ฝัง
- เผยแพร่เมื่อ 26 มิ.ย. 2024
- In this video I integrate the single-cell RNA data together with scVI and use multiple methods of label transfer from reference datasets. I then verify and annotate the individual clusters using known marker genes. This video covers advanced analysis steps, such as tuning hyperparameters in our scVI model, making custom reference datasets, and more.
Main notebook:
github.com/mousepixels/sanbom...
Example of bad mapping:
github.com/mousepixels/sanbom...
Part 1:
• 2024 updated single-ce...
0:00 Celltypist transfer
13:49 scVI transfer
21:13 Integration
29:22 Dim reduction
34:00 Annotation - วิทยาศาสตร์และเทคโนโลยี
Thanks a lot for these valuable tutorials! You really doing an incredible job for students who do not have access to formal courses in bioinformatics!
Thanks a lot, very nice especially the umap inside umap trick
Thank you for your great tutorials. I am always waiting for your next video. Thank you so much!!
Trying to get around to it this week if I have time!
Excellent tutorial Mark! I've been doing this for years now, but still managed to pick up a few new tricks. Love your careful and polite approach explaining the UMAP drama. Thank you for your work :)
I may have had to edit it to make it more polite that in was xD
eg, took out the part about using a globe while driving a car lol
I learned some new tricks, and I have been doing this for a while!
I think instead of shuffling prior to UMAP you can also just pass sort_order=False
Unless they changed it recently, I think that is probably still dependent on the order of the cells in your dataset. So DX would still seem shuffled but something like sample would still be overplotted
@@sanbomics huh that's pretty bad than.
Maybe I should write a PR
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!
I can definitely keep that in mind for a future video!
we're still looking forward to the future part ;D
I know i know xD. I was going to start working on it this weekend. I have been very busy!
someday soon...
@@sanbomics we're all hoping for this series to be completed so we can implement it, we're rooting for you! we're grateful for anything you can share :D
Hi, I've been following your videos closely lately as they are very intuitive! Thank you as always for these fantastic tutorials. I have very recently started learning about bioinfo and I have a very loose understanding of what each tool does. For example, the difference between dimensionality reduction methods such as PCA, UMAP, and Non-negative Matrix Factorization (NMF). With UMAP and PCA being very similar with the difference being one is non linear and the latter is linear. However I fail to understand why some would use NMF to analyze any type of RNA seq data, does it provide results that UMAP downstream analysis cannot perform ? or is there any other reason to use NMF? I'd be grateful if you could help me understand.
Thanks Mark for the second part, as usual it's highly informative! Just one question about SCVI-SCANVI label transfer, it also predicts the labels of reference along side the 'unknown', do you mind quickly checking ref's predicted labels with ground truth labels and find out what is the percentage of correctly predicted labels? After following your July 11 2022 video I have played with it a lot and only managed to get 87% correct prediction rate. Thanks!!
I can check. The number is a little low but doesn't sound too unreasonable, since we set n_samples_per_label to 100. If you increase that number your ground truth prediction rate might increase, but at the cost of label:unknown disparity.
Very cool video! Could you please tell us how to do something similar to your introduction with the umap transforming to the logo??
I have the video where I turn my cat into a UMAP. Let me know if that helps, if not, I can maybe post the code.
Thanks a lot for the tutorials!! Does someone has any idea if the hyperparameter tuning uses the layer counts
(with raw counts) because i dont get what it inputs to do the grid search. I just want to do tuning for just the data integration model.
if you don't specify a layer it will just use .X (which should be raw)
By the way, when I am running the scvi models, despite having a 4080hx GPU and cuda installed it barely is being employed when training the models, instead it uses the integrated GPU. When I moved the code to my friend’s computer who has a better GPU, his 4090 CPU is running at 80% when the training models as showed by the system statistics. Do you perhaps have any idea what the issue might be ? In terms of time needed to complete the task I’d say my computer is not too slow compared to his.
Sometimes the cpu is running high at the start and then the gpu gets employed. Is the same usage after couple minutes?
yeah, and I noticed the integrated GPU is getting used a bit in an inconsistent manner. While my friend s GPU is running at 70% in a consistent manner
@@laloulymounia9266 i guess you meant that you have a 4080 rtx? or just an intergrated gpu ? in general these algorithms would run in cuda which need an nvidia gpu (rather than intergrated one). If you can check if cuda is available by using nvcc -V to check if its enabled (look at drivers etc.) and in case you have both intergrated gpu and individual GPU cou can specify what to use. If you just have lets say a ryzen iGPU im not sure how possible it is to use it ( cuda is for nvidia architecture)
When you start training the model does it say the GPU is being used? If yes, and If it's still running decently fast, I wouldn't worry. GPU utilization and all the warnings/errors is a common frustration with scvi but I have been told it is being addressed
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)
You can do it after depending on how many cells you have. With this many cells it becomes almost impossible because it requires a dense matrix.
Very good tutorial!!!! I would like to ask a question .
model = scvi.model.SCVI.setup_anndata(
adata, categorical_covariate_keys=[‘sample’],
continuous_covariate_keys=[‘percent_mito’, ‘percent_ribo’]
).
Why not just specify sample as batch here.
For example.
model = scvi.model.SCVI.setup_anndata(
adata, batch_key=‘batch’,
continuous_covariate_keys=[‘percent_mito’, ‘percent_ribo’]
).
Wouldn't this more directly point out that sample is a batch. Or what is the difference between these two?
Thank you very much for your help!
Funny enough, this is a question I have asked the scVI team in the past. I was told that it wouldn't make much of a difference. Typically I save batch for when I integrate multiple different studies or technologies or species together.