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README.md

Machine Learning and Statistical Methods for Clustering Single Cell RNA-sequencing Data

Comparison among several scRNA-Seq clustering algorithms under two datasets:

PBMC dataset

Dataset was downloaded from [1], containing 10 bead-enriched subpopulations of peripheral blood mononuclear cells (PBMC) from a fresh donor (Donor A). The 19,630 genes used are expressed in at least 3 cells.

The following methods were compared:

  • k-Means
  • BackSPIN [2]
  • cellTree [3]
  • CIDR [4]
  • DendroSplit [5]
  • ICGS [6]
  • Monocle [7]
  • pcaReduce [8]
  • SC3 [9]
  • SCRAT [10]
  • Seurat 1.0 [11]
  • SNN-Cliq [12]

Source code can be found at the PBMC folder, and it assumes you have each method installed the machine. Source code was implemented in MATLAB 2018a.

Dataset processed for each method can be download at: here

Breast cancer dataset

Dataset was downloaded from [13], containing 515 cells of 11 patients with breast cancer. We extracted the top 5,000 differentially expressed genes. Cells are in three groups: Immune, Stromal or Tumor. We used 5 patients that contain cells of the three groups, with 212 cells in total.

The following methods were compared:

  • Separated k-Means
  • Pooled k-Means
  • Separated cellTree [3]
  • Pooled cellTree [3]
  • Separated SC3 [9]
  • Pooled SC3 [9]
  • Separated Monocle [7]
  • Pooled Monocle [7]
  • Seurat 2.0 [14]
  • scVDMC [15]

Source code can be found at the BRCA folder, and it assumes you have each method installed the machine. Source code was implemented in MATLAB 2018a.

Dataset processed for each method can be download at: here

References

[1] Zheng, Grace XY, Jessica M. Terry, Phillip Belgrader, Paul Ryvkin, Zachary W. Bent, Ryan Wilson, Solongo B. Ziraldo et al. "Massively parallel digital transcriptional profiling of single cells." Nature communications 8 (2017): 14049.

[2] Zeisel, Amit, Ana B. Muñoz-Manchado, Simone Codeluppi, Peter Lönnerberg, Gioele La Manno, Anna Juréus, Sueli Marques et al. "Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq." Science 347, no. 6226 (2015): 1138-1142.

[3] Yotsukura, Sohiya, Seitaro Nomura, Hiroyuki Aburatani, and Koji Tsuda. "CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data." BMC bioinformatics 17, no. 1 (2016): 363.

[4] Lin, Peijie, Michael Troup, and Joshua WK Ho. "CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data." Genome biology 18, no. 1 (2017): 59.

[5] Zhang, Jesse M., Jue Fan, H. Christina Fan, David Rosenfeld, and N. Tse David. "An interpretable framework for clustering single-cell RNA-Seq datasets." BMC bioinformatics 19, no. 1 (2018): 93.

[6] Olsson, Andre, Meenakshi Venkatasubramanian, Viren K. Chaudhri, Bruce J. Aronow, Nathan Salomonis, Harinder Singh, and H. Leighton Grimes. "Single-cell analysis of mixed-lineage states leading to a binary cell fate choice." Nature 537, no. 7622 (2016): 698.

[7] Qiu, Xiaojie, Qi Mao, Ying Tang, Li Wang, Raghav Chawla, Hannah A. Pliner, and Cole Trapnell. "Reversed graph embedding resolves complex single-cell trajectories." Nature methods 14, no. 10 (2017): 979.

[8] Yau, Christopher. "pcaReduce: hierarchical clustering of single cell transcriptional profiles." BMC bioinformatics 17, no. 1 (2016): 140.

[9] Kiselev, Vladimir Yu, Kristina Kirschner, Michael T. Schaub, Tallulah Andrews, Andrew Yiu, Tamir Chandra, Kedar N. Natarajan et al. "SC3: consensus clustering of single-cell RNA-seq data." Nature methods 14, no. 5 (2017): 483.

[10] Camp, J. Gray, Keisuke Sekine, Tobias Gerber, Henry Loeffler-Wirth, Hans Binder, Malgorzata Gac, Sabina Kanton et al. "Multilineage communication regulates human liver bud development from pluripotency." Nature 546, no. 7659 (2017): 533.

[11] Satija, Rahul, Jeffrey A. Farrell, David Gennert, Alexander F. Schier, and Aviv Regev. "Spatial reconstruction of single-cell gene expression data." Nature biotechnology 33, no. 5 (2015): 495.

[12] Xu, Chen, and Zhengchang Su. "Identification of cell types from single-cell transcriptomes using a novel clustering method." Bioinformatics 31, no. 12 (2015): 1974-1980.

[13] Chung, Woosung, Hye Hyeon Eum, Hae-Ock Lee, Kyung-Min Lee, Han-Byoel Lee, Kyu-Tae Kim, Han Suk Ryu et al. "Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer." Nature communications 8 (2017): 15081.

[14] Butler, Andrew, Paul Hoffman, Peter Smibert, Efthymia Papalexi, and Rahul Satija. "Integrating single-cell transcriptomic data across different conditions, technologies, and species." Nature biotechnology 36, no. 5 (2018): 411.

[15] Zhang, Huanan, Catherine AA Lee, Zhuliu Li, John R. Garbe, Cindy R. Eide, Raphael Petegrosso, Rui Kuang, and Jakub Tolar. "A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa." PLoS computational biology 14, no. 4 (2018): e1006053.

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