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93_cluster_cells.ipynb
{
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"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"import os\n",
"\n",
"HOME = os.path.expanduser('~')\n",
"\n",
"DATA_FOLDER = os.path.abspath(\n",
" os.path.join(HOME, 'projects', 'cshl-singlecell-2017', 'data'))\n",
"FIGURE_FOLDER = os.path.abspath(\n",
" os.path.join(HOME, 'projects', 'cshl-singlecell-2017', 'figures'))\n",
"\n",
"\n",
"notebook_name = '93_cluster_cells'\n",
"data_folder = os.path.join(DATA_FOLDER, notebook_name)\n",
"figure_folder = os.path.join(FIGURE_FOLDER, notebook_name)\n",
"\n",
"input_folder = os.path.join(DATA_FOLDER, '91_filter_genes')\n",
"\n",
"! mkdir -p $data_folder\n",
"! mkdir -p $figure_folder"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
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"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import phenograph\n",
"import seaborn as sns\n",
"import xarray as xr\n",
"\n",
"from bokeh.io import output_notebook\n",
"\n",
"# This line is required for the plots to appear in the notebooks\n",
"output_notebook()\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ignore the \"trisomy21\"... we thought the data was trisomy21 fibroblasts but turns out that they were the control, human BJ fibroblasts instead."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (cell: 13300, cell_feature: 1, gene: 1028, gene_feature: 1)\n",
"Coordinates:\n",
" * gene (gene) object 'ENSG00000002834.17' 'ENSG00000005022.5' ...\n",
" * cell (cell) object 'group1_0000' 'group1_0001' 'group1_0002' ...\n",
" * gene_feature (gene_feature) object 'symbol'\n",
" * cell_feature (cell_feature) object 'group'\n",
"Data variables:\n",
" counts (cell, gene) float64 7.0 20.0 9.0 50.0 8.0 8.0 18.0 8.0 ...\n",
" gene_metadata (gene, gene_feature) object 'LASP1' 'SLC25A5' 'POLR2J' ...\n",
" cell_metadata (cell, cell_feature) object 'group1' 'group1' 'group1' ..."
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"netcdf = os.path.join(input_folder, 'trisomy21_median_filtered.netcdf')\n",
"\n",
"ds = xr.open_dataset(netcdf)\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>gene_feature</th>\n",
" <th>symbol</th>\n",
" </tr>\n",
" <tr>\n",
" <th>gene</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ENSG00000002834.17</th>\n",
" <td>LASP1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ENSG00000005022.5</th>\n",
" <td>SLC25A5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ENSG00000005075.15</th>\n",
" <td>POLR2J</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ENSG00000006327.13</th>\n",
" <td>TNFRSF12A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ENSG00000007168.12</th>\n",
" <td>PAFAH1B1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"gene_feature symbol\n",
"gene \n",
"ENSG00000002834.17 LASP1\n",
"ENSG00000005022.5 SLC25A5\n",
"ENSG00000005075.15 POLR2J\n",
"ENSG00000006327.13 TNFRSF12A\n",
"ENSG00000007168.12 PAFAH1B1"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gene_metadata = ds.gene_metadata.to_pandas()\n",
"gene_metadata.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"symbol\n",
"ABCF1 1\n",
"AC004453.8 1\n",
"AC007969.5 1\n",
"AC009413.2 1\n",
"AC011933.2 1\n",
"AC016739.2 1\n",
"AC079250.1 1\n",
"AC090498.1 1\n",
"ACTB 1\n",
"ACTG1 1\n",
"ACTN1 1\n",
"ACTN4 1\n",
"ACTR2 1\n",
"ACTR3 1\n",
"ADI1 1\n",
"ADIRF 1\n",
"ADM 1\n",
"AHNAK 1\n",
"ALDOA 1\n",
"ANAPC11 1\n",
"ANKRD11 1\n",
"ANP32B 1\n",
"ANPEP 1\n",
"ANXA1 1\n",
"ANXA11 1\n",
"ANXA2 1\n",
"ANXA2P2 1\n",
"ANXA5 1\n",
"ANXA6 1\n",
"AP000354.2 1\n",
" ..\n",
"USMG5 1\n",
"USP53 1\n",
"UXT 1\n",
"VAMP5 1\n",
"VCL 1\n",
"VCP 1\n",
"VDAC1 1\n",
"VDAC2 1\n",
"VIM 1\n",
"VIMP 1\n",
"VKORC1 1\n",
"WBP5 1\n",
"WDR1 1\n",
"WDR83OS 1\n",
"WNT5A 1\n",
"XRCC5 1\n",
"XRCC6 1\n",
"YBX1 1\n",
"YBX3 1\n",
"YIF1A 1\n",
"YWHAB 1\n",
"YWHAE 1\n",
"YWHAQ 1\n",
"YWHAZ 1\n",
"ZC3H15 1\n",
"ZCRB1 1\n",
"ZFAND5 1\n",
"ZFAS1 1\n",
"ZNHIT1 1\n",
"ZYX 1\n",
"Length: 1028, dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gene_metadata.groupby('symbol').size()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gene_metadata"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# medians = ds.counts.groupby(ds.cell_metadata.sel('group').values).median()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (cell: 1368, cell_feature: 1, gene: 1028, gene_feature: 1)\n",
"Coordinates:\n",
" * gene (gene) object 'ENSG00000002834.17' 'ENSG00000005022.5' ...\n",
" * cell (cell) object 'group1_0014' 'group1_0015' 'group1_0016' ...\n",
" * gene_feature (gene_feature) object 'symbol'\n",
" * cell_feature (cell_feature) object 'group'\n",
"Data variables:\n",
" counts (cell, gene) float64 9.0 6.0 5.0 6.0 2.0 14.0 23.0 1.0 ...\n",
" gene_metadata (gene, gene_feature) object 'LASP1' 'SLC25A5' 'POLR2J' ...\n",
" cell_metadata (cell, cell_feature) object 'group1' 'group1' 'group1' ..."
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(0)\n",
"downsampler = np.random.uniform(size=ds.counts.shape[0]) < 0.1\n",
"downsampler.sum()\n",
"\n",
"ds_downsampled = ds.sel(cell=downsampler)\n",
"ds_downsampled"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.core.groupby.DatasetGroupBy at 0x11782d3c8>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.groupby(ds.cell_metadata.sel('group'))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.core.groupby.DatasetGroupBy at 0x136acf5f8>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_downsampled.groupby(ds_downsampled.cell_metadata.sel('group'))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from bokeh.models import ColumnDataSource\n",
"from bokeh.models.widgets import Panel, Tabs\n",
"from bokeh.plotting import figure, show, output_file, output_notebook"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'cell_metadata' (cell: 1368, cell_feature: 1)>\n",
"array([['group1'],\n",
" ['group1'],\n",
" ['group1'],\n",
" ..., \n",
" ['group8'],\n",
" ['group8'],\n",
" ['group8']], dtype=object)\n",
"Coordinates:\n",
" * cell (cell) object 'group1_0014' 'group1_0015' 'group1_0016' ...\n",
" * cell_feature (cell_feature) object 'group'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_downsampled.cell_metadata.sel('group')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_nodes_specs(positions, metadata, barcodes, communities,\n",
" community_col='community',\n",
" other_cluster_col=None, palette=None):\n",
" \"\"\"Create a dataframe of x,y position of nodes\n",
" \n",
" Parameters\n",
" ----------\n",
" positions : dict\n",
" Mapping of each node's id to its x, y position\n",
" metadata : pandas.DataFrame\n",
" Any additional data describing each node\n",
" barcodes : list\n",
" Identifiers of each node, must correspond to the index of metadata, and\n",
" must be in the exact order found in `positions`\n",
" communities : list\n",
" Cluster assignments of each cell\n",
" community_col : str, optional\n",
" What to name the column in the dataframe for where the communities are \n",
" stored\n",
" other_cluster_col : str, optional\n",
" If not None, then this is a name of a colujmn that exists in `metadata`\n",
" that you'd like to also create colors for\n",
" palette : str, optional\n",
" Name of a color palette to use. Default is \"deep\"\n",
" \"\"\"\n",
" layout = pd.DataFrame(positions, index=['xs', 'ys']).T\n",
" layout[community_col] = communities\n",
" layout[community_col] = f'{community_col} #'.capitalize() \\\n",
" + layout[community_col].astype(str)\n",
"# layout = layout.sort_values(community_col)\n",
"\n",
" layout['barcode'] = barcodes\n",
" layout_metadata = layout.join(metadata, on='barcode')\n",
" if other_cluster_col is not None:\n",
" layout_metadata = layout_metadata.sort_values(other_cluster_col)\n",
" layout_metadata['other_cluster_color'] = nplots.labels_to_colors(\n",
" layout_metadata[other_cluster_col], palette)\n",
" layout_metadata[f'{community_col}_color'] = nplots.labels_to_colors(\n",
" layout_metadata[community_col], palette)\n",
" return layout_metadata"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import networkplots as nplots\n",
"\n",
"import networkx\n",
"from networkplots import correlation_to_distance\n",
"import phenograph\n",
"\n",
"\n",
"def correl_and_cluster(dataset):\n",
" counts = dataset.counts.to_pandas()\n",
" corr = counts.T.rank().corr()\n",
" distances = correlation_to_distance(corr)\n",
" communities, graph, Q = phenograph.cluster(counts, k=30)\n",
" G = networkx.from_scipy_sparse_matrix(graph)\n",
" positions = networkx.spring_layout(G)\n",
" \n",
" metadata = subset.cell_metadata.to_pandas().unstack()\n",
" \n",
" nodes_specs = get_nodes_specs(\n",
" positions, metadata=metadata,\n",
" barcodes=corr.index, communities=communities, \n",
" other_cluster_col='group', palette='Set2')\n",
" edges_specs = nplots.get_edges_specs(G, positions)\n",
" nodes_source = ColumnDataSource(nodes_specs)\n",
" edges_source = ColumnDataSource(edges_specs)\n",
" return nodes_source, edges_source\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- group1 --- (1028, 1000)\n",
"Finding 30 nearest neighbors using minkowski metric and 'auto' algorithm\n",
"Neighbors computed in 0.5414679050445557 seconds\n",
"Jaccard graph constructed in 0.15779709815979004 seconds\n",
"Wrote graph to binary file in 0.07283711433410645 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.621501\n",
"Louvain completed 21 runs in 0.4370100498199463 seconds\n",
"PhenoGraph complete in 1.2289369106292725 seconds\n",
"--- group2 --- (1028, 1000)\n",
"Finding 30 nearest neighbors using minkowski metric and 'auto' algorithm\n",
"Neighbors computed in 0.4439229965209961 seconds\n",
"Jaccard graph constructed in 0.1742568016052246 seconds\n",
"Wrote graph to binary file in 0.07852625846862793 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.609225\n",
"After 3 runs, maximum modularity is Q = 0.615597\n",
"Louvain completed 23 runs in 0.5114140510559082 seconds\n",
"PhenoGraph complete in 1.2284479141235352 seconds\n",
"--- group3 --- (1028, 5000)\n",
"Finding 30 nearest neighbors using minkowski metric and 'auto' algorithm\n",
"Neighbors computed in 2.0500218868255615 seconds\n",
"Jaccard graph constructed in 0.21287083625793457 seconds\n",
"Wrote graph to binary file in 0.16564679145812988 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.619147\n",
"After 2 runs, maximum modularity is Q = 0.625431\n",
"After 13 runs, maximum modularity is Q = 0.627464\n",
"Louvain completed 33 runs in 0.7455739974975586 seconds\n",
"PhenoGraph complete in 3.1921558380126953 seconds\n",
"--- group4 --- (1028, 4000)\n",
"Finding 30 nearest neighbors using minkowski metric and 'auto' algorithm\n",
"Neighbors computed in 1.6094889640808105 seconds\n",
"Jaccard graph constructed in 0.19327592849731445 seconds\n",
"Wrote graph to binary file in 0.08546900749206543 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.542585\n",
"Louvain completed 21 runs in 0.4200170040130615 seconds\n",
"PhenoGraph complete in 2.3273088932037354 seconds\n",
"--- group5 --- (1028, 1500)\n",
"Finding 30 nearest neighbors using minkowski metric and 'auto' algorithm\n",
"Neighbors computed in 0.5463330745697021 seconds\n",
"Jaccard graph constructed in 0.15103411674499512 seconds\n",
"Wrote graph to binary file in 0.05750608444213867 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.633957\n",
"After 2 runs, maximum modularity is Q = 0.640059\n",
"After 18 runs, maximum modularity is Q = 0.64132\n",
"Louvain completed 38 runs in 0.7413351535797119 seconds\n",
"PhenoGraph complete in 1.5179450511932373 seconds\n",
"--- group8 --- (1028, 800)\n",
"Finding 30 nearest neighbors using minkowski metric and 'auto' algorithm\n",
"Neighbors computed in 0.33245110511779785 seconds\n",
"Jaccard graph constructed in 0.1920456886291504 seconds\n",
"Wrote graph to binary file in 0.07539820671081543 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.638517\n",
"After 2 runs, maximum modularity is Q = 0.641707\n",
"Louvain completed 22 runs in 0.5118870735168457 seconds\n",
"PhenoGraph complete in 1.13643479347229 seconds\n",
"CPU times: user 11min 59s, sys: 5.63 s, total: 12min 5s\n",
"Wall time: 11min 44s\n"
]
}
],
"source": [
"%%time\n",
"sources = {}\n",
"\n",
"grouped = ds.groupby(ds.cell_metadata.sel('group'))\n",
"\n",
"for group, subset in grouped:\n",
" print(f'--- {group} ---', subset.counts.shape)\n",
" \n",
"# np.random.seed(0)\n",
"# downsampler = np.random.uniform(size=subset.counts.shape[0]) < 0.1\n",
"\n",
"# downsampled = subset.sel(cell=downsampler)\n",
"# print(subset.cell_metadata.to_pandas().head())\n",
" sources[group] = correl_and_cluster(subset)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
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