{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "4535bad9-b859-421c-849a-12e55abb4bba", "metadata": {}, "source": [ "# Drug response predict with scDrug\n", "\n", "scDrug is a database that can be used to predict the drug sensitivity of single cells based on an existing database of drug responses. In the downstream tasks of single cell analysis, especially in tumours, we are fully interested in potential drugs and combination therapies. To this end, we have integrated scDrug's IC50 prediction and inferCNV to infer the function of tumour cells to build a drug screening pipeline.\n", "\n", "Paper: [scDrug: From single-cell RNA-seq to drug response prediction](https://www.sciencedirect.com/science/article/pii/S2001037022005505)\n", "\n", "Code: https://github.com/ailabstw/scDrug\n", "\n", "Colab_Reproducibility:https://colab.research.google.com/drive/1mayoMO7I7qjYIRjrZEi8r5zuERcxAEcF?usp=sharing" ] }, { "cell_type": "code", "execution_count": 1, "id": "c4d12bf2-8723-4223-a82d-046c3a04a46f", "metadata": {}, "outputs": [], "source": [ "import omicverse as ov\n", "import scanpy as sc\n", "import infercnvpy as cnv\n", "import matplotlib.pyplot as plt\n", "import os\n", "\n", "sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)\n", "sc.settings.set_figure_params(dpi=80, facecolor='white')\n" ] }, { "attachments": {}, "cell_type": "markdown", "id": "79a0cde6-fbb9-4699-a07a-1e2597e9f65d", "metadata": {}, "source": [ "## Infer the Tumor from scRNA-seq\n", "\n", "Here we use Infercnvpy's example data to complete the tumour analysis, you can also refer to the official tutorial for this step: https://infercnvpy.readthedocs.io/en/latest/notebooks/tutorial_3k.html\n", "\n", "So, we provide a utility function ov.utils.get_gene_annotation to supplement the coordinate information from GTF files. The following usage assumes that the adata.var_names correspond to “gene_name” attribute in the GTF file. For other cases, please check the function documentation.\n", "\n", "The GTF file used here can be downloaded from [GENCODE](http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M25/).\n", "\n", "T2T-CHM13 gtf file can be download from [figshare](https://figshare.com/ndownloader/files/40628072)" ] }, { "cell_type": "code", "execution_count": 3, "id": "3b769654-042f-4153-b2a4-f8c6ffb67405", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "try downloading from url\n", "https://github.com/icbi-lab/infercnvpy/releases/download/d0.1.0/maynard2020_3k.h5ad\n", "... this may take a while but only happens once\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "39791a36f8c04ba68720439ad1f6d0dd", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0.00/49.5M [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
| \n", " | ensg | \n", "chromosome | \n", "start | \n", "end | \n", "
|---|---|---|---|---|
| symbol | \n", "\n", " | \n", " | \n", " | \n", " |
| AL645933.5 | \n", "ENSG00000288587.1 | \n", "chr6 | \n", "31400702 | \n", "31463705 | \n", "
| AC010184.1 | \n", "ENSG00000288585.1 | \n", "chr3 | \n", "141449745 | \n", "141456434 | \n", "
| AC023296.1 | \n", "ENSG00000288580.1 | \n", "chr8 | \n", "2923568 | \n", "2926689 | \n", "
| AL117334.2 | \n", "ENSG00000288577.1 | \n", "chr20 | \n", "3406380 | \n", "3410036 | \n", "
| AC107294.4 | \n", "ENSG00000288576.1 | \n", "chr3 | \n", "184778723 | \n", "184780720 | \n", "