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scanpy repro 3168.ipynb
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score_genes_diagnostics_tests2.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "48fbf2b0",
"metadata": {},
"source": [
"## Cell cycle scoring vignette testing score_genes"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "37c3aa50",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import scanpy as sc\n",
"from matplotlib import rcParams\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4d7232fb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----\n",
"anndata 0.10.9\n",
"scanpy 1.10.3\n",
"-----\n",
"Cython 3.0.11\n",
"PIL 10.4.0\n",
"asttokens NA\n",
"charset_normalizer 3.3.2\n",
"comm 0.2.2\n",
"cycler 0.12.1\n",
"cython 3.0.11\n",
"cython_runtime NA\n",
"dateutil 2.9.0.post0\n",
"debugpy 1.8.5\n",
"decorator 5.1.1\n",
"executing 2.1.0\n",
"h5py 3.11.0\n",
"igraph 0.11.6\n",
"ipykernel 6.29.5\n",
"jaraco NA\n",
"jedi 0.19.1\n",
"joblib 1.4.2\n",
"kiwisolver 1.4.7\n",
"legacy_api_wrap NA\n",
"leidenalg 0.10.2\n",
"llvmlite 0.43.0\n",
"louvain 0.8.2\n",
"matplotlib 3.9.2\n",
"matplotlib_inline 0.1.7\n",
"more_itertools 10.5.0\n",
"mpl_toolkits NA\n",
"natsort 8.4.0\n",
"numba 0.60.0\n",
"numpy 2.0.2\n",
"packaging 24.1\n",
"pandas 2.2.3\n",
"parso 0.8.4\n",
"patsy 0.5.6\n",
"pkg_resources NA\n",
"platformdirs 4.3.3\n",
"prompt_toolkit 3.0.47\n",
"psutil 6.0.0\n",
"pure_eval 0.2.3\n",
"pydev_ipython NA\n",
"pydevconsole NA\n",
"pydevd 2.9.5\n",
"pydevd_file_utils NA\n",
"pydevd_plugins NA\n",
"pydevd_tracing NA\n",
"pygments 2.18.0\n",
"pyparsing 3.1.4\n",
"pytz 2024.2\n",
"scipy 1.14.1\n",
"seaborn 0.13.2\n",
"session_info 1.0.0\n",
"six 1.16.0\n",
"sklearn 1.5.2\n",
"stack_data 0.6.3\n",
"statsmodels 0.14.3\n",
"texttable 1.7.0\n",
"threadpoolctl 3.5.0\n",
"tornado 6.4.1\n",
"traitlets 5.14.3\n",
"vscode NA\n",
"wcwidth 0.2.13\n",
"zmq 26.2.0\n",
"-----\n",
"IPython 8.27.0\n",
"jupyter_client 8.6.2\n",
"jupyter_core 5.7.2\n",
"-----\n",
"Python 3.12.7 (main, Oct 1 2024, 11:15:50) [GCC 14.2.1 20240910]\n",
"Linux-6.11.3-zen1-1-zen-x86_64-with-glibc2.40\n",
"-----\n",
"Session information updated at 2024-10-15 16:49\n",
"Running Scanpy 1.10.3, on 2024-10-15 16:49.\n"
]
}
],
"source": [
"sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)\n",
"sc.settings.set_figure_params(dpi=80) # low dpi (dots per inch) yields small inline figures\n",
"sc.logging.print_versions()\n",
"sc.logging.print_version_and_date()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3b0d2c95",
"metadata": {},
"outputs": [],
"source": [
"adata = sc.read_csv('data/nestorawa_forcellcycle_expressionMatrix.txt', delimiter='\\t').T"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "54c33f88",
"metadata": {},
"outputs": [],
"source": [
"cell_cycle_genes = [x.strip() for x in open('data/regev_lab_cell_cycle_genes.txt')]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "701d7192",
"metadata": {},
"outputs": [],
"source": [
"s_genes = cell_cycle_genes[:43]\n",
"g2m_genes = cell_cycle_genes[43:]\n",
"cell_cycle_genes = [x for x in cell_cycle_genes if x in adata.var_names]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "aef42cbe",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"filtered out 443 genes that are detected in less than 3 cells\n",
"normalizing by total count per cell\n",
" finished (0:00:00): normalized adata.X and added\n",
" 'n_counts', counts per cell before normalization (adata.obs)\n"
]
}
],
"source": [
"sc.pp.filter_cells(adata, min_genes=200)\n",
"sc.pp.filter_genes(adata, min_cells=3)\n",
"sc.pp.normalize_per_cell(adata, counts_per_cell_after=1e4)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8ecc3f73",
"metadata": {},
"outputs": [],
"source": [
"sc.pp.scale(adata)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "57b468a9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING: genes are not in var_names and ignored: Index(['MLF1IP', 'GMNN'], dtype='object')\n"
]
}
],
"source": [
"import scanpy.tools._score_genes as score_genes\n",
"\n",
"gene_list, gene_pool, get_subset = score_genes._check_score_genes_args(adata, s_genes, None, use_raw=False)\n",
"bins = list(score_genes._score_genes_bins(gene_list, gene_pool, ctrl_as_ref=False, n_bins=25, ctrl_size=50, get_subset=get_subset))\n",
"mean_list = np.array([score_genes._nan_means(get_subset(genes), axis=1, dtype=\"float64\").mean() for genes in bins])\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1c06c97e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Mean expression value in each bin')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 800x400 with 2 Axes>"
]
},
"metadata": {
"image/png": {
"height": 360,
"width": 667
}
},
"output_type": "display_data"
}
],
"source": [
"rcParams['figure.figsize'] = 10, 5\n",
"fig, axs = plt.subplots(1, 2)\n",
"sns.barplot(x=range(len(bins)), y=list(map(len, bins)), ax=axs[0])\n",
"axs[0].set_title('Number of genes in each bin')\n",
"sns.lineplot(x=range(len(bins)), y=mean_list, ax=axs[1])\n",
"axs[1].set_ylim(-.1, .1)\n",
"axs[1].set_title('Mean expression value in each bin')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "93bf0753",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(map(len, bins))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "basic-scrna",
"language": "python",
"name": "python3"
},
"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
}
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