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2024 updated single-cell guide – Part 2: RNA Integration and
2024 updated single-cell guide – Part 2: RNA Integration and annotation
#updated #singlecell #guide #Part #RNA #Integration
“Sanbomics”
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…
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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!
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!
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.
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.
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.
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!
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
Thanks a lot, very nice especially the umap inside umap trick