version 0.2.00 ST tutorial

Package: hdWGCNA

Package: hdWGCNA

Title: hdWGCNA

Title: hdWGCNA

Version: 0.1.2.0001

Version: 0.2.00

Authors@R: c(

Authors@R: c(

    person("Sam", "Morabito", , "smorabit@uci.edu", role = c("aut", "cre"),

    person("Sam", "Morabito", , "smorabit@uci.edu", role = c("aut", "cre"),

           comment = c(ORCID = "0000-0002-7768-4856")),

           comment = c(ORCID = "0000-0002-7768-4856")),

    person("Swarup Lab", role = "fnd")

    person("Swarup Lab", role = "fnd")

  )

  )

Description: hdWGCNA is an R package for performing weighted gene co-expression network analysis in high dimensional data such as single-cell RNA-seq or spatial transcriptomics.

Description: hdWGCNA is an R package for performing weighted gene co-expression network analysis in high dimensional data such as single-cell RNA-seq or spatial transcriptomics.

License: MIT + file LICENSE

License: GNU GPLv3

Encoding: UTF-8

Encoding: UTF-8

LazyData: true

LazyData: true

Roxygen: list(markdown = TRUE)

Roxygen: list(markdown = TRUE)

YEAR: 2022

YEAR: 2022

COPYRIGHT HOLDER: scWGCNA authors

COPYRIGHT HOLDER: hdWGCNA authors

# hdWGCNA 0.2.00 (2022-09-23)

## Added

- `MetaspotsByGroups` to aggregate neighboring ST spots prior to network analysis.

- Tutorial for applying hdWGCNA to spatial transcriptomics datasets.

## Changes

- None

# hdWGCNA 0.1.2.0001 (2022-09-19)

# hdWGCNA 0.1.2.0001 (2022-09-19)

## Added

## Added

- None

- None

## Changes

## Changes

- networkType option in `TestSoftPowers`.

- networkType option in `TestSoftPowers`.

# hdWGCNA 0.1.2.0000 (2022-09-08)

# hdWGCNA 0.1.2.0000 (2022-09-08)

## Added

## Added

- Differential Module Eigengene (DME) tutorial

- Differential Module Eigengene (DME) tutorial

#' ConstructMetaspots

#'

#' Computes metaspots in a given Seurat object containing spatial transcriptomics data.

#' This function is called by MetaspotsByGroups and should NOT be run directly!

#' @param cur_seurat A Seurat object

#' @param mode "sum" or "average"

#' @param

#' @param

#' @keywords ST

#' @export

#' @examples

#'

ConstructMetaspots <- function(

  cur_seurat,

  mode = 'sum',

  assay = 'Spatial',

  slot = 'counts'

){

  # get expression matrix:

  X <- GetAssayData(cur_seurat, slot='counts')

  # boundaries

  row_range <- range(cur_seurat$row)

  col_range <- range(cur_seurat$col)

  # loop boundaries

  col_bounds <- col_range[1]:col_range[2]

  col_bounds <- col_bounds[which(1:length(col_bounds) %% 4 == 0)]

  # even or odd cols?

  if(all(col_bounds %% 2 == 0)){even = TRUE} else{even = FALSE}

  row_bounds <- row_range[1]:row_range[2]

  if(even){

    row_bounds <- row_bounds[row_bounds %% 2 == 0]

  } else{

    row_bounds <- row_bounds[row_bounds %% 2 != 0]

  }

  # note: even number rows go to even number cols etc

  coords <- cur_seurat@meta.data[,c('row', 'col')]

  # compute distances

  distances <- proxy::dist(coords, coords, method='euclidean')

  tmp <- distances[distances != 0]

  min1 <- min(tmp)

  bcs <- c()

  unique_cols <- unique(cur_seurat$col);

  unique_cols <- unique_cols[order(unique_cols)]

  combine_list <- list()

  tmp <- lapply(1:length(col_bounds), function(i){

    x = col_bounds[i]

    tmp <- lapply(1:length(row_bounds), function(j){

      y = row_bounds[j]

      cur_coords <- coords %>% subset(row == y & col == x)

      out <- c()

      if(nrow(cur_coords) > 0){

        cur_bc <- rownames(cur_coords)

        bcs <- c(bcs, cur_bc)

        # get cur distances:

        cur_dist <- distances[cur_bc,]

        cur_dist <- cur_dist[cur_dist != 0]

        cur_dist <- cur_dist[cur_dist == min1]

        # find neighbors that are close to this spot

        ix <- which(unique_cols == x)

        other_bcs <- coords %>% subset(col %in% unique_cols[c(ix-2, ix+2)] & row == y) %>% rownames

        cur_neighbors <- c(names(cur_dist), other_bcs)

        # update list of barcodes:

        combine_list[[cur_bc]] <- cur_neighbors

        if(length(cur_neighbors) < 2){

          return(c())

        }

        # aggregate expression profile for these spots:

        cur_X <- X[,c(cur_bc, cur_neighbors)]

        cur_X <- Matrix::rowSums(cur_X)

        if(mode == 'average'){

          cur_X <- cur_X / (length(cur_neighbors) + 1)

        }

        out <- cur_X

      } else{

        return()

      }

      list(cur_bc, cur_neighbors, out)

    })

    # get bc-neighbor results

    bcs <- unlist(lapply(1:length(tmp), function(k){tmp[[k]][[1]]}))

    cur_neighbors <- lapply(1:length(tmp), function(k){tmp[[k]][[2]]})

    cur_neighbors[sapply(cur_neighbors, is.null)] <- NULL

    names(cur_neighbors) <- bcs

    # combine expression results

    cur_X <- do.call(rbind, lapply(1:length(tmp), function(k){tmp[[k]][[3]]}))

    list(bcs, cur_neighbors, cur_X)

  })

  # get bc-neighbor results

  bcs <- unlist(lapply(1:length(tmp), function(k){tmp[[k]][[1]]}))

  cur_neighbors <- list()

  for(k in 1:length(tmp)){

    cur_neighbors <- c(cur_neighbors, tmp[[k]][[2]])

  }

  names(cur_neighbors) <- bcs

  # combine expression results

  agg_X <- do.call(rbind, lapply(1:length(tmp), function(k){tmp[[k]][[3]]}))

  # transpose expression matrix:

  agg_X <- t(agg_X)

  colnames(agg_X) <- bcs

  # get metadata:

  cur_meta <- cur_seurat@meta.data[bcs,]

  # make metaspot object:

  metaspot_obj <- CreateSeuratObject(

    counts = agg_X,

    meta = cur_meta,

    assay = assay

  )

  if(slot == 'scale.data'){

    metaspot_obj <- SeuratObject::SetAssayData(

      metaspot_obj,

      slot=slot,

      assay=assay,

      new.data=as.matrix(agg_X)

    )

  }

  # add neighbors:

  metaspot_obj@misc$spot_neighbors <- cur_neighbors

  # compute sparsity:

  agg_X[agg_X > 0] <- 1

  X[X > 0] <- 1

  density_agg <- sum(Matrix::colSums(agg_X) / (nrow(agg_X)*ncol(agg_X)))

  density_orig <- sum(Matrix::colSums(X) / (nrow(X)*ncol(X)))

  metaspot_obj@misc$density_agg <- density_agg

  metaspot_obj@misc$density_orig <- density_orig

  metaspot_obj

}