|
44 | 44 | }, |
45 | 45 | { |
46 | 46 | "cell_type": "code", |
47 | | - "execution_count": null, |
| 47 | + "execution_count": 4, |
48 | 48 | "metadata": {}, |
49 | 49 | "outputs": [], |
50 | 50 | "source": [ |
|
63 | 63 | }, |
64 | 64 | { |
65 | 65 | "cell_type": "code", |
66 | | - "execution_count": null, |
| 66 | + "execution_count": 6, |
67 | 67 | "metadata": {}, |
68 | 68 | "outputs": [], |
69 | 69 | "source": [ |
|
72 | 72 | }, |
73 | 73 | { |
74 | 74 | "cell_type": "code", |
75 | | - "execution_count": null, |
| 75 | + "execution_count": 7, |
76 | 76 | "metadata": {}, |
77 | 77 | "outputs": [], |
78 | 78 | "source": [ |
79 | 79 | "embeddings_mrged = rxrx3_metadata.merge(\n", |
80 | | - " openphenom_embeddings.rename(columns={\"well_id\": \"external_well_id\"}).groupby(\"external_well_id\").mean(), \n", |
| 80 | + " openphenom_embeddings.groupby(\"well_id\").mean(), \n", |
81 | 81 | " left_on=\"well_id\", \n", |
82 | 82 | " right_index=True,\n", |
83 | 83 | ")" |
84 | 84 | ] |
85 | 85 | }, |
86 | 86 | { |
87 | 87 | "cell_type": "code", |
88 | | - "execution_count": null, |
| 88 | + "execution_count": 8, |
89 | 89 | "metadata": {}, |
90 | 90 | "outputs": [], |
91 | 91 | "source": [ |
|
118 | 118 | }, |
119 | 119 | { |
120 | 120 | "cell_type": "code", |
121 | | - "execution_count": null, |
| 121 | + "execution_count": 10, |
122 | 122 | "metadata": {}, |
123 | 123 | "outputs": [], |
124 | 124 | "source": [ |
|
152 | 152 | "metadata": {}, |
153 | 153 | "outputs": [], |
154 | 154 | "source": [ |
| 155 | + "map_data_gene_only = map_data.query(\"perturbation_type == 'CRISPR'\") # only use CRISPR perturbations for gene-gene benchmarks\n", |
| 156 | + "\n", |
155 | 157 | "pert_signal_pval_cutoff = 0.05\n", |
156 | 158 | "recall_thr_pairs = [(0.05, 0.95)]\n", |
157 | 159 | "\n", |
158 | 160 | "print(\"Computing recall...\")\n", |
159 | 161 | "bmdb_metrics = known_relationship_benchmark(\n", |
160 | | - " Bunch(metadata=map_data[metadata_cols], features=map_data[features_cols]),\n", |
| 162 | + " Bunch(metadata=map_data_gene_only[metadata_cols], features=map_data_gene_only[features_cols]),\n", |
161 | 163 | " recall_thr_pairs=recall_thr_pairs,\n", |
162 | 164 | " pert_col=pert_colname,\n", |
163 | 165 | " log_stats=True,\n", |
|
309 | 311 | "\n", |
310 | 312 | " fig.show()" |
311 | 313 | ] |
| 314 | + }, |
| 315 | + { |
| 316 | + "cell_type": "code", |
| 317 | + "execution_count": null, |
| 318 | + "metadata": {}, |
| 319 | + "outputs": [], |
| 320 | + "source": [] |
312 | 321 | } |
313 | 322 | ], |
314 | 323 | "metadata": { |
|
327 | 336 | "name": "python", |
328 | 337 | "nbconvert_exporter": "python", |
329 | 338 | "pygments_lexer": "ipython3", |
330 | | - "version": "3.11.6" |
| 339 | + "version": "3.11.9" |
331 | 340 | } |
332 | 341 | }, |
333 | 342 | "nbformat": 4, |
|
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