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

spatial_lda_output

meta:map

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

*.csv:file

File with the motifs detected from SpatialLDA

*.{csv}

composition_plot

meta:map

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

*.png:file

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

*.{png}

motif_location_plot

meta:map

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

*.html:file

Plot with the locations of the motifs.

*.{html}

versions

versions.yml:file

File containing software versions

versions.yml

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.