Loads spatial transcriptomics data into a SpatialDDLS object

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).

Value

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

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.

Examples

# \donttest{
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
)
#> === Spatial transcriptomics data not provided
#> === Processing single-cell data
#>       - Filtering features:
#>          - Selected features: 40
#>          - Discarded features: 0
#> 
#> === No mitochondrial genes were found by using ^mt- as regrex
#> 
#> === Final number of dimensions for further analyses: 40

## 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"
)
#> === 1 SpatialExperiment objects provided
#>    === Processing spatial transcriptomics data
#>       - Filtering features:
#>          - Selected features: 5
#>          - Discarded features: 0
#> 
# }