Spatial Transcriptomics Analysis in Galaxy

Download RO-Crate Run on Galaxy
Workflow Type: Galaxy
Stable

A comprehensive Galaxy workflow for the end-to-end analysis of transcriptomics data, featuring automated QC, clustering, spatial statistics, cell type annotation, and cell-cell communication.

SEEK ID: https://workflowhub.eu/workflows/2174?version=3

DOI: 10.48546/workflowhub.workflow.2174.3

Inputs

ID Name Description Type
Fraction of cells to subsample Fraction of cells to subsample Fraction of cells to subsample. Default is 1.0 (no subsampling)
  • float
Maximum cell volume Maximum cell volume maximum volume of the cell
  • float
Minimum cell volume Minimum cell volume Minimum volume of a cell.
  • float
Perform scaling? Perform scaling? By default, no scaling will be done.
  • boolean
Proportion of top genes for QC Proportion of top genes for QC will be used for qc_vars param
  • string
Regress out volume and total_counts? Regress out volume and total_counts? By default no regression will be done.
  • boolean
Resolution Resolution Provide the list of resolutions to use for Leiden clustering. One resolution per line. For example: 1.0 1.2 1.4
  • File
Subsample the AnnData? Subsample the AnnData? Subsample the AnnData?
  • boolean
Use seurat flavor for highly variable gene selection Use seurat flavor for highly variable gene selection By default, it uses the Cellranger flavor with top 4000 genes
  • boolean
maximum number of counts for a cell to pass 2 maximum number of counts for a cell to pass 2 How many counts should a cell have at least?
  • int
maximum number of counts for a cell to pass 3 maximum number of counts for a cell to pass 3 What is the maximum number of counts a cell should have?
  • int
minimum number of cells for a gene to pass minimum number of cells for a gene to pass How many cells should a gene be assigned to, at least
  • int
minimum number of counts for a cell to pass minimum number of counts for a cell to pass How many counts should a cell have, at least?
  • int
minimum number of counts for a gene to pass minimum number of counts for a gene to pass How many counts should a gene have, at least
  • int
minimum number of genes for a cell to pass minimum number of genes for a cell to pass How many genes should a cell have, at least?
  • int
number of expected clusters number of expected clusters How many clusters do you expect to see in your data? A resolution that has the closest number of clusters to this value, will be selected for downstream analysis.
  • int
select celltypist model select celltypist model select celltypist model
  • string
spatialdata spatialdata input spatial data object
  • File

Steps

ID Name Description
18 extract anndata extract the AnnData from SpatialData toolshed.g2.bx.psu.edu/repos/iuc/spatialdata_operation/spatialdata_operation/0.7.2+galaxy0
19 Split file toolshed.g2.bx.psu.edu/repos/bgruening/split_file_to_collection/split_file_to_collection/0.5.2
20 add "leiden_res" toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_replace_in_line/9.5+galaxy3
21 Map parameter value toolshed.g2.bx.psu.edu/repos/iuc/map_param_value/map_param_value/0.2.0
22 Map parameter value toolshed.g2.bx.psu.edu/repos/iuc/map_param_value/map_param_value/0.2.0
23 Calculate QC metrics toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.11.5+galaxy0
24 Parse parameter value param_value_from_file
25 Transpose toolshed.g2.bx.psu.edu/repos/iuc/datamash_transpose/datamash_transpose/1.9+galaxy0
26 scatter plot toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
27 violin plot 1 toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
28 violin plot 2 toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
29 violin plot 3 toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
30 filter cells - min_genes toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.11.5+galaxy0
31 list of resolution keys toolshed.g2.bx.psu.edu/repos/iuc/compose_text_param/compose_text_param/0.1.1
32 replace tab with comma toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_replace_in_line/9.5+galaxy3
33 filter cells - min_counts toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.11.5+galaxy0
34 Parse parameter value param_value_from_file
35 filter genes - min_cells toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.11.5+galaxy0
36 filter genes - min_counts toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.11.5+galaxy0
37 filter cells - max_counts toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.11.5+galaxy0
38 filter cells - max_genes toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.11.5+galaxy0
39 Calculate QC metrics - filtered toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.11.5+galaxy0
40 scatter plot filtered toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
41 violin plot filtered 1 toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
42 violin plot filtered 2 toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
43 violin plot filtered 3 toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
44 filter min volume toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.11.5+galaxy0
45 filter max volume toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.11.5+galaxy0
46 Calculate QC metrics - filtered by volume toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.11.5+galaxy0
47 scatter plot filtered volume toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
48 violin plot filtered volume 1 toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
49 violin plot filtered volume 2 toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
50 violin plot filtered volume 3 toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
51 Manipulate AnnData toolshed.g2.bx.psu.edu/repos/iuc/anndata_manipulate/anndata_manipulate/0.11.4+galaxy3
52 Scanpy normalize toolshed.g2.bx.psu.edu/repos/iuc/scanpy_normalize/scanpy_normalize/1.11.5+galaxy0
53 log1p toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.11.5+galaxy0
54 sc.pp.highly_variable cellranger toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.11.5+galaxy0
55 sc.pp.highly_variable seurat toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.11.5+galaxy0
56 HVG selected toolshed.g2.bx.psu.edu/repos/iuc/pick_value/pick_value/0.2.0
57 scanpy regress out toolshed.g2.bx.psu.edu/repos/iuc/scanpy_remove_confounders/scanpy_remove_confounders/1.11.5+galaxy0
58 Scanpy plot toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
59 Pick parameter value toolshed.g2.bx.psu.edu/repos/iuc/pick_value/pick_value/0.2.0
60 scanpy scale toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.11.5+galaxy0
61 Pick parameter value toolshed.g2.bx.psu.edu/repos/iuc/pick_value/pick_value/0.2.0
62 scanpy PCA toolshed.g2.bx.psu.edu/repos/iuc/scanpy_cluster_reduce_dimension/scanpy_cluster_reduce_dimension/1.11.5+galaxy0
63 scanpy pca plot toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
64 scanpy neighbor toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.11.5+galaxy0
65 scanpy umap toolshed.g2.bx.psu.edu/repos/iuc/scanpy_cluster_reduce_dimension/scanpy_cluster_reduce_dimension/1.11.5+galaxy0
66 scanpy umap plot toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
67 leiden per res toolshed.g2.bx.psu.edu/repos/iuc/scanpy_cluster_reduce_dimension/scanpy_cluster_reduce_dimension/1.11.5+galaxy0
68 Get obs toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.11.4+galaxy3
69 scanpy rank_genes_groups toolshed.g2.bx.psu.edu/repos/iuc/scanpy_inspect/scanpy_inspect/1.11.5+galaxy0
70 get cell_id and leiden key values get cell_id and leiden key values toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_awk_tool/9.5+galaxy3
71 scanpy plot rank_genes toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
72 Column join toolshed.g2.bx.psu.edu/repos/iuc/collection_column_join/collection_column_join/0.0.3
73 remove cell_id remove cell_id toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/9.5+galaxy3
74 remove file name from header toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/9.5+galaxy3
75 add "c_" to clusters When adding the table back to the Anndata, it is set as an integer. This step adds "c_" before those integers so it will be fixed. toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/9.5+galaxy3
76 calculate number of clusters calculate the number of clusters per resolution toolshed.g2.bx.psu.edu/repos/iuc/table_compute/table_compute/1.2.4+galaxy2
77 Manipulate AnnData toolshed.g2.bx.psu.edu/repos/iuc/anndata_manipulate/anndata_manipulate/0.11.4+galaxy3
78 Add column addValue
79 transform string annotation to categories transform string annotation to categories toolshed.g2.bx.psu.edu/repos/iuc/anndata_manipulate/anndata_manipulate/0.11.4+galaxy3
80 Compute substract expected # cluster from real # clusters toolshed.g2.bx.psu.edu/repos/devteam/column_maker/Add_a_column1/2.1+galaxy0
81 Scanpy plot toolshed.g2.bx.psu.edu/repos/iuc/scanpy_plot/scanpy_plot/1.11.5+galaxy0
82 Sort sort to get the closest resolution. In case of a tie, the highest resolution will be selected. toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_sort_header_tool/9.5+galaxy3
83 Scanpy filter toolshed.g2.bx.psu.edu/repos/iuc/scanpy_filter/scanpy_filter/1.11.5+galaxy0
84 Table Compute Select the line with the best resolution toolshed.g2.bx.psu.edu/repos/iuc/table_compute/table_compute/1.2.4+galaxy2
85 Pick parameter value toolshed.g2.bx.psu.edu/repos/iuc/pick_value/pick_value/0.2.0
86 Cut select the resolution Cut1
87 squidpy spatial_neighbors squidpy spatial_neighbors 2 toolshed.g2.bx.psu.edu/repos/goeckslab/squidpy/squidpy_spatial/1.5.0+galaxy0
88 Parse parameter value set the value to a text input param_value_from_file
89 squidpy centrality_scores squidpy centrality_scores toolshed.g2.bx.psu.edu/repos/goeckslab/squidpy/squidpy_spatial/1.5.0+galaxy0
90 squidpy nhood_enrichment squidpy nhood_enrichment toolshed.g2.bx.psu.edu/repos/goeckslab/squidpy/squidpy_spatial/1.5.0+galaxy0
91 squidpy spatial_autocorr squidpy spatial_autocorr toolshed.g2.bx.psu.edu/repos/goeckslab/squidpy/squidpy_spatial/1.5.0+galaxy0
92 CellTypist toolshed.g2.bx.psu.edu/repos/iuc/celltypist/celltypist/1.7.1+galaxy0
93 Liana methods toolshed.g2.bx.psu.edu/repos/iuc/liana_methods/liana_methods/1.7.1+galaxy0
94 final spatialdata object toolshed.g2.bx.psu.edu/repos/iuc/spatialdata_operation/spatialdata_operation/0.7.2+galaxy0

Outputs

ID Name Description Type
ranked_gene ranked_gene n/a
  • File
spatialdata_output_processed spatialdata_output_processed n/a
  • File

Version History

Version 3 (latest) Created 19th May 2026 at 10:51 by Amirhossein Naghsh Nilchi

No revision comments

Frozen Version-3 6e657d6

Version 2 Created 19th May 2026 at 10:37 by Amirhossein Naghsh Nilchi

No revision comments

Frozen Version-2 deee199

Version 1 (earliest) Created 18th May 2026 at 13:50 by Amirhossein Naghsh Nilchi

First version of the WF


Frozen Version-1 b414b09
help Creators and Submitter
Creators
  • Amirhossein Naghsh Nilchi
  • Pavankumar Videm
Additional credit

Björn Grüning

Submitter
Tools
SpatialData
anndata
Squidpy
SCANPY
celltypist
Liana+
Citation
Naghsh Nilchi, A., & Videm, P. (2026). Spatial Transcriptomics Analysis in Galaxy. WorkflowHub. https://doi.org/10.48546/WORKFLOWHUB.WORKFLOW.2174.3
License
MIT License (MIT)
Activity

Views: 342   Downloads: 61   Runs: 21

Created: 18th May 2026 at 13:50

Last updated: 19th May 2026 at 10:56

Annotated Properties
Topic annotations
Operation annotations
Scientific disciplines
Computer Science, Medicine, Biochemistry, Genetics and Molecular Biology, Immunology and Microbiology, Neuroscience, Pharmacology, Toxicology and Pharmaceutics
help Attributions
Total size: 467 KB
Powered by
 (v.1.17.3)
About WorkflowHub | Acknowledgements | Credits | Terms & Conditions | Privacy Policy | Cite us | Contact us
Copyright © 2008 - 2026 The University of Manchester and HITS gGmbH