Picasso is a Python package for constructing phylogenetic trees from large-scale copy number alteration (CNA) data derived from single-cell sequencing. It implements an algorithm for inferring evolutionary relationships between cellular populations based on their CNA profiles.
You can install Picasso using pip:
pip install git+https://github.com/dpeerlab/picasso.git- Python 3.6+
- NumPy
- Pandas
- Seaborn
- Matplotlib
import picasso
# Load your CNA data
character_matrix = picasso.load_data()
# Create and fit the model
model = Picasso(character_matrix,
min_depth=2,
min_clone_size=5,
assignment_confidence_threshold=0.8)
model.fit()
# Get the phylogeny and clone assignments
phylogeny = model.get_phylogeny()
clone_assignments = model.get_clone_assignments()- Load and process copy number alteration (CNA) data
- Encode CNVs as ternary values for more meaningful similarity measures
- Feature selection to remove non-informative regions
- Construct phylogenetic trees using the PICASSO algorithm
- Flexible tree manipulation and rooting options
- Support for both clone-level and sample-level phylogenies
- Basic tree visualization
- Clone size plotting
- Alteration plotting
- Integration with iTOL for advanced visualization
- Support for:
- Heatmaps
- Colorstrips
- Stacked bar charts
from picasso import CloneTree
# Create and manipulate the clone tree
tree = CloneTree(phylogeny, clone_assignments, filtered_matrix, clone_aggregation='mode')
outgroup = tree.get_most_ancestral_clone()
tree.root_tree(outgroup)
# Get different tree representations
clone_tree = tree.get_clone_phylogeny()
cell_tree = tree.get_sample_phylogeny()# Generate heatmap of copy number changes
heatmap_annot = picasso.itol.dataframe_to_itol_heatmap(character_matrix)
with open('heatmap_annotation.txt', 'w') as f:
f.write(heatmap_annot)
# Generate colorstrip annotation
colorstrip_annot = picasso.itol.dataframe_to_itol_colorstrip(
data_series,
color_map,
dataset_label='Label'
)
# Generate stacked bar visualization
stackedbar_annot = picasso.itol.dataframe_to_itol_stackedbar(
proportions_df,
color_map,
dataset_label='Label'
)min_depth: Minimum depth of the phylogenetic treemax_depth: Maximum depth of the tree (None for unlimited)min_clone_size: Minimum number of samples in a cloneterminate_by: Criterion for terminating tree growthassignment_confidence_threshold: Confidence threshold for sample assignmentassignment_confidence_proportion: Required proportion of samples meeting confidence threshold
For detailed visualization, we recommend using the iTOL website/application, which accepts newick strings as input and allows for detailed customization of tree visualization. Picasso provides convenience functions for generating iTOL annotation files to visualize metadata on the tree.
If you encounter any problems, please open an issue along with a detailed description.
This project is licensed under the MIT License:
MIT License
Copyright (c) 2024 [Pe'er Lab]
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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SOFTWARE.
If you use Picasso in your research, please cite our paper.