StatQuest: MDS and PCoA

MDS (multi-dimensional scaling) and PCoA (principal coordinate analysis) are very, very similar to PCA (principal component analysis). There really only one small difference, but that difference means you need to know what you're doing if you're going to use MDS effectively. This video make sure you learn what you need to know to use MDS and PCoA.

There is a minor error at 4:14: The difference for gene 3 should be (2.2 - 1)². Instead the distance for gene 2 was repeated.

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[taoyang]

This is such a great video.
To answer a student's question in one sentence demonstrates the teacher's complete understanding of the knowledge.
The more the teacher talks to answer, the less the teacher knows what you are asking and the more confused you become.

[dsagman]

Honestly the best machine learning and stats videos available. How did we live before Statquest?

[lade_edal]

runn around all over the internet none the wiser then come across this channel and bam! It all fits so easy. Why do some people over complicate such simple things? Thanks Josh!

[son]

Thank you so much for such an easy and bite-size content that I can understand to the fullest. It's way much better visualized and informative compared with other videos I've seen !!!

[Ivaniushina]

Brilliant! so clear. Now I understand (at last!) the relations between PCA and MDS.

[초롱초록]

Thank you so much! I was confused with the concept of difference about PCA and MDS. Thanks to your explanation, I could understand.

[alejandrotenorio]

In MDS where does the minimizing of the Raw Stress go? I'm not getting how you can do that while performing EVD to reduce the dimensions

[abcdzxc]

Thanks so much for your video, but I still have a question; I really don't understand what is the difference between PCoA and MDS.
It would be a great help if anyone could explain the difference between PCoA and MDS.

[shahbazsiddiqi]

Unlike PCA where we compared Genes variation in order to give weight to calculate the value for each cell and then map them accordingly to PC1 and PC2. Here we are calculating the distance between cells with reference to each genes. What is the calculation for MDS1 and MDS2 . I am confused because we are taking 2 cells at a time, instead of one and are we plotting the difference of each gene with respect to cell 1 along x axis and cell 2 along y axis. Could you please explain what to consider for MDS1 and MDS2 ? Thanks a ton

[nikhiljoyappa]

very helpful in the world of people who are always helpfool.

[Stephanbitterwolf]

Very helpful. Not sure if this has been pointed out yet but at around 4:17 you talk about distance for gene 3 but the numbers aren't accurate for that gene difference.

[poojakunte]

difference for Gene 3 should be (2.2 - 1)^2 right ?

[medazzouzi]

Heyy josh i m confuse in the pca statement "correlations among samples" isn't suppose to be correlation among variables? Since we are reducing dimension of variables in this case ( genes) not the samples?

[malteneumeier]

@Josh Starmer: in minute 4:14 there is a tiny mistake in the formula: the difference for gene 3 should be (2.2 - 1)². Instead the distance for gene 2 was repeated.

[ahmetlacin]

ı just have no idea how to thank you. Viva Josh!

[AlonKedem]

I love your videos. Just want to mention that in 4:18 you calculated the euclidian distances for gene 2 twice while saying its gene number 3. :)

[takethegaussian]

Thank you very much! This is a really really good explanation.

[sofiagreen]

Hello Josh and thank you for your videos, they are really helpful. Would you mind making a video on Canonical Correlations please?

[ranitchatterjee]

To plot the data, do we select the cells with maximum distances? Like for example if cell 1 & 2 and cell 3&4 have maximum distances, do we plot with respect to them?

[liranzaidman]

Hi Josh,
have you ever encountered a clustering model where there were more than 3-4 clusters? I've done it many times, and it looks like the number of optimal clusters (3-4) is "natural".