We will discuss about \u201cA statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples
\n\u201d, Nature communications<\/i>\u00a014.1 (2023): 7286.<\/p>\n
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Abstract<\/p>\n Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here, we introduce\u00a0Lamian<\/span>, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis.\u00a0Lamian<\/span>\u00a0can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions while adjusting for batch effects, and to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability,\u00a0Lamian<\/span>\u00a0draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both real scRNA-seq\u00a0and simulation data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of\u00a0Lamian<\/span>\u00a0in decoding cellular gene expression programs in continuous biological processes.<\/p>\n<\/div>\n<\/div>\n<\/section>\n <\/p>\n","protected":false},"excerpt":{"rendered":" We will discuss about \u201cA statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples \u201d, Nature communications\u00a014.1 (2023): 7286. Abstract Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data … <\/p>\n