A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies

Sun, Zhe; Chen, Li; Xin, Hongyi; Jiang, Yale; Huang, Qizhao; Cillo, Anthony R.; Tabib, Tracy; Kolls, Jay K.; Bruno, Tullia C.; Lafyatis, Robert; Vignali, Dario A.A.; Chen, Kong; Ding, Ying; Hu, Ming; Chen, Wei

doi:10.1038/s41467-019-09639-3

Cited by 73 publications

(65 citation statements)

References 31 publications

(31 reference statements)

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“…Furthermore, SAME-clustering results are stable when parameters are changed for individual methods (Supplementary Tables 4 and 5). Our method is flexible and can easily accommodate additional sets of clustering solutions, as new clustering methods continue to be proposed (36). Supplementary Figure 10 shows that adding one more set of cluster results may improve ensemble results.…”

Section: Discussionmentioning

confidence: 99%

SAME-clustering: Single-cell Aggregated Clustering via Mixture Model Ensemble

Huh

Yang

Jiang

et al. 2019

Preprint

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Clustering is an essential step in the analysis of single cell RNA-seq (scRNA-seq) data to shed light on tissue complexity including the number of cell types and transcriptomic signatures of each cell type. Due to its importance, novel methods have been developed recently for this purpose. However, different approaches generate varying estimates regarding the number of clusters and the single-cell level cluster assignments. This type of unsupervised clustering is challenging and it is often times hard to gauge which method to use because none of the existing methods outperform others across all scenarios. We present SAME-clustering, a mixture model-based approach that takes clustering solutions from multiple methods and selects a maximally diverse subset to produce an improved ensemble solution. We tested SAMEclustering across 15 scRNA-seq datasets generated by different platforms, with number of clusters varying from 3 to 15, and number of single cells from 49 to 32,695. Results show that our SAME-clustering ensemble method yields enhanced clustering, in terms of both cluster assignments and number of clusters.The mixture model ensemble clustering is not limited to clustering scRNA-seq data and may be useful to a wide range of clustering applications.

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Section: Discussionmentioning

confidence: 99%

SAME-clustering: Single-cell Aggregated Clustering via Mixture Model Ensemble

Huh

Yang

Jiang

et al. 2019

Preprint

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“…We tested CITE-sort with three in-house PBMC CITE-seq dataset as well as 5 public CITE-seq datasets. For the three in house datasets, cells were drawn from two healthy donor described in a previous study (Sun, et al, 2019). The cell assays were prepared using the 10x Genomics platform with Gel Bead Kit V2, and was subsequently sequenced on an Illumina HiSeq with a depth of 50K reads per cell.…”

Section: Real Datasetsmentioning

confidence: 99%

Artificial-Cell-Type Aware Cell Type Classification in CITE-seq

Lian

Xin

et al. 2020

Preprint

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Motivation: Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types and complicates the automation of cell surface phenotyping. Results: We propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITEsort is aware of and is robust to multiplet-induced artificial cell types. We benchmarked CITE-sort with real and simulated CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITEsort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real biological-cell-type droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell type annotation with domain knowledge in CITE-seq.

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“…A summary of the three datasets is provided in Table 2 Table 2: Summary of cell-hashing datasets. Cells in the PBMC-1 dataset are drawn from a healthy donor described in a previous study [38]. These cells are divided into four samples.…”

Section: Real Datasetsmentioning

confidence: 99%

Sample demultiplexing, multiplet detection, experiment planning and novel cell type verification in single cell sequencing

Xin

Yan

Jiang

et al. 2019

Preprint

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Identifying and removing multiplets from downstream analysis is essential to improve the scalability and reliability of single cell RNA sequencing (scRNA-seq). High multiplet rates create artificial cell types in the dataset. Sample barcoding, including the cell hashing technology and the MULTI-seq technology, enables analytical identification of a fraction of multiplets in a scRNA-seq dataset.We propose a Gaussian-mixture-model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes the sample-barcoding-detectable multiplets and estimates the percentage of sample-barcodingundetectable multiplets in the remaining dataset. GMM-Demux describes the droplet formation process with an augmented binomial probabilistic model, and uses the model to authenticate cell types discovered from a scRNA-seq dataset.We conducted two cell-hashing experiments, collected a public cell-hashing dataset, and generated a simulated cellhashing dataset. We compared the classification result of GMM-Demux against a state-of-the-art heuristic-based classifier. We show that GMM-Demux is more accurate, more stable, reduces the error rate by up to 69×, and is capable of reliably recognizing 9 multiplet-induced fake cell types and 8 real cell types in a PBMC scRNA-seq dataset.

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A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies

Cited by 73 publications

References 31 publications

SAME-clustering: Single-cell Aggregated Clustering via Mixture Model Ensemble

SAME-clustering: Single-cell Aggregated Clustering via Mixture Model Ensemble

Artificial-Cell-Type Aware Cell Type Classification in CITE-seq

Sample demultiplexing, multiplet detection, experiment planning and novel cell type verification in single cell sequencing

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