Mixed cell type prediction with NNLS (experimental)

This function can be used to obtain proportion estimates for a mixture of cell types and unknown factors. The input spatial expression matrix is first deconvolved using Non-negative Matrix Factorization (NMF) with the rank set to number of cell types + k additional factors. The estimated factor expression profiles are are then compared with the cell type expression profiles by computing pairwise correlation scores. Factor expression profiles with the lowest correlation scores are kept and combined with the cell type expression profiles to predict proportions directly from the spatial data using Non-negative Least Squares (NNLS).

Usage

RunMixedNNLS(object, ...)

# Default S3 method
RunMixedNNLS(
  object,
  singlecell_matrix,
  groups,
  k = 10L,
  nCells_per_group = 50L,
  minCells_per_celltype = 10L,
  min_prop = 0.01,
  L1 = 0.01,
  seed = 1337L,
  return_expression_profiles = FALSE,
  verbose = TRUE,
  ...
)

# S3 method for class 'Seurat'
RunMixedNNLS(
  object,
  singlecell_object,
  groups = NULL,
  k = 10L,
  features = NULL,
  singlecell_assay = "RNA",
  spatial_assay = "Spatial",
  slot = "data",
  min_prop = 0.01,
  nCells_per_group = 50L,
  minCells_per_celltype = 10L,
  assay_name = "celltypepropsmixed",
  dimred_name = "nnlsmixed",
  return_as_dimred = FALSE,
  L1 = 0.01,
  seed = 1337L,
  verbose = TRUE,
  ...
)

Arguments

object: A matrix-like object with 10x Visium data
...: Arguments passed to other methods
singlecell_matrix: A matrix-like object with scRNA-seq data
groups: A character vector of length ncol(singlecell_matrix) with cell type labels. For Seurat objects, one can omit groups in which case the current identity is used instead. Alternatively, a string can be provided specifying a column to use from the singlecell_object meta.data slot.
k: An integer specifying the number of factors to compute
nCells_per_group: Each cell type is down sampled to this number before computing cell type expression profiles. This is mainly used to speed up computation. Note that if a cell type has fewer than nCells_per_group cells, the cells will be sampled with replacement to obtain nCells_per_group cells.
minCells_per_celltype: Minimum number of cells allowed per cell type
min_prop: Minimum proportion allowed. Any proportion values lower than this threshold will be removed and the remaining proportions will be rescaled.
L1: lasso penalty
seed: An integer to set seed for reproducibility
return_expression_profiles: Logical specifying if the expression profile matrix should also be returned
verbose: Print messages
singlecell_object: A Seurat object with single-cell gene expression data
features: Features to use for computation. If no features are provided, the intersection of variable features between sc_object and st_object will be used. Note that the intersect can be quite small, in which case you can increase the number of variable features by rerunning FindVariableFeatures and increase the number with the nfeatures argument.
singlecell_assay: Assay in single cell data object to use for deconvolution
spatial_assay: Assay in Visium data object to use for deconvolution
slot: Name of slot in singlecell_assay and spatial_assay to use for deconvolution
assay_name: Sets the name of the returned Assay object. Only active if return.as.dimred = FALSE.
dimred_name: Sets the name of the returned DimReduc object. Only active if return.as.dimred = TRUE.
return_as_dimred: By default, the results are returned as an Assay named "celltypeprops", where each feature is named after the cell type IDs. Alternatively, you can return the results as a DimReduc object. This will also provide the cell gene loadings for each cell type. However, the proportions will instead be named "factor_1", "factor_2", ...

Value

An object with mixed cell type and factor proportion estimates

default method

Input object is a matrix-like object with 10x Visium data. The function returns a matrix with estimated proportions of dimensions (nCellTypes + k) x nSpots, where nCellTypes is the number of cell types, k is the number of factors and nSpots is the number of spots. If return_expression_profiles=TRUE, the returned object will be a list with estimated proportions prop and the cell type/factor expression profile matrix W.

Seurat method

Input object is a Seurat object with 10x Visium data. The function returns the Seurat object with either a new Assay or DimReduc object containing estimated proportions for cell types and additional factors.