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

Song, Dongyuan; Li, Jingyi Jessica

doi:10.1186/s13059-021-02341-y

Cited by 49 publications

(53 citation statements)

References 63 publications

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“…However, there is a growing need to generalize our framework to identify features across more than two conditions. For example, temporal analysis of scRNA-seq data aims to identify genes whose expression levels change along cell pseudotime [ 31 ]. To tailor Clipper for such analysis, we could define a new contrast score that differentiates the genes with stationary expression (uninteresting features) from the other genes with varying expression (interesting features).…”

Section: Discussionmentioning

confidence: 99%

“…This issue is evidenced by serious concerns about the widespread miscalculation and misuse of p -values in the scientific community [ 30 ]. As a result, bioinformatics tools using questionable p -values either cannot reliably control the FDR to a target level [ 23 ] or lack power to make discoveries [ 31 ]; see the “ Results ” section. Therefore, p -value-free control of FDR is desirable, as it would make data analysis more transparent and thus improve the reproducibility of scientific research.…”

Section: Introductionmentioning

confidence: 99%

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Clipper: p-value-free FDR control on high-throughput data from two conditions

Chen

Song

et al. 2021

Genome Biol

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High-throughput biological data analysis commonly involves identifying features such as genes, genomic regions, and proteins, whose values differ between two conditions, from numerous features measured simultaneously. The most widely used criterion to ensure the analysis reliability is the false discovery rate (FDR), which is primarily controlled based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions. Clipper is a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper outperforms existing methods for a broad range of applications in high-throughput data analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clipper: p-value-free FDR control on high-throughput data from two conditions

Chen

Song

et al. 2021

Genome Biol

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“…If the mean expression level of a gene can be changed along pseudotime, the gene is indicated as differentially expressed which can be crucial for the underlying cellular process that generated the pseudotime. 47,48 Trajectory inference can thus illuminate the underlying biological processes by identifying key genes that play important roles in the development of particular lineages and genes differentially expressed between different lineages. Recently, Phansalkar et al applied the trajectory inference methods in developing human coronary arteries to illustrate coronary blood vessels from distinct origins can converge to equivalent states.…”

Section: Trajectory Inference Discovers Transition States In Heart Developmentmentioning

confidence: 99%

Single-Cell RNA Sequencing (scRNA-seq) in Cardiac Tissue: Applications and Limitations

Wang¹,

Gu²,

Liu³

et al. 2021

VHRM

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Cardiovascular diseases (CVDs) are a group of disorders of the blood vessels and heart, which are considered as the leading causes of death worldwide. The pathology of CVDs could be related to the functional abnormalities of multiple cell types in the heart. Single-cell RNA sequencing (scRNA-seq) technology is a powerful method for characterizing individual cells and elucidating the molecular mechanisms by providing a high resolution of transcriptomic changes at the single-cell level. Specifically, scRNA-seq has provided novel insights into CVDs by identifying rare cardiac cell types, inferring the trajectory tree, estimating RNA velocity, elucidating the cell-cell communication, and comparing healthy and pathological heart samples. In this review, we summarize the different scRNAseq platforms and published single-cell datasets in the cardiovascular field, and describe the utilities and limitations of this technology. Lastly, we discuss the future perspective of the application of scRNA-seq technology into cardiovascular research.

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“…The major goal of the single-cell clustering is making a consistent group of cells because current single-cell sequencing protocols cannot provide the auxiliary information such as cell types even though it can simultaneously detect the relative gene expressions for a larger number of cells. Since a prior knowledge for the true cell type can play a pivotal role in a comprehensive analysis of the single-cell sequencing such as pseudo-temporal ordering [32][33][34] and gene regulatory networks [35][36][37], it is important to develop the accurate computational methods to predict the groups of cells with consistent labels.…”

Section: Performance Assessment Based On the True Cell-type Labelsmentioning

confidence: 99%

Accurate Single-Cell Clustering through Ensemble Similarity Learning

Jeong

Shin

Yeom

2021

Genes

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Single-cell sequencing provides novel means to interpret the transcriptomic profiles of individual cells. To obtain in-depth analysis of single-cell sequencing, it requires effective computational methods to accurately predict single-cell clusters because single-cell sequencing techniques only provide the transcriptomic profiles of each cell. Although an accurate estimation of the cell-to-cell similarity is an essential first step to derive reliable single-cell clustering results, it is challenging to obtain the accurate similarity measurement because it highly depends on a selection of genes for similarity evaluations and the optimal set of genes for the accurate similarity estimation is typically unknown. Moreover, due to technical limitations, single-cell sequencing includes a larger number of artificial zeros, and the technical noise makes it difficult to develop effective single-cell clustering algorithms. Here, we describe a novel single-cell clustering algorithm that can accurately predict single-cell clusters in large-scale single-cell sequencing by effectively reducing the zero-inflated noise and accurately estimating the cell-to-cell similarities. First, we construct an ensemble similarity network based on different similarity estimates, and reduce the artificial noise using a random walk with restart framework. Finally, starting from a larger number small size but highly consistent clusters, we iteratively merge a pair of clusters with the maximum similarities until it reaches the predicted number of clusters. Extensive performance evaluation shows that the proposed single-cell clustering algorithm can yield the accurate single-cell clustering results and it can help deciphering the key messages underlying complex biological mechanisms.

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

Cited by 49 publications

References 63 publications

Clipper: p-value-free FDR control on high-throughput data from two conditions

Clipper: p-value-free FDR control on high-throughput data from two conditions

Single-Cell RNA Sequencing (scRNA-seq) in Cardiac Tissue: Applications and Limitations

Accurate Single-Cell Clustering through Ensemble Similarity Learning

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