Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters

Domanskyi, Sergii; Szedlak, Anthony; Hawkins, Nathaniel T.; Wang, Jiayin; Paternostro, Giovanni; Piermarocchi, Carlo

doi:10.1101/539833

Cited by 21 publications

(23 citation statements)

References 26 publications

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“…The widely adopted approaches for cell type identification are mainly based on unsupervised clustering to assign cells into distinct groups, and the cell type of each group is further identified using canonical markers or differential genes by manual annotation [56,57,58]. Supervised learning algorithms have also been proposed to improve the efficiency and accuracy of cell type identification [24,47,59,60,61,62,63]. To better understand the effect of scRNA-seq data processing pipelines on cell type identification, we compared the performance of the unsupervised clustering-based method and SuperCT [47], a supervised learning-based method, using the expression matrices generated by different pipelines.…”

Section: Clustering and Cell Type Identificationmentioning

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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Section: Clustering and Cell Type Identificationmentioning

confidence: 99%

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

Gao

Ling

Tang

et al. 2020

Preprint

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“…Single-cell RNA sequencing (scRNA-seq) has revolutionized traditional transcriptomic studies by extracting the transcriptome information at the resolution of a single cell; therefore, this approach is able to detect heterogeneous information that cannot be obtained by sequencing mixed cells and to reveal the genetic structure and gene expression status of a single cell [ 1 – 7 ]. Moreover, it helps to identify new cell types [ 8 , 9 ], provides new research ideas and opens up new directions for in-depth research on the occurrence, development mechanisms, diagnosis and treatment of complex diseases [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data

2020

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Background Advances in single-cell RNA-seq technology have led to great opportunities for the quantitative characterization of cell types, and many clustering algorithms have been developed based on single-cell gene expression. However, we found that different data preprocessing methods show quite different effects on clustering algorithms. Moreover, there is no specific preprocessing method that is applicable to all clustering algorithms, and even for the same clustering algorithm, the best preprocessing method depends on the input data. Results We designed a graph-based algorithm, SC3-e, specifically for discriminating the best data preprocessing method for SC3, which is currently the most widely used clustering algorithm for single cell clustering. When tested on eight frequently used single-cell RNA-seq data sets, SC3-e always accurately selects the best data preprocessing method for SC3 and therefore greatly enhances the clustering performance of SC3. Conclusion The SC3-e algorithm is practically powerful for discriminating the best data preprocessing method, and therefore largely enhances the performance of cell-type clustering of SC3. It is expected to play a crucial role in the related studies of single-cell clustering, such as the studies of human complex diseases and discoveries of new cell types.

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“…This manuscript focuses on the question of unsupervised clustering. Recent work in supervised clustering [25][26][27][28] has proposed labeling cells in a new dataset by relying on information contained in other datasets or even cell atlases. In practice, these methods define marker genes for known cell types and build classifiers to assign new cells to these cell types.…”

Section: Discussionmentioning

confidence: 99%

Improving replicability in single-cell RNA-Seq cell type discovery with Dune

Bézieux

Street

Fischer

et al. 2020

Preprint

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Single-cell transcriptome sequencing (scRNA-Seq) has allowed many new types of investigations at unprecedented and unique levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into potentially novel cell types. Many approaches build on the existing clustering literature to develop tools specific to single-cell applications. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters identified. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist to select these tuning parameters, most of them are quite ad hoc. In general, there is little assurance that any given set of parameters will represent an optimal choice in the ever-present trade-off between cluster resolution and replicability. For instance, it may be the case that another set of parameters will result in more clusters that are also more replicable, or in fewer clusters that are also less replicable.Here, we propose a new method called Dune for optimizing the trade-off between the resolution of the clusters and their replicability across datasets. Our method takes as input a set of clustering results on a single dataset, derived from any set of clustering algorithms and associated tuning parameters, and iteratively merges clusters within partitions in order to maximize their concordance between partitions. As demonstrated on a variety of scRNA-Seq datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters. It provides an objective approach for identifying replicable consensus clusters most likely to represent common biological features across multiple datasets.

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Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters

Cited by 21 publications

References 26 publications

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

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

Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data

Improving replicability in single-cell RNA-Seq cell type discovery with Dune

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