Visualize gene expression data in R using ggplot2 | Bioinformatics for beginners

[@alicemostafa8717]

you are just amazing. I always felt afraid from R and RNA-seq analysis, but you made it super clear for me. Thanks!

[@coolalexpcs]

It's a really fantastic lecture teaching ggplot specifically for gene expression data! Really helpful!

[@usmanasghar1127]

You are doing really amazing work keep it up.

[@lavanyakakimallaiah]

I was finding it difficult to understand from online resources.... your video about gene expression really superb...I was able to understand all the things Thanks for the video

[@tulikabhardwaj484]

I am so much addicted to your videos. Please kindly made some videos on metatranscriptomics

[@Bioinformagician]

Thank you, I will surely think about making a video on it :)

[@tulikabhardwaj484]

@@Bioinformagician then surely, I am eagerly waiting for that day. Good luck to you girl.😀

[@Bioinformagician]

@@tulikabhardwaj484 Thank you! 😃

[@setarehsohail5422]

Unbelievable! you are genius! How do you teach these complex issues so easy and understandable?! Thanks from bottom of heart!

[@Bioinformagician]

I really appreciate your kind words. Thank you very much :)

[@kimiaslk9348]

your videos really help me through my masters, you are so smart and inspiring ! thank you

[@lincysubi6735]

Excellent explanation...

[@nikitamaurya4518]

Thank you so much!

[@jowrow7665]

Thanks sister. You are just doing nothing but a great job. I wish you all the best.

[@sanjaisrao484]

Exellent explaination Thank you very much Keep uploading 🥰

[@o1kun]

These videos are really really helpful! You really saved my research 🎉

[@shrishteekandoi120]

Fabulous Explanation ! I love how you make things so much easier to understand !

[@nagavenicavuturu9179]

Yes your explanation is really great

[@bzaruk]

All your content is PRICELESS - thank you so much! :)

[@gaellelelandais2624]

Very nice ! Thank you, you're doing a great job, very useful for other teachers (as I am). Thank you.

[@giulianacoatti8163]

I simply LOVE YOUR CHANEL!!! THANK YOU SO MUCH FOR SHARING!!!

[@vahidgorganli8895]

Great😀

[@luischipres283]

¡Gracias!

[@RicardoRiveroHerrera]

Really good content! Keep it up!

[@Bioinformagician]

Thank you! :)

[@parthibanm756]

Can u please put lectures on comparative metabolomics data analysis, you have been amazing

[@tushardhyani3931]

Thank you for this video !!

[@umarsheikh1992]

Hie, Thank you the helpful tutorial for understanding and analyzing the gene expression data sets. As u have started the videos series beginning from how to download the data set to visualisation. Could you please add a video where in you can show how to perform differential gene expression with edgeR, limma, and DESeq2 of the same data set. We now we have the data in the dat. Long format, could we explore the differentially expressing gene between tumor and normal sample and plot the data using heatmap. Regards

[@Bioinformagician]

That's a great suggestion. I'll surely plan on making a video in continuation with this dataset and performing downstream analysis.

[@neetimittal9189]

Hi Could you please make a tutorial on workflow for processing, qc and analysis of a DNA methylation data on Illumina 450K or EPIC platforms? Your videos are really helpful!

[@sciencetutor_businessguy]

i think the data set you used is different from the one downloaded as there was no gene BRCA1 AND BRCA2 in the genes in the dataset. While i filtered , it showed no result with the filtering using both gene names. Does anyone face the same challenge?

[@vijethavk]

For the heatmaps, can the sample names be made perpendicular to the columns and therefore more readable?

[@yahyayozbatiran]

Hello, how can i plot a specific gene expression in cancer subtypes from tcga, for example; I want to plot> MSH2 gene expressions in Colon Mucinous versus Colon Adenocarcinoma

[@user-uq3qh2cy9v]

in this sample, you used "fill=tissue", then bars of different tissue colors are grouped together. However, if you change it into "fill=metastasis", like below coded, then the bars of different colors are mingled. Is there any way we can group it again? dat.long %>% filter(gene=='BRAC1') %>% ggplot(., aes(x=sample, y=FPKM, fill=metastasis)) + geom_col()

[@manuelsokolov]

This video was very helpfull! If I may ask can you compare like this across samples having the data normalized in FPKM?

[@zulfakhan2655]

can you please explain how can we generate Differentially expressed gene via this type of data... that is gene expression data.

[@dharmeshbisoriya7728]

Can we use the FPKM values directly to compare between samples.?

[@shreymehta4973]

Hi! Quick question -- is it possible to use other values besides FPKM for comparison? What other types of normalized values do you recommend using for all these plots? Thank you!

[@nagavenicavuturu9179]

Does the software s mentioned will be available at free of cost???

[@karthibiotech426]

i want to know about the file of your input GSE183947_long format.txt...is this the previous .CSV file you used...?

[@Bioinformagician]

It is the CSV file associated with the record GSE183947, just manipulated the shape of the data from wide to long and saved it as GSE183947_long format.txt.

[@ashutoshs1966]

Can these things be done in Studio as well

[@ducphucngo3938]

Is this method still available for microarray data? Thanks in advance

[@user-no1jk9km7r]

i love you

[@user-ft5bh9yr1j]

Hey, great tutorial! Just a note that you do a lot of "uhhhh/ahh" while you talk, it's a little distracting hahaha

[@Bioinformagician]

I am happy you find the tutorial great inspite of uhh ahh :P But in all good spirit point noted, thanks for bringing that to my notice :)

[@bnb7462]

Tidy video ❤