The main ideas behind PCA are actually super simple and that means it’s easy to interpret a PCA plot: Samples that are correlated will cluster together apart from samples that are not correlated with them. In this video, I walk through the ideas so that you will have an intuitive sense of how PCA plots are draw. If you’d like more details, check out my full length PCA video here:

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0:00 Awesome song and introduction

0:27 Motivation for using PCA

1:23 Correlations among samples

3:36 PCA converts correlations into a 2-D graph

4:26 Interpreting PCA plots

5:08 Other options for dimension reduction

#statquest #PCA #ML

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Xem thêm bài viết khác: https://12m-15m.org/category/cong-nghe

Special thanks to PROTIST for the Russian subtitles!!! 🙂

Okay, but how do you translate the gene expression values into principle components?

Wasn't expecting to see someone can introduce PCA concept in such a way that a dummy can understand!

Not really related but relevant question, how will you be able to explain PCA in layman terms during an interview?

This question comes from an acquaintance of mine who happens to be dealing with statistics for 2 decades, I thought that it is a pretty interesting question on its own and figured that you might be able to answer it considering how well you did in this video!

Thanks!

awesome stuff thank u

So it’s basically finding the correlation between groups of data? Seems pretty simple

Holy moly. I finally understand the concept of PCA plots :O THANK YOU SO MUCH

Do you have a multivariate analysis video or link ?

So, is PCA the same as a Cluster Analysis?

Has anyone told you your intro songs sound like they belong on a Magnetic Fields album?

very useful <3

Thank you for existing!

Thanks for the video. It is really helpful.

I am curious which programm was used to plot it. I have downloaded Excel with XCLStat but I cannot get smth similar from there….

Concise, and clear!

Great explanation as always! Thanks a lot for your effort!

Very insightful. Thanks!

You are awesome

thanks a lot!

Can you please enable payment through Mobile apps so that it's easier to join.

Thank you for these sequencing, singing, and recipe videos, this channel needs more subscribers.

oh man!! thank you, I needed this so bad

you are the best <3

This is saving my mind. I am an archaeologist trying to understand statistics and really reading abount ir has been nothing but torture XD

Waoooo love the way you explained….specially the graphical parts that you explain in each video…. 🙂 Please if possible try to make a video on LightGBM model …how it works! 🙂

I’ve watched your PCA videos but I still have major questions around 2 things:

1. What is PCA used for? In what cases would I want to perform PCA instead of other statistical analyses?

2. How can I develop my intuition on what each dimension “means”. I understand the meaning isn’t clear/obvious since they are linear combinations, but any help would be good.

Thank you ! you're brilliant :'D

why bother with the 'non bio language'…idk who would watch this if they weren't doing bio of some sort…

StatQuest is really the best! that you so much to prevent my brain to explode!!!

Hellow sir , can you please explain how PLS works differently than PCA.

Awesome! Thanks!

Thank you

Sorry, i just want to know why did you say that in the PC plot that the data points are some clusters for the cells; inspite of the fact that each data point represents the transcription of each gene in a cell, and since the cells represented the axes at the beginning, therefore the principle components must be a linear combination of these axes represented by the cells, while the clusters in the PC plot should then represent the clusters of genes; actually i got confused when you said that the cells cluster together in the PC plot (in 3:50); can you please clarify this point? thanks in advance!

thanks, this helped me much!!!!!!!!

Wow, this sheds a lot of light on dimension reduction. Very clearly explained & illustrated. TQVM!!!

love this content. Thanks!

You mentioned the heatmap as one of the dimension reduction methods. Could you instruct how it could be used as a dimension reduction method? I tried to find the answer in your Heatmaps video but still not sure how to do it. Could you explain it a bit? Thank you!