added metacells_by_groups function

#' construct_metacells

#'

#' This function takes a Seurat object and constructs averaged 'metacells' based

#' @export

#' @examples

#' construct_metacells(pbmc)

construct_metacells <- function(seurat_obj, k=50, name='agg', reduction='umap', assay='RNA', slot='data'){

construct_metacells <- function(seurat_obj, name='agg', k=50, reduction='umap', assay='RNA', slot='data'){

  reduced_coordinates <- as.data.frame(seurat_obj@reductions[[reduction]]@cell.embeddings)

  nn_map <- FNN::knn.index(reduced_coordinates, k = (k - 1))

  new_exprs <- (exprs_old %*% mask) / k

  colnames(new_exprs) <- paste0(name, '_', 1:ncol(new_exprs))

  rownames(cell_sample) <- paste0(name, '_', 1:ncol(new_exprs))

  colnames(cell_sample) <- paste0('knn_', 1:ncol(cell_sample))

  # make seurat obj:

  seurat_aggr <- CreateSeuratObject(

    counts = new_exprs

  )

  list(seurat_aggr, cell_sample)

  seurat_aggr

}

#' metacells_by_groups

#'

#' This function takes a Seurat object and constructs averaged 'metacells' based

#' on neighboring cells in provided groupings, such as cluster or cell type.

#' @param seurat_obj A Seurat object

#' @param group.by A character vector of Seurat metadata column names representing groups for which metacells will be computed. Default = 'seurat_clusters'

#' @param k Number of nearest neighbors to aggregate. Default = 50

#' @param name A string appended to resulting metalcells. Default = 'agg'

#' @param reduction A dimensionality reduction stored in the Seurat object. Default = 'umap'

#' @param assay Assay to extract data for aggregation. Default = 'RNA'

#' @param slot Slot to extract data for aggregation. Default = 'data'

#' @keywords scRNA-seq

#' @export

#' @examples

#' metacells_by_groups(pbmc)

metacells_by_groups <- function(seurat_obj, group.by=c('seurat_clusters'), k=50, reduction='umap', assay='RNA', slot='data'){

  # should replace this apply with something faster

  if(length(group.by) > 1){

    seurat_obj$metacell_grouping <- apply(seurat_obj@meta.data[, group.by], 1, paste, collapse='_')

  } else {

    seurat_obj$metacell_grouping <- seurat_obj@meta.data[[group.by]]

  }

  groupings <- unique(seurat_obj$metacell_grouping)

  # split seurat obj by groupings

  seurat_list <- lapply(groupings, function(x){seurat_obj[,seurat_obj$metacell_grouping == x]})

  names(seurat_list) <- groupings

  # construct metacells

  out <- future_mapply(scWGCNA::construct_metacells, seurat_list, groupings, MoreArgs = list(k=k, reduction=reduction, assay=assay, slot=slot))

  names(out) <- groupings

  # merge seurat objects:

  for(i in 1:length(out)){

    if(length(group.by) > 1){

      cur_groups <- unlist(strsplit(groupings[i], '_'))

      for(j in length(group.by)){

        out[[groupings[i]]]@meta.data[[group.by[j]]] <- cur_groups[j]

      }

    } else{

      out[[groupings[i]]]@meta.data[[group.by]] <- groupings[i]

    }

  }

  seurat_merged <- merge(out[[1]], out[2:length(out)])

  seurat_merged

}