Mercurial > repos > iuc > scanpy_cluster_reduce_dimension

comparison cluster_reduce_dimension.xml @ 14:4d8f983cd751 draft default tip

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 9d49b2a98de059ae9a053dc1c5a23537cf0311de
author iuc
date Sat, 18 May 2024 18:28:35 +0000
parents 6f83f8fd381f
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13:6f83f8fd381f 14:4d8f983cd751
646 analysis by Levine et al, 2015. 646 analysis by Levine et al, 2015.
647 647
648 This requires to run `pp.neighbors`, first. 648 This requires to run `pp.neighbors`, first.
649 649
650 More details on the `tl.louvain scanpy documentation 650 More details on the `tl.louvain scanpy documentation
651 <https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.louvain.html>`_ 651 <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.louvain.html>`_
652 652
653 Cluster cells into subgroups (`tl.leiden`) 653 Cluster cells into subgroups (`tl.leiden`)
654 ========================================== 654 ==========================================
655 655
656 Cluster cells using the Leiden algorithm (Traag et al, 2018), an improved version of the Louvain algorithm (Blondel et al, 2008). 656 Cluster cells using the Leiden algorithm (Traag et al, 2018), an improved version of the Louvain algorithm (Blondel et al, 2008).
657 657
658 The Louvain algorithm has been proposed for single-cell analysis by Levine et al, 2015. 658 The Louvain algorithm has been proposed for single-cell analysis by Levine et al, 2015.
659 659
660 More details on the `tl.leiden scanpy documentation 660 More details on the `tl.leiden scanpy documentation
661 <https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.leiden.html>`_ 661 <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.leiden.html>`_
662 662
663 Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca` 663 Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca`
664 ============================================================================================================ 664 ============================================================================================================
665 665
666 @CMD_pca_outputs@ 666 @CMD_pca_outputs@
667 667
668 More details on the `pp.pca scanpy documentation 668 More details on the `pp.pca scanpy documentation
669 <https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.pca.html>`__ 669 <https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.pca.html>`__
670 670
671 Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca` 671 Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca`
672 ============================================================================================================ 672 ============================================================================================================
673 673
674 @CMD_pca_outputs@ 674 @CMD_pca_outputs@
675 675
676 More details on the `tl.pca scanpy documentation 676 More details on the `tl.pca scanpy documentation
677 <https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.pca.html>`__ 677 <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.pca.html>`__
678 678
679 Diffusion Maps, using `tl.diffmap` 679 Diffusion Maps, using `tl.diffmap`
680 ================================== 680 ==================================
681 681
682 Diffusion maps (Coifman et al 2005) has been proposed for visualizing single-cell 682 Diffusion maps (Coifman et al 2005) has been proposed for visualizing single-cell
694 The diffusion map representation of data are added to the return AnnData in the multi-dimensional 694 The diffusion map representation of data are added to the return AnnData in the multi-dimensional
695 observations annotation (obsm). It is the right eigen basis of the transition matrix with eigenvectors 695 observations annotation (obsm). It is the right eigen basis of the transition matrix with eigenvectors
696 as colum. It can be accessed using the inspect tool for AnnData 696 as colum. It can be accessed using the inspect tool for AnnData
697 697
698 More details on the `tl.diffmap scanpy documentation 698 More details on the `tl.diffmap scanpy documentation
699 <https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.diffmap.html>`__ 699 <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.diffmap.html>`__
700 700
701 t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne` 701 t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne`
702 ======================================================================= 702 =======================================================================
703 703
704 t-distributed stochastic neighborhood embedding (tSNE) (Maaten et al, 2008) has been 704 t-distributed stochastic neighborhood embedding (tSNE) (Maaten et al, 2008) has been
706 we use the implementation of *scikit-learn* (Pedregosa et al, 2011). 706 we use the implementation of *scikit-learn* (Pedregosa et al, 2011).
707 707
708 It returns `X_tsne`, tSNE coordinates of data. 708 It returns `X_tsne`, tSNE coordinates of data.
709 709
710 More details on the `tl.tsne scanpy documentation 710 More details on the `tl.tsne scanpy documentation
711 <https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.tsne.html>`__ 711 <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.tsne.html>`__
712 712
713 Embed the neighborhood graph using UMAP, using `tl.umap` 713 Embed the neighborhood graph using UMAP, using `tl.umap`
714 ======================================================== 714 ========================================================
715 715
716 UMAP (Uniform Manifold Approximation and Projection) is a manifold learning 716 UMAP (Uniform Manifold Approximation and Projection) is a manifold learning
726 726
727 The UMAP coordinates of data are added to the return AnnData in the multi-dimensional 727 The UMAP coordinates of data are added to the return AnnData in the multi-dimensional
728 observations annotation (obsm). This data is accessible using the inspect tool for AnnData 728 observations annotation (obsm). This data is accessible using the inspect tool for AnnData
729 729
730 More details on the `tl.umap scanpy documentation 730 More details on the `tl.umap scanpy documentation
731 <https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.umap.html>`__ 731 <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.umap.html>`__
732 732
733 Force-directed graph drawing, using `tl.draw_graph` 733 Force-directed graph drawing, using `tl.draw_graph`
734 =================================================== 734 ===================================================
735 735
736 Force-directed graph drawing describes a class of long-established algorithms for visualizing graphs. 736 Force-directed graph drawing describes a class of long-established algorithms for visualizing graphs.
745 745
746 The coordinates of graph layout are added to the return AnnData in the multi-dimensional 746 The coordinates of graph layout are added to the return AnnData in the multi-dimensional
747 observations annotation (obsm). This data is accessible using the inspect tool for AnnData. 747 observations annotation (obsm). This data is accessible using the inspect tool for AnnData.
748 748
749 More details on the `tl.draw_graph scanpy documentation 749 More details on the `tl.draw_graph scanpy documentation
750 <https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.draw_graph.html>`__ 750 <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.draw_graph.html>`__
751 751
752 Infer progression of cells through geodesic distance along the graph (`tl.dpt`) 752 Infer progression of cells through geodesic distance along the graph (`tl.dpt`)
753 =============================================================================== 753 ===============================================================================
754 754
755 Reconstruct the progression of a biological process from snapshot 755 Reconstruct the progression of a biological process from snapshot
774 - dpt_groups : Array of dim (number of samples) that stores the subgroup id ('0','1', ...) for each cell. The groups typically correspond to 'progenitor cells', 'undecided cells' or 'branches' of a process. 774 - dpt_groups : Array of dim (number of samples) that stores the subgroup id ('0','1', ...) for each cell. The groups typically correspond to 'progenitor cells', 'undecided cells' or 'branches' of a process.
775 775
776 The tool is similar to the R package `destiny` of Angerer et al (2016). 776 The tool is similar to the R package `destiny` of Angerer et al (2016).
777 777
778 More details on the `tl.dpt scanpy documentation 778 More details on the `tl.dpt scanpy documentation
779 <https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.dpt.html>`_ 779 <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.dpt.html>`_
780 780
781 781
782 Generate cellular maps of differentiation manifolds with complex topologies (`tl.paga`) 782 Generate cellular maps of differentiation manifolds with complex topologies (`tl.paga`)
783 ======================================================================================= 783 =======================================================================================
784 784
805 - Adjacency matrix of the tree-like subgraph that best explains the topology (connectivities_tree) 805 - Adjacency matrix of the tree-like subgraph that best explains the topology (connectivities_tree)
806 806
807 These datasets are stored in the unstructured annotation (uns) and can be accessed using the inspect tool for AnnData objects 807 These datasets are stored in the unstructured annotation (uns) and can be accessed using the inspect tool for AnnData objects
808 808
809 More details on the `tl.paga scanpy documentation 809 More details on the `tl.paga scanpy documentation
810 <https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.paga.html>`_ 810 <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.paga.html>`_
811 ]]></help> 811 ]]></help>
812 <expand macro="citations"/> 812 <expand macro="citations"/>
813 </tool> 813 </tool>