loadSTProfiles: Loads spatial transcriptomics data into a SpatialDDLS object

In SpatialDDLS: Deconvolution of Spatial Transcriptomics Data Based on Neural Networks

Loads spatial transcriptomics data into a SpatialDDLS object

Description

This function loads a SpatialExperiment object (or a list with several SpatialExperiment objects) into a SpatialDDLS object.

Usage

loadSTProfiles(
  object,
  st.data,
  st.spot.ID.column,
  st.gene.ID.column,
  st.min.counts = 0,
  st.min.spots = 0,
  st.n.slides = 3,
  verbose = TRUE
)

Arguments

object	A SpatialDDLS object.
st.data	A SpatialExperiment object (or a list with several SpatialExperiment objects) to be deconvoluted.
st.spot.ID.column	Name or number of the column in spots metadata corresponding to spot names in the expression matrix.
st.gene.ID.column	Name or number of the column in genes metadata corresponding to names used for features/genes.
st.min.counts	Minimum gene counts to filter (0 by default).
st.min.spots	Minimum of spots with more than min.counts (0 by default).
st.n.slides	Minimum number of slides (SpatialExperiment objects) in which a gene has to be expressed in order to keep it. This parameter is applicable only when multiple SpatialExperiment objects are provided. Genes not present in at least st.n.slides will be discarded. If no filtering is desired, set st.n.slides = 1.
verbose	Show informative messages during execution (TRUE by default).

Details

It is recommended to perform this step when creating the SpatialDDLS object using the createSpatialDDLSobject function in order to only keep genes shared between the spatial transcriptomics and the single-cell transcriptomics data used as reference. In addition, please, make sure the gene identifiers used the spatial and single-cell transcriptomics data are consistent.

Value

A SpatialDDLS object with the provided spatial trainscriptomics data loaded into the spatial.experiments slot.

See Also

createSpatialDDLSobject trainDeconvModel

Examples

set.seed(123)
sce <- SingleCellExperiment::SingleCellExperiment(
  assays = list(
    counts = matrix(
      rpois(100, lambda = 5), nrow = 40, ncol = 30,
      dimnames = list(paste0("Gene", seq(40)), paste0("RHC", seq(30)))
    )
  ),
  colData = data.frame(
    Cell_ID = paste0("RHC", seq(30)),
    Cell_Type = sample(x = paste0("CellType", seq(4)), size = 30,
                       replace = TRUE)
  ),
  rowData = data.frame(
    Gene_ID = paste0("Gene", seq(40))
  )
)
SDDLS <- createSpatialDDLSobject(
  sc.data = sce,
  sc.cell.ID.column = "Cell_ID",
  sc.gene.ID.column = "Gene_ID",
  sc.filt.genes.cluster = FALSE
)

## simulating a SpatialExperiment object
counts <- matrix(rpois(30, lambda = 5), ncol = 6)
rownames(counts) <- paste0("Gene", 1:5)
colnames(counts) <- paste0("Spot", 1:6)
coordinates <- matrix(
  c(1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3), ncol = 2
)
ste <- SpatialExperiment::SpatialExperiment(
  assays = list(counts = as.matrix(counts)),
  rowData = data.frame(Gene_ID = paste0("Gene", 1:5)),
  colData = data.frame(Cell_ID = paste0("Spot", 1:6)),
  spatialCoords = coordinates
)

## previous SpatialDDLS object
SDDLS <- loadSTProfiles(
  object = SDDLS,
  st.data = ste,
  st.spot.ID.column = "Cell_ID",
  st.gene.ID.column = "Gene_ID"
)

  

SpatialDDLS documentation built on Oct. 31, 2024, 5:07 p.m.