first commit

Package: scWGCNA

Title: What the Package Does (One Line, Title Case)

Version: 0.0.0.9000

Authors@R: 

    person(given = "First",

           family = "Last",

           role = c("aut", "cre"),

           email = "first.last@example.com",

           comment = c(ORCID = "YOUR-ORCID-ID"))

Description: What the package does (one paragraph).

License: `use_mit_license()`, `use_gpl3_license()` or friends to

    pick a license

Encoding: UTF-8

LazyData: true

Roxygen: list(markdown = TRUE)

RoxygenNote: 7.1.1

# Generated by roxygen2: do not edit by hand

export(construct_metacells)

#' construct_metacells

#'

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

#' on neighboring cells.

#' @param seurat_obj A Seurat object

#' @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

#' construct_metacells(pbmc)

construct_metacells <- function(seurat_obj, k=50, name='agg', 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))

  row.names(nn_map) <- row.names(reduced_coordinates)

  nn_map <- cbind(nn_map, seq_len(nrow(nn_map)))

  good_choices <- seq_len(nrow(nn_map))

  choice <- sample(seq_len(length(good_choices)), size = 1,

      replace = FALSE)

  chosen <- good_choices[choice]

  good_choices <- good_choices[good_choices != good_choices[choice]]

  it <- 0

  k2 <- k * 2

  get_shared <- function(other, this_choice) {

      k2 - length(union(cell_sample[other, ], this_choice))

  }

  while (length(good_choices) > 0 & it < 5000) {

      it <- it + 1

      choice <- sample(seq_len(length(good_choices)), size = 1,

          replace = FALSE)

      new_chosen <- c(chosen, good_choices[choice])

      good_choices <- good_choices[good_choices != good_choices[choice]]

      cell_sample <- nn_map[new_chosen, ]

      others <- seq_len(nrow(cell_sample) - 1)

      this_choice <- cell_sample[nrow(cell_sample), ]

      shared <- sapply(others, get_shared, this_choice = this_choice)

      if (max(shared) < 0.9 * k) {

          chosen <- new_chosen

      }

  }

  cell_sample <- nn_map[chosen, ]

  combs <- combn(nrow(cell_sample), 2)

  shared <- apply(combs, 2, function(x) {

      k2 - length(unique(as.vector(cell_sample[x, ])))

  })

  message(paste0("Overlap QC metrics:\nCells per bin: ",

      k, "\nMaximum shared cells bin-bin: ", max(shared),

      "\nMean shared cells bin-bin: ", mean(shared), "\nMedian shared cells bin-bin: ",

      median(shared)))

  if (mean(shared)/k > 0.1)

      warning("On average, more than 10% of cells are shared between paired bins.")

  exprs_old <- GetAssayData(seurat_obj, assay=assay, slot=slot)

  mask <- sapply(seq_len(nrow(cell_sample)), function(x) seq_len(ncol(exprs_old)) %in%

      cell_sample[x, , drop = FALSE])

  mask <- Matrix::Matrix(mask)

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

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

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

  # make seurat obj:

  seurat_aggr <- CreateSeuratObject(

    counts = new_exprs

  )

  list(seurat_aggr, cell_sample)

}

% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/scWGCNA.R

\name{construct_metacells}

\alias{construct_metacells}

\title{construct_metacells}

\usage{

construct_metacells(

  seurat_obj,

  k = 50,

  name = "agg",

  reduction = "umap",

  assay = "RNA",

  slot = "data"

)

}

\arguments{

\item{seurat_obj}{A Seurat object}

\item{k}{Number of nearest neighbors to aggregate. Default = 50}

\item{name}{A string appended to resulting metalcells. Default = 'agg'}

\item{reduction}{A dimensionality reduction stored in the Seurat object. Default = 'umap'}

\item{assay}{Assay to extract data for aggregation. Default = 'RNA'}

\item{slot}{Slot to extract data for aggregation. Default = 'data'}

}

\description{

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

on neighboring cells.

}

\examples{

construct_metacells(pbmc)

}

\keyword{scRNA-seq}