Datasets
Models
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{
"cells": [
{
"cell_type": "code",
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"metadata": {},
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"source": [
"# import libraries\n",
"import pandas as pd\n",
"import numpy as np\n",
"import umap\n",
"import plotly.express as px\n",
"from sklearn.preprocessing import StandardScaler"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Normalize the data\n",
"def is_data_normalized(data, tolerance=1e-3):\n",
" \"\"\"\n",
" Check if the data is normalized (mean ~ 0 and std ~ 1 for each column).\n",
"\n",
" Parameters:\n",
" data (np.ndarray): Data to check.\n",
" tolerance (float): Tolerance level for mean and std deviation.\n",
"\n",
" Returns:\n",
" bool: True if data is normalized, False otherwise.\n",
" \"\"\"\n",
" mean_check = np.allclose(np.mean(data, axis=0), 0, atol=tolerance)\n",
" std_check = np.allclose(np.std(data, axis=0), 1, atol=tolerance)\n",
" return mean_check and std_check\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Visualize single-cell data using UMAP\n",
"def visualize_umap(data, labels=None, n_neighbors=15, min_dist=0.1, n_components=2, random_state=42):\n",
" \"\"\"\n",
" Visualize single-cell gene expression data on a UMAP plot.\n",
"\n",
" Parameters:\n",
" data (pd.DataFrame or np.ndarray): Gene expression data. Rows are cells, columns are features/genes.\n",
" labels (pd.Series or np.ndarray, optional): Labels or annotations for the cells (e.g., cell type, tissue).\n",
" n_neighbors (int): Number of neighbors for UMAP.\n",
" min_dist (float): Minimum distance between points on the UMAP plot.\n",
" n_components (int): Number of dimensions for the UMAP embedding (default: 2 for 2D visualization).\n",
" random_state (int): Random state for reproducibility.\n",
"\n",
" Returns:\n",
" fig (plotly.graph_objects.Figure): Interactive UMAP plot.\n",
" \"\"\"\n",
" # Ensure data is in the right format\n",
" if isinstance(data, pd.DataFrame):\n",
" data_array = data.values\n",
" else:\n",
" data_array = data\n",
"\n",
" # Check if data is normalized\n",
" if not is_data_normalized(data_array):\n",
" print(\"Data is not normalized. Performing normalization...\")\n",
" data_scaled = StandardScaler().fit_transform(data_array)\n",
" else:\n",
" print(\"Data is already normalized. Skipping normalization...\")\n",
" data_scaled = data_array\n",
"\n",
" # Apply UMAP\n",
" umap_model = umap.UMAP(\n",
" n_neighbors=n_neighbors,\n",
" min_dist=min_dist,\n",
" n_components=n_components,\n",
" random_state=random_state\n",
" )\n",
" embedding = umap_model.fit_transform(data_scaled)\n",
"\n",
" # Create a DataFrame for the embedding\n",
" umap_df = pd.DataFrame(embedding, columns=[f\"UMAP_{i+1}\" for i in range(n_components)])\n",
"\n",
" # Add labels if provided\n",
" if labels is not None:\n",
" umap_df[\"Labels\"] = labels\n",
"\n",
" # Plot using Plotly\n",
" if n_components == 2:\n",
" fig = px.scatter(\n",
" umap_df, x=\"UMAP_1\", y=\"UMAP_2\",\n",
" color=\"Labels\" if labels is not None else None,\n",
" title=\"UMAP Visualization of Single-Cell Data\",\n",
" labels={\"Labels\": \"Annotations\"},\n",
" hover_data=umap_df.columns\n",
" )\n",
" elif n_components == 3:\n",
" fig = px.scatter_3d(\n",
" umap_df, x=\"UMAP_1\", y=\"UMAP_2\", z=\"UMAP_3\",\n",
" color=\"Labels\" if labels is not None else None,\n",
" title=\"UMAP Visualization of Single-Cell Data (3D)\",\n",
" labels={\"Labels\": \"Annotations\"},\n",
" hover_data=umap_df.columns\n",
" )\n",
" else:\n",
" raise ValueError(\"Only 2D and 3D visualizations are supported.\")\n",
"\n",
" # Customize plot appearance\n",
" fig.update_traces(marker=dict(size=5, opacity=0.8), selector=dict(mode=\"markers\"))\n",
" fig.update_layout(legend_title=\"Cell Annotations\", legend=dict(itemsizing=\"constant\"))\n",
"\n",
" return fig\n"
]
},
{
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jaydeepbhat/Documents/Hackathon/2024_AI_Agent/scripts/talk2cells/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"
]
},
{
"name": "stdout",
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"text": [
"Data is not normalized. Performing normalization...\n"
]
},
{
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"text": [
"/Users/jaydeepbhat/Documents/Hackathon/2024_AI_Agent/scripts/talk2cells/lib/python3.11/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
" warn(\n"
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},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "UMAP Visualization of Single-Cell Data"
},
"xaxis": {
"anchor": "y",
"domain": [
0,
1
],
"title": {
"text": "UMAP_1"
}
},
"yaxis": {
"anchor": "x",
"domain": [
0,
1
],
"title": {
"text": "UMAP_2"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Example usage\n",
"if __name__ == \"__main__\":\n",
" # Simulated gene expression data (e.g., 100 cells with 50 genes each)\n",
" data = np.random.rand(100, 50)\n",
" labels = np.random.choice([\"Type A\", \"Type B\", \"Type C\"], size=100)\n",
"\n",
" # Create UMAP plot\n",
" fig = visualize_umap(data, labels=labels, n_components=2)\n",
" fig.show()\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "talk2cells",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}