Fit FAST model for single-section SRT data

Source: R/main.R

Fit FAST model for single-section SRT data.

FAST_single(
  seu,
  Adj_sp,
  q = 15,
  fit.model = c("poisson", "gaussian"),
  slot = "data",
  assay = NULL,
  reduction.name = "fast",
  verbose = TRUE,
  ...
)

Arguments

seu

a Seurat object.

Adj_sp

a sparse matrix, specify the adjacency matrix among spots.

q

an optional integer, specify the number of low-dimensional embeddings to extract in FAST. Larger q means more information extracted.

fit.model

an optional string, specify the version of FAST to be fitted. The Gaussian version models the log-count matrices while the Poisson verions models the count matrices; default as possion model.

slot

an optional string, specify the slot in Seurat object as the input of FAST model, default as `data`.

assay

an optional string, specify the assay in Seurat object, default as `NULL` that means the default assay in Seurat object.

reduction.name

an optional string, specify the reduction name for the fast embedding, default as `fast`.

verbose

a logical value, whether output the information in iteration.

...

other arguments passed to FAST_run.

Value

return a list including the parameters set in the arguments.

See also

FAST_run