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@@ -6,8 +6,8 @@ tags:
6
  - genomics
7
  - single-cell
8
  - model_cls_name:SCVI
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- - scvi_version:1.2.0
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- - anndata_version:0.11.1
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  - modality:rna
12
  - tissue:various
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  - annotated:True
@@ -20,7 +20,7 @@ The learned low-dimensional latent representation of the data can be used for vi
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  clustering.
21
 
22
  scVI takes as input a scRNA-seq gene expression matrix with cells and genes.
23
- We provide an extensive [user guide](https://docs.scvi-tools.org/en/1.2.0/user_guide/models/scvi.html).
24
 
25
  - See our original manuscript for further details of the model:
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  [scVI manuscript](https://www.nature.com/articles/s41592-018-0229-2).
@@ -28,7 +28,7 @@ We provide an extensive [user guide](https://docs.scvi-tools.org/en/1.2.0/user_g
28
  to leverage pre-trained models.
29
 
30
  This model can be used for fine tuning on new data using our Arches framework:
31
- [Arches tutorial](https://docs.scvi-tools.org/en/1.0.0/tutorials/notebooks/scarches_scvi_tools.html).
32
 
33
 
34
  # Model Description
@@ -49,24 +49,14 @@ space might still be useful for analysis.
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50
  **Cell-wise Coefficient of Variation**:
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- | Metric | Training Value | Validation Value |
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- |-------------------------|----------------|------------------|
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- | Mean Absolute Error | 1.25 | 1.34 |
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- | Pearson Correlation | 0.95 | 0.94 |
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- | Spearman Correlation | 0.93 | 0.92 |
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- | R² (R-Squared) | 0.89 | 0.86 |
58
 
59
  The gene-wise coefficient of variation summarizes how well variation between different genes is
60
  preserved by the generated model expression. This value is usually quite high.
61
 
62
  **Gene-wise Coefficient of Variation**:
63
 
64
- | Metric | Training Value |
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- |-------------------------|----------------|
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- | Mean Absolute Error | 19.41 |
67
- | Pearson Correlation | 0.69 |
68
- | Spearman Correlation | 0.77 |
69
- | R² (R-Squared) | -0.95 |
70
 
71
  </details>
72
 
@@ -81,31 +71,7 @@ cell-type.
81
 
82
  **Differential expression**:
83
 
84
- | Index | gene_f1 | lfc_mae | lfc_pearson | lfc_spearman | roc_auc | pr_auc | n_cells |
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- | --- | --- | --- | --- | --- | --- | --- | --- |
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- | acinar cell of salivary gland | 0.96 | 0.60 | 0.74 | 0.96 | 0.14 | 0.94 | 10147.00 |
87
- | memory B cell | 0.93 | 1.22 | 0.69 | 0.92 | 0.38 | 0.88 | 2879.00 |
88
- | fibroblast | 0.97 | 1.06 | 0.69 | 0.92 | 0.44 | 0.88 | 2169.00 |
89
- | CD8-positive, alpha-beta T cell | 0.94 | 2.11 | 0.67 | 0.88 | 0.21 | 0.77 | 1979.00 |
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- | macrophage | 0.87 | 1.23 | 0.69 | 0.93 | 0.48 | 0.88 | 1761.00 |
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- | duct epithelial cell | 0.93 | 1.11 | 0.72 | 0.91 | 0.39 | 0.83 | 1693.00 |
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- | CD4-positive helper T cell | 0.97 | 2.08 | 0.62 | 0.87 | 0.32 | 0.83 | 1221.00 |
93
- | naive B cell | 0.93 | 2.37 | 0.66 | 0.84 | 0.33 | 0.84 | 1120.00 |
94
- | basal cell | 0.87 | 1.44 | 0.71 | 0.92 | 0.55 | 0.90 | 922.00 |
95
- | adventitial cell | 0.91 | 2.36 | 0.65 | 0.87 | 0.37 | 0.80 | 442.00 |
96
- | monocyte | 0.89 | 2.59 | 0.68 | 0.83 | 0.40 | 0.80 | 426.00 |
97
- | plasma cell | 0.85 | 2.38 | 0.67 | 0.80 | 0.21 | 0.85 | 345.00 |
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- | endothelial cell | 0.85 | 2.70 | 0.64 | 0.79 | 0.50 | 0.84 | 323.00 |
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- | endothelial cell of lymphatic vessel | 0.82 | 2.51 | 0.68 | 0.83 | 0.52 | 0.84 | 320.00 |
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- | mature NK T cell | 0.82 | 3.07 | 0.66 | 0.79 | 0.38 | 0.80 | 317.00 |
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- | T cell | 0.73 | 3.99 | 0.51 | 0.56 | 0.30 | 0.59 | 227.00 |
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- | myoepithelial cell | 0.92 | 3.63 | 0.61 | 0.68 | 0.35 | 0.68 | 196.00 |
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- | CD4-positive, alpha-beta T cell | 0.46 | 5.79 | 0.50 | 0.60 | 0.21 | 0.72 | 182.00 |
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- | pericyte | 0.80 | 3.19 | 0.67 | 0.72 | 0.42 | 0.75 | 146.00 |
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- | B cell | 0.35 | 2.73 | 0.59 | 0.77 | 0.43 | 0.75 | 137.00 |
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- | neutrophil | 0.90 | 4.36 | 0.61 | 0.59 | 0.35 | 0.77 | 115.00 |
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- | ionocyte | 0.83 | 4.53 | 0.61 | 0.63 | 0.39 | 0.72 | 87.00 |
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- | salivary gland cell | 0.50 | 3.99 | 0.62 | 0.68 | 0.55 | 0.81 | 45.00 |
109
 
110
  </details>
111
 
@@ -125,6 +91,7 @@ These provide the settings to setup the original model:
125
  "dropout_rate": 0.05,
126
  "dispersion": "gene",
127
  "gene_likelihood": "nb",
 
128
  "latent_distribution": "normal",
129
  "use_batch_norm": "none",
130
  "use_layer_norm": "both",
@@ -140,9 +107,9 @@ These provide the settings to setup the original model:
140
  Arguments passed to setup_anndata of the original model:
141
  ```json
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  {
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- "layer": null,
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  "batch_key": "donor_assay",
145
- "labels_key": "cell_ontology_class",
146
  "size_factor_key": null,
147
  "categorical_covariate_keys": null,
148
  "continuous_covariate_keys": null
@@ -155,15 +122,15 @@ Arguments passed to setup_anndata of the original model:
155
  <summary><strong>Data Registry</strong></summary>
156
 
157
  Registry elements for AnnData manager:
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- | Registry Key | scvi-tools Location |
159
- |-------------------|--------------------------------------|
160
- | X | adata.X |
161
- | batch | adata.obs['_scvi_batch'] |
162
- | labels | adata.obs['_scvi_labels'] |
163
- | latent_qzm | adata.obsm['scvi_latent_qzm'] |
164
- | latent_qzv | adata.obsm['scvi_latent_qzv'] |
165
- | minify_type | adata.uns['_scvi_adata_minify_type'] |
166
- | observed_lib_size | adata.obs['observed_lib_size'] |
167
 
168
  - **Data is Minified**: False
169
 
@@ -172,16 +139,16 @@ Registry elements for AnnData manager:
172
  <details>
173
  <summary><strong>Summary Statistics</strong></summary>
174
 
175
- | Summary Stat Key | Value |
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  |--------------------------|-------|
177
- | n_batch | 4 |
178
- | n_cells | 27199 |
179
- | n_extra_categorical_covs | 0 |
180
- | n_extra_continuous_covs | 0 |
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- | n_labels | 23 |
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- | n_latent_qzm | 20 |
183
- | n_latent_qzv | 20 |
184
- | n_vars | 3000 |
185
 
186
  </details>
187
 
 
6
  - genomics
7
  - single-cell
8
  - model_cls_name:SCVI
9
+ - scvi_version:1.4.2
10
+ - anndata_version:0.12.7
11
  - modality:rna
12
  - tissue:various
13
  - annotated:True
 
20
  clustering.
21
 
22
  scVI takes as input a scRNA-seq gene expression matrix with cells and genes.
23
+ We provide an extensive [user guide](https://docs.scvi-tools.org/en/stable/user_guide/models/scvi.html).
24
 
25
  - See our original manuscript for further details of the model:
26
  [scVI manuscript](https://www.nature.com/articles/s41592-018-0229-2).
 
28
  to leverage pre-trained models.
29
 
30
  This model can be used for fine tuning on new data using our Arches framework:
31
+ [Arches tutorial](https://docs.scvi-tools.org/en/stable/tutorials/notebooks/scrna/scarches_scvi_tools.html).
32
 
33
 
34
  # Model Description
 
49
 
50
  **Cell-wise Coefficient of Variation**:
51
 
52
+ Not provided by uploader
 
 
 
 
 
53
 
54
  The gene-wise coefficient of variation summarizes how well variation between different genes is
55
  preserved by the generated model expression. This value is usually quite high.
56
 
57
  **Gene-wise Coefficient of Variation**:
58
 
59
+ Not provided by uploader
 
 
 
 
 
60
 
61
  </details>
62
 
 
71
 
72
  **Differential expression**:
73
 
74
+ Not provided by uploader
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
75
 
76
  </details>
77
 
 
91
  "dropout_rate": 0.05,
92
  "dispersion": "gene",
93
  "gene_likelihood": "nb",
94
+ "use_observed_lib_size": true,
95
  "latent_distribution": "normal",
96
  "use_batch_norm": "none",
97
  "use_layer_norm": "both",
 
107
  Arguments passed to setup_anndata of the original model:
108
  ```json
109
  {
110
+ "layer": "counts",
111
  "batch_key": "donor_assay",
112
+ "labels_key": "cell_type",
113
  "size_factor_key": null,
114
  "categorical_covariate_keys": null,
115
  "continuous_covariate_keys": null
 
122
  <summary><strong>Data Registry</strong></summary>
123
 
124
  Registry elements for AnnData manager:
125
+ | Registry Key | scvi-tools Location |
126
+ |--------------------------|--------------------------------------|
127
+ | X | adata.layers['counts'] |
128
+ | batch | adata.obs['_scvi_batch'] |
129
+ | labels | adata.obs['_scvi_labels'] |
130
+ | latent_qzm | adata.obsm['scvi_latent_qzm'] |
131
+ | latent_qzv | adata.obsm['scvi_latent_qzv'] |
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+ | minify_type | adata.uns['_scvi_adata_minify_type'] |
133
+ | observed_lib_size | adata.obs['observed_lib_size'] |
134
 
135
  - **Data is Minified**: False
136
 
 
139
  <details>
140
  <summary><strong>Summary Statistics</strong></summary>
141
 
142
+ | Summary Stat Key | Value |
143
  |--------------------------|-------|
144
+ | n_batch | 6 |
145
+ | n_cells | 39821 |
146
+ | n_extra_categorical_covs | 0 |
147
+ | n_extra_continuous_covs | 0 |
148
+ | n_labels | 31 |
149
+ | n_latent_qzm | 20 |
150
+ | n_latent_qzv | 20 |
151
+ | n_vars | 3000 |
152
 
153
  </details>
154