Mercurial > repos > iuc > scanpy_cluster_reduce_dimension
comparison cluster_reduce_dimension.xml @ 14:4d8f983cd751 draft
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 9d49b2a98de059ae9a053dc1c5a23537cf0311de
author | iuc |
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date | Sat, 18 May 2024 18:28:35 +0000 |
parents | 6f83f8fd381f |
children | 178242b82297 |
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13:6f83f8fd381f | 14:4d8f983cd751 |
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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> |