Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

Zappia, Luke; Phipson, Belinda; Oshlack, Alicia

doi:10.1101/206573

Cited by 81 publications

(101 citation statements)

References 54 publications

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“…Because of the importance of DR in scRNAseq analysis, many DR methods have been developed and are routinely used in scRNAseq software tools that include, but not limited to, cell clustering tools [12,13] and lineage reconstruction tools [14]. Indeed, most commonly used scRNAseq clustering methods rely on DR as the first analytic step [15]. For example, Seurat applies clustering algorithms directly on a low dimensional space inferred from principal component analysis (PCA) [16].…”

Section: Introductionmentioning

confidence: 99%

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

Sun

Zhu

et al. 2019

Preprint

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Background: Dimensionality reduction (DR) is an indispensable analytic component for many areas of single cell RNA sequencing (scRNAseq) data analysis. Proper DR can allow for effective noise removal and facilitate many downstream analyses that include cell clustering and lineage reconstruction. Unfortunately, despite the critical importance of DR in scRNAseq analysis and the vast number of DR methods developed for scRNAseq studies, however, few comprehensive comparison studies have been performed to evaluate the effectiveness of different DR methods in scRNAseq.Results: Here, we aim to fill this critical knowledge gap by providing a comparative evaluation of a variety of commonly used DR methods for scRNAseq studies. Specifically, we compared 18 different DR methods on 30 publicly available scRNAseq data sets that cover a range of sequencing techniques and sample sizes. We evaluated the performance of different DR methods for neighborhood preserving in terms of their ability to recover features of the original expression matrix, and for cell clustering and lineage reconstruction in terms of their accuracy and robustness. We also evaluated the computational scalability of different DR methods by recording their computational cost. Conclusions:Based on the comprehensive evaluation results, we provide important guidelines for choosing DR methods for scRNAseq data analysis. We also provide all analysis scripts used in the present study at www.xzlab.org/reproduce.html. Together, we hope that our results will serve as an important practical reference for practitioners to choose DR methods in the field of scRNAseq analysis. our results can serve as an important guideline for practitioners to choose DR methods in the field of scRNAseq analysis.

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Section: Introductionmentioning

confidence: 99%

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

Sun

Zhu

et al. 2019

Preprint

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“…A standard scRNA-seq analysis involves several tasks that can be performed by various bioinformatics or biostatistics techniques. Zappia et al [55] categorized these tasks into four broad phases of analysis: data acquisition, data cleaning, cell assignment, and gene identification. The first two phases are generally referred to as the pre-processing steps, and the last two phases are referred to as the statistical analysis steps.…”

Section: Pre-processing Stepsmentioning

confidence: 99%

Subpopulation identification for single-cell RNA-sequencing data using functional data analysis

Ahn

Fujiwara

2019

Preprint

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Motivation:In single-cell RNA-sequencing (scRNA-seq) analysis, a number of statistical tools in multivariate data analysis (MDA) have been developed to help analyze the gene expression data. This MDA approach is typically focused on examining discrete genomic units of genes that ignores the dependency between the data components. In this paper, we propose a functional data analysis (FDA) approach on scRNA-seq data whereby we consider each cell as a single function that does not allow permutation of the data components. To avoid a large number of dropouts (zero or zeroclosed values) and reduce the high dimensionality of the data, we first perform a principal component analysis (PCA) and assign PCs to be the amplitude of the function. For the phase components, we propose two criteria: we use the PCs directly from PCA, and we sort the PCs by the genetic spatial information. For the latter, we embed the spatial information of genes by aligning the genomic gene locations to be the phase of the function. These two approaches allow us to apply FDA clustering methods to scRNA-seq analysis. Results: To demonstrate the robustness of our method, we apply several existing FDA clustering algorithms to the gene expression data to improve the accuracy of the classification of the cell types against the conventional clustering methods in MDA. As a result, the FDA clustering algorithms achieve superior accuracy on simulated data as well as real data such as human and mouse scRNAseq data.

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“…Massively parallel single-cell RNA sequencing (scRNA-Seq) has been increasingly used over the past few years as a powerful alternative to bulk RNA-Seq [1][2][3] . While the first scRNA-Seq dataset in 2009 consisted of only eight cells 4 , the number of cells in a typical experiment today is approaching tens or even hundreds of thousands 5,6 .…”

Section: Introductionmentioning

confidence: 99%

Optimal design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

Mandric

Schwarz

Majumdar

et al. 2019

Preprint

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Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to simultaneously measure cellular composition and state which is impossible with bulk profiling approaches. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage percell sequencing of more samples rather than high-coverage sequencing of fewer samples. We also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power.

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Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

Cited by 81 publications

References 54 publications

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

Subpopulation identification for single-cell RNA-sequencing data using functional data analysis

Optimal design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

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