# PCA vs PCoA - which is the appropriate one for microbiome data

**URL:** https://forum.qiime2.org/t/pca-vs-pcoa-which-is-the-appropriate-one-for-microbiome-data/5974
**Category:** User Support
**Created:** [September 11, 2018, 2:56am UTC](https://forum.qiime2.org/t/pca-vs-pcoa-which-is-the-appropriate-one-for-microbiome-data/5974 "2018-09-11T02:56:17Z")
**Posts on this page:** 1
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### Author: ![colinbrislawn](https://forum.qiime2.org/user_avatar/forum.qiime2.org/colinbrislawn/32/6221_2.png) [@colinbrislawn](https://forum.qiime2.org/u/colinbrislawn)
#### Post date: [September 12, 2018, 5:32pm UTC](https://forum.qiime2.org/t/pca-vs-pcoa-which-is-the-appropriate-one-for-microbiome-data/5974/6 "2018-09-12T17:32:40Z")

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Good question.

I want to take a moment to differentiate the ordination method vs the distance metric.

```plaintext
ordination methods
 - PCoA
 - NMDS
 - CCA
distance metrics
 - unifrac
 - jaccard
 - euclidian

```

I think you probably know that already, but I just wanted to post that for future users to see.

* * *

I'm not sure if euclidian is bad, but other methods are arguably more clear or more biologically relevant. Let's take Jaccard and UniFrac as examples.

Jaccard distances are simple:

```plaintext
percentage of taxa not found in both samples

```

So if 30% of taxa are in both samples, this means 70% are only found in one sample, and the Jaccard distance is 0.7. Very easy!

UniFrac distances are equally easy, and add phylogenetic information:

```plaintext
percentage of phylogenetic branch length not found in both samples

```

This makes UniFrac a tremendously powerful method for measuring the difference between samples because it incorporates the underlying phylogenetic tree of the taxa. 🌴 🎆

Now let's look at Euclidian distance

```plaintext
the square root of
    the sum of
        the squares of
            the percentage of unique taxa in each sample

```

How could that possibly be useful!?!  
Who was crazy enough to invent the Euclidean distance? 😉

Colin

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