{
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"cell_type": "markdown",
"id": "4a3d0b1b",
"metadata": {},
"source": [
"# Tutorial 3 - Generating metacells for reverse-engineering of ARACNe gene regulatory networks \n",
"\n",
"This tutorial explores different approaches to generate metacells from scRNA-seq data. Metacells are used as a means to counteract sparsity in single-cell transcriptomics and improve gene regulatory network (GRN) inference, for instance using the [ARACNe3](https://github.com/califano-lab/ARACNe3) and [ARACNe-AP](https://pubmed.ncbi.nlm.nih.gov/27153652/) algorithms for GRN reconstruction by mutual information estimation. After a brief description of the pyVIPER installation procedure and the modules needed, this notebook is organized in the following sections.\n",
"\n",
"**Table of Contents**\n",
"\n",
"[Step 1. Load a gene expression matrix and associated metadata](#1)\\\n",
"[Step 2. Perform basic preprocessing and clustering](#2)\\\n",
"[Step 3. Generate metacells](#3)\\\n",
"[Key Takeaways](#4) "
]
},
{
"cell_type": "markdown",
"id": "03b2b639",
"metadata": {},
"source": [
"### Install Pyviper\n",
"Install `pyviper` from PyPI using pip. Alternatively, refer to the README in the current GitHub to install from the local directory."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "309d7375",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"#!pip install viper-in-python"
]
},
{
"cell_type": "markdown",
"id": "e2590a58",
"metadata": {},
"source": [
"### Import modules\n",
"Load `pyviper` and additional modules required used in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "41321fbe",
"metadata": {
"execution": {
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"shell.execute_reply": "2023-12-14T20:41:46.766449Z",
"shell.execute_reply.started": "2023-12-14T20:41:44.576262Z"
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/lucazanella7/mambaforge/envs/pyviper_development/lib/python3.11/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"
]
}
],
"source": [
"import pyviper\n",
"import scanpy as sc\n",
"import anndata \n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import rc_context\n",
"from sklearn.metrics import silhouette_samples\n",
"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\", message=\".*The 'nopython' keyword.*\") # for jit decorator issue with sc.pp.neighbors (09/30/2023)"
]
},
{
"cell_type": "markdown",
"id": "d428eb41",
"metadata": {},
"source": [
"### Step 1. Load a gene expression matrix and associated metadata\n",
"\n",
"Load the same gene expression matrix (UMIs) used in Tutorial 2 and store it into an [AnnData](https://anndata.readthedocs.io/en/latest/) object to enable interoperability with [scanpy](https://scanpy-tutorials.readthedocs.io/en/latest/#). Cells used in this tutorial were sampled from scRNA-seq data published in [Peng et al., 2019](https://www.nature.com/articles/s41422-019-0195-y). \n",
"\n",
"Display matrix dimensions (cells x genes)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "03cb7f44",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-14T20:41:46.768058Z",
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"shell.execute_reply.started": "2023-12-14T20:41:46.768042Z"
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},
"outputs": [
{
"data": {
"text/plain": [
"AnnData object with n_obs × n_vars = 4632 × 24005"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gExpr_url = \"https://zenodo.org/records/10059791/files/Tutorial_2_counts_mixed_4632.tsv.gz\" # path to gene expression matrix (UMI counts)\n",
"adata_gExpr = pd.read_csv(gExpr_url, sep=\"\\t\") # read from remote \n",
"adata_gExpr = sc.AnnData(adata_gExpr) # convert to AnnData object\n",
"\n",
"adata_gExpr"
]
},
{
"cell_type": "markdown",
"id": "717ba697",
"metadata": {},
"source": [
"Load cell-associated metadata."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d694957f",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-14T20:42:22.397916Z",
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"iopub.status.idle": "2023-12-14T20:42:23.009847Z",
"shell.execute_reply": "2023-12-14T20:42:23.009221Z",
"shell.execute_reply.started": "2023-12-14T20:42:22.397901Z"
}
},
"outputs": [],
"source": [
"metadata_url = \"https://zenodo.org/records/10059791/files/Tutorial_2_metadata_mixed_4632.tsv.gz\" # path to cells metadata\n",
"cells_metadata = pd.read_csv(metadata_url, sep=\"\\t\") # load it"
]
},
{
"cell_type": "markdown",
"id": "149296dd",
"metadata": {},
"source": [
"Store the metadata in the `adata_gExpr` object and display the observation annotation."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8b87dd88",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-14T20:42:23.012504Z",
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{
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"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Cell_Type | \n",
"
\n",
" \n",
" \n",
" \n",
" | T1_AACCATGCACAACTGT | \n",
" Ductal cell type 2 | \n",
"
\n",
" \n",
" | T1_AAGACCTAGTCATGCT | \n",
" Ductal cell type 2 | \n",
"
\n",
" \n",
" | T1_ACATACGAGACTCGGA | \n",
" Ductal cell type 2 | \n",
"
\n",
" \n",
" | T1_ACCAGTATCTTGCAAG | \n",
" Ductal cell type 2 | \n",
"
\n",
" \n",
" | T1_ACGATGTTCACGAAGG | \n",
" Ductal cell type 2 | \n",
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\n",
" \n",
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\n",
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"
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"text/plain": [
" Cell_Type\n",
"T1_AACCATGCACAACTGT Ductal cell type 2\n",
"T1_AAGACCTAGTCATGCT Ductal cell type 2\n",
"T1_ACATACGAGACTCGGA Ductal cell type 2\n",
"T1_ACCAGTATCTTGCAAG Ductal cell type 2\n",
"T1_ACGATGTTCACGAAGG Ductal cell type 2"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata_gExpr.obs = pd.merge(adata_gExpr.obs, cells_metadata, how=\"left\",left_index=True, right_index=True) # store cell-specific metadata as annotation observation\n",
"adata_gExpr.obs.head()"
]
},
{
"cell_type": "markdown",
"id": "15319a83",
"metadata": {},
"source": [
"The observation annotations include the patient from which each cell was sequenced and the annotated cell type. List the number of cells for each cell types."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "87a47a53",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-14T20:42:23.033921Z",
"iopub.status.busy": "2023-12-14T20:42:23.033592Z",
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"shell.execute_reply": "2023-12-14T20:42:23.043397Z",
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{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Cell_Type | \n",
" n | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" B cell | \n",
" 1000 | \n",
"
\n",
" \n",
" | 1 | \n",
" Ductal cell type 2 | \n",
" 1455 | \n",
"
\n",
" \n",
" | 2 | \n",
" Fibroblast cell | \n",
" 1277 | \n",
"
\n",
" \n",
" | 3 | \n",
" Stellate cell | \n",
" 900 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Cell_Type n\n",
"0 B cell 1000\n",
"1 Ductal cell type 2 1455\n",
"2 Fibroblast cell 1277\n",
"3 Stellate cell 900"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata_gExpr.obs.groupby('Cell_Type').size().reset_index(name='n') # show cell types and number of cells for each type in AnnData"
]
},
{
"cell_type": "markdown",
"id": "808dc5f7",
"metadata": {},
"source": [
"### Step 2. Perform basic preprocessing and clustering\n",
"\n",
"Perform some standard preprocessing. The UMI matrix can be processed using the standard Scanpy preprocessing workflow. Since the quality of the provided data was pre-assessed and found to be high, cell filtering will be minimal. For a more detailed explanation of quality control steps, refer to the preprocessing tutorials by [Scanpy](https://scanpy-tutorials.readthedocs.io/en/latest/pbmc3k.html) or [Seurat](https://satijalab.org/seurat/articles/pbmc3k_tutorial). "
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "08d3b344",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-14T20:42:23.044788Z",
"iopub.status.busy": "2023-12-14T20:42:23.044597Z",
"iopub.status.idle": "2023-12-14T20:42:31.500470Z",
"shell.execute_reply": "2023-12-14T20:42:31.500055Z",
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},
"outputs": [],
"source": [
"sc.pp.calculate_qc_metrics(adata_gExpr, inplace=True)\n",
"sc.pp.filter_cells(adata_gExpr, min_genes=200) # filter out cells with <200 genes expressed\n",
"sc.pp.filter_genes(adata_gExpr, min_counts=10) # filter out genes that are present with <10 UMIs\n",
"\n",
"#adata_gExpr = calcMitoRiboPercent(adata_gExpr)\n",
"#adata_gExpr = filterCellsHighMito(adata_gExpr,0.2)\n",
"\n",
"# Store UMI in .raw\n",
"adata_gExpr.raw = adata_gExpr\n",
"\n",
"sc.pp.normalize_total(adata_gExpr, inplace=True,target_sum=1e4)\n",
"sc.pp.log1p(adata_gExpr)\n",
"sc.pp.highly_variable_genes(adata_gExpr, flavor=\"seurat\", n_top_genes=3000, inplace=True)\n",
"\n",
"sc.pp.scale(adata_gExpr)"
]
},
{
"cell_type": "markdown",
"id": "02e141f6",
"metadata": {},
"source": [
"scRNA-seq data are known to exhibit high sparsity, typically >80-90%. Compute the sparsity of cells labelled as Fibroblasts as the percentage of zero-entries in the dataset."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a859ee5f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sparsity (fibroblasts, single cells): 85.0%\n"
]
}
],
"source": [
"Fibroblast_matrix = adata_gExpr[adata_gExpr.obs['Cell_Type'] == 'Fibroblast cell']\n",
"sparsity = np.mean(Fibroblast_matrix.raw.X == 0)\n",
"del Fibroblast_matrix\n",
"\n",
"print(\"Sparsity (fibroblasts, single cells): \" + str(round(sparsity,2)*100) + \"%\")\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "7432b70c",
"metadata": {},
"source": [
"### 3. Generate Metacells \n",
"\n",
"Compute a distance matrix between each pair of cells on the PCA-projected space in the dataset using the `corr_distance` function. Any distance matrix can be used for metacell generation by setting 'dist_slot' to the corresponding value in the `repr_metacells` function (see below)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d700cd3b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 5/5 [00:00<00:00, 43.53it/s]\n"
]
}
],
"source": [
"sc.tl.pca(adata_gExpr, svd_solver='arpack', random_state=0)\n",
"pyviper.pp.corr_distance(adata_gExpr) # compute correlation distance"
]
},
{
"cell_type": "markdown",
"id": "227ea7cd",
"metadata": {},
"source": [
"Generate a set of metacell matrices for each cellular population (cluster), as represented by the 'Cell_Type' annotation in .obs. The metacell generation function allows several combination of inputs. Specifically, exactly two of the following parameters must be set: \n",
"**1.** 'size', **2a.** 'min_median_depth' or **2b.** 'n_cells_per_metacell', **3a.** 'perc_data_to_use' or **3b.** 'perc_incl_data_reused'. \n",
"\n",
"Please notice that 2a-2b and 3a-3b are mutually exclusive, i.e. they cannot be set together.\n",
"\n",
"- **Example 1**: generate 400 metacells per cell population requiring an attempt minimum depth of 12,000 UMI/metacell."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ce8c7727",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-14T20:42:31.501524Z",
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"shell.execute_reply": "2023-12-14T20:42:33.298309Z",
"shell.execute_reply.started": "2023-12-14T20:42:31.501506Z"
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},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 1/1 [00:00<00:00, 114.17it/s], ?it/s]\n",
"100%|██████████| 2/2 [00:00<00:00, 149.43it/s]<00:02, 1.20it/s]\n",
"/Users/lucazanella7/mambaforge/envs/pyviper_development/lib/python3.11/site-packages/numpy/_core/fromnumeric.py:3860: RuntimeWarning: Mean of empty slice.\n",
" return _methods._mean(a, axis=axis, dtype=dtype,\n",
"/Users/lucazanella7/mambaforge/envs/pyviper_development/lib/python3.11/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
" ret = ret.dtype.type(ret / rcount)\n",
"/Users/lucazanella7/mambaforge/envs/pyviper_development/lib/python3.11/site-packages/numpy/_core/_methods.py:222: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
" ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n",
"/Users/lucazanella7/mambaforge/envs/pyviper_development/lib/python3.11/site-packages/numpy/_core/_methods.py:180: RuntimeWarning: invalid value encountered in divide\n",
" arrmean = um.true_divide(arrmean, div, out=arrmean,\n",
"/Users/lucazanella7/mambaforge/envs/pyviper_development/lib/python3.11/site-packages/numpy/_core/_methods.py:214: RuntimeWarning: invalid value encountered in scalar divide\n",
" ret = ret.dtype.type(ret / rcount)\n",
"100%|██████████| 2/2 [00:00<00:00, 174.99it/s]<00:01, 1.19it/s]\n",
"cluster metacells: 100%|██████████| 4/4 [00:03<00:00, 1.18it/s]\n"
]
}
],
"source": [
"n_metacells=400\n",
"min_target_depth=12000\n",
"\n",
"pyviper.pp.repr_metacells(adata_gExpr, \n",
" counts=None, \n",
" pca_slot='X_pca', \n",
" dist_slot='corr_dist', \n",
" size=n_metacells, \n",
" min_median_depth=min_target_depth, \n",
" clusters_slot=\"Cell_Type\",\n",
" key_added=\"metacells_approach_1\") # size=n_meta, n_cells_per_metacell=k, clusters_slot=\"putative_tumor_infercnv_output\""
]
},
{
"cell_type": "markdown",
"id": "e28733da",
"metadata": {},
"source": [
"While the generation of the minimum target depth cannot be guaranteed in all cases, pyVIPER attempts to satisfy this request by tuning the number of nearest neighbors used in metacell generation. \n",
"\n",
"Display `adata_gExpr`."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "8bb06d47",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AnnData object with n_obs × n_vars = 4632 × 18160\n",
" obs: 'Cell_Type', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'n_genes'\n",
" var: 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'\n",
" uns: 'log1p', 'hvg', 'pca', 'metacells_approach_1_B cell', 'metacells_approach_1_Ductal cell type 2', 'metacells_approach_1_Fibroblast cell', 'metacells_approach_1_Stellate cell'\n",
" obsm: 'X_pca'\n",
" varm: 'PCs'\n",
" obsp: 'corr_dist'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata_gExpr"
]
},
{
"cell_type": "markdown",
"id": "9fb28d00",
"metadata": {},
"source": [
"4 new matrices, one for each cell type, were stored as Pandas dataframe in `adata.uns`. \n",
"\n",
"Display the metacell matrix for the population of Ductal cell type 2. "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f2d1a02e",
"metadata": {},
"outputs": [
{
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" | metacell_4 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \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",
" | metacell_395 | \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
" | metacell_396 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
" | metacell_397 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
" | metacell_398 | \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
" | metacell_399 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" ... | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
"
\n",
"
400 rows × 18160 columns
\n",
"
"
],
"text/plain": [
" AL627309.1 AP006222.2 RP11-206L10.3 RP11-206L10.2 \\\n",
"metacell_0 0 0 0 0 \n",
"metacell_1 0 0 0 0 \n",
"metacell_2 0 0 0 0 \n",
"metacell_3 0 0 0 0 \n",
"metacell_4 0 0 0 0 \n",
"... ... ... ... ... \n",
"metacell_395 0 1 0 0 \n",
"metacell_396 0 0 0 0 \n",
"metacell_397 0 0 0 0 \n",
"metacell_398 0 1 0 0 \n",
"metacell_399 0 0 0 0 \n",
"\n",
" RP11-206L10.9 LINC00115 FAM41C RP11-54O7.3 SAMD11 NOC2L \\\n",
"metacell_0 0 0 0 0 0 0 \n",
"metacell_1 0 0 0 0 0 0 \n",
"metacell_2 0 0 0 0 0 0 \n",
"metacell_3 0 0 0 0 0 0 \n",
"metacell_4 0 0 0 0 0 1 \n",
"... ... ... ... ... ... ... \n",
"metacell_395 0 0 0 0 0 0 \n",
"metacell_396 0 0 0 0 0 2 \n",
"metacell_397 0 0 0 0 0 0 \n",
"metacell_398 0 0 2 0 0 0 \n",
"metacell_399 0 0 0 0 0 1 \n",
"\n",
" ... RP11-332K15.1 RP11-157E21.1 DMRT2 RP11-309M23.1 NLRP7 \\\n",
"metacell_0 ... 0 0 0 0 0 \n",
"metacell_1 ... 0 0 0 0 0 \n",
"metacell_2 ... 0 0 0 0 0 \n",
"metacell_3 ... 0 0 0 0 0 \n",
"metacell_4 ... 0 0 0 0 0 \n",
"... ... ... ... ... ... ... \n",
"metacell_395 ... 0 0 0 0 0 \n",
"metacell_396 ... 0 0 0 0 0 \n",
"metacell_397 ... 0 0 0 0 0 \n",
"metacell_398 ... 0 0 0 0 0 \n",
"metacell_399 ... 0 0 0 0 0 \n",
"\n",
" IGLL1 EGFR-AS1 RSPO2 KCNC2 AGTR2 \n",
"metacell_0 0 0 0 0 0 \n",
"metacell_1 0 0 0 0 0 \n",
"metacell_2 0 0 0 0 0 \n",
"metacell_3 0 0 0 0 0 \n",
"metacell_4 0 0 0 0 0 \n",
"... ... ... ... ... ... \n",
"metacell_395 0 0 0 0 0 \n",
"metacell_396 0 0 0 0 0 \n",
"metacell_397 0 0 0 0 0 \n",
"metacell_398 0 0 0 0 0 \n",
"metacell_399 0 0 0 0 0 \n",
"\n",
"[400 rows x 18160 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata_gExpr.uns[\"metacells_approach_1_Ductal cell type 2\"]"
]
},
{
"cell_type": "markdown",
"id": "eb11effd",
"metadata": {},
"source": [
"Each metacell matrix can be used as input to ARACNe3 to reverse engineer the corresponding GRN which can then be provided as an input to VIPER. Each metacell matrix is associated with a dictionary collecting relevant metacell statistics. \n",
"\n",
"Display metacell-associated statistics for the population of fibroblasts."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c5d7dc15",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Values | \n",
"
\n",
" \n",
" \n",
" \n",
" | size | \n",
" 400.000000 | \n",
"
\n",
" \n",
" | n_cells_per_metacell | \n",
" 2.000000 | \n",
"
\n",
" \n",
" | min_median_depth | \n",
" 12000.000000 | \n",
"
\n",
" \n",
" | median_depth | \n",
" 18332.000000 | \n",
"
\n",
" \n",
" | perc_total_samples_used | \n",
" 58.339859 | \n",
"
\n",
" \n",
" | perc_included_samples_used_mult_times | \n",
" 7.248322 | \n",
"
\n",
" \n",
" | mean_n_times_samples_used | \n",
" 1.073826 | \n",
"
\n",
" \n",
" | stdev_n_times_samples_used | \n",
" 0.266571 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Values\n",
"size 400.000000\n",
"n_cells_per_metacell 2.000000\n",
"min_median_depth 12000.000000\n",
"median_depth 18332.000000\n",
"perc_total_samples_used 58.339859\n",
"perc_included_samples_used_mult_times 7.248322\n",
"mean_n_times_samples_used 1.073826\n",
"stdev_n_times_samples_used 0.266571"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"attrs_dict = adata_gExpr.uns[\"metacells_approach_1_Fibroblast cell\"].attrs\n",
"attrs_df = pd.DataFrame.from_dict(attrs_dict, orient='index', columns=['Values']) # convert to df\n",
"\n",
"attrs_df\n"
]
},
{
"cell_type": "markdown",
"id": "942b47b3",
"metadata": {},
"source": [
"The report is highly informative. The metacell matrix contains 400 metacells, each generated by aggregation of 2 nearest neighbors after requesting an \"attempt\" minimum target depth of 12,000 UMI/metacell. The median depth for the inferred metacells is ~18,000 UMI/metacell. 58% of the original single-cells from the fibroblasts population was used for metacell generation, with <7% of cells being aggregated in more than 1 metacell. \n",
"\n",
"The data sparsity is expected to be decreased in the metacells."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "c4f2268b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sparsity (fibroblasts, metacells example 1): 77.0%\n"
]
}
],
"source": [
"sparsity = np.mean(np.array(adata_gExpr.uns[\"metacells_approach_1_Fibroblast cell\"]) == 0)\n",
"print(\"Sparsity (fibroblasts, metacells example 1): \" + str(round(sparsity,2)*100) + \"%\")\n"
]
},
{
"cell_type": "markdown",
"id": "6410f82c",
"metadata": {},
"source": [
"Display the statistics for metacells of another cluster (B cells)."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e3f4b56b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Values | \n",
"
\n",
" \n",
" \n",
" \n",
" | size | \n",
" 400.000000 | \n",
"
\n",
" \n",
" | n_cells_per_metacell | \n",
" 3.000000 | \n",
"
\n",
" \n",
" | min_median_depth | \n",
" 12000.000000 | \n",
"
\n",
" \n",
" | median_depth | \n",
" 11481.500000 | \n",
"
\n",
" \n",
" | perc_total_samples_used | \n",
" 81.700000 | \n",
"
\n",
" \n",
" | perc_included_samples_used_mult_times | \n",
" 31.456548 | \n",
"
\n",
" \n",
" | mean_n_times_samples_used | \n",
" 1.468788 | \n",
"
\n",
" \n",
" | stdev_n_times_samples_used | \n",
" 0.855148 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Values\n",
"size 400.000000\n",
"n_cells_per_metacell 3.000000\n",
"min_median_depth 12000.000000\n",
"median_depth 11481.500000\n",
"perc_total_samples_used 81.700000\n",
"perc_included_samples_used_mult_times 31.456548\n",
"mean_n_times_samples_used 1.468788\n",
"stdev_n_times_samples_used 0.855148"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"attrs_dict = adata_gExpr.uns[\"metacells_approach_1_B cell\"].attrs\n",
"attrs_df = pd.DataFrame.from_dict(attrs_dict, orient='index', columns=['Values']) # convert to df\n",
"\n",
"attrs_df\n"
]
},
{
"cell_type": "markdown",
"id": "a08d5fec",
"metadata": {},
"source": [
"As mentioned before, `repr_metacells` allows several combinations of parameters. Let's consider another combination.\n",
"\n",
"- **Example 2**: generate 400 metacells per cluster such that each metacell should incorporate 20 single-cells (`n_cells_per_metacell=20`; `min_median_depth=None`).\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "b9ceab10",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 1/1 [00:00<00:00, 121.91it/s], ?it/s]\n",
"100%|██████████| 2/2 [00:00<00:00, 141.73it/s]<00:04, 1.42s/it]\n",
"100%|██████████| 2/2 [00:00<00:00, 108.01it/s]<00:03, 1.68s/it]\n",
"cluster metacells: 100%|██████████| 4/4 [00:06<00:00, 1.56s/it]\n"
]
}
],
"source": [
"n_cells_per_metacell=20\n",
"\n",
"pyviper.pp.repr_metacells(adata_gExpr, \n",
" counts=None, \n",
" pca_slot='X_pca', \n",
" dist_slot='corr_dist', \n",
" size=400,\n",
" n_cells_per_metacell=n_cells_per_metacell,\n",
" min_median_depth=None, \n",
" clusters_slot=\"Cell_Type\",\n",
" key_added=\"metacells_approach_2\") "
]
},
{
"cell_type": "markdown",
"id": "28b82629",
"metadata": {},
"source": [
"Display metacell-associated statistics for the Fibroblast population. The median depth is expected to be much higher than before, due to the larger number of cells per metacells specified."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "da4d17ae",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Values | \n",
"
\n",
" \n",
" \n",
" \n",
" | size | \n",
" 400.000000 | \n",
"
\n",
" \n",
" | n_cells_per_metacell | \n",
" 20.000000 | \n",
"
\n",
" \n",
" | min_median_depth | \n",
" NaN | \n",
"
\n",
" \n",
" | median_depth | \n",
" 196897.500000 | \n",
"
\n",
" \n",
" | perc_total_samples_used | \n",
" 97.259201 | \n",
"
\n",
" \n",
" | perc_included_samples_used_mult_times | \n",
" 91.384863 | \n",
"
\n",
" \n",
" | mean_n_times_samples_used | \n",
" 6.441224 | \n",
"
\n",
" \n",
" | stdev_n_times_samples_used | \n",
" 4.286650 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Values\n",
"size 400.000000\n",
"n_cells_per_metacell 20.000000\n",
"min_median_depth NaN\n",
"median_depth 196897.500000\n",
"perc_total_samples_used 97.259201\n",
"perc_included_samples_used_mult_times 91.384863\n",
"mean_n_times_samples_used 6.441224\n",
"stdev_n_times_samples_used 4.286650"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"attrs_dict = adata_gExpr.uns[\"metacells_approach_2_Fibroblast cell\"].attrs\n",
"attrs_df = pd.DataFrame.from_dict(attrs_dict, orient='index', columns=['Values']) # convert to df\n",
"\n",
"attrs_df\n"
]
},
{
"cell_type": "markdown",
"id": "bd9642f0",
"metadata": {},
"source": [
"Data sparsity is also strongly decreased due to the higher number of nearest neighbors being aggregated."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "fd05b2d2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sparsity (fibroblasts, metacells example 2): 40.0%\n"
]
}
],
"source": [
"sparsity = np.mean(np.array(adata_gExpr.uns[\"metacells_approach_2_Fibroblast cell\"]) == 0)\n",
"\n",
"print(\"Sparsity (fibroblasts, metacells example 2): \" + str(round(sparsity,2)*100) + \"%\")\n"
]
},
{
"cell_type": "markdown",
"id": "3311b6df",
"metadata": {},
"source": [
"- **Example 3**: Generate 400 metacells and allow for only the 20% of the cells to be incorporated in more than 1 metacell (`perc_incl_data_reused=20`)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "d1bf7600",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 1/1 [00:00<00:00, 109.47it/s], ?it/s]\n",
"100%|██████████| 2/2 [00:00<00:00, 95.07it/s]1<00:03, 1.04s/it]\n",
"100%|██████████| 2/2 [00:00<00:00, 161.72it/s]<00:02, 1.30s/it]\n",
"cluster metacells: 100%|██████████| 4/4 [00:05<00:00, 1.27s/it]\n"
]
}
],
"source": [
"perc_incl_data_reused=20\n",
"\n",
"pyviper.pp.repr_metacells(adata_gExpr, \n",
" counts=None, \n",
" pca_slot='X_pca', \n",
" dist_slot='corr_dist', \n",
" size=400,\n",
" perc_incl_data_reused=perc_incl_data_reused,\n",
" min_median_depth=None, \n",
" clusters_slot=\"Cell_Type\",\n",
" key_added=\"metacells_approach_3\") \n"
]
},
{
"cell_type": "markdown",
"id": "db3c16d3",
"metadata": {},
"source": [
"Display metacell statistics for the B cell population: the percentage of included cells is now lower than in the example 1. This approach can be useful if the goal is to generate metacells that are independent."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "2b0a08fa",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Values | \n",
"
\n",
" \n",
" \n",
" \n",
" | size | \n",
" 400.000000 | \n",
"
\n",
" \n",
" | n_cells_per_metacell | \n",
" 2.000000 | \n",
"
\n",
" \n",
" | min_median_depth | \n",
" NaN | \n",
"
\n",
" \n",
" | median_depth | \n",
" 7612.500000 | \n",
"
\n",
" \n",
" | perc_total_samples_used | \n",
" 69.700000 | \n",
"
\n",
" \n",
" | perc_included_samples_used_mult_times | \n",
" 12.912482 | \n",
"
\n",
" \n",
" | mean_n_times_samples_used | \n",
" 1.147776 | \n",
"
\n",
" \n",
" | stdev_n_times_samples_used | \n",
" 0.404031 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Values\n",
"size 400.000000\n",
"n_cells_per_metacell 2.000000\n",
"min_median_depth NaN\n",
"median_depth 7612.500000\n",
"perc_total_samples_used 69.700000\n",
"perc_included_samples_used_mult_times 12.912482\n",
"mean_n_times_samples_used 1.147776\n",
"stdev_n_times_samples_used 0.404031"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"attrs_dict = adata_gExpr.uns[\"metacells_approach_3_B cell\"].attrs\n",
"attrs_df = pd.DataFrame.from_dict(attrs_dict, orient='index', columns=['Values']) # convert to df\n",
"\n",
"attrs_df"
]
},
{
"cell_type": "markdown",
"id": "8bc2a264",
"metadata": {},
"source": [
"##### Comparing metacell and original single-cells\n",
"As a simple and quick sanity check, we compare the original cells and the derived cell metacells. Extract all the metacell matrices generated with the first approach."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "becc4fdf",
"metadata": {},
"outputs": [],
"source": [
"# Extract and concatenate all population-specific metacell matrices\n",
"B_meta=adata_gExpr.uns[\"metacells_approach_1_B cell\"]\n",
"Ductal_meta=adata_gExpr.uns[\"metacells_approach_1_Ductal cell type 2\"]\n",
"Fibroblast_meta=adata_gExpr.uns[\"metacells_approach_1_Fibroblast cell\"]\n",
"Stellate_meta=adata_gExpr.uns[\"metacells_approach_1_Stellate cell\"]\n",
"\n",
"# rename all indexes before merging\n",
"B_meta.index = \"B_\"+B_meta.index\n",
"Ductal_meta.index = \"Ductal_\"+Ductal_meta.index\n",
"Fibroblast_meta.index = \"Fibroblast_\"+Fibroblast_meta.index\n",
"Stellate_meta.index = \"Stellate_\"+Stellate_meta.index\n",
"\n",
"# concatenate all dataframes\n",
"metacells_df = pd.concat([B_meta, Ductal_meta, Fibroblast_meta, Stellate_meta], ignore_index=False)\n"
]
},
{
"cell_type": "markdown",
"id": "5997567a",
"metadata": {},
"source": [
"Annotate each metacell with its cell type."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "2ebb53ce",
"metadata": {},
"outputs": [],
"source": [
"# Dictionary mapping index prefixes to cell types\n",
"cell_type_map = {\n",
" 'B': 'B cell',\n",
" 'Ductal': 'Ductal cell type 2',\n",
" 'Fibroblast': 'Fibroblast cell',\n",
" 'Stellate': 'Stellate cell'\n",
"}\n",
"\n",
"# Generate the \"Cell_Type\" column based on the index\n",
"cell_types = [cell_type_map[index.split('_')[0]] for index in metacells_df.index]\n",
"\n",
"# metacell observation dataframe\n",
"observation_df = pd.DataFrame({'Cell_Type': cell_types}, index=metacells_df.index)\n"
]
},
{
"cell_type": "markdown",
"id": "a5c0341f",
"metadata": {},
"source": [
"Generate an AnnData object from metacells."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "be6a2422",
"metadata": {},
"outputs": [],
"source": [
"adata_metacell = anndata.AnnData(X=metacells_df, obs=observation_df)"
]
},
{
"cell_type": "markdown",
"id": "26989bfe",
"metadata": {},
"source": [
"Preprocess the concatenated metacells for PCA and UMAP visualization."
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "4a241691",
"metadata": {},
"outputs": [],
"source": [
"adata_metacell.raw = adata_metacell\n",
"\n",
"sc.pp.normalize_total(adata_metacell, inplace=True,target_sum=1e4)\n",
"sc.pp.log1p(adata_metacell)\n",
"sc.pp.highly_variable_genes(adata_metacell, flavor=\"seurat\", n_top_genes=3000, inplace=True)\n",
"\n",
"sc.pp.scale(adata_metacell)\n",
"\n",
"sc.tl.pca(adata_metacell, svd_solver='arpack', random_state=0)\n",
"sc.pp.neighbors(adata_metacell, n_neighbors=15, n_pcs=30)\n",
"sc.tl.umap(adata_metacell)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "b356970e",
"metadata": {},
"source": [
"Visualize the original single-cells in PCA and UMAP embeddings."
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "168c8717",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sc.pp.neighbors(adata_gExpr, n_neighbors=15, n_pcs=30)\n",
"sc.tl.umap(adata_gExpr)\n",
"\n",
"fig, axs = plt.subplots(1, 2, figsize=(10, 3.5))\n",
"\n",
"with rc_context({'figure.figsize': (3.5, 3.5)}):\n",
" sc.pl.pca(adata_gExpr, color=\"Cell_Type\", use_raw=False, palette=\"Set2\", title=\"PCA by Cell Types\", add_outline=True, ax=axs[0], show=False)\n",
"with rc_context({'figure.figsize': (3.5, 3.5)}):\n",
" sc.pl.umap(adata_gExpr, color=\"Cell_Type\", use_raw=False, palette=\"Set2\", title=\"UMAP by Cell Types\", add_outline=True, ax=axs[1], show=False)\n",
"\n",
"plt.suptitle(\"Gene expression - single cells\")\n",
"plt.tight_layout()\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "213559ad",
"metadata": {},
"source": [
"Visualize the cell population-specific metacells in PCA and UMAP embeddings."
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "aa3ab719",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(1, 2, figsize=(10, 3.5))\n",
"\n",
"with rc_context({'figure.figsize': (3.5, 3.5)}):\n",
" sc.pl.pca(adata_metacell, color=\"Cell_Type\", use_raw=False, palette=\"Set2\", title=\"PCA by Cell Types\", add_outline=True, ax=axs[0], show=False)\n",
"with rc_context({'figure.figsize': (3.5, 3.5)}):\n",
" sc.pl.umap(adata_metacell, color=\"Cell_Type\", use_raw=False, palette=\"Set2\", title=\"UMAP by Cell Types\", add_outline=True, ax=axs[1], show=False)\n",
"plt.suptitle(\"Gene expression - metacells\")\n",
"plt.tight_layout()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "d598b66e",
"metadata": {},
"source": [
"The cell type distribution is consistent between single-cells and metacells, with stellate cells and fibroblasts partially overlapping in the PCA as expected, and all the three lineages been separated along the directions of maximal variance."
]
},
{
"cell_type": "markdown",
"id": "b1e82953",
"metadata": {},
"source": [
"### Key takeaways\n",
"\n",
"This Tutorial describes how to generate metacells with pyVIPER. Metacells can be used for pseudo-bulk analysis of specific cellular populations or as input to [ARACNe](https://github.com/califano-lab/ARACNe3), when the original cell depth is not adequate to robustly infer regulatory networks (typically below 10,000 UMI/cell). The `repr_metacell` function computes several metacell-associated statistics and allows the users to specify several parameter combinations, among which the current Tutorial provides three examples."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "pyviper_development",
"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": "3.11.14"
}
},
"nbformat": 4,
"nbformat_minor": 5
}