Description

SpatialLDA uses an LDA based approach for the identification of cellular neighborhoods, using cell type identities.

Input

Name (Type)
Description
Pattern

meta (map)

Groovy Map containing sample information
e.g. [ id:'sample1', single_end:false ]

phenotyped (file)

Phenotyped CSV file, it must contain the columns, sampleID, X, Y and Phenotype.

*.{csv}

Output

Name (Type)
Description
Pattern

meta (map)

Groovy Map containing sample information
e.g. [ id:'sample1', single_end:false ]

versions (file)

File containing software versions

versions.yml

spatial_lda_output (file)

File with the motifs detected from SpatialLDA

*.{csv}

composition_plot (file)

Plot with the motif composition and the cell type composition of motifs.

*.{png}

motif_location_plot (file)

Plot with the locations of the motifs.

*.{html}
maintainers
Github user migueLib
Github user chiarasch
get in touch
Ask a question on Slack Open an issue on GitHub

Tools

scimap
MIT licence

Scimap is a scalable toolkit for analyzing spatial molecular data. The underlying framework is generalizable to spatial datasets mapped to XY coordinates. The package uses the anndata framework making it easy to integrate with other popular single-cell analysis toolkits. It includes preprocessing, phenotyping, visualization, clustering, spatial analysis and differential spatial testing. The Python-based implementation efficiently deals with large datasets of millions of cells.

scimap.xyz scimap.xyz/tutorials/1-scimap-tutorial-getting-started https://github.com/labsyspharm/scimap 10.5281/zenodo.7854095