Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

Liu, Fenglin; Zhang, Yuanyuan; Zhang, Lei; Li, Ziyi; Qiao, Fang; Gao, Ranran

doi:10.1186/s13059-019-1863-4

Cited by 100 publications

(94 citation statements)

References 53 publications

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“…LACE can also return the posterior probability of the output model with respect to each data point, therefore allowing to quantitatively assess the statistical confidence of the inference. Importantly, the robustness of our approach allows its application to the highly-available scRNA-seq data -which are usually employed to characterize the gene expression patterns of single-cells in a variety of experimental settings [23] -, by calling somatic variants in transcribed regions with standard pipelines [24] and by selecting a set of putative drivers. This allows to overcome the limitation of relying on longitudinal single-cell whole genome/exome sequencing experiments, which are currently rarer and significantly more expensive.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal cancer evolution from single cells

Ramazzotti

Angaroni

Maspero

et al. 2020

Preprint

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The rise of longitudinal single-cell sequencing experiments on patient-derived cell cultures, xenografts and organoids is opening new opportunities to track cancer evolution in single tumors and to investigate intra-tumor heterogeneity. This is particularly relevant when assessing the efficacy of therapies over time on the clonal composition of a tumor and in the identification of resistant subclones. We here introduce LACE (Longitudinal Analysis of Cancer Evolution), the first algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. Our approach solves a Boolean matrix factorization problem with phylogenetic constraints, by maximizing a weighted likelihood function computed on multiple time points, and we show with simulations that it outperforms state-of-the-art methods for both bulk and single-cell sequencing data. Remarkably, as the results are robust with respect to high levels of data-specific errors, LACE can be employed to process single-cell mutational profiles as generated by calling variants from the increasingly available scRNA-seq data, thus obviating the need of relying on rarer and more expensive genome sequencing experiments. This also allows to investigate the relation between genomic clonal evolution and phenotype at the single-cell level. To illustrate the capabilities of LACE, we show its application to a longitudinal scRNA-seq dataset of patient-derived xenografts of BRAF V600E/K mutant melanomas, in which we characterize the impact of concurrent BRAF/MEK-inhibition on clonal evolution, also by showing that distinct genetic clones reveal different sensitivity to the therapy.

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Section: Introductionmentioning

confidence: 99%

Longitudinal cancer evolution from single cells

Ramazzotti

Angaroni

Maspero

et al. 2020

Preprint

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“…The progression of various high-throughput scRNA-seq data processing pipelines has empowered end-users, including biologists, chemists and developers of novel scRNA-seq platforms, to process their data conveniently and challenged them to select a proper pipeline for their data in hand. Recently, several studies have been published to evaluate scRNA-seq data analysis strategies [18,19,20,21,22,23,24,25,26]. These studies mainly focused on downstream analysis algorithms including normalization, data imputation, clustering, and differential expression.…”

Section: Introductionmentioning

confidence: 99%

Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

Gao

Ling

Tang

et al. 2020

Preprint

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With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNAseq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biased if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performances in terms of running time, computational resource consumption, and data processing consistency using nine public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performances on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.Since the emergence of the first single-cell RNA sequencing (scRNA-seq) platform [1], many research achievements have been made at the cellular and subcellular levels with unprecedented resolutions with the aid of this technology. Recent advances in microfluidics and next generation sequencing (NGS) have further increased the efficiency and throughput of scRNA-seq, enabling more cells to be identified and the expression information of more genes for each cell to be quantified simultaneously [2,3,4]. From microcapillary pipettes in 2012 [5,6] and nanoliter droplet-based microfluidic chips in 2015 [7,8] to the latest liquid barcoding combined with split-pool methods [9], the number of cells analysed in parallel has increased from several to tens of thousands [10], bringing new challenges for the processing of a large amount of barcoded NGS data for efficient and accurate quantification of the transcript information of single cells.To meet the needs of high-throughput scRNA-seq data processing, several pipelines that integrate multiple functions have been developed. As one of the first high-throughput scRNA-seq platforms, Drop-seq [7] was introduced in 2015. Moreover, Drop-seq-tools in combination with Picard tools were introduced and provided a user-friendly Application Programming Interface (API) to process the data from Drop-seq. Later in 2017, three additional pipelines were published including Cell Ranger [11], umis [12] and UMItools [13]. Cell Ranger [11] was developed along with the widely adopted 10X Genomics platform which can process multiple biological samples separated by sample barcodes in paral...

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“…Efforts towards minimizing bias in transcriptomic datasets are indispensable not only for enhancing the reproducibility of findings, but also to avoid biological misinterpretation of datasets within studies. A way to obviate such biases is the use of single nucleus sequencing (snSeq), where whole tissue is homogenized and cells are lysed to release single nuclei that can be processed for sequencing (Bakken et al, 2018; Liu et al, 2019). This method is at present prevalently used for frozen human brain tissue samples (Habib et al, 2017), and represents a good alternative for single cell characterization of brain cells with less depth as compared to ordinary scSeq (Bakken et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Enzymatic dissociation induces transcriptional and proteotype bias in brain cell populations

Mattei

Ivanov

Oostrum

et al. 2020

Preprint

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2Different cell isolation techniques exist for transcriptomic and proteotype profiling of brain cells. 3Here, we provide a systematic investigation of the influence of different cell isolation protocols on 4 transcriptional and proteotype profiles in mouse brain tissue by taking into account single-cell 5 transcriptomics of brain cells, proteotypes of microglia and astrocytes, and flow cytometric analysis 6 of microglia. We show that standard enzymatic digestion of brain tissue at 37°C induces profound 7 and consistent alterations in the transcriptome and proteotype of neuronal and glial cells, as compared 8 to an optimized mechanical dissociation protocol at 4°C. These findings emphasize the risk of 9 introducing technical biases and biological artefacts when implementing enzymatic digestion-based 10 isolation methods for brain cell analyses. 11

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Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

Cited by 100 publications

References 53 publications

Longitudinal cancer evolution from single cells

Longitudinal cancer evolution from single cells

Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

Enzymatic dissociation induces transcriptional and proteotype bias in brain cell populations

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