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PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data.

ABSTRACT: To investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed p-values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p-values. Comprehensive simulations and real-data applications verify that PseudotimeDE outperforms existing methods in false discovery rate control and power.

SUBMITTER: Song D

PROVIDER: S-EPMC8082818 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.

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Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference.

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Correction: Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference.

Project description:[This corrects the article DOI: 10.1371/journal.pcbi.1005212.].

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Statistical inference of differential RNA-editing sites from RNA-sequencing data by hierarchical modeling.

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Project description:Pseudotime analysis from scRNA-seq data enables to characterize the continuous progression of various biological processes, such as the cell cycle. Cell cycle plays an important role in cell fate decisions and differentiation and is often regarded as a confounder in scRNA-seq data analysis when analyzing the role of other factors. Therefore, accurate prediction of cell cycle pseudotime and identification of cell cycle stages are important steps for characterizing the development-related biological processes. Here, we develop CCPE, a novel cell cycle pseudotime estimation method to characterize cell cycle timing and identify cell cycle phases from scRNA-seq data. CCPE uses a discriminative helix to characterize the circular process of the cell cycle and estimates each cell's pseudotime along the cell cycle. We evaluated the performance of CCPE based on a variety of simulated and real scRNA-seq datasets. Our results indicate that CCPE is an effective method for cell cycle estimation and competitive in various applications compared with other existing methods. CCPE successfully identified cell cycle marker genes and is robust to dropout events in scRNA-seq data. Accurate prediction of the cell cycle using CCPE can also effectively facilitate the removal of cell cycle effects across cell types or conditions.

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