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Hello, Would you please make a tut on advance workflow (based on good paper) on sc RNA Seq by using R?

Please make this same tutorial for R🙏

Thank you for the informative tutorial video; it has been immensely beneficial to my scientific research!😄

great content, languages just different, don't have to be good or bad.

do i have to do this for each sample or what ?

1:12, why do we have to predict doublet at each sample separately??

Well it was really awesome. Im still undergreadute so it was little bit hard to understand bioinfo part but python code part was clear. Can you or did you do other integration methods or can you record another video ?

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!

Man, you absolutely saved me! Suddenly, everything makes sense now. Subscribed!

Thank you so much for your videos! I am a grad student who recently started a sing cell project and since I found your channel, your explanations and code have been getting me through this tough time. I was wondering if you will be planning on doing cNMF in the future? It is something that I and our lab have had difficulty with. Thanks again!

Thank you for this video! I am confused why you can use the Ensembl version 109 for TxImport--but you ran salmon with the Gencode transcripts fasta. Doesn't gencode differ in what transcripts are included versus Ensembl? Or this doesn't really matter?

Hi there, thanks. But I have this error.I changed it to 6 but still I have this error !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=154478, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 7

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

While this is technically correct, it is not very statistically sound. When adjusting for the bonferroni and bh procedures, we typically change the cutoff point, not the actual p-value. multiplying the p-value by the number of tests can lead to p-values greater than one (specifically for the bonferroni method, the bh method is already accounted for by dividing by the rank), which is impossible since a p-value is a probability between 0 and 1. while the end conclusion is the same, it doesn't make sense from a statistical standpoint. you can absolutely use this method to get the right significance, but if you are presenting this to a statistician or publishing this work, you would need to adjust the p-value cutoff instead of the actual p-value or change any p-value that is greater than 1 to be exactly 1, but even this is a little more nuanced than just multiplication.

This man is an absolute God send, I can't even begin to count the amount of times he has came in clutch with a solution to issues I encounter in my personal projects and during my internship!

Thank you for this very helpful tutorial. is there a function for plotting the euclidean distance map for each sample?

@Sanbomics... good content but you really need to learn how to talk!

we're still looking forward to the future part ;D

Thanks for your work. It's really useful.

Can you cover batch correction in python?

I love this, thank you!

thank you very much. Great work. Maybe a dumb question, but how would you process bam files that contain SCdata from several cells? Essentially what I need is a table similar to yours with genes in the rows and cells in the columns (instead of whole bam files).

Thank you so much! This video is perfect for those who want to analyze scRNA-seq data!

Firstly thank you so much for your videos are very useful. I have used featureCounts to generate the count table, but I obtain a percentage of Unassigned_NoFeatures to high (around 50%). I checked that the annotation file used to the alignment and to generate the count table is the same, also I checked the type of stranded of the assay and I continue having the same problem. I tried to change the GTF.featureType to exon by gene and the % of Unassigned_NoFeatures decrease until 15% more or less. These results suggest me that I have a high content of introns or intergenic regiones in my results but when I checked with the IGV I don't observe that. I don't know if you can help me with this or tell me is this results are normal for human data. Thank you so much!!

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.

Hello! I am curious if you will have a much expanded version of the Spatial Seq Data analysis, also curious if there is any Proteomic analysis tutorial coming up! Thanks for your great work!

hey mate Am getting this error followed all your steps except the filter one pls help I have 5 columns in my matrix (M10,M11,M12,M3 and M5) and EMBSEL gene ids to it The dds step is not working Error in checkForExperimentalReplicates(object, modelMatrix) : The design matrix has the same number of samples and coefficients to fit, so estimation of dispersion is not possible. Treating samples as replicates was deprecated in v1.20 and no longer

I never comment on youtube videos but thank you so much for this. It was so simple and straight to the point. I am new to coding and needed to get all of the outputs from 20 featurecount reads into one output file, and this was the only thing I could find that not only made sense, but also worked. Thank you thank you thank you!!!

Do you have a code for doing the same in R studio, I've been trying to built a Seurat object with public available data, using the counts, position and images, with no success. Thanks

Hi Sam, do you have a video of how you’re downloading the data from NCBI (papers) because that part I don’t understand.

great video, I learn a lot. But i was wondering what device you use? I did the same analysis but i could load the model because im out of memory.

Dear sanbomic! I am wondering what is your first options for gene regulatory network analysis? when you have only single cell rna seq data or also have single cell ATAC data!!

Thank you, very usefull !

thank you for the nice video, regarding to the part for making the cell typ fraction plot (form this part of the code till end of this part: adata.obs.groupby(['sample']).count()) may you also please explain how to do it in R with the Seurat objecet? thanks

Your older videos are also very helpful. Thank you for everything

Hi! thanks for this! just wondering if you have tried comparing results of CellOracle from SCENIC?

Hello I'm use Star to align rna .fq using --quantmode when i cat ReadPerGene.out.tap it's indicate that my read only map to rRNAs ncRNA but when i check in IGV it shown that my reads mapped to coding gene ,how do i fix this I'm trying to use ReadPerGene.out.tab to further analysis.

Hi! Im memory limited, so I can only load in my dataset using the backed = 'r' option. How would I subset in this scenario?

Not sure if the DeseqDataSet parameters have changed since this tutorial but I had to change clinical to metadata when running: dds = DeseqDataSet( counts = counts, metadata=pb.obs, design_factors="tumour")

there is no complex heat map package

haii may I know if it runs .tsv file?

Could you please help how to make a violin plot using AUCell package like you did for dimplot?

Your tutorials are always incredibly helpful! Do you have a scripts that implements UMAP to spatial transition animation for the Xenium dataset? We have a beautiful new dataset that can share. sqiuatarizonadotedu. Thanks so much.

Hey! Could you please explain what these computed counts are?

Men U R a real hero

Very cool video! Could you please tell us how to do something similar to your introduction with the umap transforming to the logo??

my name is zeinab bahari . you can find me in research gat... i need help in rna seq data analysis

Hi.thanks for your good video.how can acsess to you dr. i need some emergenecy help in my data analysis.. please help me

Im not a scientist. I came here from cancer research. I gave it a thumbs up. Clear and informational.

Is there a reason you used CellTypist before integration? It means that the overclustering done by CellTypist is different to the overclustering done post-integration when annotating (which is making annotation a bit confusing in my case)