Comments (1)
You can recompute the UMAP embeddings. Let adata_raw
be the AnnData object for the raw count data. I use the following code for generating the processed data adata
with the UMAP embeddings:
adata = adata_raw.copy()
sc.pp.normalize_per_cell(adata, counts_per_cell_after=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, subset = False, min_disp=.5,
min_mean=.0125, max_mean=10, n_bins=20, n_top_genes=None)
sc.pp.scale(adata, max_value=10, zero_center=False)
sc.pp.pca(adata, n_comps=50, use_highly_variable=True, svd_solver='arpack')
sc.pp.neighbors(adata, n_neighbors=15, n_pcs=20)
sc.tl.louvain(adata, resolution = 0.5)
sc.tl.leiden(adata, resolution = 0.5)
sc.tl.umap(adata)
The UMAP embeddings should be stored at adata.obsm['X_umap']
.
Let me know if you have more questions.
Best,
Martin
from scdrs.
Related Issues (20)
- scRNA data input HOT 6
- perform-downstream outputs zero significant cells HOT 1
- plot_group_stats() got an unexpected keyword argument 'plot_kws' HOT 15
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- gene weights for the control genes HOT 5
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- Gene weights or without gene weights HOT 3
- compute-score is not reading .gs file properly HOT 2
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- The graphs from a single result and multiple results are inconsistent HOT 13
- Subsetting cells for downstream analysis. HOT 1
- TypeError: '<' not supported between instances of 'str' and 'float' HOT 3
- About compute-score
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- TypeError: plot_group_stats() got an unexpected keyword argument 'plot_kws' HOT 9
- trait=z_score: skipped due to small size (n_gene=5, sys_time=116.1s) HOT 1
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