bugfixes

# hdWGCNA 0.1.1.9007 (2022-07-20)

## Added

- None

## Changes

- New warning message in `MetacellsByGroups` if there are any groups that are excluded by `min_cells`.

- Assay in Metacell seurat object is now the same as the assay supplied to `MetacellsByGroups`, instead of the default "RNA".

- `ModuleEigengenes` takes `assay` as an argument, clears up some issues with `RunHarmony`.

- `ModuleEigengenes` doesn't require a "counts" slot to be present in the given assay, but now it throws an error if the normalized data slot is missing.

# hdWGCNA 0.1.1.9006 (2022-07-14)

## Added

- None

#' @param verbose logical indicating whether to print messages

#' @param vars.to.regress character vector of variables in seurat_obj@meta.data to regress when running ScaleData

#' @param scale.model.use model to scale data when running ScaleData choices are "linear", "poisson", or "negbinom"

#' @param pc_dim Which PC to use as the module eigengene? Default to 1.

#' @param assay Assay in seurat_obj to compute module eigengenes. Default is DefaultAssay(seurat_obj)

#' @param wgcna_name name of the WGCNA experiment

#' @keywords scRNA-seq

#' @export

  vars.to.regress = NULL,

  scale.model.use = 'linear',

  pc_dim = 1,

  assay = NULL,

  wgcna_name=NULL, ...

){

  # set as active assay if wgcna_name is not given

  if(is.null(wgcna_name)){wgcna_name <- seurat_obj@misc$active_wgcna}

  ass <- DefaultAssay(seurat_obj)

  # get the assay

  if(is.null(assay)){assay <- DefaultAssay(seurat_obj)}

  if(dim(seurat_obj@assays[[assay]]@data)[1] == 0){

    stop(paste0("Normalized data slot not found in selected assay ", assay))

  }

  # get genes in this module:

  cur_genes <- modules %>% subset(module == cur_mod) %>% .$gene_name

  print(cur_genes)

  # subset seurat object by these genes only:

  X <- GetAssayData(seurat_obj, slot='counts')[cur_genes,]

  X_dat <- GetAssayData(seurat_obj, slot='data')[cur_genes,]

  cur_seurat <- CreateSeuratObject(X, assay = ass, meta.data = seurat_obj@meta.data)

  cur_seurat <- SetAssayData(cur_seurat, slot='data', new.data=X_dat, assay=ass)

  X_dat <- GetAssayData(seurat_obj, slot='data', assay = assay)[cur_genes,]

  if(dim(seurat_obj@assays[[assay]]@counts)[1] == 0){

    X <- X_dat

  } else{

    X <- GetAssayData(seurat_obj, slot='counts', assay = assay)[cur_genes,]

  }

  # create seurat obj with just these genes

  cur_seurat <- CreateSeuratObject(X, assay = assay, meta.data = seurat_obj@meta.data)

  cur_seurat <- SetAssayData(cur_seurat, slot='data', new.data=X_dat, assay=assay)

  # scale the subsetted expression dataset:

  if(is.null(vars.to.regress)){

  # compute average expression of each gene

  cur_expr <- GetAssayData(cur_seurat, slot='data')

  expr <- t(as.matrix(cur_expr))

  averExpr <- rowSums(expr) / ncol(expr)

  expr <- t(cur_expr)

  averExpr <- Matrix::rowSums(expr) / ncol(expr)

  # run PCA with Seurat function

  cur_pca <- Seurat::RunPCA(

  pc <- cur_pca@cell.embeddings[,pc_dim]

  pc_loadings <- cur_pca@feature.loadings[,pc_dim]

  print('here')

  # correlate average expression with eigengene

  pca_cor <- cor(averExpr, pc)

    # add this PCA as its own reduction in the seurat object

    seurat_obj@reductions$ME <- Seurat::CreateDimReducObject(

      embeddings = cur_pca@cell.embeddings,

      assay = Seurat::DefaultAssay(cur_seurat)

      assay = assay

    )

    cur_harmony <- harmony::RunHarmony(

      seurat_obj,

      group.by.vars=group.by.vars,

      reduction="ME", verbose=verbose, ...

      reduction="ME", verbose=verbose, assay.use=assay, ...

    )@reductions$harmony

    ha <- cur_harmony@cell.embeddings[,pc_dim]

    ha_loadings <- cur_pca@feature.loadings[,pc_dim]

    # add harmonized PCA as its own reduction in the seurat object

    seurat_obj@reductions$ME_harmony <- Seurat::CreateDimReducObject(

      embeddings = cur_harmony@cell.embeddings,

      assay = Seurat::DefaultAssay(seurat_obj)

      assay = assay

    )

    seurat_obj <- SetMELoadings(

  # add this PCA as its own reduction in the seurat object

  seurat_obj@reductions$ME <- Seurat::CreateDimReducObject(

    embeddings = cur_pca@cell.embeddings,

    assay = Seurat::DefaultAssay(cur_seurat)

    assay = assay

  )

  seurat_obj <- SetMELoadings(

#' @param verbose logical indicating whether to print messages

#' @param vars.to.regress character vector of variables in seurat_obj@meta.data to regress when running ScaleData

#' @param scale.model.use model to scale data when running ScaleData choices are "linear", "poisson", or "negbinom"

#' @param pc_dim Which PC to use as the module eigengene? Default to 1.

#' @param assay Assay in seurat_obj to compute module eigengenes. Default is DefaultAssay(seurat_obj)

#' @param wgcna_name name of the WGCNA experiment

#' @keywords scRNA-seq

#' @export

  vars.to.regress = NULL,

  scale.model.use = 'linear',

  verbose=TRUE,

  assay = NULL,

  pc_dim = 1,

  wgcna_name=NULL, ...

){

      stop('Need to run ScaleData before running ModuleEigengenes with group.by.vars option.')

  }

  # get the assay

  if(is.null(assay)){assay <- DefaultAssay(seurat_obj)}

  # check for the data slot in this assay

  if(dim(seurat_obj@assays[[assay]]@data)[1] == 0){

    stop(paste0("Normalized data slot not found in selected assay ", assay))

  }

  me_list <- list()

  harmonized_me_list <- list()

      scale.model.use = scale.model.use,

      verbose=verbose,

      pc_dim = pc_dim,

      assay = assay,

      wgcna_name=wgcna_name,

      ...

    )

  shared_old <- shared

  cell_sample <- nn_map[chosen, ]

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

  #

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

  combs <- tryCatch(

    {combn(nrow(cell_sample), 2)},

    error = function(cond){return(NA)}

  )

  if(any(is.na(combs))){

    warning('Metacell failed')

    return(NULL)

  }

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

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

  })

  # make seurat obj:

  metacell_obj <- CreateSeuratObject(

    counts = new_exprs

    counts = new_exprs,

    assay = assay

  )

  # calculate stats:

MetacellsByGroups <- function(

  seurat_obj, group.by=c('seurat_clusters'),

  ident.group='seurat_clusters',

  k=25, reduction='pca', assay='RNA',

  k=25, reduction='pca', assay=NULL,

  cells.use = NULL, # if we don't want to use all the cells to make metacells, good for train/test split

  slot='counts', mode = 'average', min_cells=100,

  max_shared=15,

    stop(paste0("Invalid reduction (", reduction, "). Reductions in Seurat object: ", paste(names(seurat_obj@reductions), collapse=', ')))

  }

  # check assay:

  if(is.null(assay)){

    assay <- DefaultAssay(seurat_obj)

  } else if(!(assay %in% names(seurat_obj@assays))){

    stop(paste0('Assay ', assay, ' not found in seurat_obj. Select a valid assay: ', paste0(names(seurat_obj@assays), collapse = ', ')))

  }

  # subset seurat object by seleted cells:

  if(!is.null(cells.use)){

    seurat_full <- seurat_obj

  groupings <- groupings[order(groupings)]

  # remove groups that are too small:

  # TODO: add a warning to let the user know that some groups are skipped?

  group_counts <- table(seurat_obj$metacell_grouping) >= min_cells

  warning(paste0("Removing the following groups that did not meet min_cells: ", paste(names(group_counts)[group_counts], collapse=', ')))

  groupings <- groupings[table(seurat_obj$metacell_grouping) >= min_cells]

  print(groupings)

  if(length(groupings) == 0 ){

    stop("No groups met the min_cells requirement.")

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

  names(seurat_list) <- groupings

  print(meta_list)

  # construct metacells

  metacell_list <- mapply(

    ConstructMetacells,

  )

  names(metacell_list) <- groupings

  # remove NULL

  remove <- which(sapply(metacell_list, is.null))

  if(length(remove) > 1){

    metacell_list <- metacell_list[-remove]

  }

  # get the run stats:

  run_stats <- as.data.frame(do.call(rbind, lapply(metacell_list, function(x){x@misc$run_stats})))

  rownames(run_stats) <- 1:nrow(run_stats)

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<pre>YEAR: 2022

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