Thank you very much ❤❤❤

Thank you so much for this lecture! Really appreciate it.

Can I use PICRUSt2 on Oxford Nanopore long reads data?

Sweet whistling Geronimo, don't try to be funny or draw comparisons to a growing human brain. You got it totally wrong. Please!

One thing i dont understand is the enrichment ratio in enrichment analysis. Since we have two groups, how do we know in which group is the pathway "enriched"?

Thanks a lot.

Thanks a lot.

Thanks for sharing this. I've watched all the videos on CBW Proteomics. They are very informative for beginners like me. I've learned a lot, from the basics to advanced topics. It has also helped me to develop new ideas that will be useful in my experiments.

It is very informative and helpful for beginners. Thanks for sharing. I will watch these series.

Wilson Brenda Garcia Anthony Gonzalez Amy

Williams Donald Lee Kimberly Lee Paul

Thank you!

This was a good introduction. If you have domain knowledge, you can use the various tools to enhance your technique, say you are a plant breeder and wish to change the color of a flower. Could we use ML for that purpose?

Thanks you for the clear class🌟

Really very much useful for beginners in microbiome research

The audio volume is low.

voice is not clear most of the time

This helped a lot! Thank you

Thanks so much. when the next lecture

This is well explained and very informative

Video to make wonders ❤

Great video thnx for sharing <3

In this series, voice is breaking between a lot... Please ask the speakers to speak to microphone...

what is this channel about? I am interested in bioinfomatics in canada and i'm currently in undergrad and wanted to know more about the feild.

Great channel

Thanks to the Bioinformatics DotCa team for making this wonderful series of videos public.

Hi thank you for this valuable workshop. I would like to ask if the functional analysis for untarget metabolomics can also be applied for NMR data? Thank you.

Good comprehensive video. One comment regarding differential expression analysis post-integration (starting at 56:27). In either bulk or single-cell sequencing, most tools highly recommend NOT doing DE analysis on corrected data, exactly because of the concerns regarding under- or over-correction and removing potential biological variation. If you read the literature around older batch correction tools such as ComBat, most recommend that DE analysis correct for batch effects within the statistical model (i.e. including a model of ~ Batch + Condition). Obviously, Seurat doesn't have that functionality, but if you perform pseudobulk aggregation for DE analysis (literature suggests this is more statistically sound), then tools capable of including batch effects in the regression model (DESeq2, edgeR, limma-voom, etc.) are available.

Hello thank you for the lecture. I would like to ask which metabolite set library I should use for enrichment analysis if I am doing human cell extract metabolomics? Thank you.

Excellent lecture. Thank you

Very helpful lecture

A million dollar lecture. Thank you 👍👍

Thank you so much!

hello, do you have de DOI or any information about the comparation between RNA shield and RNA later that was shown in the presentation? thanks

Thanks a lot for sharing these materials online. Learning bioinformatics by myself in Kenya

List of DBs to use in metabolomics

thank you so much❣

Very intersting and informative tutorial! Thanks! I'd be happy if your email is avaipable.

did they remove the analysis report feature from this version I have five and can't find it

Does the PPT available, Hamza bhai?

thank you

Thanks!!!

bhai speak slower

Can you please upload a tutorial on EnrichmentMap with GSEA data results and network analysis? And how to generate a heat map from the GSEA data pathways. I would really appreciate it. Great tutorial on this one.

Wonderful

I need help finding sessions two and three of introduction to R. Can you help?

Great 👍

this is great! Thank you

Excellent video. Thanks for sharing!

Hi, I have some questions regarding this. I am trying to make my own network. I have got 1000 gene IDs which are sorted in 6 clusters. All ID's contain descriptive information about the gene, and have got relative expressions for 8 different times (healthy vs sick). I have got all this data, but do not know how to import it in the right way into cytoscape. Could you help me with this? With kind regards, Jip