Comments (1)
You can download the cell type annotation or reproduce the labels as follows:
For hArtery and hBone, you can reproduce the labels following the methods and cell type markers described in Liang, et.al, 2021 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8685327/ , Fig. 2B and Table S1) and Chou, et.al, 2020 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331607/ , Table 1), respectively.
For mBrain, you can download from:
https://drive.google.com/drive/folders/1QQXDuUjKG8CTnwWW_u83MDtdrBXr8Kpq
We use the ‘cell_type’ as label.
For mAtlas, you can download from:
https://figshare.com/articles/dataset/Processed_files_to_use_with_scanpy_/8273102/2
We use the ‘cell_ontology_class’ as label.
For mPancreas:
https://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132188/suppl/GSE132188_adata.h5ad.h5
We use the sub-type from 'clusters_fig6_broad_final' and 'clusters_fig6_fine_final' as label.
And we use the Scanpy to normalize the counts data. The coda is shown as blow:
import scanpy as sc
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata)
adata = adata[:, adata.var.highly_variable]
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Related Issues (20)
- Share test data file in the tutorial HOT 1
- Sharing code to reproduce manuscript figures HOT 1
- Quick question about prediction result HOT 3
- What will happen if the number or order of var_names is different? HOT 3
- [bug] There is a bug when I install the software HOT 2
- could you share what's hardware(GPU) used to train this model? HOT 2
- Questions about reading certain datasets HOT 1
- model depth=2, cause more 8 times GPU usage than depth=1 in hPancreas dataset train HOT 3
- TOSICA install problem HOT 1
- use TOSICA for cell classification HOT 4
- Problem with TOSICA metrics comparing HOT 1
- Cannot import TOSICA due to issue with np.long HOT 3
- Memory leakage HOT 3
- Data preprocessing HOT 1
- Code Consulting HOT 1
- TOSICA pre function gives very different accuracy than evaluate in train function HOT 2
- The test data runs differently than the example HOT 8
- Mask matrix issues HOT 1
- Script problem HOT 1
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