models based on gene expression data do not leverage information related to gene spl...
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Powerful eQTL mapping through low coverage RNA sequencing
Mapping genetic variants that regulate gene expression (eQTLs) in large-scale RNA sequencing (RNA-se...