Package: palantir
PalantirPalantir 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 and dependenciesPalantir has been implemented in Python3 and can be installed using:pip install palantirUsageA tutorial on Palantir usage and results visualization for single cell RNA-seq data can be found in this notebook:http://nbviewer.jupyter.org/github/dpeerlab/Palantir/blob/master/notebooks/Palantir_sample_notebook.ipynbProcessed data and metadatascanpy anndataobjects 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 inPythonandR:http://nbviewer.jupyter.org/github/dpeerlab/Palantir/blob/master/notebooks/manuscript_data.ipynbComparison to trajectory detection algorithmsNotebooks detailing the generation of results comparing Palantir to trajectory detection algorithms are availablehereCitationsPalantir manuscript is available fromNature 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 NotesVersion 1.3.2requirepython>=3.8implement CI for testingfixes for edge cases discoverd through extended testingimplementplot_trajectoryfunction to show trajectory on the umapscale pseudotime to unit intervall in anndataVersion 1.3.1implementedpalantir.plot.plot_statsto plot arbitray cell-wise statistics as x-/y-positions.reduce memory usgae ofpalantir.presults.compute_gene_trendsremoved seaborn dependencyrefactorrun_diffusion_mapsto split outcompute_kernelanddiffusion_maps_from_kernelremove unused dependenciestables,Cython,cmake, andtzlocal.fixes inrun_pca(return correct projections and do not use too many components)Version 1.3.0New FeaturesEnable an AnnData-centric workflow for improved usability and interoperability with other single-cell analysis tools.Introduced new utility functionspalantir.utils.early_cellTo automate fining an early cell based on cell type and diffusion components.palantir.utils.find_terminal_statesTo automate finding terminal cell states based on cell type and diffusion components.palantir.presults.select_branch_cellsTo find cells associated to each branch based on fate probability.palantir.plot.plot_branch_selectionTo inspect the cell to branch association.palantir.utils.run_local_variabilityTo compute local gene expression variability.palantir.utils.run_densityA wrapper formellon.DensityEstimator.palantir.utils.run_density_evaluationEvaluate 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 ofpalantir.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.EnhancementsUpdated tutorial notebook to reflect the new workflow, guiding users through the updated processes.Implemented gene trend computation usingMellon, providing more robust and efficient gene trend analysis.Enable annotation inpalantir.plot.highight_cells_on_umap.ChangesReplaced PhenoGraph dependency withscanpy.tl.leidenfor gene trend clustering.Deprecated therun_tsne,determine_cell_clusters, andplot_cell_clustersfunctions. Use corresponding implementations fromScanpy, widely used single-cell analysis library and direct dependecy of Palantir.Renamepalantir.plot.highight_cells_on_tsnetopalantir.plot.highight_cells_on_umapDepend onanndata>=0.8.0to avoid issues writing dataframes inad.obsm.FixesAddressed the issue of variability when reproducing results (issue#64), enhancing the reproducibility and reliability of Palantir.Version 1.1.0Replaced rpy2 with pyGAM for computing gene expression trends.Updated tutorial and plotting functionsVersion 1.0.0A fix toissue#41A fix toissue#42Revamped tutorial with support for Anndata and force directed layoutsVersion 0.2.6A fix toissue#33andissue#31Version 0.2.5A fix related toissue#28. When identifying terminal states, duplicate values were generated instead of unique ones.