Відео

Thanks for the great explanation of PCA 😊

Well explained! Peace out

Thank youuuu

You make very helpful videos! Thanks :)

This was so helpful, thank you. I was trying to analyse the heat map of a publication and could not understand anything. THis video really broke it down and now I know what to look for.

Fantastic clear explanation, thank you!

Best videos on internet . Need more statistics topics to be covered. Thanks a lot

For real a very good video! This actually helped me to understand the subject quite well.

Thank you for this lesson, i found it very useful and easy to understand. Grettings from Mexico! your videos have helped me to get through my masters degree

Thank you

This is nicely explained and clear. Thank you so much!

This was brilliant, thank you

Basics of Survival Analysis made so lucid. Well Done!

thanks for your video. Every scientific video is very valuable.

Could you please show how to add a portion/command to run the same chosen variables for the Cox regression to do univariate analysis simultaneously and tabukate the results. Excellent video, thank you so much for making this and making the code available!

very informative video

Do you have more information on the DEG data? Is it disease vs control? Which disease and which tissue?

Teacher, df and significant_df, which one is suitable for bg_gene? Tanks so much.

Teacher, i need ur help, since the dataset is df gene, why many p value is above 0.05? how do i get the df gene set for my scRNA?

Hello, teacher. Why not set ontology = "Reactome" in the new function? Thanks so much.

At last i fully understood the concept. Thank you

I have a question: Let's say I want to calculate the PCA of children's grades at school to know how it impacts the final grade average of each child. For that I have my sample, which would be all the children in a class, for example. Then I have the grades of those children in different subjects such as Math, History, Physical Education, etc. And I also have the average. Do I add the average as another variable to the PCA analysis? Or should I make a correlation, for example, between PC1 and the average and see the PCA loadings?

I need help, teacher. Since df include all the gene not just the differential genes, how to get the whole genes list since i saw that some p value is above 0.05, how do i get that list for my scRNA analysis. Appreciate it.

The necklace makes the whole video

very clear explanation

Thanks so much. I wondered that the y-axis should use adjusted p value instead of raw p value, is that true?

Amazing explanation, thank you! The graphics make it so much easier to understand and the video is also very entertaining to watch.

good video and nicely explained with good visuals. The only small issue is that your voice was too low.

Very clear explanation! Thanks!

Very good teacher! Thanks!

This is a great video. Thanks!

good work. thanks

thanks for your help!

Lady you are good. But you only tell the facts half only. We get easily confused in certain points like gmt files in half way. If are decided to teach just do it correctly. For beginners in bioinformatics you are mocking is. Half truth is worse and worst than lying.

kuch smjh na aya!!!

Thank you!

hi thank you for a very good video, how would you interpret the HR 7,4 for smoking in one of the slides? 7,4 times higher risc of death compared to non smokers?

Thank you so much for this video but we would like to see the codes you're running so that we can replicate the same

what is the career opportunities in this field

That's a german accent right? Many thanks for the video.. really enjoyed the explanations!

Thanks for the video, this really explains it in a nutshell! This may be not in your alley, but what would PC2 to be if you would plot geochemical data with each variable being an element (PC1 seems to be rock type). Thanks in advance!

Awesome! Thank you for sharing these insights. That's very helpful for my current research project and I decided to switch from SPSS to R ;-)

SUPERB THANKS <3

Perfect explanation for Principal Component Analysis

Thanks for your valuable tutorial.🙏🌹

First and foremost, thank you for providing such a detailed video. I am having an unusual difficulty with my data. I have 2345 significantly differentially expressed genes, and when I try to plot all of the significant genes, I can see all of the gene names on the volcano plot. However, when I try to visualize merely 30 or 100 genes, the codes work correctly but I am unable to see them. Could you help provide me some advice on how to solve this issue? Thank you in advance.

fix your mic

❤

Thanks

Thanks a lot