2019
DOI: 10.1101/539833
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Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters

Abstract: Background: Single cell RNA sequencing (scRNA-seq) brings unprecedented opportunities for mapping the heterogeneity of complex cellular environments such as bone marrow, and provides insight into many cellular processes. Single cell RNA-seq, however, has a far larger fraction of missing data reported as zeros (dropouts) than traditional bulk RNA-seq. This makes difficult not only the clustering of cells, but also the assignment of the resulting clusters into predefined cell types based on known molecular signa… Show more

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Cited by 21 publications
(23 citation statements)
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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%
“…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%
“…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%
“…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%