Clustering 3K PBMCs with Scanpy

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# Clustering 3K PBMCs with Scanpy

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		<a href="/training-material/hall-of-fame/mtekman/" class="contributor-badge contributor-mtekman"><img src="https://avatars.githubusercontent.com/mtekman?s=36" alt="Mehmet Tekman avatar" width="36" class="avatar" />
    Mehmet Tekman</a>
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## Requirements

Before diving into this slide deck, we recommend you to have a look at:

- [Introduction to Galaxy Analyses](/training-material/topics/introduction)

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### <i class="far fa-question-circle" aria-hidden="true"></i><span class="visually-hidden">question</span> Questions

- What are the main steps of scRNA-seq?

- What kind of variation can confound an analysis?

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### <i class="fas fa-bullseye" aria-hidden="true"></i><span class="visually-hidden">objectives</span> Objectives

- Learn the main stages of an scRNA-seq analysis

- Understand the methods and concepts underlying scRNA-seq

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### Single Cell RNA Pre-processing

![fig01](../../images/scrna-scanpy-pbmc3k/scrna_preproc.svg)

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1. Barcode Extraction
1. Mapping
1. Gene Annotation
1. Batch count matrices

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### Single Cell RNA Downstream Analysis

![fig10](../../images/scrna-scanpy-pbmc3k/scrna_downstream.svg)

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1. Filtering
1. Normalising
   * Confounder Removal
1. Dimension Reduction
1. Clustering
   * Annotation

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### Barcoding Cells

![fig1](../../images/scrna-intro/scrna_pbb_barcodes_add.svg)

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### Filtering: Cell and Gene

![fig2](../../images/scrna-raceid/libsize.svg)

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### Normalisation: Technical Variation

![fig3](../../images/scrna-raceid/technical_variation.svg)

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### Normalisation: Biological Variation

![fig4](../../images/scrna-raceid/cellcycle.svg)

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### Dimension Reduction: Relatedness of Cells

![fig5](../../images/scrna-intro/scrna_knn.svg)

???
Build a KNN graph from distance matrix:
  * If P and q share distance which is

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### Dimension Reduction: Projection

![fig6](../../images/scrna-raceid/dimred.svg)

???
- Can use tSNE, PCA, UMAP

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### Community Clustering: Louvain

Aim: Maximise internal links and minimise external links

![fig7](../../images/scrna-intro/commgraph1.svg)

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### Community Clustering: Louvain

Pick a cell, place in neighbour, and accept if *internal:external* increases

![fig8](../../images/scrna-intro/commgraph2.svg)

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### Cell Type: Identifying Cluster Types

.left-column50[.image-100[![fig9](../../images/scrna-scanpy-pbmc3k/singlecellplot3.png)]]
.left-column50[.image-100[![fig10](../../images/scrna-scanpy-pbmc3k/singlecellplot4.png)]]

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### Clustering: Hard vs Soft

.pull-left[

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.image-100[![fig11](../../images/scrna-scanpy-pbmc3k/singlecellplot3.png)]

*Hard*
  * Big spaces between clusters
  * Cell types are well defined and the clustering reflects that

]

.pull-right[
.image-100[![fig12](../../images/scrna-intro/10xdata.png)]

*Soft*
  * Clusters bleed into one another
  * Cell types seem to intermingle with one another.

]

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Why? Why would there be clusters so close to one another?

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### Continuous Phenotypes:

.image-100[![fig13](../../images/scrna-raceid/contpheno.svg)]

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* Cells aren't discrete, they transition

* Continuously changing over time from a less mature type to more mature type

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### Interactive Environments: live.usegalaxy.eu

<br/>

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What is Differential Expression in scRNA-seq?

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### CellxGene Local Test

* We can probe clusters to see how they are so differentially expressed

pip3 install cellxgene
        cellxgene launch https://cellxgene-example-data.czi.technology/pbmc3k.h5ad

* Launch locally: http://127.0.0.1:5005

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### <i class="fas fa-key" aria-hidden="true"></i><span class="visually-hidden">keypoints</span> Key points

- Understanding the purpose of barcoding

- Knowing the difference between hard and soft clustering

- A KNN graph can be generated from a count matrix.

- Community clustering can be generated from a KNN graph.

- Interpreting scRNA-seq plots

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## Thank You!

This material is the result of a collaborative work. Thanks to the [Galaxy Training Network](https://training.galaxyproject.org) and all the contributors!

<div class="contributors-line">
		Authors: <a href="/training-material/hall-of-fame/mtekman/" class="contributor-badge contributor-mtekman"><img src="https://avatars.githubusercontent.com/mtekman?s=36" alt="Mehmet Tekman avatar" width="36" class="avatar" />
    Mehmet Tekman</a>
	</div>

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Tutorial Content is licensed under <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.<br/>