U md @sddlmZddlmZmZmZddlZddl Z ddl m Z ddl mZde eeeeeeeefd d d Ze d ddZdS)N)Iterable CollectionUnion)AnnData)loggingTr)adata start_cellbranchk components num_waypointsc CsFzddlm}Wntk r,tdYnXt|j|kd}t|dkr^td|dt|t j rt ||j j }|jdkrtd|}n||jdkrtd|d}t|}||jdd d |f|||||d } | d } | t| t| t| } t| |j d <|rB| d t} t| |j d<d S)a Wishbone identifies bifurcating developmental trajectories from single-cell data [Setty16]_. Wishbone is an algorithm for positioning single cells along bifurcating developmental trajectories with high resolution. Wishbone uses multi-dimensional single-cell data, such as mass cytometry or RNA-Seq data, as input and orders cells according to their developmental progression, and it pinpoints bifurcation points by labeling each cell as pre-bifurcation or as one of two post-bifurcation cell fates. .. note:: More information and bug reports `here `__. Parameters ---------- adata Annotated data matrix. start_cell Desired start cell from `obs_names`. branch Use True for Wishbone and False for Wanderlust. k Number of nearest neighbors for graph construction. components Components to use for running Wishbone. num_waypoints Number of waypoints to sample. Returns ------- Updates `adata` with the following fields: `trajectory_wishbone` : (`adata.obs`, dtype `float64`) Computed trajectory positions. `branch_wishbone` : (`adata.obs`, dtype `int64`) Assigned branches. Example ------- >>> import scanpy.external as sce >>> import scanpy as sc **Loading Data and Pre-processing** >>> adata = sc.datasets.pbmc3k() >>> sc.pp.normalize_per_cell(adata) >>> sc.pp.pca(adata) >>> sc.tl.tsne(adata=adata, n_pcs=5, perplexity=30) >>> sc.pp.neighbors(adata, n_pcs=15, n_neighbors=10) >>> sc.tl.diffmap(adata, n_comps=10) **Running Wishbone Core Function** Usually, the start cell for a dataset should be chosen based on high expression of the gene of interest: >>> sce.tl.wishbone( ... adata=adata, start_cell='ACAAGAGACTTATC-1', ... components=[2, 3], num_waypoints=150, ... ) **Visualizing Wishbone results** >>> sc.pl.tsne(adata, color=['trajectory_wishbone', 'branch_wishbone']) >>> markers = ['C1QA', 'PSAP', 'CD79A', 'CD79B', 'CST3', 'LYZ', 'MALAT1'] >>> sce.pl.wishbone_marker_trajectory(adata, markers, show=True) For further demonstration of Wishbone methods and visualization please follow the notebooks in the package `Wishbone_for_single_cell_RNAseq.ipynb `_. r)wishbonez@ please install wishbone: https://github.com/dpeerlab/wishbonez Start cell z9 not found in data. Please rerun with correct start cell.zJSome of the specified waypoints are not in the data. These will be removedzinum_waypoints parameter is higher than the number of cells in the dataset. Please select a smaller number X_diffmapN)srlrrZ Trajectorytrajectory_wishboneZBranchesbranch_wishbone)Z wishbone.corer ImportErrornpwhere obs_nameslen RuntimeError isinstancecabcrZ setdiff1dobsindexsizerwarningtolistshapelistobsmminmaxZasarrayZastypeint) r rrrrrZ c_wishbonerdiffres trajectoryZbranchesr/U/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/scanpy/external/tl/_wishbone.pyr sPR        r)r cCsXddlm}m}||}tj|jd|jd|_||}|j d|_ |j d|_ |S)Nr)SCDataWishboner)r"rr) Z wishbone.wbr1r2Zto_dfpdZ DataFramer(rZdiffusion_eigenvectorsr!r.r)r r1r2Zscdatawbr/r/r0_anndata_to_wishbones   r5)Trr r )collections.abcabcr typingrrrnumpyrZpandasr3Zanndatarrstrboolr+rr5r/r/r/r0s&