Commit 4d3b01d7 authored by smorabit's avatar smorabit
Browse files

Wrote all Seurat wrapper functions

parent 14f8b3b5

R/Seurat_wrappers.R

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#' NormalizeMetacells

#'

#' Wrapper function to run Seurat's NormalizeData function on the metacell object.

#'

#' @param seurat_obj A Seurat object

#' @keywords scRNA-seq

#' @export

#' @examples

#' NormalizeMetadata

NormalizeMetacells <- function(seurat_obj, ...){

  seurat_obj@misc$wgcna_metacell_obj <- Seurat::NormalizeData(seurat_obj@misc$wgcna_metacell_obj, ...)

  seurat_obj

}



#' ScaleMetacells

#'

#' Wrapper function to run Seurat's ScaleData function on the metacell object.

#'

#' @param seurat_obj A Seurat object

#' @keywords scRNA-seq

#' @export

#' @examples

#' ScaleMetadata

ScaleMetacells <- function(seurat_obj, ...){

  if(!exists("features")){

    features = VariableFeatures(seurat_obj)

  }

  seurat_obj@misc$wgcna_metacell_obj <- Seurat::ScaleData(seurat_obj@misc$wgcna_metacell_obj, ...)

  seurat_obj

}



#' RunPCAMetacells

#'

#' Wrapper function to run Seurat's RunPCA function on the metacell object.

#'

#' @param seurat_obj A Seurat object

#' @keywords scRNA-seq

#' @export

#' @examples

#' NormalizeMetadata

RunPCAMetacells <- function(seurat_obj, ...){

  seurat_obj@misc$wgcna_metacell_obj <- Seurat::RunPCA(seurat_obj@misc$wgcna_metacell_obj, ...)

  seurat_obj

}



#' RunHarmonyMetacells

#'

#' Wrapper function to run harmony's RunHarmony function on the metacell object.

#'

#' @param seurat_obj A Seurat object

#' @keywords scRNA-seq

#' @export

#' @examples

#' NormalizeMetadata

RunHarmonyMetacells <- function(seurat_obj, ...){

  seurat_obj@misc$wgcna_metacell_obj <- harmony::RunHarmony(seurat_obj@misc$wgcna_metacell_obj, ...)

  seurat_obj

}



#' RunUMAPMetacells

#'

#' Wrapper function to run Seurat's RunUMAP function on the metacell object.

#'

#' @param seurat_obj A Seurat object

#' @keywords scRNA-seq

#' @export

#' @examples

#' NormalizeMetadata

RunUMAPMetacells <- function(seurat_obj, ...){

  seurat_obj@misc$wgcna_metacell_obj <- Seurat::RunUMAP(seurat_obj@misc$wgcna_metacell_obj, ...)

  seurat_obj

}





#' DimPlotMetacells

#'

#' Wrapper function to run Seurat's DimPlot function on the metacell object.

#'

#' @param seurat_obj A Seurat object

#' @keywords scRNA-seq

#' @export

#' @examples

#' NormalizeMetadata

DimPlotMetacells <- function(seurat_obj, ...){

  Seurat::DimPlot(seurat_obj@misc$wgcna_metacell_obj, ...)

}

R/WGCNA_functions.R

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#' SelectNetworkGenes

#'

#' This function

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

#' @param seurat_obj A Seurat object

#' @param type How to select genes? Select "variable", "fraction", "all", or "custom".

#' @param fraction A numeric that determines the minimum cells that a gene must be expressed in order to be included. For example, fraction = 0.05 means that 5% of cells must express a gene (count > 0) for it to be included.

#' @param gene_list A character string of gene names, only used if type = "custom"

#' @keywords scRNA-seq

#' @export

#' @examples

#' MetacellsByGroups(pbmc)

SelectNetworkGenes <- function(seurat_obj, type="variable", fraction=0.05, gene_list=NULL){



  # validate inputs:

  if(!(type %in% c("variable", "fraction", "all", "custom"))){

    stop(paste0("Invalid selection type: ", type, '. Valid types are variable, fraction, all, or custom.'))

  }



  # handle different selection strategies

  if(type == "fraction"){



    # binarize counts matrix

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

    expr_mat[expr_mat>0] <- 1



    # identify genes that are expressed in at least some fraction of cells

    gene_filter <- rowSums(expr_mat) >= round(fraction*ncol(cur_seurat));

    gene_list <- rownames(seurat_obj)[gene_filter]



  } else if(type == "variable"){

    gene_list <- VariableFeatures(seurat_obj)



  } else if(type == "all"){

    gene_list <- rownames(seurat_obj)



  } else if(type == "custom"){



    gene_list <- gene_list



    # check that custom genes are present in the seurat object:

    check_genes <- gene_list %in% rownames(seurat_obj)

    if(sum(check_genes) < length(gene_list)){

      stop(paste("Some genes not present in seurat object:", paste(gene_list[!check_genes], collapse=', ')))

    }

  }



  # make sure there's more than 0 genes:

  if(length(gene_list) == 0){

    stop("No genes found")

  }



  # throw a warning if there's very few genes:

  if(length(gene_list) <= 100){

    warning(paste0("Very few genes selected (", length(gene_list), "), perhaps use a different method to select genes."))

  }



  # add genes to gene list

  seurat_obj@misc$wgcna_genes <- gene_list



  # set VariableFeatures slot for metacell object as these genes

  VariableFeatures(seurat_obj@misc$wgcna_metacell_obj) <- gene_list



  # return updated seurat obj

  seurat_obj



}

R/scWGCNA_new.R→R/metacells.R

+0 −99
Original line number Diff line number Diff line
@@ -125,38 +125,6 @@ ConstructMetacells <- function(seurat_obj, name='agg', k=50, reduction='umap', a 


}



#' NormalizeMetacells

#'

#' Wrapper function to run Seurat's NormalizeData function on the metacell object.

#'

#' @param seurat_obj A Seurat object

#' @keywords scRNA-seq

#' @export

#' @examples

#' NormalizeMetadata

NormalizeMetacells <- function(seurat_obj, ...){

  seurat_obj@misc$wgcna_metacell_obj <- NormalizeData(seurat_obj@misc$wgcna_metacell_obj, ...)

}



#' ScaleMetacells

#'

#' Wrapper function to run Seurat's ScaleData function on the metacell object.

#'

#' @param seurat_obj A Seurat object

#' @keywords scRNA-seq

#' @export

#' @examples

#' ScaleMetadata

ScaleMetacells <- function(seurat_obj, ...){

  if(!exists("features")){

    features = VariableFeatures(seurat_obj)

  }

  print(features)

  seurat_obj@misc$wgcna_metacell_obj <- ScaleData(seurat_obj@misc$wgcna_metacell_obj, ...)

}







#' MetacellsByGroups

#'

#' This function takes a Seurat object and constructs averaged 'metacells' based
@@ -185,7 +153,6 @@ MetacellsByGroups <- function(seurat_obj, group.by=c('seurat_clusters'), k=50, r 
    seurat_obj$metacell_grouping <- as.character(seurat_obj@meta.data[[group.by]])

  }

  groupings <- unique(seurat_obj$metacell_grouping)

  print(groupings)



  # unique meta-data for each group

  meta_df <- as.data.frame(do.call(rbind, strsplit(groupings, '_')))
@@ -235,69 +202,3 @@ MetacellsByGroups <- function(seurat_obj, group.by=c('seurat_clusters'), k=50, r 
  }

  seurat_obj

}
 




#' SelectNetworkGenes

#'

#' This function

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

#' @param seurat_obj A Seurat object

#' @param type How to select genes? Select "variable", "fraction", "all", or "custom".

#' @param fraction A numeric that determines the minimum cells that a gene must be expressed in order to be included. For example, fraction = 0.05 means that 5% of cells must express a gene (count > 0) for it to be included.

#' @param gene_list A character string of gene names, only used if type = "custom"

#' @keywords scRNA-seq

#' @export

#' @examples

#' MetacellsByGroups(pbmc)

SelectNetworkGenes <- function(seurat_obj, type="variable", fraction=0.05, gene_list=NULL){



  # validate inputs:

  if(!(type %in% c("variable", "fraction", "all", "custom"))){

    stop(paste0("Invalid selection type: ", type, '. Valid types are variable, fraction, all, or custom.'))

  }



  # handle different selection strategies

  if(type == "fraction"){



    # binarize counts matrix

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

    expr_mat[expr_mat>0] <- 1



    # identify genes that are expressed in at least some fraction of cells

    gene_filter <- rowSums(expr_mat) >= round(fraction*ncol(cur_seurat));

    gene_list <- rownames(seurat_obj)[gene_filter]



  } else if(type == "variable"){

    gene_list <- VariableFeatures(seurat_obj)



  } else if(type == "all"){

    gene_list <- rownames(seurat_obj)



  } else if(type == "custom"){



    gene_list <- gene_list



    # check that custom genes are present in the seurat object:

    check_genes <- gene_list %in% rownames(seurat_obj)

    if(sum(check_genes) < length(gene_list)){

      stop(paste("Some genes not present in seurat object:", paste(gene_list[!check_genes], collapse=', ')))

    }

  }



  # make sure there's more than 0 genes:

  if(length(gene_list) == 0){

    stop("No genes found")

  }



  # throw a warning if there's very few genes:

  if(length(gene_list) <= 100){

    warning(paste0("Very few genes selected (", length(gene_list), "), perhaps use a different method to select genes."))

  }



  # add genes to gene list

  seurat_obj@misc$wgcna_genes <- gene_list



  # return updated seurat obj

  seurat_obj



}

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