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@biostatsquid 4 күн бұрын
Here's part 1: kzbin.info/www/bejne/a6uncmx7ma-UY5I
@antonrosenfeld6861 5 күн бұрын
A very clear and engaging introduction to PCA. It was new to me, and I came away with a good impression of how it would be used. Thanks very much!😀
@shrivastava3892 7 күн бұрын
The differential data that you loaded in the r script initially, which has approx 30 thousand something genes and four variables, are they pre-processed data, like removing the duplicates and adjusting the p values and log FC?? Or are they raw data tT saved from r script?
@mdabidafridi2961 8 күн бұрын
Hi there. Your videos are really helpful. Can you make a video on RNA sequencing profile?
@biostatsquid 5 күн бұрын
Hi, thanks for your feedback! What do you mean by profile? single-cell or bulk?
@yashdeepsingh1790 8 күн бұрын
This is really helpful , thank you!
@singh_nimisha 10 күн бұрын
Hi Dear Biostatsquid, can you please check out Plotnine in Python too? It provides a great visualization for statistical outputs. 😊
@biostatsquid 11 күн бұрын
Here's the link to the step-by-step tutorial: biostatsquid.com/easy-violin-plots-tutorial-ggplot2/
@odothomas1851 14 күн бұрын
Amazing. Thank you
@amritabhattacharjee4596 15 күн бұрын
Hi. This is a nice video. I am new to data visualisation and I find it very complex as to how to memorise the code or understand how to use it with various datasets. Could you please share some tips on how you do that?
@biostatsquid 14 күн бұрын
Hi, thanks so much for your comment! My recommendation is... don't memorise code! You'll end up remembering the most common functions and bits and pieces anyway if you use them a lot - but a lot of bioinformatics is just googling:) As for what to use in which case and with which data... honestly, it comes with practice. Seeing and reading what other people do with similar problems / datasets definitely helps, e.g., from publications, tools, github repos... if you encounter a problem, odds are someone already did too! And probably solved it:) Good luck, you'll see how it gets easier the more you do it! Just have fun with it:)
@omonzejieimaralu7677 16 күн бұрын
Your videos are great and very easy to follow. For the background genes, how do you download GSEA GMT files for only genes expressed in the specific tissue you are interested in.
@biostatsquid 14 күн бұрын
Thanks so much for your feedback! Hmm as far as I know, you cannot do that. But you can download the full .gmt file and then just filter it for all of the genes you detected in your tissue.
@reregad590 16 күн бұрын
This was very helpful, your way of teach just keep me engaged and understanding, thanks ❤
@folenspill 17 күн бұрын
Thank you for a very nice video. I have trouble understanding the fold change for gene 1 in the table example. Wouldn't the fold change (FC) be 3 (9 divided by 3) and log2(FC) 1.585?
@biostatsquid 17 күн бұрын
Yes, apologies, that was a typo! You are correct:)
@mercedesdebernardi4215 18 күн бұрын
Tus videos me estan ayudando muchisimo!!! Sigue asi!!
@sreeram6416 21 күн бұрын
Could you please make a video on DEWSeq or any other tool to analyse the eCLIP data to find the motifs in rna through which it is bound to a protein
@CynthiaFrancis-sv4rc 22 күн бұрын
Absolutely amazing! Thank you for doing this! Great job
@charlesmusengeyi3484 23 күн бұрын
Your accent is very good. Thank you!
@mondayakhuetie9284 24 күн бұрын
This is great 👍, it was well explained.
@zazoudunet5756 27 күн бұрын
Thank you, very useful !
@ZullyPulido Ай бұрын
Eres la mejor!! Saludos desde Colombia :)
@hmmusik2095 Ай бұрын
Can you do a video for pathway enrichment analysis using pathfindR package in R
@user-God-s-child-0101 Ай бұрын
Whole world creator's godfather bless you all always and you all love and remember godfather with your pure hearts.
@NAVYAB-eb2jp Ай бұрын
Thank you for explaining it well.. Can you pls provide information on the inputs needed to perform ssGSEA ...
@tareknahle9578 Ай бұрын
Thank you for this amazing video!
@shivavyavahare Ай бұрын
How to explain which factors contribute to PC1 and PC2? by biplot graph.
@sanariaaljaf9619 Ай бұрын
This was such an informative video! Helped explain so much for me as I have never been exposed to Volcano plots before. Will definitely be tuning in more for more videos! Thank you.
@kyaw94 Ай бұрын
I'm currently watching without logging into my Google account. 😊 However, halfway through, I made the decision to log in, hit the like button, and subscribe to your channel. 🎉 Thank you for your valuable content-it's truly helpful, and I encourage you to keep up the great work! 👍
@jules6731 Ай бұрын
Thank you so much!!
@markcolgan3262 Ай бұрын
Thank you for a very clear explanation
@haili1649 Ай бұрын
Thanks for uploading the valuable video. I could not install the Rqc and QuasQ packages in R 4.3.2. Do you think I should use a lower version?
@elmoelmo6505 Ай бұрын
Hi thank you so much for explaining PCA in such a clear way. I've been really stressed about understanding it for my uni stats exam, but now I feel much more confident :)
@souvikghosh5825 Ай бұрын
nice explanation
@nunziofazio1143 Ай бұрын
Thank you a lot! I'm struggling with my data. is there any option to create a clustering within a group on the same heatmap? I have many groups of species I want to analyze but I just want the clustering only within the same group.
@hozifaelgadal623 Ай бұрын
thank you very much , that was very informative and joy to watch .
@huiminlu8436 Ай бұрын
I am with zero experience, and failed so many times by following youtubers, you script works and I can easily catch up, even different methods. Thankyou sooooooooomuch.
@user-il4jz8mu6o Ай бұрын
How I can do interrogating the sample PBMC clusters for the following genes : CD68 CD45 Sox10 CD44 any similar video will be great ? thank you
@HH-ew5pd Ай бұрын
Super helpful video! Please make more videos with easy explanations for basic concepts in this field.
@HH-ew5pd Ай бұрын
Thanks for the wonderful video! I'm interested in marker-based method. Hope to see the video soon!!:)
@h1bB0ilzZ Ай бұрын
Many thanks for this video. It was extremely helpful! Just a quick question, do you have a link to any papers that use the same method for ranking genes? I've gone for the same approach, but will need to defend it in my viva and I am struggling to find publications using this method. Secondly, I just want to confirm that you use regular p-values rather than adjusted p-values for the ranking calculation?
@nehapimpalwar7339 Ай бұрын
VERY INFORMATIVE VIDEO, THANKS A LOT IT MADE MY LIFR EASIER
@dannggg Ай бұрын
Very good high level video!
@HH-ew5pd Ай бұрын
Thank you for the clear explanation!! Great help!! Looking forward to upcoming videos:)
@amandamirandamartins2014 Ай бұрын
you explain so well!! thank you
@amandamirandamartins2014 Ай бұрын
this video helped me so much!!!!!
@jackdawson7385 Ай бұрын
Please can u tell me how can we calculate principal loading. I am a bit confused to this part.
@Myri912 Ай бұрын
Hello! thank you very much for the video, it has helped me a lot. However I had a query as I have played the whole script on my computer with my own SDR data. I have run the whole script and everything seems to be correct except when I run the last step "target_pws <- unique(res_df$ID[res_df$p.adjust < padj_cutoff])" and "res_df <- res_df[res_df$ID %in% target_pws, ]" which reduces the list to a single gene. I have looked at the default values of the function and they are not exaggerated, but what would you recommend me to do to have a larger list? Thanks in advance!
@Myri912 Ай бұрын
I have another query, I have tried to use another data set and I get this result directly when running ClusterProfile: --> No gene can be mapped.... --> Expected input gene ID: HSD11B2,PTPN11,ABCG1,GALE,WASL,PLA2G12A --> return NULL... --> No gene can be mapped.... --> Expected input gene ID: APBB1,BID,GALT,NDUFA1,ABCB4,RUNX1 --> return NULL... It's like my genes don't match...how can that happen? Thanks in advance!
@KeshavSharma-lh7zf Ай бұрын
can i follow the same for proteomics data
@wobby7055 Ай бұрын
So well explained. Thanks a bunch!
@kiplimosimon1429 Ай бұрын
I enjoyed the explanation. it is very clear. Thanks so much
@danielgladish2502 Ай бұрын
2:14 I am not clear on what contributes to the magnitude of the increase/decrease of the running statistic (i.e. what number specifically is the input for the running statistic calculation). Is it the rank value? In the video you focus explicitly on fold change, but in the previous video you mentioned that rank is determined by both fold change AND significance. Great video by the way :)
@biostatsquid Ай бұрын
Hey Daniel, thanks for your comment, great question! I tend to use -log10(pval)*sign(FC), to get a combination of both, but there's not a consensus in the community as far as I know. There's a few blogs/papers that discuss it: www.biostars.org/p/375584/
@danielgladish2502 Ай бұрын
@@biostatsquid ah makes sense! So it sounds like there are a number of different ways of doing this. Thanks for clarifying and the quick reply! I will have a look at the link.
@danielgladish2502 Ай бұрын
Great video! Really helpful for getting an understanding of the analysis workflow! A small critique / suggestion for improvement that I think could be made is in terminology being used, specifically referring to genes in the ranked list as being overrepresented. As you said in the video, one is not filtering any genes, so when looking at your gene set in GSEA, you aren't looking at the proportion of the genes being part of your list, but rather where are the genes located in the unfiltered ranked list containing all the genes.
@biostatsquid Ай бұрын
Totally agree! Thanks for your comment:)