{ "cells": [ { "cell_type": "markdown", "id": "cite-interp-00", "metadata": {}, "source": [ "# RNA/ADT interpretability tutorial\n", "\n", "This tutorial shows how to run Champollion on a processed RNA/ADT dataset and then inspect the learned interaction matrix `A`. The goal is to illustrate the main steps of the workflow and the main result objects, not to provide a full analysis recipe.\n", "\n", "The dataset used here is already preprocessed. For guidance on preparing a new dataset, see the Data Inputs section of the documentation.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "44e0f2e0-2283-4d87-8ea3-975cfd45aae2", "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "cite-interp-02", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.\n" ] } ], "source": [ "from pathlib import Path\n", "\n", "import muon as mu\n", "import numpy as np\n", "import pandas as pd\n", "from gprofiler import GProfiler\n", "from IPython.display import display\n", "\n", "from champollion import Champollion\n", "from champollion.plot import plot_aggregated_transport_plan, top_interactions_bar" ] }, { "cell_type": "markdown", "id": "cite-interp-03", "metadata": {}, "source": "## Configuration" }, { "cell_type": "code", "execution_count": 8, "id": "cite-interp-04", "metadata": {}, "outputs": [], "source": [ "DATA_PATH = Path(\n", " \"../semi-paired/data/cite_opp_tuto_processed.h5mu\"\n", ")\n", "\n", "RNA_MOD = \"rna\"\n", "ADT_MOD = \"adt\"\n", "CELLTYPE_KEY = \"celltype\"" ] }, { "cell_type": "markdown", "id": "cite-interp-05", "metadata": {}, "source": [ "## Prepare the tutorial inputs\n", "\n", "This dataset is fully paired, but in this tutorial we split it to mimic the semi-paired setting Champollion is designed for. This setup detail is made optional as it is not central to the point of this tutorial. For your own data and preprocessing choices, see the Data Inputs section of the documentation.\n", "\n", "After this preparation step, we will have three objects: one bridge `MuData` object used for fitting, plus one pseudo-unpaired RNA object and one pseudo-unpaired ADT object used for transport and interpretation.\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "cite-interp-06", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MuData object with n_obs × n_vars = 10966 × 14087\n", " obs:\t'celltype', 'fine_celltype', 'paired_mask'\n", " uns:\t'dataset_name'\n", " 2 modalities\n", " adt:\t10966 × 134\n", " obs:\t'batch', 'Site', 'DonorNumber', 'celltype', 'fine_celltype'\n", " var:\t'feature_types', 'gene_id', 'variability_score', 'mean', 'std'\n", " obsm:\t'X_pca', 'prior_data'\n", " layers:\t'counts', 'scaled'\n", " rna:\t10966 × 13953\n", " obs:\t'batch', 'Site', 'DonorNumber', 'celltype', 'fine_celltype', 'n_genes'\n", " var:\t'feature_types', 'gene_id', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'variability_score', 'mean', 'std'\n", " uns:\t'hvg', 'log1p'\n", " obsm:\t'X_pca', 'prior_data'\n", " layers:\t'counts', 'scaled'\n", "modalities: ['adt', 'rna']\n" ] } ], "source": [ "if not DATA_PATH.exists():\n", " raise FileNotFoundError(\n", " f\"Could not find {DATA_PATH}. Update DATA_PATH for your local data location.\"\n", " )\n", "\n", "mdata = mu.read_h5mu(DATA_PATH)\n", "print(mdata)\n", "print(\"modalities:\", list(mdata.mod.keys()))" ] }, { "cell_type": "code", "execution_count": 11, "id": "cite-interp-09", "metadata": {}, "outputs": [], "source": [ "PAIRED_MASK_KEY = \"paired_mask\"\n", "\n", "DEBUG = False\n", "N_HVG = 250 if DEBUG else 4000\n", "DEBUG_TRAIN_CELLS = 60\n", "DEBUG_TEST_CELLS = 80\n", "\n", "coding_genes = [\"TNFRSF4\", \"TNFRSF14\", \"TNFRSF9\", \"CD52\", \"CD58\", \"CD2\", \"CD101\", \"FCGR1A\", \"CD1D\", \"CD1C\", \"FCER1A\", \"SLAMF6\", \"CD48\", \"SLAMF7\", \"CD244\", \"FCGR2A\", \"FCGR3A\", \"SELL\", \"PTPRC\", \"CR2\", \"CR1\", \"CD8A\", \"DPP4\", \"ITGA6\", \"ITGA4\", \"CD28\", \"CTLA4\", \"ICOS\", \"CCR4\", \"CX3CR1\", \"CCR2\", \"CCR5\", \"CD47\", \"BTLA\", \"TIGIT\", \"CD86\", \"TFRC\", \"CD38\", \"IL7R\", \"ITGA1\", \"CD14\", \"CSF1R\", \"CD83\", \"HLA-A\", \"HLA-E\", \"HLA-C\", \"HLA-B\", \"HLA-DRA\", \"NT5E\", \"CD24\", \"IFNGR1\", \"CCR6\", \"CD36\", \"CD72\", \"ENG\", \"IL2RA\", \"ITGB1\", \"NRP1\", \"FAS\", \"ENTPD1\", \"CD81\", \"CD44\", \"CD82\", \"MS4A1\", \"CD5\", \"NCAM1\", \"CD3G\", \"CXCR5\", \"CD9\", \"CD27\", \"LAG3\", \"CD4\", \"CD163\", \"CLEC4C\", \"KLRG1\", \"KLRB1\", \"CD69\", \"KLRD1\", \"KLRK1\", \"ITGB7\", \"CD63\", \"SELPLG\", \"LAMP1\", \"TRAC\", \"IGHD\", \"IGHM\", \"ANPEP\", \"IL4R\", \"CD19\", \"ITGAL\", \"ITGAM\", \"ITGAX\", \"ITGAE\", \"GP1BA\", \"CD79B\", \"PECAM1\", \"CD7\", \"CD226\", \"FCER2\", \"ICAM1\", \"CD22\", \"PVR\", \"NECTIN2\", \"C5AR1\", \"SIGLEC7\", \"CD33\", \"LILRB1\", \"NCR1\", \"SIRPA\", \"THBD\", \"CD93\", \"CD40\", \"ITGB2\", \"IL2RB\", \"TNFRSF13C\", \"IL3RA\", \"CD40LG\"]\n", "\n", "CELLTYPE_ORDER = [\"HSC\", \"Lymph prog\", \"G/M prog\", \"MK/E prog\", \"Proerythroblast\", \"Erythroblast\", \"Normoblast\", \"Reticulocyte\", \"CD14+ Mono\", \"CD16+ Mono\", \"cDC2\", \"pDC\", \"Naive CD20+ B\", \"Transitional B\", \"B1 B\", \"Plasma cell\", \"Plasmablast\", \"CD4+ T naive\", \"CD4+ T\", \"CD4+ T activated\", \"CD8+ T naive\", \"CD8+ T\", \"ILC1\", \"NK\", \"MAIT\"]\n", "\n", "def get_hvg_names(adata, n_features=4000, keep_features=None):\n", " \"\"\"Return HVGs, optionally forcing a set of biologically linked genes to stay.\"\"\"\n", " if \"variability_score\" not in adata.var:\n", " raise KeyError(\n", " \"Expected adata.var['variability_score'] in the processed RNA object.\"\n", " )\n", "\n", " var_annot = adata.var.sort_values(ascending=False, by=\"variability_score\")\n", " n_features = min(n_features, len(var_annot))\n", " min_score = var_annot[\"variability_score\"].iloc[n_features - 1]\n", " mask = adata.var[\"variability_score\"] >= min_score\n", "\n", " if keep_features is not None:\n", " keep_features = [gene for gene in keep_features if gene in mask.index]\n", " mask.loc[keep_features] = True\n", "\n", " return list(adata.var_names[mask])\n", "\n", "\n", "gene_names = get_hvg_names(\n", " mdata.mod[RNA_MOD], n_features=N_HVG, keep_features=coding_genes\n", ")\n", "\n", "paired_mask = np.asarray(mdata.obs[PAIRED_MASK_KEY].to_numpy(), dtype=bool)\n", "celltypes = mdata.obs[CELLTYPE_KEY].astype(str).to_numpy()\n", "\n", "def stratified_indices(mask, labels, n=None, random_state=0):\n", " \"\"\"Return indices within mask, optionally stratified down to n cells.\"\"\"\n", " idx = np.flatnonzero(mask)\n", " if n is None or n >= len(idx):\n", " return idx\n", "\n", " rng = np.random.default_rng(random_state)\n", " selected = []\n", " present_labels = [\n", " label for label in pd.unique(labels[idx]) if np.any(labels[idx] == label)\n", " ]\n", " per_group = max(1, int(np.ceil(n / len(present_labels))))\n", " for label in present_labels:\n", " label_idx = idx[labels[idx] == label]\n", " take = min(per_group, len(label_idx))\n", " selected.extend(rng.choice(label_idx, size=take, replace=False))\n", " selected = np.asarray(selected, dtype=int)\n", " if len(selected) > n:\n", " selected = rng.choice(selected, size=n, replace=False)\n", " return np.sort(selected)\n", "\n", "\n", "train_idx = stratified_indices(\n", " paired_mask,\n", " labels=celltypes,\n", " n=DEBUG_TRAIN_CELLS if DEBUG else None,\n", " random_state=0,\n", ")\n", "test_idx = stratified_indices(\n", " ~paired_mask,\n", " labels=celltypes,\n", " n=DEBUG_TEST_CELLS if DEBUG else None,\n", " random_state=1,\n", ")\n", "\n", "train_mdata = mu.MuData(\n", " {\n", " RNA_MOD: mdata.mod[RNA_MOD][train_idx, gene_names].copy(),\n", " ADT_MOD: mdata.mod[ADT_MOD][train_idx].copy(),\n", " }\n", ")\n", "\n", "adata_rna = mdata.mod[RNA_MOD][test_idx, gene_names].copy()\n", "adata_adt = mdata.mod[ADT_MOD][test_idx].copy()" ] }, { "cell_type": "code", "execution_count": 12, "id": "c34faf3c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "training bridge data:\n", "MuData object with n_obs × n_vars = 8783 × 4165\n", " 2 modalities\n", " rna:\t8783 × 4031\n", " obs:\t'batch', 'Site', 'DonorNumber', 'celltype', 'fine_celltype', 'n_genes'\n", " var:\t'feature_types', 'gene_id', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'variability_score', 'mean', 'std'\n", " uns:\t'hvg', 'log1p'\n", " obsm:\t'X_pca', 'prior_data'\n", " layers:\t'counts', 'scaled'\n", " adt:\t8783 × 134\n", " obs:\t'batch', 'Site', 'DonorNumber', 'celltype', 'fine_celltype'\n", " var:\t'feature_types', 'gene_id', 'variability_score', 'mean', 'std'\n", " obsm:\t'X_pca', 'prior_data'\n", " layers:\t'counts', 'scaled'\n", "pseudo-unpaired RNA: (2183, 4031)\n", "pseudo-unpaired ADT: (2183, 134)\n", "RNA features kept for A: 4031\n", "ADT proteins kept for A: 134\n" ] } ], "source": [ "print(\"training bridge data:\")\n", "print(train_mdata)\n", "print(\"pseudo-unpaired RNA:\", adata_rna.shape)\n", "print(\"pseudo-unpaired ADT:\", adata_adt.shape)\n", "print(f\"RNA features kept for A: {len(gene_names)}\")\n", "print(f\"ADT proteins kept for A: {adata_adt.n_vars}\")" ] }, { "cell_type": "markdown", "id": "cite-interp-10", "metadata": {}, "source": [ "## Fit Champollion\n", "\n", "Fitting learns the matrix `A` from the bridge cells. Because this tutorial uses direct scaled RNA and ADT features, rows and columns of `A` can be read as gene-protein association scores. \n" ] }, { "cell_type": "code", "execution_count": 13, "id": "ea988fd575725c1e", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 5/300000 [00:00<12:34:06, 6.63it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train_total_loss: 83.64341735839844\n", "train_obj_loss: 29.6660099029541\n", "train_reg_loss: 53.97740936279297\n", "train_trace_term: 0.28358766436576843\n", "train_err_1: 1.7106705904006958\n", "train_plan_mass: 1.0000009536743164\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 44/300000 [00:02<2:43:33, 30.56it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "train_total_loss: 36.16069793701172\n", "train_obj_loss: -0.7098903656005859\n", "train_reg_loss: 36.87059020996094\n", "train_trace_term: -17.491703033447266\n", "train_err_1: 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\n", "
" ], "text/plain": [ " train_total_loss train_obj_loss train_reg_loss train_trace_term \\\n", "245 -4.325231 -7.067060 2.741828 -16.841217 \n", "246 -4.325246 -7.067102 2.741857 -16.843895 \n", "247 -4.325257 -7.067130 2.741873 -16.846838 \n", "248 -4.325271 -7.067163 2.741892 -16.849289 \n", "249 NaN NaN NaN NaN \n", "\n", " train_err_1 train_plan_mass \n", "245 0.001022 1.0 \n", "246 0.001050 1.0 \n", "247 0.001272 1.0 \n", "248 0.000937 1.0 \n", "249 0.000937 1.0 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = Champollion(\n", " epsilon=1.0,\n", " gamma=0.01,\n", " lambda_prior=20.0,\n", " use_keops=False,\n", " device=\"auto\",\n", " random_state=0,\n", " max_iter=2 if DEBUG else 300_000,\n", " learning_rate=1e-3,\n", " sinkhorn_tol=1e-3,\n", " log_every=1 if DEBUG else 40,\n", " verbose=True,\n", ")\n", "model.fit(\n", " train_mdata,\n", " modality_1=RNA_MOD,\n", " modality_2=ADT_MOD,\n", " x_1_rep=\"layers/scaled\",\n", " x_2_rep=\"layers/scaled\",\n", ")\n", "\n", "A_df = model.A_dataframe()\n", "print(\"A shape:\", A_df.shape)\n", "print(\"A device:\", model.A_.device)\n", "pd.DataFrame(\n", " {key: pd.Series(value) for key, value in model.training_history_.items()}\n", ").tail()" ] }, { "cell_type": "markdown", "id": "d3f0c96f-b36a-4f17-9a68-9f2d842b8326", "metadata": {}, "source": [ "## Transport pseudo-unpaired cells and aggregate the plan\n", "\n", "After fitting, we use the learned cost to transport pseudo-unpaired RNA cells to pseudo-unpaired ADT cells.\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "cite-interp-13", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "plan shape: (2183, 2183)\n", "plan mass: 1.0000002\n" ] }, { "data": { "text/plain": [ "{'mass': 1.000000238418579,\n", " 'mass_abs_error': 2.384185791015625e-07,\n", " 'x_1_marginal_l1_error': 0.0008025987190194428,\n", " 'x_2_marginal_l1_error': 2.4001928977668285e-07,\n", " 'finite': True,\n", " 'passed': True,\n", " 'tol': 0.001}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transport = model.transport(\n", " {RNA_MOD: adata_rna, ADT_MOD: adata_adt},\n", " x_reps={RNA_MOD: \"layers/scaled\", ADT_MOD: \"layers/scaled\"},\n", " store_cost=True,\n", " store_plan=True,\n", " max_iter_sink=20 if DEBUG else 1000,\n", ")\n", "\n", "plan = transport.materialize_plan().detach().cpu().numpy()\n", "print(\"plan shape:\", plan.shape)\n", "print(\"plan mass:\", plan.sum())\n", "transport.plan_diagnostics" ] }, { "cell_type": "markdown", "id": "b364115d-e64b-4e33-b97d-df4b68eb8f8a", "metadata": {}, "source": [ "## Examine the transport plan\n", "\n", "For a quick visualization of the validity of the inferred transport plan, we can use provided cell type annotations to aggregate the transported mass across those labels and check mass is transported between coherent cell populations." ] }, { "cell_type": "code", "execution_count": 15, "id": "cite-interp-14", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels_rna = adata_rna.obs[CELLTYPE_KEY].astype(str).to_numpy()\n", "labels_adt = adata_adt.obs[CELLTYPE_KEY].astype(str).to_numpy()\n", "\n", "aggregated_plan, fig, ax = plot_aggregated_transport_plan(\n", " heat_mtx=plan,\n", " annotations=labels_rna,\n", " annotations_ordered=CELLTYPE_ORDER,\n", " annotations_2=labels_adt,\n", " annotations_ordered_2=CELLTYPE_ORDER,\n", " cbar_label=\"transport mass\",\n", " reduction=\"sum\",\n", ")" ] }, { "cell_type": "markdown", "id": "cite-interp-15", "metadata": {}, "source": [ "## Inspect one marker-specific column of A\n", "\n", "A column of `A` ranks RNA genes by their contribution to the learned cost for one ADT protein. Here we inspect one familiar B-cell marker as an example of how to query and plot the learned interactions." ] }, { "cell_type": "code", "execution_count": 16, "id": "cite-interp-16", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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geneA_score
0CD240.021163
1WHAMM0.019635
2CXXC50.019152
3LINC009260.017681
4MS4A10.015109
5PDLIM10.014692
6BLK0.014095
7PAX50.013725
8TRIM590.013483
9ZNF2840.013372
\n", "
" ], "text/plain": [ " gene A_score\n", "0 CD24 0.021163\n", "1 WHAMM 0.019635\n", "2 CXXC5 0.019152\n", "3 LINC00926 0.017681\n", "4 MS4A1 0.015109\n", "5 PDLIM1 0.014692\n", "6 BLK 0.014095\n", "7 PAX5 0.013725\n", "8 TRIM59 0.013483\n", "9 ZNF284 0.013372" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "MARKER_PROTEIN = \"CD19\"\n", "top = model.top_interactions(\n", " MARKER_PROTEIN,\n", " modality=ADT_MOD,\n", " k=10,\n", " direction=\"positive\",\n", ")\n", "display(\n", " top[[\"target_feature\", \"weight\"]].rename(\n", " columns={\"target_feature\": \"gene\", \"weight\": \"A_score\"},\n", " )\n", ")\n", "bar_fig = top_interactions_bar(\n", " interactions=top,\n", " title=f\"Top positive interactions with {MARKER_PROTEIN}\",\n", " colors=\"#CA4B55\",\n", ")" ] }, { "cell_type": "markdown", "id": "cite-interp-17", "metadata": {}, "source": [ "## Enrichment from one protein-specific A column\n", "\n", "The same `A` column can also be used to define a gene set for enrichment. This is only a small example of the downstream analyses enabled by the learned interaction matrix; it requires internet access for g:Profiler." ] }, { "cell_type": "code", "execution_count": 17, "id": "cite-interp-18", "metadata": {}, "outputs": [], "source": [ "def genes_for_enrichment(A_df, protein, frac_max=100, max_genes=100):\n", " if protein not in A_df.columns:\n", " raise KeyError(f\"Protein {protein!r} was not found in A columns.\")\n", " scores = A_df[protein]\n", " max_abs_score = scores.abs().max()\n", " if max_abs_score == 0:\n", " return pd.Series(dtype=float, name=protein)\n", " selected = scores[scores.abs() > (max_abs_score / frac_max)]\n", " selected = selected.loc[selected.abs().sort_values(ascending=False).index]\n", " selected = selected.iloc[:max_genes]\n", " selected.name = protein\n", " return selected" ] }, { "cell_type": "code", "execution_count": 18, "id": "cite-interp-19", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CD18: top 100 genes\n" ] }, { "data": { "text/html": [ "
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A_score
CD840.029304
AC092279.1-0.026720
ALOX5AP0.026240
PCNA0.023267
KBTBD20.022768
NRIP1-0.021693
AC007262.20.021638
CD740.021350
ITGB20.020158
CA5B0.019774
ISCA10.019579
IRF80.018257
PGD-0.017852
SAMD9L0.017842
ZFAND2B0.017396
IGSF60.017214
GZMH0.017020
AC010168.2-0.016554
MTIF2-0.016174
TANGO20.015917
\n", "
" ], "text/plain": [ " A_score\n", "CD84 0.029304\n", "AC092279.1 -0.026720\n", "ALOX5AP 0.026240\n", "PCNA 0.023267\n", "KBTBD2 0.022768\n", "NRIP1 -0.021693\n", "AC007262.2 0.021638\n", "CD74 0.021350\n", "ITGB2 0.020158\n", "CA5B 0.019774\n", "ISCA1 0.019579\n", "IRF8 0.018257\n", "PGD -0.017852\n", "SAMD9L 0.017842\n", "ZFAND2B 0.017396\n", "IGSF6 0.017214\n", "GZMH 0.017020\n", "AC010168.2 -0.016554\n", "MTIF2 -0.016174\n", "TANGO2 0.015917" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ENRICHMENT_PROTEIN = \"CD18\"\n", "gp = GProfiler(return_dataframe=True)\n", "background = list(A_df.index)\n", "\n", "enrichment_input = genes_for_enrichment(\n", " A_df,\n", " protein=ENRICHMENT_PROTEIN,\n", " frac_max=100,\n", " max_genes=100,\n", ")\n", "print(f\"{ENRICHMENT_PROTEIN}: top {len(enrichment_input)} genes\")\n", "display(enrichment_input.to_frame(\"A_score\").head(20))" ] }, { "cell_type": "code", "execution_count": 19, "id": "cite-interp-20", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sourcenativenamep_valuesignificantdescriptionterm_sizequery_sizeintersection_sizeeffective_domain_sizeprecisionrecallqueryparents
0WPWP:WP615Senescence and autophagy in cancer0.024924TrueSenescence and autophagy in cancer3187835210.0919540.258065query_1[WP:000000]
\n", "
" ], "text/plain": [ " source native name p_value significant \\\n", "0 WP WP:WP615 Senescence and autophagy in cancer 0.024924 True \n", "\n", " description term_size query_size \\\n", "0 Senescence and autophagy in cancer 31 87 \n", "\n", " intersection_size effective_domain_size precision recall query \\\n", "0 8 3521 0.091954 0.258065 query_1 \n", "\n", " parents \n", "0 [WP:000000] " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "enrichment_results = gp.profile(\n", " organism=\"hsapiens\",\n", " sources=[\"KEGG\", \"REAC\", \"WP\", \"GO\"],\n", " query=list(enrichment_input.index),\n", " background=background,\n", ")\n", "display(enrichment_results)" ] }, { "cell_type": "code", "execution_count": null, "id": "a8df1ae4-4d36-45c2-916a-ee3a36706503", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python (test_champo)", "language": "python", "name": "test_champo" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }