2024 updated single-cell guide - Part 1: RNA preprocessing and quality control 

Amazing series idea. I hope they keep coming.

You rock! Thank you for doing this, looking forward to following this series!

Your work is fantastic, great content!

Amazing! Thanks very much for the tutorial, I'm learning a lot!

really appreciate your videos🎉❤cannot wait to see spatial omics tutorial in the future😊

Thank you very much for creating this tutorial! Looking forward to the next lessons!😊❤

Good to see you back😊 and thank you for your update

i love sanbomics so much!!!!!!!!!!!!!!!!!!!

Amazing work, hope we will see second part soon

I was waiting for your video. your video is so helpful for beginner like me. Thank you so much for sharing your knowledge and experience

this is brilliant! can't wait for part two!! Ridge plot look awesome! thank you Mark! :-)

I started with the video camparing different intergration method. That one really helped me! I eventually choose scanorama for my dataset, which worked out. Looking forward to this series! I appreciate your videoes!

I look forward to your videos. Your grasp on the subject and the ability to teach are amazing. Thanks a lot 👍🏻

this is fantastic and really helps people with limited bioinformatics background to independently analyze data-thanks so much for making these videos, ive been using them with python ever since you shared a few years ago!

You trully are an inspiration for rna-seq! Love your videos and your communication skills. Hope to see the rest of the 2024 tutotial soon :D

Thx for the update ！

welcome back, bro. Your channel is better than before.

Waiting impatiently for the next part

Thank you for your work!

gratefull Mark!!

You were great.

I just started your sc guide and I really enjoy it. Just for some clarifications about the tools, I use mamba (conda) with python 3.8 and a lower version of pandas (

cool good job😁

🎉🎉🎉thanks！

best page ever

Thanks for the Videos. Currently, I'm embarking on the journey of analyzing single-cell RNA sequencing (scRNA-seq) data combined with CITE-seq data. However, I'm facing challenges related to duplicate discrimination and assigning sub-samples via hashtags. Given your expertise in this area, I was hoping you could provide some guidance and advice on how to navigate these challenges effectively.

I would like to thank you immensely because you’re one of the few bioinfo channels I can follow along, I have a question regarding a result I obtained from a following the previous full scRnA seq walkthrough you posted a year ago. I tried applying the code to a before and after chemotherapy treatment. Everything worked perfectly until i got to the deg analysis part with heat maps, With 25 top upregulated and downregulated genes and the filtering codes it didn’t yield more than 12 degs, so I had to reduce the filtering and kept genes with significant fold change above 0.05 . And I ended up with more differentially expressed genes, however in both cases my heat map was devoid of pattern, both the condition and control looked mostly downregulated. Should I conclude that there is no deg or expression signatures in both cancer sample before and aftertreatment? Because the original paper i took my data from didn’t do a deg analysis for the whole dataset but selected 4 patients out of 12 to create a deg heatmap with less than 10 genes. thank you, I’d highly appreciate your insight on my results

Thanks for making very useful videos. I was wondering if you would like to make a video related to single cell analysis using Julius AI a data analysis AI.

amazing work as always ! on a side note, if I were to download a fastq data from GEO with no specification of whether the adapters were removed or not in the paper, how should I check if they were removed on python.

Thank you for such a great video. Which is better for removing doublets, doubletdetection or the previous SCVI method?

Thanks for the great video and series. I have a question at around 36:40 on how to interpret the graph. If the experiment had loaded say 14000 cells it appears that around 8000 would be recovered which I assume we would interpret as the number called by cellranger... For 14000 cells loaded the multiplet rate appears to be 6%, 6% of 14000 being 840 expected multiplets. However, all the blue recovery dots are aligned around 4.5%. 4.5% of 8000 would be only 360 expected multiplets. The document from which the graph is extracted says "Generally an increased number of cells per sample will increase the doublet rate". I've not been able to find clarification. Thank you

thx for sharing! if i use a filtered matrix for analysis, do i still need to remove the background RNA? since i dont have a 4090🤣

Another question: if you were to choose between SCVi model for detecting doublets and this clf doubletdetection method, which one is more straightforward? I feel like this method needs some tinkering around depending on the specific dataset

Hello! Thanks for the Video, I will begin my PhD in Bioinformatics in August, what computer do you have?

Hi! Thanks so much for such a great tutorial! Have a naïve question of someone who just started in this world: When raw data is not available, for example, you can only download normalised filtered values, do you skip the pre-processing step? Is it correct to pre-process normalised values, let's say tmm? Again, thanks so much for all the videos!

Your videos are amazing. Thanks a lot. Could I use 3050 with 64 GB RAM for this kind of analysis? Thanks a lot.

Sir, I have count matrix and want generate annotation matrix out of it then do the batch correction and then DGA plz help via process as i am not getting suitable results.

are you're still going to develop workflows for R or you're sticking with python?

Have a beer on me bro🍺

F%(k. Seems super useful but you could have been speaking any random language and I would have understood about the same.