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.

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

pip install palantir

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

conda install -c conda-forge -c bioconda palantir

mamba install -c conda-forge -c bioconda palantir

micromamba install -c conda-forge -c bioconda palantir

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

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

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

• Replicate 1 (Rep1)
• Replicate 2 (Rep2)
• Replicate 3 (Rep3)

This notebook details how to use the data in Python and R: https://github.com/dpeerlab/Palantir/blob/master/notebooks/manuscript_data.ipynb

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

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}
    }

• 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

• avoid devision 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

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

• require python>=3.8
• implement CI for testing
• fixes for edge cases discoverd through extended testing
• implement plot_trajectory function to show trajectory on the umap
• scale pseudotime to unit intervall in anndata

• implemented palantir.plot.plot_stats to plot arbitray cell-wise statistics as x-/y-positions.
• reduce memory usgae 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)

• 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 fining 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-postion and coloring.
  • palantir.plot.plot_trend. To plot the gene trend ontop 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.

• 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.highight_cells_on_umap.

• 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 dependecy of Palantir.
• Rename palantir.plot.highight_cells_on_tsne to palantir.plot.highight_cells_on_umap
• Depend on anndata>=0.8.0 to avoid issues writing dataframes in ad.obsm.

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

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

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

• A fix to issue#33 and issue#31

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