Generic function that runs the Tessera algorithm on single-cell spatial data

GetTiles is a generic function that runs the main Tessera algorithm. If working with a Seurat object, please refer to the documentation of the appropriate generic API: GetTiles.Seurat(). If users work with other forms of the input, they can pass them directly to Tessera using the GetTiles.default() API. The function arguments listed here are common in all GetTiles interfaces.

Usage

GetTiles(...)

Arguments

...

Arguments passed on to GetTiles.default

meta_vars_include

Names of columns in meta_data to include in dmt$pts.

group.by

Name of column in meta_data that provides the group IDs. Tessera tiles are constructed separately for each group (which could be separate experimental samples or FOVs).

npcs

Number of PCs to compute for input to segmentation. Ignored if embeddings are provided directly.

smooth_emb

Number of smoothing iterations to perform on the cell embeddings prior to gradient computation. If a vector, then embeddings after each specified iteration are concatenated. If 0 is included, then the original embeddings are also included.

prune_thresh_quantile

Floating point value between 0 and 1, inclusive. Quantile of edge length above which edges are pruned. Defaults to 0.95.

prune_min_cells

Minimum number of cells required for a connected component of triangles to be kept. Defaults to 10.

prune_thresh

Edge length above which edges are pruned. If equal to NA, then this value is ignored and thresh_quantile is used to compute the threshold. Otherwise, if thresh is set, then thresh_quantile is ignored. Defaults to NA.

smooth_distance

One of c('none', 'euclidean', 'projected', 'constant'). If either smooth_distance or smooth_similarity is 'none', then no smoothing of the gradient field is conducted. Defaults to 'projected'.

smooth_similarity

One of c('none', 'euclidean', 'projected', 'constant'). If either smooth_distance or smooth_similarity is 'none', then no smoothing of the gradient field is conducted. Defaults to 'projected'.

smooth_iter

Number of rounds of gradient smoothing.

max_npts

Maximum number of cells allowed in each tile during the agglomerative clustering phase.

min_npts

Minimum number of cells allowed in each tile during the agglomerative clustering phase.

alpha

Parameter for scoring transcriptional similarity between adjacent tiles during the agglomerative clustering phase. For alpha, 0.2 = conservative merging, 2 = liberal merging.

future.globals.maxSize

Maximum allowed size (in bytes) of global variables that are exported to each parallel worker. Increase this value if you get an error about global object size. Default is 8*1024^3 (8 GB).

consolidate

Whether to consolidate results from multiple groups into a single collection of points and tiles (TRUE) or to return a list of separate results for each group (FALSE).

verbose

Whether to print progress messages for each stage of the segmentation pipeline.

Value

If used with a Seurat object, it will return a pair of Seurat objects:

1. the input single-cell object updated with tile assignments for each cell, and

2. a Seurat object where each item represents an individual Tessera tile.

For standalone operation, it returns Lists with the output of Tessera segmentation (see GetTiles.default()).

See also

Other GetTiles: GetTiles.Seurat(), GetTiles.default()