Palantir¶

Palantir is an algorithm to align cells along differentiation trajectories. Palantir models differentiation as a stochastic process where stem cells differentiate to terminally differentiated cells by a series of steps through a low dimensional phenotypic manifold. Palantir effectively captures the continuity in cell states and the stochasticity in cell fate determination. Palantir has been designed to work with multidimensional single cell data from diverse technologies such as Mass cytometry and single cell RNA-seq.

Installation¶

Palantir has been implemented in Python3 and can be installed using:

Using pip¶

pip install palantir

Using conda, mamba, or micromamba from the bioconda channel¶

You can also install Palantir via conda, mamba, or micromamba from the bioconda channel:

Using conda¶

conda install -c conda-forge -c bioconda palantir

Using mamba¶

mamba install -c conda-forge -c bioconda palantir

Using micromamba¶

micromamba install -c conda-forge -c bioconda palantir

These methods ensure that all dependencies are resolved and installed efficiently.

Usage¶

A tutorial on Palantir usage and results visualization for single cell RNA-seq data can be found in this notebook: https://github.com/dpeerlab/Palantir/blob/master/notebooks/Palantir_sample_notebook.ipynb

More tutorials and a documentation of all the Palantir components can be found here: https://palantir.readthedocs.io

Processed data and metadata¶

scanpy anndata objects are available for download for the three replicates generated in the manuscript:

This notebook details how to use the data in Python and R: http://nbviewer.jupyter.org/github/dpeerlab/Palantir/blob/master/notebooks/manuscript_data.ipynb

Comparison to trajectory detection algorithms¶

Notebooks detailing the generation of results comparing Palantir to trajectory detection algorithms are available here

Citations¶

Palantir manuscript is available from Nature Biotechnology. If you use Palantir for your work, please cite our paper.

@article{Palantir_2019,
        title = {Characterization of cell fate probabilities in single-cell data with Palantir},
        author = {Manu Setty and Vaidotas Kiseliovas and Jacob Levine and Adam Gayoso and Linas Mazutis and Dana Pe'er},
        journal = {Nature Biotechnology},
        year = {2019},
        month = {March},
        url = {https://doi.org/10.1038/s41587-019-0068-4},
        doi = {10.1038/s41587-019-0068-4}
}

Release Notes¶

Version 1.4.4¶

Fix: reorder dependencies to work around uv resolver order-dependence (astral-sh/uv#5161), which caused uv pip install palantir to resolve to incompatible ancient versions of scanpy/numba/llvmlite

Version 1.4.3¶

Avoid warning about coloring in plot_trajectories
Fix: forward eigvec_key to fallback method in early_cell()
Performance optimizations (thanks to @svdrecbd for PR #176):
- Faster max-min waypoint sampling: O(N·K²) → O(N·K)
- PCA slicing optimization to avoid redundant recomputation
- Sparse LU solver for absorption probabilities
- Vectorized perspective matrix and Dijkstra shortest paths
- Vectorized Markov chain construction and terminal state detection
- Optional sklearn backend for diffusion kernel (kernel_backend parameter)
Fix: run_pca now correctly preserves existing X_pca when using custom pca_key

Version 1.4.2¶

Fix “lightgray” error in plot_trend
Address pygam incompatibility with newer scipy versions (automatic skip in tests, user warnings)
Address fcsparser incompatibility with NumPy 2.0 (automatic skip in tests, user warnings)
Use different joblib backend in Python 3.12+ with joblib < 1.5 to avoid ResourceTracker warnings
More convenient cell-name handling for integers (e.g., in spatial data) with automatic type conversion

Version 1.4.1¶

update LICENSE file to be consistent with MIT - license
implement plot_trajectories to show multiple paths on the UMAP
Drop leiden dependency to allow Python >= 3.13. igraph is used instead.

Version 1.4.0¶

Made pygam an optional dependency that can be installed with pip install palantir[gam] or pip install palantir[full]
Added proper conditional imports and improved error handling for pygam
Enhanced run_magic_imputation to return appropriate data types for different inputs
Updated code to use direct AnnData imports for newer compatibility
Improved version detection using importlib.metadata with graceful fallbacks
Fixed Series indexing deprecation warnings in early cell detection functions
Expanded and standardized documentation with NumPy-style docstrings throughout the codebase
Added comprehensive type hints to improve code quality and IDE support
Remove dependency from _ methods in scanpy for plotting.
add pseudotime_interval argument to control path length in palantir.plot.plot_trajectory

Testing and Quality Improvements¶

Added comprehensive tests for optional pygam dependency
Improved test coverage for run_magic_imputation with various input/output types
Added integration tests against expected results
Enhanced test infrastructure to work with newer library versions
Expanded test coverage to catch edge cases in data processing

Version 1.3.6¶

run_magic_imputation now has a boolean parameter sparse to control output sparsity
bugfix: run_local_variability for dense expression arrays now runs much faster and more accurate

Version 1.3.4¶

avoid division by zero in select_branch_cells for very small datasets
make branch selection robust against NaNs
do not plot unclustered trends (NaN cluster) in plot_gene_trend_clusters

Version 1.3.3¶

optional progress bar with progress=True in palantir.utils.run_local_variability
avoid NaN in local variablility output
compatibility with scanpy>=1.10.0

Version 1.3.2¶

require python>=3.9
implement CI for testing
fixes for edge cases discovered through extended testing
implement plot_trajectory function to show trajectory on the umap
scale pseudotime to unit interval in anndata

Version 1.3.1¶

implemented palantir.plot.plot_stats to plot arbitrary cell-wise statistics as x-/y-positions.
reduce memory usage of palantir.presults.compute_gene_trends
removed seaborn dependency
refactor run_diffusion_maps to split out compute_kernel and diffusion_maps_from_kernel
remove unused dependencies tables, Cython, cmake, and tzlocal.
fixes in run_pca (return correct projections and do not use too many components)

Version 1.3.0¶

New Features¶

Enable an AnnData-centric workflow for improved usability and interoperability with other single-cell analysis tools.
Introduced new utility functions
- palantir.utils.early_cell To automate finding an early cell based on cell type and diffusion components.
- palantir.utils.find_terminal_states To automate finding terminal cell states based on cell type and diffusion components.
- palantir.presults.select_branch_cells To find cells associated to each branch based on fate probability.
- palantir.plot.plot_branch_selection To inspect the cell to branch association.
- palantir.utils.run_local_variability To compute local gene expression variability.
- palantir.utils.run_density A wrapper for mellon.DensityEstimator.
- palantir.utils.run_density_evaluation Evaluate computed density on a different dataset.
- palantir.utils.run_low_density_variability. To aggregate local gene expression variability in low density.
- palantir.plot.plot_branch. To plot branch-selected cells over pseudotime in arbitrary y-position and coloring.
- palantir.plot.plot_trend. To plot the gene trend on top of palantir.plot.plot_branch.
Added input validation for better error handling and improved user experience.
Expanded documentation within docstrings, providing additional clarity for users and developers.

Enhancements¶

Updated tutorial notebook to reflect the new workflow, guiding users through the updated processes.
Implemented gene trend computation using Mellon, providing more robust and efficient gene trend analysis.
Enable annotation in palantir.plot.highlight_cells_on_umap.

Changes¶

Replaced PhenoGraph dependency with scanpy.tl.leiden for gene trend clustering.
Deprecated the run_tsne, determine_cell_clusters, and plot_cell_clusters functions. Use corresponding implementations from Scanpy, widely used single-cell analysis library and direct dependency of Palantir.
Rename palantir.plot.highlight_cells_on_tsne to palantir.plot.highlight_cells_on_umap
Depend on anndata>=0.8.0 to avoid issues writing dataframes in ad.obsm.

Fixes¶

Addressed the issue of variability when reproducing results (issue#64), enhancing the reproducibility and reliability of Palantir.

Version 1.1.0¶

Replaced rpy2 with pyGAM for computing gene expression trends.
Updated tutorial and plotting functions

Version 1.0.0¶

A fix to issue#41
A fix to issue#42
Revamped tutorial with support for Anndata and force directed layouts

Version 0.2.6¶

A fix to issue#33 and issue#31

Version 0.2.5¶

A fix related to issue#28. When identifying terminal states, duplicate values were generated instead of unique ones.

Index: Index