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:
Using conda, mamba, or micromamba from the bioconda channelYou 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
Processed data and metadatascanpy 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
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}
}
plot_trendLICENSE file to be consistent with MIT - licenseplot_trajectories to show multiple poaths on the UMAPpip install palantir[gam] or pip install palantir[full]run_magic_imputation to return appropriate data types for different inputsimportlib.metadata with graceful fallbacks_ methods in scanpy for plotting.pseudotime_interval argument to control path length in palantir.plot.plot_trajectoryrun_magic_imputation now has a boolean parameter sparse to control output sparsityrun_local_variability for dense expression arrays now runs much faster and more accurateselect_branch_cells for very small datasetsplot_gene_trend_clustersprogress=True in palantir.utils.run_local_variabilityscanpy>=1.10.0python>=3.9plot_trajectory function to show trajectory on the umappalantir.plot.plot_stats to plot arbitrary cell-wise statistics as x-/y-positions.palantir.presults.compute_gene_trendsrun_diffusion_maps to split out compute_kernel and diffusion_maps_from_kerneltables, Cython, cmake, and tzlocal.run_pca (return correct projections and do not use too many components)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.palantir.plot.highlight_cells_on_umap.scanpy.tl.leiden for gene trend clustering.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.palantir.plot.highlight_cells_on_tsne to palantir.plot.highlight_cells_on_umapanndata>=0.8.0 to avoid issues writing dataframes in ad.obsm.RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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