Single Cell Analysis and Visualization

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# Single Cell Analysis and Visualization
## Lab Meeting: 2019-04-12
### Kevin Stachelek
### 2019/04/08 (updated: 2019-04-12)

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### What are our resources for computing in the lab?

We have two computers with the power to run pipelines and host apps

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Thor (ip address 10.134.4.240)

<img src="img/tower.png" width="200" height="200" />
]

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SRT server (ip address 10.134.4.105)

]

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### We also have one back up server located on the USC campus

USC server (ip address 10.112.120.117)

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### How can we use these resources?

+ Use [rstudio](10.134.4.105:8787) on one of your servers via your web browser

+ Connect in a terminal

+ Use a graphical user interface (gui) that manages this connection for you:

1. Cyberduck (Mac) 
2. filezilla (Mac/Windows)
3. PuTTY (Windows)

+ Use 'Shiny' Web Apps

+ Use jupyter notebooks (python)

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### Projects

- Transcriptional Response to Rb Loss in the Retinoblastoma Cell of Origin
  Single Cell RNA Sequencing (scRNAseq)

- Changes associated with retinal cone precursor development
  Single Cell RNA Sequencing (scRNAseq)

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### What are the transcriptomic changes involved in Retinoblastoma Tumorigenesis?

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### Study Design

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### Analysis Pipeline

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### Plot hierarchical clustering
<img src="img/Capture.PNG" width="900" style="display: block; margin: auto;" />

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### Calculate Principle Components

<div id="wrap">
<iframe id="frame" frameborder="0" scrolling="no" src="img/Plot_1.html"></iframe>
</div>
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### Plot Trajectory

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### Calculate Pseudotime

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### Find Genes Correlated with Pseudotime

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### Why did this not work well?

+ Normalization

+ Batch Effects

+ Incomplete Dimensional Reduction

+ Naive Clustering Approach

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###  The Next Generation: Seurat

#### What is Seurat?

Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data.

Written in R so fits easily into existing analysis

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### Dimensional Reduction by PCA

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### Graph Construction and Clustering

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### Further Dimensional Reduction

#### Several Techniques: most common is tSNE or UMAP

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### Seurat

Advantages:

1. Batch Correction aka 'integration'.

2. Label Transfer across experiments

3. Normalization

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### Batch Correction aka integration.

Seurat v3 implements methods to identify ‘anchors’ across diverse single-cell data types to construct harmonized references, or to transfer information across experiments.
Stuart, Butler, Hoffman, Hafemeister, Papalexi, Mauck, Stoeckius, Smibert, and Satija (2018)

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### Label Transfer across experiments

We can use the same batch correction technique to predict the cluster that a cell from a 'query' dataset would fall into in a reference dataset.

Useful for comparison to published studies of retinal transcriptomes

#### Mouse
Macosko, Basu, Satija, Nemesh, Shekhar, Goldman, Tirosh, Bialas, Kamitaki, Martersteck, Trombetta, Weitz, Sanes, Shalek, Regev, and McCarroll (2015)
#### Mouse/Macaque
Peng, Shekhar, Yan, Herrmann, Sappington, Bryman, van
Zyl, Do, Regev, and Sanes (2019)
#### Human Bulk
Eldred, Hadyniak, Hussey, Brenerman, Zhang, Chamling, Sluch, Welsbie, Hattar, Taylor, Wahlin, Zack, and Johnston (2018); Mellough, Bauer, Collin, Dorgau, Zerti, Dolan, Jones, Izuogu, Yu, Hallam, Steyn, White, Steel, Santibanez-Koref, Elliott, Jackson, Lindsay, Grellscheid, and Lako (2019)

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### Normalization

Seurat v3 includes sctransform, a new modeling approach for the normalization of single-cell data. Compared to standard log-normalization, sctransform effectively removes technically-driven variation while preserving biological heterogeneity.

Hafemeister and Satija (2019)

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### Other Methods

#### [Scanpy](https://scanpy.readthedocs.io/en/latest/) Strengths and Weaknesses

1. Speed

2. Pseudotime Integration - PAGA

3. Makes several machine learning approaches easier to use

+ Denoising Auto Encoder
  
  Eraslan, Simon, Mircea, Mueller, and Theis (2019)

+ Integrating Datasets (Batch Correction) using Machine Learning 
  
  Lotfollahi, Wolf, and Theis (2018)
  
  + Transfer Learning
  
  Lotfollahi, Wolf, and Theis (2018)
  
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#### [bigScale 2](https://github.com/iaconogi/bigSCale2)

* disclaimer: I haven't tried this sofware; and haven't yet gotten a sense of popularity

+ Sensitive and accurate marker detection and classification. No method is used to reduce dimensions, all information is retained.
  
  + Infer gene regulatory networks for any single cell dataset.
  
  + Compress large datasets of any size into a smaller datasets of higher quality, without loss of information.
  
  + Reduce a dataset of many cells to one with fewers cells of increased quality

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### So many tools!

<blockquote class="bluesky-tweet" data-lang="en"><p lang="en" dir="ltr">Too many awesome new datasets, biological findings, ML methods every second of every day. And they are spawning too many new ideas. I luv science &amp; I&#39;m going crazy (in a good way) that I can&#39;t keep up with all the coolness and just have one brain and two hands and no time. Fk!!!!</p>&mdash; ANSHUL KUNDAJE (@anshulkundaje) <a href="https://bluesky.com/anshulkundaje/status/1116398619072942080?ref_src=twsrc%5Etfw">April 11, 2019</a></blockquote>
<script async src="https://platform.bluesky.com/widgets.js" charset="utf-8"></script>

### What's the most efficient way to pursue our (biological) questions?

#### We need to manage our data well and ask the right questions

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### Step 1: (General) Data Science for all

.pull-left[

<span style="font-size: 200%">[R for Data Science](https://r4ds.had.co.nz/)</span>

<span style="font-size: 200%">[rstudio cheat sheets](https://www.rstudio.com/resources/cheatsheets/)</span>
]

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![](img/r4ds.png)

]

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# Data Science Workflow

![](img/data-science_cycle.png)

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### Step 2: Graphical User Interfaces(GUIs) for some

#### Shiny & Plotly!

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### What is Shiny?

Shiny is an R package that makes it easy to build interactive web apps straight from R.

[Our Apps on Thor (10.134.4.240)](10.134.4.105:3838)

[Our Apps on SRT server (10.134.4.105)](10.134.4.105:3838)

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There are more possiblities!

Let's think about how graphical tools can help us make progress more quickly

[interactive genome browser using Shiny](https://shiny.rstudio.com/gallery/genome-browser.html)

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### References

Eldred, K. C, S. E. Hadyniak, K. A. Hussey, et al. (2018).
"Thyroid hormone signaling specifies cone subtypes in human
retinal organoids". En. In: _Science_ 362.6411, p. eaau6348. ISSN:
0036-8075, 1095-9203. DOI:
[10.1126/science.aau6348](https://doi.org/10.1126%2Fscience.aau6348).
URL:
[http://www.sciencemag.org/lookup/doi/10.1126/science.aau6348](http://www.sciencemag.org/lookup/doi/10.1126/science.aau6348)
(visited on Mar. 26, 2019).

Eraslan, G, L. M. Simon, M. Mircea, et al. (2019). "Single-cell
RNA-seq denoising using a deep count autoencoder". En. In: _Nature
Communications_ 10.1, p. 390. ISSN: 2041-1723. DOI:
[10.1038/s41467-018-07931-2](https://doi.org/10.1038%2Fs41467-018-07931-2).
URL:
[https://www.nature.com/articles/s41467-018-07931-2](https://www.nature.com/articles/s41467-018-07931-2)
(visited on Apr. 11, 2019).

Hafemeister, C. and R. Satija (2019). "Normalization and variance
stabilization of single-cell RNA-seq data using regularized
negative binomial regression". En. In: _bioRxiv_, p. 576827. DOI:
[10.1101/576827](https://doi.org/10.1101%2F576827). URL:
[https://www.biorxiv.org/content/10.1101/576827v2](https://www.biorxiv.org/content/10.1101/576827v2)
(visited on Apr. 11, 2019).

Lotfollahi, M, F. A. Wolf, and F. J. Theis (2018). "Generative
modeling and latent space arithmetics predict single-cell
perturbation response across cell types, studies and species". En.
In: _bioRxiv_, p. 478503. DOI:
[10.1101/478503](https://doi.org/10.1101%2F478503). URL:
[https://www.biorxiv.org/content/10.1101/478503v2](https://www.biorxiv.org/content/10.1101/478503v2)
(visited on Apr. 11, 2019).

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### References cont.

Macosko, E, A. Basu, R. Satija, et al. (2015). "Highly Parallel
Genome-wide Expression Profiling of Individual Cells Using
Nanoliter Droplets". In: _Cell_ 161.5, pp. 1202-1214. ISSN:
0092-8674. DOI:
[10.1016/j.cell.2015.05.002](https://doi.org/10.1016%2Fj.cell.2015.05.002).
URL:
[http://www.sciencedirect.com/science/article/pii/S0092867415005498](http://www.sciencedirect.com/science/article/pii/S0092867415005498)
(visited on Apr. 11, 2019).

Mellough, C. B, R. Bauer, J. Collin, et al. (2019). "An integrated
transcriptional analysis of the developing human retina". En. In:
_Development_ 146.2, p. dev169474. ISSN: 0950-1991, 1477-9129.
DOI: [10.1242/dev.169474](https://doi.org/10.1242%2Fdev.169474).
URL:
[http://dev.biologists.org/lookup/doi/10.1242/dev.169474](http://dev.biologists.org/lookup/doi/10.1242/dev.169474)
(visited on Feb. 05, 2019).

Peng, Y, K. Shekhar, W. Yan, et al. (2019). "Molecular
Classification and Comparative Taxonomics of Foveal and Peripheral
Cells in Primate Retina". En. In: _Cell_. ISSN: 00928674. DOI:
[10.1016/j.cell.2019.01.004](https://doi.org/10.1016%2Fj.cell.2019.01.004).
URL:
[https://linkinghub.elsevier.com/retrieve/pii/S0092867419300376](https://linkinghub.elsevier.com/retrieve/pii/S0092867419300376)
(visited on Feb. 03, 2019).

Stuart, T., A. Butler, P. Hoffman, et al. (2018). _Comprehensive
integration of single cell data_. En. preprint. Genomics. DOI:
[10.1101/460147](https://doi.org/10.1101%2F460147). URL:
[http://biorxiv.org/lookup/doi/10.1101/460147](http://biorxiv.org/lookup/doi/10.1101/460147)
(visited on Apr. 11, 2019).

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### Thanks!