<scp>SAREV</scp>: A review on statistical analytics of single‐cell RNA sequencing data

Ellis, Dorothy; Wu, Dongyuan; Datta, Susmita

doi:10.1002/wics.1558

Cited by 2 publications

(3 citation statements)

References 141 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Before applying the algorithm, we must perform quality control (QC) and normalization. These are vital steps for downstream analyses (Ellis et al, 2021). For QC, it is appropriate to perform standard QC, including filtering out low-quality cells, such as those with a high percentage of mitochondrial genes, low gene expression, or very high gene expression in the scRNA-seq modality.…”

Section: Quality Control and Normalizationmentioning

confidence: 99%

“…Among these deeper insights is improved cell-type clustering. Expression of omics data varies among cell types, and this cellular heterogeneity is not captured in bulk data (Ellis et al, 2021). Accurately clustering cells can, for example, enable insights into and analysis of cell-type-specific responses to disease.…”

mentioning

confidence: 99%

“…scATAC-seq identifies accessible regions (peaks) within the chromatin of a singlecell; the data consist of matrices of counts of nucleosome free region (NFR) fragments, where each column corresponds to a cell and each row corresponds to a given range of base pairs (Yan et al, 2020). Due to several challenges such as batch effects, technical noise, and sparsity, these data require extensive quality control, normalization, and batch effect correction before downstream analyses, including cell clustering and annotation, network analysis, and differential expression analysis, can proceed (Yan et al, 2020;Ellis et al, 2021).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Clustering single-cell multimodal omics data with jrSiCKLSNMF

2023

Self Cite

View full text Add to dashboard Cite

Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise.Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced “junior sickles NMF”) that extracts latent factors shared across omics modalities within the same set of single cells.Results: We compare our clustering algorithm to several existing methods on four sets of data simulated from third party software. We also apply our algorithm to a real set of cell line data.Discussion: We show overwhelmingly better clustering performance than several existing methods on the simulated data. On a real multimodal omics dataset, we also find our method to produce scientifically accurate clustering results.

show abstract

Section: Quality Control and Normalizationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Clustering single-cell multimodal omics data with jrSiCKLSNMF

2023

Self Cite

View full text Add to dashboard Cite

show abstract

An evaluation of statistical differential analysis methods in single-cell RNA-seq data

Zand

Dye

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: Single-cell RNA Sequencing is gaining popularity in recent years. Compared to bulk RNA-Seq, single-cell RNA Sequencing allows the gene expression being measured within individual cells instead of mean gene expression levels across all cells in the sample. Thus, cell-to-cell variation of gene expressions could be examined. Gene differential expression analysis remains the major purpose in most single-cell RNA sequencing experiments and many methods have been developed in recent years to conduct gene differential expression analysis for single-cell RNA sequencing data. Results:Through simulation studies and real data examples, we evaluated the performance of five open-source popular methods used for gene differential expression analysis in single-cell RNA sequencing data. The five methods included DEsingle (Zero-inflated negative binomial model), Linnorm (Empirical Bayes method on transformed count data using the limma package), monocle (An approximate Chi-Square likelihood ratio test), MAST (A generalized linear hurdle model), and DESeq2 (A generalized linear model with empirical Bayes approach and also commonly used for bulk RNA sequencing differential express analyses). We assessed the false discovery rate (FDR) control, sensitivity, specificity, accuracy, and area under the receiver operating characteristics (AUROC) curve for all five methods under different sample sizes, distribution assumptions, and proportions of zeros in the data. Conclusions: We found the MAST method performed the best among the five methods compared with the largest AUROC values across all tested sample sizes and different proportion of truly differential expressed genes, when the data followed negative binomial distributions. When the sample size increased to $100$ in each group, the MAST method showed the best performance with the highest AUROC regardless of the data distributions. If the excess zeros were first filtered out before the gene differential analyses, the DESingle, Linnorm, and DESeq2 performed relatively better than the MAST and the monocle methods with higher AUROC values.

show abstract

SAREV: A review on statistical analytics of single‐cell RNA sequencing data

Cited by 2 publications

References 141 publications

Clustering single-cell multimodal omics data with jrSiCKLSNMF

Clustering single-cell multimodal omics data with jrSiCKLSNMF

An evaluation of statistical differential analysis methods in single-cell RNA-seq data

Contact Info

Product

Resources

About