---
title: "Clustering and Embedding Methods for scRNA-Seq"
linkTitle: "scRNA-Seq Embedding"
description: >
type: docs
weight: 405
---

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## RNA-Seq Workflow  

1. Read quality assessment, filtering and trimming 
2. Map reads against reference genome 
3. Perform read counting for required ranges (_e.g._ exonic gene ranges)
4. Normalization of read counts
5. Identification of differentially expressed genes (DEGs)
6. Clustering of gene expression profiles 
7. Gene set enrichment analysis

## Challenge Project

### Clustering and Embedding Methods for scRNA-Seq 

+ Run the above workflow from start to finish (steps 1-7) on the full RNA-Seq data set from Howard et al. (2013).
+ Challenge project tasks
    + Group 1 and 2 compare the partition performance of at least 3 clustering and 3 embedding methods, respectively, for high-dimensional gene expression data using single cell RNA-Seq data. 
    + The clustering methods can include SC3, TSCAM, Seurat, PCAkmeans, etc (for additional methods, see table 3 in Duò et al, 2018).
    + The dimensionality reduction methods can include PCA, MDS, [SC3](http://bioconductor.org/packages/release/bioc/html/SC3.html), [isomap](https://bioconductor.org/packages/release/bioc/html/RDRToolbox.html), [t-SNE](https://cran.r-project.org/web/packages/Rtsne/), [FIt-SNE](https://github.com/KlugerLab/FIt-SNE), [UMAP](https://cran.r-project.org/web/packages/umap/index.html), [runUMAP in scater Bioc package](https://bioconductor.org/packages/release/bioc/vignettes/scater/inst/doc/overview.html), etc. 
    + To obtain meaningful test results, choose an scRNA-Seq data set (here pre-processed count data) where the correct cell clustering is known (ground truth). For simplicity the data could be obtained from the [scRNAseq](https://bioconductor.org/packages/release/data/experiment/html/scRNAseq.html) package (Risso and Cole, 2020) or loaded from GEO (e.g. Shulse et al., 2019). For learning purposes, organize the data in a [SingleCellExperiment](https://bioconductor.org/packages/3.12/bioc/html/SingleCellExperiment.html) object. How to work with `SingleCellExperiment` objects with embedding methods like t-SNE, the tutorial ([here](https://bioconductor.org/packages/3.12/bioc/vignettes/scran/inst/doc/scran.html)) of the scran package provides an excellent introduction. 
    + Optional: plot the (partitioning) performance in the form of ROC curves and/or record their AUC values.
    + Compare your test results with published performance test results, e.g. Sun et al. (2019) or Duò et al. (2018).

## References

+ Duò A, Robinson MD, Soneson C (2018) A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000Res 7: 1141. [PubMed](https://pubmed.ncbi.nlm.nih.gov/30271584/)
+ Howard, B.E. et al., 2013. High-throughput RNA sequencing of pseudomonas-infected Arabidopsis reveals hidden transcriptome complexity and novel splice variants. PloS one, 8(10), p.e74183. [PubMed](http://www.ncbi.nlm.nih.gov/pubmed/24098335)
+ Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, Natarajan KN, Reik W, Barahona M, Green AR, et al (2017) SC3: consensus clustering of single-cell RNA-seq data. Nat Methods 14: 483–486. [PubMed](https://pubmed.ncbi.nlm.nih.gov/28346451/)
+ L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9 (Nov) : 2579-2605, 2008. 
+ Linderman GC, Rachh M, Hoskins JG, Steinerberger S, Kluger Y (2019) Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. Nat Methods 16: 243–245 [PubMed](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6402590/) (Note: this could be used as a more recent pub on t-SNE; the speed improved version is also available for R with a C)
+ McInnes L, Healy J, Melville J (2018) UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. [arXiv](https://arxiv.org/abs/1802.03426) 
+ Risso D, Cole M (2020). scRNAseq: Collection of Public Single-Cell RNA-Seq Datasets. R package version 2.4.0. -> Choose one scRNA-Seq data set from this Bioc data package for testing embedding methods. [URL](https://bioconductor.org/packages/release/data/experiment/html/scRNAseq.html)
+ Senabouth A, Lukowski SW, Hernandez JA, Andersen SB, Mei X, Nguyen QH, Powell JE (2019) ascend: R package for analysis of single-cell RNA-seq data. Gigascience. doi: 10.1093/gigascience/giz087. [PubMed](https://pubmed.ncbi.nlm.nih.gov/31505654/)
+ Shulse CN, Cole BJ, Ciobanu D, Lin J, Yoshinaga Y, Gouran M, Turco GM, Zhu Y, O’Malley RC, Brady SM, et al (2019) High-Throughput Single-Cell Transcriptome Profiling of Plant Cell Types. Cell Rep 27: 2241–2247.e4 [PubMed](https://pubmed.ncbi.nlm.nih.gov/31091459/)
+ Sun S, Zhu J, Ma Y, Zhou X (2019) Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. Genome Biol 20: 269. [PubMed](https://pubmed.ncbi.nlm.nih.gov/31823809/)
+ Sun S, Zhu J, Zhou X (2020) Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat Methods. doi: 10.1038/s41592-019-0701-7. [PubMed](https://pubmed.ncbi.nlm.nih.gov/31988518/)