##(1)Run diffusion maps dm_res=palantir.utils.run_diffusion_maps(pca_projections,n_components=5)ms_data=palantir.utils.determine_multiscale_space(dm_res)ad.layers['MAGIC_imputed_data']=palantir.utils.run_magic_imputation(ad,dm_res)#基因表达量可视化 sc.pl.embedding(ad,basis='umap',layer='...
* `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...
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.plot.plot_gene_expression(imp_df, fdl, ['CD34', 'MPO', 'GATA1', 'IRF8']) Diffusion maps visualization The computed diffusion components can be visualized with the following snippet. palantir.plot.plot_diffusion_components(tsne,dm_res) 图片.png Running Palantir 可以通过指定一个近似的...
dm_res = palantir.utils.run_diffusion_maps(pca_projections, n_components=5) ms_data = palantir.utils.determine_multiscale_space(dm_res) Palantir可以使用MAGIC算法对单细胞的表达数据进行imputation处理 # MAGIC imputation imp_df = palantir.utils.run_magic_imputation(norm_df, dm_res) ...