{
"cells": [
{
"cell_type": "markdown",
"id": "0f8d8db0",
"metadata": {},
"source": [
"Downloaded from the Single Cell Portal: https://singlecell.broadinstitute.org/single_cell/study/SCP1064/multi-modal-pooled-perturb-cite-seq-screens-in-patient-models-define-novel-mechanisms-of-cancer-immune-evasion"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e9234341",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/scanpy/_settings.py:447: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
" IPython.display.set_matplotlib_formats(*ipython_format)\n",
"2022-02-08 23:14:15.755003: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
"2022-02-08 23:14:15.755029: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"scanpy==1.8.2 anndata==0.7.6 umap==0.5.2 numpy==1.20.3 scipy==1.5.3 pandas==1.3.4 scikit-learn==1.0.2 statsmodels==0.11.1 python-igraph==0.8.3 leidenalg==0.8.3 pynndescent==0.5.5\n"
]
},
{
"data": {
"text/plain": [
"AnnData object with n_obs × n_vars = 218331 × 23712\n",
" obs: 'library_preparation_protocol', 'condition', 'MOI', 'sgRNA', 'UMI_count'\n",
" uns: 'protein_names'\n",
" obsm: 'protein_expression'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# %load block0_load.py\n",
"author_year = 'Frangieh_2021'\n",
"is_counts = False\n",
"var_genes = None\n",
"doi = '10.1038/s41588-021-00779-1'\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import scanpy as sc\n",
"sc.set_figure_params(dpi=100, frameon=False)\n",
"sc.logging.print_header()\n",
"\n",
"# verify\n",
"assert(doi in pd.read_csv('../personal.csv').DOI.values)\n",
"\n",
"adata = sc.read(f'{author_year}_raw.h5ad')\n",
"adata"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "904d339f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" count | \n",
" unique | \n",
" top | \n",
" freq | \n",
"
\n",
" \n",
" \n",
" \n",
" | library_preparation_protocol | \n",
" 218331 | \n",
" 1 | \n",
" 10X 3' v3 sequencing | \n",
" 218331 | \n",
"
\n",
" \n",
" | condition | \n",
" 218331 | \n",
" 3 | \n",
" IFNγ | \n",
" 87590 | \n",
"
\n",
" \n",
" | MOI | \n",
" 218331 | \n",
" 20 | \n",
" 1 | \n",
" 126966 | \n",
"
\n",
" \n",
" | sgRNA | \n",
" 126966 | \n",
" 818 | \n",
" IFNGR2_2 | \n",
" 358 | \n",
"
\n",
" \n",
" | UMI_count | \n",
" 218331 | \n",
" 33077 | \n",
" 7977.0 | \n",
" 33 | \n",
"
\n",
" \n",
"
\n",
"