From a0b9e848d791c4cd4a9ec808c8b4a492b5624111 Mon Sep 17 00:00:00 2001 From: Lukas Heumos Date: Thu, 28 May 2026 10:03:45 +0200 Subject: [PATCH] milo: switch miloDE example to kang_2018 for biologically meaningful results MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit bhattacherjee only ships 6 samples (2 per condition), so nhoods rarely satisfy the per-condition replicate requirement and almost every test came back NaN. kang_2018 (8 patients × ctrl/stim) keeps raw counts in .X and yields a clear signal: top hits are ISG15 and ISG20 (canonical IFN-β response). Also bumps n_neighbors to 100 so nhood sizes are large enough to pseudobulk by sample. Tutorial-side warning filter is just warnings.filterwarnings("ignore"); the previous per-nhood pertpy logger noise is now collapsed into a single end-of-run summary inside de_nhoods itself. Co-Authored-By: Claude Opus 4.7 (1M context) --- milo.ipynb | 128 +++++++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 114 insertions(+), 14 deletions(-) diff --git a/milo.ipynb b/milo.ipynb index 132afb2..b0c755e 100644 --- a/milo.ipynb +++ b/milo.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "express-survivor", "metadata": { "pycharm": { @@ -47,13 +47,16 @@ }, "outputs": [], "source": [ + "import warnings\n", + "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pertpy as pt\n", "import scanpy as sc\n", "\n", - "plt.rcParams[\"figure.figsize\"] = (7, 7)" + "warnings.filterwarnings(\"ignore\")\n", + "plt.rcParams[\"figure.figsize\"] = (7, 7)\n" ] }, { @@ -193,7 +196,7 @@ "metadata": {}, "outputs": [], "source": [ - "## Exclude LPS samples\n", + "##\u00a0Exclude LPS samples\n", "adata = adata[adata.obs[\"Status\"] != \"LPS\"].copy()\n", "\n", "## Initialize object for Milo analysis\n", @@ -218,7 +221,7 @@ { "data": { "text/html": [ - "
MuData object with n_obs × n_vars = 59873 × 16299\n",
+       "
MuData object with n_obs \u00d7 n_vars = 59873 \u00d7 16299\n",
        "  2 modalities\n",
        "    rna:\t59873 x 16299\n",
        "      obs:\t'Collection_Day', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id', 'author_cell_type', 'organism', 'sex', 'tissue', 'ethnicity', 'disease', 'assay', 'cell_type', 'dataset_group', 'COVID_severity'\n",
@@ -228,7 +231,7 @@
        "    milo:\t0 x 0
" ], "text/plain": [ - "MuData object with n_obs × n_vars = 59873 × 16299\n", + "MuData object with n_obs \u00d7 n_vars = 59873 \u00d7 16299\n", " 2 modalities\n", " rna:\t59873 x 16299\n", " obs:\t'Collection_Day', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id', 'author_cell_type', 'organism', 'sex', 'tissue', 'ethnicity', 'disease', 'assay', 'cell_type', 'dataset_group', 'COVID_severity'\n", @@ -456,7 +459,7 @@ " \n", " \n", "\n", - "

4307 rows × 2 columns

\n", + "

4307 rows \u00d7 2 columns

\n", "" ], "text/plain": [ @@ -589,7 +592,7 @@ { "data": { "text/html": [ - "
MuData object with n_obs × n_vars = 59873 × 16299\n",
+       "
MuData object with n_obs \u00d7 n_vars = 59873 \u00d7 16299\n",
        "  2 modalities\n",
        "    rna:\t59873 x 16299\n",
        "      obs:\t'Collection_Day', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id', 'author_cell_type', 'organism', 'sex', 'tissue', 'ethnicity', 'disease', 'assay', 'cell_type', 'dataset_group', 'COVID_severity', 'nhood_ixs_random', 'nhood_ixs_refined', 'nhood_kth_distance'\n",
@@ -602,7 +605,7 @@
        "      uns:\t'sample_col'
" ], "text/plain": [ - "MuData object with n_obs × n_vars = 59873 × 16299\n", + "MuData object with n_obs \u00d7 n_vars = 59873 \u00d7 16299\n", " 2 modalities\n", " rna:\t59873 x 16299\n", " obs:\t'Collection_Day', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id', 'author_cell_type', 'organism', 'sex', 'tissue', 'ethnicity', 'disease', 'assay', 'cell_type', 'dataset_group', 'COVID_severity', 'nhood_ixs_random', 'nhood_ixs_refined', 'nhood_kth_distance'\n", @@ -633,7 +636,7 @@ { "data": { "text/plain": [ - "AnnData object with n_obs × n_vars = 113 × 4307\n", + "AnnData object with n_obs \u00d7 n_vars = 113 \u00d7 4307\n", " var: 'index_cell', 'kth_distance'\n", " uns: 'sample_col'" ] @@ -965,7 +968,7 @@ " \n", " \n", "\n", - "

113 rows × 3 columns

\n", + "

113 rows \u00d7 3 columns

\n", "" ], "text/plain": [ @@ -1207,7 +1210,7 @@ " \n", " \n", "\n", - "

4307 rows × 11 columns

\n", + "

4307 rows \u00d7 11 columns

\n", "" ], "text/plain": [ @@ -2043,9 +2046,9 @@ "## Get IDs of enriched neighbourhoods\n", "pl_nhoods = mdata[\"milo\"].var_names[\n", " (mdata[\"milo\"].var[\"SpatialFDR\"] < 0.1) & (mdata[\"milo\"].var[\"logFC\"] > 0)\n", - "] ## notice how here logFCs are much smaller\n", + "] ##\u00a0notice how here logFCs are much smaller\n", "\n", - "## Add COVID severity labels to mdata['milo'].obs\n", + "##\u00a0Add COVID severity labels to mdata['milo'].obs\n", "milo.add_covariate_to_nhoods_var(mdata, [\"COVID_severity\"])\n", "mdata[\"milo\"].obs[\"COVID_severity\"] = (\n", " mdata[\"milo\"].obs[\"COVID_severity\"].astype(\"category\").cat.reorder_categories(severity_order)\n", @@ -2054,6 +2057,103 @@ "## Visualize cell counts by condition (x-axis) and individuals on all signif neighbourhoods\n", "milo.plot_nhood_counts_by_cond(mdata, test_var=\"COVID_severity\", subset_nhoods=pl_nhoods, log_counts=False)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Per-neighbourhood differential expression with miloDE\n", + "\n", + "`Milo.de_nhoods` runs differential expression *inside each neighbourhood*.\n", + "For every nhood it pseudobulks cells by sample, fits the existing pertpy DE model (`PyDESeq2` or `Statsmodels`), and corrects p-values both across genes within each nhood and across nhoods per gene (density-weighted Benjamini-Hochberg, same correction `da_nhoods` uses).\n", + "Unlike DA testing this requires *raw counts*, so we use the `kang_2018` IFN-\u03b2 stimulation dataset (8 patients \u00d7 ctrl/stim, raw counts in `.X`) for this short demo.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Load a dataset with raw counts and prepare the Milo object\n", + "adata_de = pt.dt.kang_2018()\n", + "adata_de.layers[\"counts\"] = adata_de.X.copy()\n", + "adata_de.obs[\"sample\"] = adata_de.obs[\"replicate\"].astype(str) + \"_\" + adata_de.obs[\"label\"].astype(str)\n", + "sc.pp.normalize_total(adata_de)\n", + "sc.pp.log1p(adata_de)\n", + "sc.pp.pca(adata_de)\n", + "sc.pp.neighbors(adata_de, use_rep=\"X_pca\", n_neighbors=100)\n", + "sc.tl.umap(adata_de)\n", + "\n", + "milo_de = pt.tl.Milo()\n", + "mdata_de = milo_de.load(adata_de)\n", + "milo_de.make_nhoods(mdata_de[\"rna\"], prop=0.1)\n", + "mdata_de = milo_de.count_nhoods(mdata_de, sample_col=\"sample\")\n", + "milo_de.build_nhood_graph(mdata_de)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "247 of 901 nhoods tested\n" + }, + { + "output_type": "execute_result", + "metadata": {}, + "data": { + "text/plain": " nhood variable log_fc p_value adj_p_value \\\n10079094 641 ISG20 3.646139 1.156167e-73 7.463060e-70 \n9340912 594 ISG20 3.270801 2.198864e-72 1.469281e-68 \n13377354 851 ISG20 3.405809 2.125853e-70 1.330784e-66 \n2905622 185 ISG15 3.740931 1.534661e-68 1.152070e-64 \n10067558 641 ISG15 5.653214 1.489707e-54 4.808031e-51 \n\n pval_corrected_across_nhoods test_performed \n10079094 3.280868e-71 True \n9340912 3.155836e-70 True \n13377354 2.055798e-68 True \n2905622 3.177080e-66 True \n10067558 1.783145e-52 True ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nhoodvariablelog_fcp_valueadj_p_valuepval_corrected_across_nhoodstest_performed
10079094641ISG203.6461391.156167e-737.463060e-703.280868e-71True
9340912594ISG203.2708012.198864e-721.469281e-683.155836e-70True
13377354851ISG203.4058092.125853e-701.330784e-662.055798e-68True
2905622185ISG153.7409311.534661e-681.152070e-643.177080e-66True
10067558641ISG155.6532141.489707e-544.808031e-511.783145e-52True
\n
" + }, + "execution_count": 3 + } + ], + "source": [ + "# Run miloDE: stim vs ctrl\n", + "de = milo_de.de_nhoods(\n", + " mdata_de,\n", + " design=\"~label\",\n", + " column=\"label\",\n", + " baseline=\"ctrl\",\n", + " group_to_compare=\"stim\",\n", + " layer=\"counts\",\n", + ")\n", + "tested = int(de.groupby(\"nhood\")[\"test_performed\"].first().sum())\n", + "print(f\"{tested} of {mdata_de['milo'].n_vars} nhoods tested\")\n", + "de.loc[de[\"test_performed\"]].sort_values(\"adj_p_value\").head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a long DataFrame with one row per (nhood, gene): `log_fc`, `p_value`, `adj_p_value` (BH across genes within each nhood), `pval_corrected_across_nhoods` (density-weighted BH across nhoods per gene), plus a `test_performed` flag.\n", + "Column names match the rest of pertpy's DE module, so the existing `plot_volcano` accepts a per-nhood slice directly.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "metadata": {}, + "data": { + "text/plain": "
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" + } + } + ], + "source": [ + "# Visualize per-nhood logFC of one gene on the nhood graph.\n", + "# ISG15 is a canonical IFN-\u03b2 response gene -- expect strong positive logFC in many nhoods.\n", + "milo_de.plot_de_nhood_graph(mdata_de, de, gene=\"ISG15\", min_size=2)\n" + ] } ], "metadata": { @@ -2077,4 +2177,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file