<p>tSNE often offers better visual representation (separation) on such complicated data than PCA. As Micheal pointed out, computing a tSNE embedding over 20.000 gene dimensions is computationally unfeasible, so a number of PCs are normally calculated and these are used as input for calculating the tSNE. They are used in tandem.</p>

<p>As for global vs. local, we are much more interested to see similarity of cells to a limited number of neighbours indicating a celltype and grouping these close together. This is more important than the distance between such celltypes. (assuming the separation in your tSNE is driven by a biologically meaningful factor such as celltype    and not some confounder)</p>

<p>Edit:</p>

<p>Since I just made these images for myself anyway I might as well post them. The first 2 PCs show some separation by celltype, tSNE computed over 100 PCs gets very nice separation.</p>

<p><a href="https://i.sstatic.net/anIfM.png" rel="noreferrer"><img src="https://i.sstatic.net/anIfM.png" alt=""></a>
<a href="https://i.sstatic.net/IzQ2o.png" rel="noreferrer"><img src="https://i.sstatic.net/IzQ2o.png" alt=" "></a></p>

CC BY-SA 4.0
2019-05-28T14:32:20.327Z
[UMAP](https://umap-learn.readthedocs.io/en/latest/) is another dimensionality reduction algorithm that better respects global structure.
GWW
CC BY-SA 4.0
2019-05-30T09:34:19.497Z
Alse checkout sleepwalk which helps you see where your embedding works well and where it doesn't https://anders-biostat.github.io/sleepwalk/
Martin Modrák
CC BY-SA 4.0
2019-06-04T11:50:09.157Z
Woooaaa this a quite a dramatic demonstration. Only one upvote per post ... pity
M__
CC BY-SA 4.0
2019-06-04T12:18:46.483Z
Woaaw! Super clear now!
jxx_fa
CC BY-SA 4.0
2019-05-28T14:22:52.430Z
2019-06-04T11:00:10.367Z
2019-06-04T11:00:10.367Z
Pallie
<p><strong>What makes tSNE being the preferred dimensional reduction</strong> for visualization in single cell RNA-seq over PCA?</p>

<p>I am aware that tSNE works better at showing local structures and fails to capture global structures of the data.</p>

<p>But I think I don't fully get the reason of <strong>why is this an advantage</strong>? It offers better resolution? Or better separation of the cells as compared to PCA?  </p>

CC BY-SA 4.0
2019-05-28T12:38:54.143Z
They are often stacked together, i.e. first PCA then tSNE. They do quite different things.
M__
<scrnaseq><ngs><single-cell>
PCA vs tSNE in single cell RNA-seq
# PCA vs tSNE in single cell RNA-seq

(Asked by: username_0 on 2019-05-28)

**What makes tSNE being the preferred dimensional reduction** for visualization in single cell RNA-seq over PCA?

I am aware that tSNE works better at showing local structures and fails to capture global structures of the data.

But I think I don't fully get the reason of **why is this an advantage**? It offers better resolution? Or better separation of the cells as compared to PCA?

**Tags:** scrnaseq, ngs, single-cell
> **Comments:**
> **username_1**: They are often stacked together, i.e. first PCA then tSNE. They do quite different things.  

---

## 1 answers

### Answer by username_2 on 2019-05-28. Score: 11 | Accepted answer

tSNE often offers better visual representation (separation) on such complicated data than PCA. As Micheal pointed out, computing a tSNE embedding over 20.000 gene dimensions is computationally unfeasible, so a number of PCs are normally calculated and these are used as input for calculating the tSNE. They are used in tandem.

As for global vs. local, we are much more interested to see similarity of cells to a limited number of neighbours indicating a celltype and grouping these close together. This is more important than the distance between such celltypes. (assuming the separation in your tSNE is driven by a biologically meaningful factor such as celltype and not some confounder)

Edit:

Since I just made these images for myself anyway I might as well post them. The first 2 PCs show some separation by celltype, tSNE computed over 100 PCs gets very nice separation.

[![](https://i.sstatic.net/anIfM.png)](https://i.sstatic.net/anIfM.png)
[![ ](https://i.sstatic.net/IzQ2o.png)](https://i.sstatic.net/IzQ2o.png)

> **Comments:**
> **username_3**: [UMAP](https://umap-learn.readthedocs.io/en/latest/) is another dimensionality reduction algorithm that better respects global structure.  
> **username_4**: Alse checkout sleepwalk which helps you see where your embedding works well and where it doesn't https://anders-biostat.github.io/sleepwalk/  
> **username_1**: Woooaaa this a quite a dramatic demonstration. Only one upvote per post ... pity  
> **username_0**: Woaaw! Super clear now!  

---

