Linnorm: improved statistical analysis for single cell RNA-seq expression data

Yip, Shun H.; Wang, Panwen; Kocher, Jean Pierre A.; Sham, Pak C.; Wang, Junwen

doi:10.1093/nar/gkx828

Cited by 105 publications

(73 citation statements)

References 33 publications

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“…Single-cell RNA sequencing is becoming popular in recent years to better understand the stochastic process and gene regulations in a granular resolution (13,15,16,39). The commonly used gene differential expression analysis in single-cell RNA sequencing can be classified into two categories, with one category modeling excess zeros (SCDE, MAST, scDD, DEsingle, and SigEMD) (40)(41)(42)(43)(44) and the other category without modeling the excess zeros in the single-cell RNA sequencing data (DESeq2, SINCERA, D 3 E, EMDomics, Monocle2, Linnorm, and Discriminative Learning) (12,27,(45)(46)(47)(48)(49)(50). DESeq2 is a popular method used for bulk RNA sequencing data analysis, which is also often used for analyzing single-cell RNA sequencing data for testing of differential expression between groups.…”

Section: Statistical Methods For Single-cell Rna Sequencing Differentmentioning

confidence: 99%

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Statistical Methods for RNA Sequencing Data Analysis

2019

Computational Biology

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This chapter will review the statistical methods used in RNA sequencing data analysis, including bulk RNA sequencing and single-cell RNA sequencing. RNA sequencing data analysis has been widely used in biomedical and biological research to identify genes associated with certain conditions or diseases. Many statistical methods have been proposed to analyze bulk and single-cell RNA sequencing data. Several studies have compared the performance of different statistical methods for RNA sequencing data analysis through simulation studies and real data evaluations. This chapter will summarize the statistical methods and the evaluation results for comparing different statistical analysis methods used for RNA sequencing data analysis. It will cover the statistical models, model assumptions, and challenges encountered in the RNA sequencing data analysis. It is hoped that this chapter will help researchers learn more about the statistical perspective of the RNA sequencing data analysis and enable them to choose appropriate statistical analysis methods for their own RNA sequencing data analysis.

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Section: Statistical Methods For Single-cell Rna Sequencing Differentmentioning

confidence: 99%

“…Linnorm proposes a new normalization and transformation method for single-cell RNA sequencing data analysis (49). The normalization and transformation parameters are calculated based on stably expressed genes across different cells.…”

Section: Linnormmentioning

confidence: 99%

Statistical Methods for RNA Sequencing Data Analysis

2019

Computational Biology

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“…We also excluded any methods that continually failed to run (e.g. Linnorm 22 and Monocle 23 ). This resulted in the evaluation of 12 methods (see Table 1).…”

Section: Methodsmentioning

confidence: 99%

Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data

et al. 2018

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Background: The commercially available 10x Genomics protocol to generate droplet-based single-cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use. Methods: Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as three silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also robustness of a dozen methods. Results: We found that some methods, including Seurat and Cell Ranger, outperform other methods, although performance seems to be dependent on the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other. Conclusions: In light of this, we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.

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“…Additionally, principal component (PC) scores are also used as the input of other non-linear dimensionality reduction [67][68][69][70][71][72][73] and clustering methods [74][75][76][77] in order to preserve the global structure, avoid the "curse of dimensionality" [78][79][80][81], and save memory space. A wide variety of scRNA-seq data analysis tools actually include PCA as an internal function or utilize PC scores as input for downstream analyses [22,[82][83][84][85][86][87][88][89].…”

Section: Review Of Pca Algorithms and Implementationsmentioning

confidence: 99%

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

Tsuyuzaki

Sato

et al. 2020

Genome Biol

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Background: Principal component analysis (PCA) is an essential method for analyzing single-cell RNA-seq (scRNA-seq) datasets, but for large-scale scRNA-seq datasets, computation time is long and consumes large amounts of memory. Results: In this work, we review the existing fast and memory-efficient PCA algorithms and implementations and evaluate their practical application to large-scale scRNA-seq datasets. Our benchmark shows that some PCA algorithms based on Krylov subspace and randomized singular value decomposition are fast, memory-efficient, and more accurate than the other algorithms. Conclusion: We develop a guideline to select an appropriate PCA implementation based on the differences in the computational environment of users and developers.

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Linnorm: improved statistical analysis for single cell RNA-seq expression data

Cited by 105 publications

References 33 publications

Statistical Methods for RNA Sequencing Data Analysis

Statistical Methods for RNA Sequencing Data Analysis

Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

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