Lazy analysis of scRNA-seq¶
We all know that preprocessing for single-cell analysis is tedious and boring. In future OmicVerse development, we will gradually introduce agents to make the entire analysis fully automated. In version 1.7.0, we already support fully automated data preprocessing, and in the next release we expect to automate cell annotation.
- We provide a function
omicverse.single.lazy, which can preprocess single-cell data automatically, with adjustable parameters. - We offer an analysis-report generator
omicverse.single.generate_scRNA_report, similar to MultiQC, to visualize our preprocessing results. - Moreover, for every analysis invoked within OmicVerse, you can now generate reference citations and DOIs via
ov.generate_reference_table.
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import scanpy as sc
#import pertpy as pt
import omicverse as ov
ov.plot_set()
import scanpy as sc
#import pertpy as pt
import omicverse as ov
ov.plot_set()
🔬 Starting plot initialization...
🧬 Detecting CUDA devices…
✅ [GPU 0] NVIDIA H100 80GB HBM3
• Total memory: 79.1 GB
• Compute capability: 9.0
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/ /_/ / / / / / / / /__ | |/ / __/ / (__ ) __/
\____/_/ /_/ /_/_/\___/ |___/\___/_/ /____/\___/
🔖 Version: 1.7.0 📚 Tutorials: https://omicverse.readthedocs.io/
✅ plot_set complete.
Data Preparation¶
The data we use in the following example comes from Haber et al., 2017. It contains samples from the small intestinal epithelium of mice with different conditions. We first load the raw cell-level data. The dataset contains gene expressions of 9842 cells. They are annotated with their sample identifier (batch), the condition of the subjects and the type of each cell (cell_label).
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import pertpy as pt
adata = pt.dt.haber_2017_regions()
adata
import pertpy as pt
adata = pt.dt.haber_2017_regions()
adata
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ov.settings.cpu_gpu_mixed_init()
ov.settings.cpu_gpu_mixed_init()
CPU-GPU mixed mode activated
Lazy analysis¶
Currently, only human and mouse species are supported; we may add more species in the future. If you don’t specify any parameters, the analysis will run using the default settings.
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%time
adata=ov.single.lazy(adata,species='mouse',sample_key='batch')
%time
adata=ov.single.lazy(adata,species='mouse',sample_key='batch')
CPU times: user 2 μs, sys: 0 ns, total: 2 μs Wall time: 4.53 μs 🔧 The mode of lazy is cpu-gpu-mixed ✅ All packages used in lazy are installed ❌ QC step didn't start, we will start it now 🔧 The argument of qc we set mito_perc: 0.2 nUMIs: 500 detected_genes: 250 doublets_method: scrublet batch_key: batch ⚙️ Using torch CPU/GPU mixed mode... 📊 [GPU 0] ------------------------------ 4/81559 MiB (0.0%) Calculate QC metrics End calculation of QC metrics. Original cell number: 9842 !!!It should be noted that the `scrublet` detection is too old and may not work properly.!!! !!!if you want to use novel doublet detection, please set `doublets_method=sccomposite`!!! Begin of post doublets removal and QC plot using`scrublet` Running Scrublet🔍 filtered out 3076 genes that are detected in less than 3 cells normalizing counts per cell finished (0:00:00) extracting highly variable genes finished (0:00:00) --> added 'highly_variable', boolean vector (adata.var) 'means', float vector (adata.var) 'dispersions', float vector (adata.var) 'dispersions_norm', float vector (adata.var) normalizing counts per cell finished (0:00:00) normalizing counts per cell finished (0:00:00) Embedding transcriptomes using PCA... using data matrix X directly Automatically set threshold at doublet score = 0.24 Detected doublet rate = 0.6% Estimated detectable doublet fraction = 5.1% Overall doublet rate: Expected = 5.0% Estimated = 11.8% filtered out 3227 genes that are detected in less than 3 cells normalizing counts per cell finished (0:00:00) extracting highly variable genes finished (0:00:00) --> added 'highly_variable', boolean vector (adata.var) 'means', float vector (adata.var) 'dispersions', float vector (adata.var) 'dispersions_norm', float vector (adata.var) normalizing counts per cell finished (0:00:00) normalizing counts per cell finished (0:00:00) Embedding transcriptomes using PCA... using data matrix X directly Automatically set threshold at doublet score = 0.24 Detected doublet rate = 0.3% Estimated detectable doublet fraction = 30.8% Overall doublet rate: Expected = 5.0% Estimated = 1.0% filtered out 4938 genes that are detected in less than 3 cells normalizing counts per cell finished (0:00:00) extracting highly variable genes finished (0:00:00) --> added 'highly_variable', boolean vector (adata.var) 'means', float vector (adata.var) 'dispersions', float vector (adata.var) 'dispersions_norm', float vector (adata.var) normalizing counts per cell finished (0:00:00) normalizing counts per cell finished (0:00:00) Embedding transcriptomes using PCA... using data matrix X directly Automatically set threshold at doublet score = 0.13 Detected doublet rate = 2.5% Estimated detectable doublet fraction = 28.0% Overall doublet rate: Expected = 5.0% Estimated = 8.9% filtered out 2755 genes that are detected in less than 3 cells normalizing counts per cell finished (0:00:00) extracting highly variable genes finished (0:00:00) --> added 'highly_variable', boolean vector (adata.var) 'means', float vector (adata.var) 'dispersions', float vector (adata.var) 'dispersions_norm', float vector (adata.var) normalizing counts per cell finished (0:00:00) normalizing counts per cell finished (0:00:00) Embedding transcriptomes using PCA... using data matrix X directly Automatically set threshold at doublet score = 0.29 Detected doublet rate = 0.2% Estimated detectable doublet fraction = 3.9% Overall doublet rate: Expected = 5.0% Estimated = 4.1% filtered out 3179 genes that are detected in less than 3 cells normalizing counts per cell finished (0:00:00) extracting highly variable genes finished (0:00:00) --> added 'highly_variable', boolean vector (adata.var) 'means', float vector (adata.var) 'dispersions', float vector (adata.var) 'dispersions_norm', float vector (adata.var) normalizing counts per cell finished (0:00:00) normalizing counts per cell finished (0:00:00) Embedding transcriptomes using PCA... using data matrix X directly Automatically set threshold at doublet score = 0.25 Detected doublet rate = 0.6% Estimated detectable doublet fraction = 21.1% Overall doublet rate: Expected = 5.0% Estimated = 3.0% filtered out 2892 genes that are detected in less than 3 cells normalizing counts per cell finished (0:00:00) extracting highly variable genes finished (0:00:00) --> added 'highly_variable', boolean vector (adata.var) 'means', float vector (adata.var) 'dispersions', float vector (adata.var) 'dispersions_norm', float vector (adata.var) normalizing counts per cell finished (0:00:00) normalizing counts per cell finished (0:00:00) Embedding transcriptomes using PCA... using data matrix X directly Automatically set threshold at doublet score = 0.30 Detected doublet rate = 0.3% Estimated detectable doublet fraction = 2.2% Overall doublet rate: Expected = 5.0% Estimated = 11.9% filtered out 3305 genes that are detected in less than 3 cells normalizing counts per cell finished (0:00:00) extracting highly variable genes finished (0:00:00) --> added 'highly_variable', boolean vector (adata.var) 'means', float vector (adata.var) 'dispersions', float vector (adata.var) 'dispersions_norm', float vector (adata.var) normalizing counts per cell finished (0:00:00) normalizing counts per cell finished (0:00:00) Embedding transcriptomes using PCA... using data matrix X directly Automatically set threshold at doublet score = 0.23 Detected doublet rate = 0.6% Estimated detectable doublet fraction = 12.9% Overall doublet rate: Expected = 5.0% Estimated = 4.9% filtered out 2804 genes that are detected in less than 3 cells normalizing counts per cell finished (0:00:00) extracting highly variable genes finished (0:00:00) --> added 'highly_variable', boolean vector (adata.var) 'means', float vector (adata.var) 'dispersions', float vector (adata.var) 'dispersions_norm', float vector (adata.var) normalizing counts per cell finished (0:00:00) normalizing counts per cell finished (0:00:00) Embedding transcriptomes using PCA... using data matrix X directly Automatically set threshold at doublet score = 0.27 Detected doublet rate = 0.6% Estimated detectable doublet fraction = 17.6% Overall doublet rate: Expected = 5.0% Estimated = 3.4% filtered out 2682 genes that are detected in less than 3 cells normalizing counts per cell finished (0:00:00) extracting highly variable genes finished (0:00:00) --> added 'highly_variable', boolean vector (adata.var) 'means', float vector (adata.var) 'dispersions', float vector (adata.var) 'dispersions_norm', float vector (adata.var) normalizing counts per cell finished (0:00:00) normalizing counts per cell finished (0:00:00) Embedding transcriptomes using PCA... using data matrix X directly Automatically set threshold at doublet score = 0.31 Detected doublet rate = 0.5% Estimated detectable doublet fraction = 6.2% Overall doublet rate: Expected = 5.0% Estimated = 8.4% filtered out 3331 genes that are detected in less than 3 cells normalizing counts per cell finished (0:00:00) extracting highly variable genes finished (0:00:00) --> added 'highly_variable', boolean vector (adata.var) 'means', float vector (adata.var) 'dispersions', float vector (adata.var) 'dispersions_norm', float vector (adata.var) normalizing counts per cell finished (0:00:00) normalizing counts per cell finished (0:00:00) Embedding transcriptomes using PCA... using data matrix X directly Automatically set threshold at doublet score = 0.23 Detected doublet rate = 0.0% Estimated detectable doublet fraction = 28.9% Overall doublet rate: Expected = 5.0% Estimated = 0.0% Scrublet finished✅ (0:00:26) Cells retained after scrublet: 9798, 44 removed. End of post doublets removal and QC plots. Filters application (seurat or mads) Lower treshold, nUMIs: 500; filtered-out-cells: 0 Lower treshold, n genes: 250; filtered-out-cells: 0 Lower treshold, mito %: 0.2; filtered-out-cells: 0 Filters applicated. Total cell filtered out with this last --mode seurat QC (and its chosen options): 0 Cells retained after scrublet and seurat filtering: 9798, 44 removed. filtered out 626 genes that are detected in less than 3 cells ❌ Preprocess step didn't start, we will start it now 🔧 The argument of preprocess we set mode: shiftlog|pearson n_HVGs: 2000 target_sum: 500000.0 Begin robust gene identification After filtration, 14589/14589 genes are kept. Among 14589 genes, 14589 genes are robust. End of robust gene identification. Begin size normalization: shiftlog and HVGs selection pearson normalizing counts per cell. The following highly-expressed genes are not considered during normalization factor computation: ['Cck', 'Defa24', 'Fabp6', 'Gcg', 'Ghrl', 'Gip', 'Nts', 'Reg3b', 'Reg3g', 'Reg4', 'Sct', 'Spink4', 'Sst', 'Tff3', 'Zg16'] finished (0:00:00) extracting highly variable genes --> added 'highly_variable', boolean vector (adata.var) 'highly_variable_rank', float vector (adata.var) 'highly_variable_nbatches', int vector (adata.var) 'highly_variable_intersection', boolean vector (adata.var) 'means', float vector (adata.var) 'variances', float vector (adata.var) 'residual_variances', float vector (adata.var) Time to analyze data in cpu: 9.195535898208618 seconds. End of size normalization: shiftlog and HVGs selection pearson ❌ Scaled step didn't start, we will start it now ❌ PCA step didn't start, we will start it now 🔧 The argument of PCA we set layer: scaled n_pcs: 50 use_highly_variable: True 🚀 Using GPU to calculate PCA... 📊 [GPU 0] ------------------------------ 4/81559 MiB (0.0%) computing PCA🔍 with n_comps=50 [KeOps] Compiling cuda jit compiler engine ... OK [pyKeOps] Compiling nvrtc binder for python ... OK finished✅ (0:00:17) ❌ Cell cycle scoring step didn't start, we will start it now calculating cell cycle phase computing score 'S_score' finished: added 'S_score', score of gene set (adata.obs). 642 total control genes are used. (0:00:00) computing score 'G2M_score' WARNING: genes are not in var_names and ignored: Index(['Cks2', 'Jpt1', 'Pimreg'], dtype='object') finished: added 'G2M_score', score of gene set (adata.obs). 597 total control genes are used. (0:00:00) --> 'phase', cell cycle phase (adata.obs) ❌ Batch Correction: `Harmony` step didn't start, we will start it now ...Begin using harmony to correct batch effect
2025-05-27 02:50:31,149 - harmonypy - INFO - Computing initial centroids with sklearn.KMeans... 2025-05-27 02:50:35,626 - harmonypy - INFO - sklearn.KMeans initialization complete. 2025-05-27 02:50:35,740 - harmonypy - INFO - Iteration 1 of 10 2025-05-27 02:50:40,198 - harmonypy - INFO - Iteration 2 of 10 2025-05-27 02:50:42,596 - harmonypy - INFO - Iteration 3 of 10 2025-05-27 02:50:43,724 - harmonypy - INFO - Iteration 4 of 10 2025-05-27 02:50:44,843 - harmonypy - INFO - Iteration 5 of 10 2025-05-27 02:50:45,594 - harmonypy - INFO - Iteration 6 of 10 2025-05-27 02:50:46,093 - harmonypy - INFO - Iteration 7 of 10 2025-05-27 02:50:46,490 - harmonypy - INFO - Iteration 8 of 10 2025-05-27 02:50:46,934 - harmonypy - INFO - Iteration 9 of 10 2025-05-27 02:50:47,438 - harmonypy - INFO - Iteration 10 of 10 2025-05-27 02:50:47,844 - harmonypy - INFO - Stopped before convergence
🖥️ Using Scanpy CPU to calculate neighbors...
computing neighbors
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:25)
🔍 [2025-05-27 02:51:13] Running UMAP in 'cpu-gpu-mixed' mode...
🚀 Using torch GPU to calculate UMAP...
📊 [GPU 0] ------------------------------ 4/81559 MiB (0.0%)
computing UMAP🚀
finished ✅: added
'X_umap', UMAP coordinates (adata.obsm)
'umap', UMAP parameters (adata.uns) (0:00:09)
✅ UMAP completed successfully.
⚙️ Using torch CPU/GPU mixed mode to calculate t-SNE...
📊 [GPU 0] ------------------------------ 1084/81559 MiB (1.3%)
computing tSNE🔍
using sklearn.manifold.TSNE
finished ✅: added
'X_tsne', tSNE coordinates (adata.obsm)
'tsne', tSNE parameters (adata.uns) (0:00:15)
❌ Batch Correction: `scVI` step didn't start, we will start it now
...Begin using scVI to correct batch effect
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
You are using a CUDA device ('NVIDIA H100 80GB HBM3') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
Training: 0%| | 0/400 [00:00<?, ?it/s]
`Trainer.fit` stopped: `max_epochs=400` reached.
🖥️ Using Scanpy CPU to calculate neighbors...
computing neighbors
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:02)
🔍 [2025-05-27 02:54:44] Running UMAP in 'cpu-gpu-mixed' mode...
🚀 Using torch GPU to calculate UMAP...
📊 [GPU 0] ------------------------------ 1158/81559 MiB (1.4%)
computing UMAP🚀
finished ✅: added
'X_umap', UMAP coordinates (adata.obsm)
'umap', UMAP parameters (adata.uns) (0:00:03)
✅ UMAP completed successfully.
⚙️ Using torch CPU/GPU mixed mode to calculate t-SNE...
📊 [GPU 0] ------------------------------ 1234/81559 MiB (1.5%)
computing tSNE🔍
using sklearn.manifold.TSNE
finished ✅: added
'X_tsne', tSNE coordinates (adata.obsm)
'tsne', tSNE parameters (adata.uns) (0:00:24)
❌ Best Bench Correction Eval step didn't start, we will start it now
The Best Bench Correction Method is X_scVI
We can found it in `adata.uns['bench_best_res']`
❌ Best Clusters step didn't start, we will start it now
Automatic clustering using sccaf
Dimensionality using :X_scVI
computing neighbors
finished: added to `.uns['neighbors']`
`.obsm['X_mde']`, MDE coordinates
`.obsp['neighbors_distances']`, distances for each pair of neighbors
`.obsp['neighbors_connectivities']`, weighted adjacency matrix (0:00:02)
🖥️ Using Scanpy CPU to calculate neighbors...
computing neighbors
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:02)
Automatic clustering using leiden for preprocessed
running Leiden clustering
finished: found 23 clusters and added
'leiden_r1.5', the cluster labels (adata.obs, categorical) (0:00:03)
Automatic clustering using sccaf, Times: 0
Processing items: 0%| | 0/10 [00:00<?, ?it/s]
Mean CV accuracy: 0.8337 Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8068
Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8142
Processing item 0: 10%|█ | 1/10 [00:18<02:48, 18.69s/it, start_iter=0, R1norm_cutoff=0.296, R2norm_cutoff=0.00908, Accuracy=0.803, m1=0.306, m2=0.0101]
Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8030 Converge SCCAF_optimize no. cluster! Mean CV accuracy: 0.8278 Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8180
Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.7950 Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8111
Mean CV accuracy: 0.8311 Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8231
Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8131
Processing item 1: 20%|██ | 2/10 [00:22<01:19, 9.97s/it, start_iter=1, R1norm_cutoff=0.189, R2norm_cutoff=0.00727, Accuracy=0.813, m1=0.199, m2=0.00827]
Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8279 Converge SCCAF_optimize no. cluster! Mean CV accuracy: 0.8231 Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8234
Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8235 Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8199
Mean CV accuracy: 0.8442 Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8340
Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8334 Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8330
Mean CV accuracy: 0.8845 Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8582
Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8548 Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8625
Mean CV accuracy: 0.9144 Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8748
Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8778 Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.8907
Mean CV accuracy: 0.9710 Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.9797
Accuracy on the training set: 1.0000 Accuracy on the hold-out set: 0.9875 Accuracy on the training set: 1.0000
Processing item 1: 20%|██ | 2/10 [00:29<01:59, 14.97s/it, start_iter=1, R1norm_cutoff=0.189, R2norm_cutoff=0.00727, Accuracy=0.813, m1=0.199, m2=0.00827]
Accuracy on the hold-out set: 0.9726 Reached the minimum accuracy! Smoothing the effect of clustering, Times: 0 running Leiden clustering
finished: found 14 clusters and added
'leiden_clusters_L1', the cluster labels (adata.obs, categorical) (0:00:04)
running Louvain clustering
using the "louvain" package of Traag (2017)
finished: found 12 clusters and added
'louvain_clusters_L1', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
finished: found 8 clusters and added
'leiden_clusters_L2', the cluster labels (adata.obs, categorical) (0:00:02)
running Louvain clustering
using the "louvain" package of Traag (2017)
finished: found 8 clusters and added
'louvain_clusters_L2', the cluster labels (adata.obs, categorical) (0:00:00)
✅ UMAP step already finished, skipping it
✅ tSNE step already finished, skipping it
Of course, you can also manually specify parameters. Here are some examples.
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adata=ov.single.lazy(
adata,
species='mouse',
reforce_steps=['qc','preprocess','scaled','pca',
'cell_cycle','Harmony','scVI',
'eval_bench','umap','tsne'],
sample_key='batch',
qc_kwargs={
'tresh': {'mito_perc': 0.2, 'nUMIs': 500, 'detected_genes': 250},
'doublets_method': 'scrublet',
'batch_key': 'batch'
},
preprocess_kwargs={
'mode': 'shiftlog|pearson',
'n_HVGs': 2000,
'target_sum': 50*1e4
},
pca_kwargs={
'layer':'scaled',
'n_pcs':50,
'use_highly_variable': True,
},
harmony_kwargs={
'n_pcs':50,
},
scvi_kwargs={
'n_layers':2,
'n_latent':30,
'gene_likelihood':"nb"
},
)
adata=ov.single.lazy(
adata,
species='mouse',
reforce_steps=['qc','preprocess','scaled','pca',
'cell_cycle','Harmony','scVI',
'eval_bench','umap','tsne'],
sample_key='batch',
qc_kwargs={
'tresh': {'mito_perc': 0.2, 'nUMIs': 500, 'detected_genes': 250},
'doublets_method': 'scrublet',
'batch_key': 'batch'
},
preprocess_kwargs={
'mode': 'shiftlog|pearson',
'n_HVGs': 2000,
'target_sum': 50*1e4
},
pca_kwargs={
'layer':'scaled',
'n_pcs':50,
'use_highly_variable': True,
},
harmony_kwargs={
'n_pcs':50,
},
scvi_kwargs={
'n_layers':2,
'n_latent':30,
'gene_likelihood':"nb"
},
)
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ov.plot_set()
ov.pl.embedding(
adata,
basis='X_umap',
color=['leiden_clusters_L1','cell_label']
)
ov.plot_set()
ov.pl.embedding(
adata,
basis='X_umap',
color=['leiden_clusters_L1','cell_label']
)
🔬 Starting plot initialization...
🧬 Detecting CUDA devices…
✅ [GPU 0] NVIDIA H100 80GB HBM3
• Total memory: 79.1 GB
• Compute capability: 9.0
✅ plot_set complete.
Report Generation¶
If you’d like to obtain the analysis report from the lazy function, it’s just as simple: run
omicverse.single.generate_scRNA_report
using the same parameters you passed to lazy. We’ll continue to refine the report’s layout in future releases, and we welcome your suggestions via GitHub Issues: https://github.com/Starlitnightly/omicverse/issues
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# Usage example:
html_report = ov.single.generate_scRNA_report(
adata, species='mouse', sample_key='batch',
output_path="scRNA_analysis_report.html",
)
# Usage example:
html_report = ov.single.generate_scRNA_report(
adata, species='mouse', sample_key='batch',
output_path="scRNA_analysis_report.html",
)
✅ MultiQC-style report generated successfully! 📄 Report saved to: scRNA_analysis_report.html 📊 Visualizations included: 7 plots 🎨 Features: White theme, Dark mode toggle, OmicVerse logo 🎯 Clustering methods detected: 5 🧬 Integration methods: Harmony ✓, scVI ✓ 🏆 Best integration method: X_scVI
<Figure size 640x480 with 0 Axes>
Reference Generation¶
Starting from version 1.7.0, we record in the adata object all the packages you’ve invoked through OmicVerse for easy citation. Please note that this function is still under development, so some packages may be missing.
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ov.generate_reference_table(adata)
ov.generate_reference_table(adata)
Out[4]:
| method | reference | content | |
|---|---|---|---|
| 0 | SCCAF | clustering optimization with SCCAF | |
| 1 | omicverse | Zeng, Z., Ma, Y., Hu, L., Tan, B., Liu, P., Wa... | This analysis is performed with omicverse fram... |
| 2 | pymde | Agrawal, A., Ali, A., & Boyd, S. (2021). Minim... | MDE with pymde |
| 3 | scanpy | Wolf, F. A., Angerer, P., & Theis, F. J. (2018... | neighbors with scanpy |
| 4 | tsne | Van der Maaten, L., & Hinton, G. (2008). Visua... | t-SNE with omicverse |
| 5 | umap | McInnes, L., Healy, J., & Melville, J. (2018).... | UMAP with pymde |
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from IPython.display import IFrame
IFrame(
src='https://starlitnightly.github.io/single/scRNA_analysis_report.html',
width=1280,
height=1000,
)
from IPython.display import IFrame
IFrame(
src='https://starlitnightly.github.io/single/scRNA_analysis_report.html',
width=1280,
height=1000,
)
Out[14]:
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