{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Normalizing full-length gene sequencing data\n", "\n", "This tutorial shows you how to fetch full-length gene sequencing data from the Census and normalize it to account for gene length.\n", "\n", "**Contents**\n", "\n", "1. Opening the census\n", "2. Fetching example full-length sequencing data (Smart-Seq2)\n", "3. Normalizing expression to account for gene length\n", "4. Validation through clustering exploration\n", "\n", "⚠️ Note that the Census RNA data includes duplicate cells present across multiple datasets. Duplicate cells can be filtered in or out using the cell metadata variable `is_primary_data` which is described in the [Census schema](https://github.com/chanzuckerberg/cellxgene-census/blob/main/docs/cellxgene_census_schema.md#repeated-data).\n", "For this notebook we will focus on individual datasets, therefore we can ignore this variable.\n", "\n", "## Opening the census\n", "\n", "First we open the Census, if you are not familiar with the basics of the Census API you should take a look at notebook [Learning about the CZ CELLxGENE Census](https://chanzuckerberg.github.io/cellxgene-census/notebooks/analysis_demo/comp_bio_census_info.html)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:01.325351Z", "iopub.status.busy": "2023-07-28T14:30:01.325095Z", "iopub.status.idle": "2023-07-28T14:30:04.665645Z", "shell.execute_reply": "2023-07-28T14:30:04.665030Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The \"stable\" release is currently 2023-07-25. Specify 'census_version=\"2023-07-25\"' in future calls to open_soma() to ensure data consistency.\n" ] } ], "source": [ "import cellxgene_census\n", "import scanpy as sc\n", "from scipy.sparse import csr_matrix\n", "\n", "census = cellxgene_census.open_soma()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can learn more about the all of the `cellxgene_census` methods by accessing their corresponding documention via `help()`. For example `help(cellxgene_census.open_soma)`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fetching full-length example sequencing data (Smart-Seq)\n", "\n", "Let's get some example data, in this case we'll fetch all cells from a relatively small dataset derived from the Smart-Seq2 technology which performs full-length gene sequencing:\n", "\n", "- Collection: [Tabula Muris Senis](https://cellxgene.cziscience.com/collections/0b9d8a04-bb9d-44da-aa27-705bb65b54eb)\n", "- Dataset: [Liver - A single-cell transcriptomic atlas characterizes ageing tissues in the mouse - Smart-seq2](https://cellxgene.cziscience.com/e/524179b0-b406-4723-9c46-293ffa77ca81.cxg/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first find this dataset's id by using the dataset table of the Census" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:04.668922Z", "iopub.status.busy": "2023-07-28T14:30:04.668453Z", "iopub.status.idle": "2023-07-28T14:30:05.295251Z", "shell.execute_reply": "2023-07-28T14:30:05.294682Z" } }, "outputs": [ { "data": { "text/html": [ "
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05250b9d8a04-bb9d-44da-aa27-705bb65b54ebTabula Muris Senis10.1038/s41586-020-2496-14546e757-34d0-4d17-be06-538318925fcdLiver - A single-cell transcriptomic atlas cha...4546e757-34d0-4d17-be06-538318925fcd.h5ad2859
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" ], "text/plain": [ " soma_joinid collection_id collection_name \\\n", "0 525 0b9d8a04-bb9d-44da-aa27-705bb65b54eb Tabula Muris Senis \n", "\n", " collection_doi dataset_id \\\n", "0 10.1038/s41586-020-2496-1 4546e757-34d0-4d17-be06-538318925fcd \n", "\n", " dataset_title \\\n", "0 Liver - A single-cell transcriptomic atlas cha... \n", "\n", " dataset_h5ad_path dataset_total_cell_count \n", "0 4546e757-34d0-4d17-be06-538318925fcd.h5ad 2859 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "liver_dataset = (\n", " census[\"census_info\"][\"datasets\"]\n", " .read(\n", " value_filter=\"dataset_title == 'Liver - A single-cell transcriptomic atlas characterizes ageing tissues in the mouse - Smart-seq2'\"\n", " )\n", " .concat()\n", " .to_pandas()\n", ")\n", "liver_dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can use this id to fetch the data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:05.297790Z", "iopub.status.busy": "2023-07-28T14:30:05.297526Z", "iopub.status.idle": "2023-07-28T14:30:10.267215Z", "shell.execute_reply": "2023-07-28T14:30:10.266569Z" }, "scrolled": true }, "outputs": [], "source": [ "liver_dataset_id = \"4546e757-34d0-4d17-be06-538318925fcd\"\n", "liver_adata = cellxgene_census.get_anndata(\n", " census,\n", " organism=\"Mus musculus\",\n", " obs_value_filter=f\"dataset_id=='{liver_dataset_id}'\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make sure this data only contains Smart-Seq2 cells" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:10.270401Z", "iopub.status.busy": "2023-07-28T14:30:10.269976Z", "iopub.status.idle": "2023-07-28T14:30:10.275834Z", "shell.execute_reply": "2023-07-28T14:30:10.275293Z" } }, "outputs": [ { "data": { "text/plain": [ "assay\n", "Smart-seq2 2859\n", "Name: count, dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "liver_adata.obs[\"assay\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! As you can see this a small dataset only containing **2,859** cells. Now let's proceed to normalize by gene lengths.\n", "\n", "For good practice let's add the number of genes expressed by cell, and the total counts per cell." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Don't forget to close the census" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:10.278589Z", "iopub.status.busy": "2023-07-28T14:30:10.278107Z", "iopub.status.idle": "2023-07-28T14:30:10.281385Z", "shell.execute_reply": "2023-07-28T14:30:10.280887Z" } }, "outputs": [], "source": [ "census.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Normalizing expression to account for gene length\n", "\n", "By default `cellxgene_census.get_anndata()` fetches all genes in the Census. So let's first identify the genes that were measured in this dataset and subset the `AnnData` to only include those. \n", "\n", "To this goal we can use the \"Dataset Presence Matrix\" in `census[\"census_data\"][\"mus_musculus\"].ms[\"RNA\"][\"feature_dataset_presence_matrix\"]`. This is a boolean matrix `N x M` where `N` is the number of datasets and `M` is the number of genes in the Census, `True` indicates that a gene was measured in a dataset." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:10.283904Z", "iopub.status.busy": "2023-07-28T14:30:10.283600Z", "iopub.status.idle": "2023-07-28T14:30:10.287325Z", "shell.execute_reply": "2023-07-28T14:30:10.286707Z" } }, "outputs": [ { "data": { "text/plain": [ "52392" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "liver_adata.n_vars" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get the genes measured in this dataset." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:10.289922Z", "iopub.status.busy": "2023-07-28T14:30:10.289467Z", "iopub.status.idle": "2023-07-28T14:30:10.952112Z", "shell.execute_reply": "2023-07-28T14:30:10.951512Z" } }, "outputs": [], "source": [ "liver_dataset_id = liver_dataset[\"soma_joinid\"][0]\n", "\n", "presence_matrix = cellxgene_census.get_presence_matrix(census, \"Mus musculus\", \"RNA\")\n", "presence_matrix = presence_matrix[liver_dataset_id, :]\n", "gene_presence = presence_matrix.nonzero()[1]\n", "\n", "liver_adata = liver_adata[:, gene_presence].copy()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:10.954969Z", "iopub.status.busy": "2023-07-28T14:30:10.954680Z", "iopub.status.idle": "2023-07-28T14:30:10.958409Z", "shell.execute_reply": "2023-07-28T14:30:10.957932Z" } }, "outputs": [ { "data": { "text/plain": [ "17992" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "liver_adata.n_vars" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that out of the all genes in the Census **17,992** were measured in this dataset. \n", "\n", "Now let's normalize these genes by gene length. We can easily do this because the Census has gene lengths included in the gene metadata under `feature_length`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:10.960903Z", "iopub.status.busy": "2023-07-28T14:30:10.960406Z", "iopub.status.idle": "2023-07-28T14:30:10.964680Z", "shell.execute_reply": "2023-07-28T14:30:10.964217Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n", " [0.000e+00, 0.000e+00, 5.590e+02, 0.000e+00, 0.000e+00],\n", " [0.000e+00, 0.000e+00, 1.969e+03, 0.000e+00, 8.280e+02],\n", " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.000e+00],\n", " [0.000e+00, 2.250e+03, 0.000e+00, 0.000e+00, 5.400e+01]],\n", " dtype=float32)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "liver_adata.X[:5, :5].toarray()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:10.966944Z", "iopub.status.busy": "2023-07-28T14:30:10.966531Z", "iopub.status.idle": "2023-07-28T14:30:11.952451Z", "shell.execute_reply": "2023-07-28T14:30:11.951841Z" } }, "outputs": [], "source": [ "gene_lengths = liver_adata.var[[\"feature_length\"]].to_numpy()\n", "liver_adata.X = csr_matrix((liver_adata.X.T / gene_lengths).T)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:11.955315Z", "iopub.status.busy": "2023-07-28T14:30:11.955039Z", "iopub.status.idle": "2023-07-28T14:30:11.959535Z", "shell.execute_reply": "2023-07-28T14:30:11.959053Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", " 0.00000000e+00],\n", " [0.00000000e+00, 0.00000000e+00, 6.58654413e-02, 0.00000000e+00,\n", " 0.00000000e+00],\n", " [0.00000000e+00, 0.00000000e+00, 2.32001885e-01, 0.00000000e+00,\n", " 2.74444813e-01],\n", " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", " 3.31455088e-04],\n", " [0.00000000e+00, 4.71500419e-01, 0.00000000e+00, 0.00000000e+00,\n", " 1.78985747e-02]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "liver_adata.X[:5, :5].toarray()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All done! You can see that we now have real numbers instead of integers.\n", "\n", "## Validation through clustering exploration\n", "\n", "Let's perform some basic clustering analysis to see if cell types cluster as expected using the normalized counts.\n", "\n", "First we do some basic filtering of cells and genes." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:11.961835Z", "iopub.status.busy": "2023-07-28T14:30:11.961516Z", "iopub.status.idle": "2023-07-28T14:30:12.203292Z", "shell.execute_reply": "2023-07-28T14:30:12.202671Z" } }, "outputs": [], "source": [ "sc.pp.filter_cells(liver_adata, min_genes=500)\n", "sc.pp.filter_genes(liver_adata, min_cells=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we normalize to account for sequencing depth and transform data to log scale." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:12.206318Z", "iopub.status.busy": "2023-07-28T14:30:12.205823Z", "iopub.status.idle": "2023-07-28T14:30:12.272548Z", "shell.execute_reply": "2023-07-28T14:30:12.271937Z" } }, "outputs": [], "source": [ "sc.pp.normalize_total(liver_adata, target_sum=1e4)\n", "sc.pp.log1p(liver_adata)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we subset to highly variable genes." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:12.275522Z", "iopub.status.busy": "2023-07-28T14:30:12.275036Z", "iopub.status.idle": "2023-07-28T14:30:12.453613Z", "shell.execute_reply": "2023-07-28T14:30:12.453014Z" } }, "outputs": [], "source": [ "sc.pp.highly_variable_genes(liver_adata, n_top_genes=1000)\n", "liver_adata = liver_adata[:, liver_adata.var.highly_variable].copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally we scale values across the gene axis." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:12.456562Z", "iopub.status.busy": "2023-07-28T14:30:12.456068Z", "iopub.status.idle": "2023-07-28T14:30:12.476372Z", "shell.execute_reply": "2023-07-28T14:30:12.475787Z" } }, "outputs": [], "source": [ "sc.pp.scale(liver_adata, max_value=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can proceed to do clustering analysis" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2023-07-28T14:30:12.478967Z", "iopub.status.busy": "2023-07-28T14:30:12.478681Z", "iopub.status.idle": "2023-07-28T14:30:24.268320Z", "shell.execute_reply": "2023-07-28T14:30:24.267752Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ssm-user/cellxgene-census/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n", "/home/ssm-user/cellxgene-census/venv/lib/python3.10/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n", " cax = scatter(\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sc.tl.pca(liver_adata)\n", "sc.pp.neighbors(liver_adata)\n", "sc.tl.umap(liver_adata)\n", "sc.pl.umap(liver_adata, color=\"cell_type\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With a few exceptions we can see that all cells from the same cell type cluster near each other which serves as a sanity check for the gene-length normalization that we applied." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.10" }, "vscode": { "interpreter": { "hash": "3da8ec1c162cd849e59e6ea2824b2e353dce799884e910aae99411be5277f953" } } }, "nbformat": 4, "nbformat_minor": 2 }