Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking

Gagnon, Jake; Pi, Lira; Ryals, Matthew; Wan, Qingwen; Hu, Wenxing; Ouyang, Zhengyu; Zhang, Baohong; Li, Kejie

doi:10.3390/life12060850

Cited by 11 publications

(8 citation statements)

References 47 publications

(91 reference statements)

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“…If needed, scRNASequest also offers pseudo-bulk-based differential expression (DE) analysis using DESeq2 [ 61 ], limma [ 62 ], and edgeR [ 63 ]. However, as the benchmarking study reported previously, NEBULA outperforms other methods in general [ 64 ]. Thus, the pipeline uses NEBULA to perform DE analysis by default, and the parameters for running this step are defined in the configuration file (Table 2 ).…”

Section: Methodsmentioning

confidence: 82%

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing

Sun

Ouyang³

et al. 2023

BMC Genomics

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Background Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights. Results Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files. Conclusions We developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest. We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/. Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.

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Section: Methodsmentioning

confidence: 82%

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing

Sun

Ouyang³

et al. 2023

BMC Genomics

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“…DEseq2 and edgeR which are based on negative binomial distribution have high precision, but relatively low sensitivity, so that some TPs are easily missed. NEBULA and glmmTMB were benchmarked to have outstanding performance for differential gene expression on simulated and real multi-subject scRNA-seq data of the 10x Genomics platform [ 64 ]. However, another reference on benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data shows the pseudo-bulk methods such as DEseq2 and edgeR performed generally best [ 65 ].…”

Section: Discussionmentioning

confidence: 99%

“…To benchmark DEGman, we considered eight popular tools, DEsingle [ 7 ], DEseq2 [ 21 ], SigEMD [ 8 ], scDD [ 3 ], edgeR [ 10 ], Monocle2 [ 5 ], glmmTMB [ 12 ] and NEBULA [ 13 ] which have been shown to have superior performance in multiple method comparisons [ 20 , 35 , 61 , 64 , 65 ]. We also compared DEGman with a new DEG finding method, singleCellHaystack [ 14 ], which uses the coordinates of all cells in a low-dimensional space produced by a dimensionality reduction methods, such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), or uniform manifold approximation and projection(UMAP) [ 66 , 67 ].…”

Section: Methodsmentioning

confidence: 99%

Detecting Fear-Memory-Related Genes from Neuronal scRNA-seq Data by Diverse Distributions and Bhattacharyya Distance

Zhang

Xie²,

Cui³

et al. 2022

Biomolecules

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The detection of differentially expressed genes (DEGs) is one of most important computational challenges in the analysis of single-cell RNA sequencing (scRNA-seq) data. However, due to the high heterogeneity and dropout noise inherent in scRNAseq data, challenges in detecting DEGs exist when using a single distribution of gene expression levels, leaving much room to improve the precision and robustness of current DEG detection methods. Here, we propose the use of a new method, DEGman, which utilizes several possible diverse distributions in combination with Bhattacharyya distance. DEGman can automatically select the best-fitting distributions of gene expression levels, and then detect DEGs by permutation testing of Bhattacharyya distances of the selected distributions from two cell groups. Compared with several popular DEG analysis tools on both large-scale simulation data and real scRNA-seq data, DEGman shows an overall improvement in the balance of sensitivity and precision. We applied DEGman to scRNA-seq data of TRAP; Ai14 mouse neurons to detect fear-memory-related genes that are significantly differentially expressed in neurons with and without fear memory. DEGman detected well-known fear-memory-related genes and many novel candidates. Interestingly, we found 25 DEGs in common in five neuron clusters that are functionally enriched for synaptic vesicles, indicating that the coupled dynamics of synaptic vesicles across in neurons plays a critical role in remote memory formation. The proposed method leverages the advantage of the use of diverse distributions in DEG analysis, exhibiting better performance in analyzing composite scRNA-seq datasets in real applications.

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“…Accordingly, numerous methods for differential expression testing have been proposed and benchmarked (Soneson and Robinson 2018;T. Wang et al 2019;Crowell et al 2020;Squair et al 2021;Gagnon et al 2022). Methods originally developed for bulk RNA-seq, such as limma-voom (Ritchie et al 2015), edgeR (Robinson, McCarthy, and Smyth 2010;McCarthy, Chen, and Smyth 2012;Y.…”

Section: Introductionmentioning

confidence: 99%

“…DE analysis, at both the bulk and single-cell level, has yielded valuable insights into the mechanisms of numerous diseases (X.-S. Wang et al 2006; Adams et al 2020; Elmentaite et al 2020) and enabled the identification of drug targets (Montoro et al 2018; van den Hurk et al 2022). Accordingly, numerous methods for differential expression testing have been proposed and benchmarked (Soneson and Robinson 2018; T. Wang et al 2019; Crowell et al 2020; Squair et al 2021; Gagnon et al 2022). Methods originally developed for bulk RNA-seq, such as limma-voom (Ritchie et al 2015), edgeR (Robinson, McCarthy, and Smyth 2010; McCarthy, Chen, and Smyth 2012; Y.…”

Section: Introductionmentioning

confidence: 99%

Sensitive cluster-free differential expression testing.

Missarova

Rosen

Dann

et al. 2023

Preprint

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Comparing molecular features, including the identification of genes with differential expression (DE) between conditions, is a powerful approach for characterising disease-specific phenotypes. When testing for DE in single-cell RNA sequencing data, current pipelines first assign cells into discrete clusters (or cell types), followed by testing for differences within each cluster. Consequently, the sensitivity and specificity of DE testing are limited and ultimately dictated by the granularity of the cell type annotation, with discrete clustering being especially suboptimal for continuous trajectories. To overcome these limitations, we present miloDE - a cluster-free framework for differential expression testing. We build on the Milo approach, introduced for differential cell abundance testing, which leverages the graph representation of single-cell data to assign relatively homogenous, neighbouring cells into overlapping neighbourhoods. We address key differences between differential abundance and expression testing at the level of neighbourhood assignment, statistical testing, and multiple testing correction. To illustrate the performance of miloDE we use both simulations and real data, in the latter case identifying a transient haemogenic endothelia-like state in chimeric mouse embryos lacking Tal1 as well as uncovering distinct transcriptional programs that characterise changes in macrophages in patients with Idiopathic Pulmonary Fibrosis. miloDE is available as an open-source R package at https://github.com/MarioniLab/miloDE.

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Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking

Cited by 11 publications

References 47 publications

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing

Detecting Fear-Memory-Related Genes from Neuronal scRNA-seq Data by Diverse Distributions and Bhattacharyya Distance

Sensitive cluster-free differential expression testing.

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