plotDmeLollipop function

Package: hdWGCNA

Title: hdWGCNA

Version: 0.2.15

Version: 0.2.16

Authors@R: c(

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

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

    person("Zechuan", "Shi", , "zechuas@uci.edu", role = c("ctb"),

          comment = c(ORCID = "0000-0002-2844-5816")),

    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.

export(OverlapDotPlot)

export(OverlapModulesDEGs)

export(OverlapModulesMotifs)

export(PlotDMEsLollipop)

export(PlotDMEsVolcano)

export(PlotDendrogram)

export(PlotKMEs)

import(harmony)

import(magrittr)

import(patchwork)

import(uwot)

importFrom(Matrix,colSums)

importFrom(Matrix,rowSums)

importFrom(dplyr,"%>%")

# hdWGCNA 0.2.16 (2023-03-02)

## Added

- `PlotDMEsLollipop` function to visualize differential module eigengenes.

## Changes

- New error checks in `MetacellsByGroups`

# hdWGCNA 0.2.15 (2023-03-02)

## Added

- None

#' @param seurat_obj A Seurat object

#' @param n_hubs number of hub genes to use in the UMAP computation

#' @param exclude_grey logical indicating whether to include grey module

#' @param genes_use character vector of genes to use for the UMAP, must already be present in GetModules(seurat_obj)

#' @param wgcna_name The name of the hdWGCNA experiment in the seurat_obj@misc slot

#' @param ... Additional parameters supplied to uwot::umap

#' @keywords scRNA-seq

#' @import uwot

#' @import Seurat

#' @export

#' @examples

RunModuleUMAP <- function(

  seurat_obj,

  features = "TOM", # "TOM" or "kME"

  n_hubs = 10,

  exclude_grey = TRUE,

  genes_use = NULL,

  wgcna_name = NULL,

  n_neighbors= 25,

  metric = "cosine",

    stop('Eigengene-based connectivity (kME) not found. Did you run ModuleEigengenes and ModuleConnectivity?')

  }

  # handle excluding grey module

  if(exclude_grey){

    mods <- mods[mods != 'grey']

    modules <- subset(modules, module != 'grey')

  # get all genes that aren't in gray mod

  selected_genes <- modules[modules$module %in% mods,'gene_name']

  if(!is.null(genes_use)){

    selected_genes <- selected_genes[selected_genes %in% genes_use]

  } 

  # get the TOM

  TOM <- GetTOM(seurat_obj, wgcna_name)

    stop('Invalid character # found in group.by, please re-name the group.')

  }

  # check ident.group

  if(!(ident.group %in% group.by)){

    stop('ident.group must be in group.by')

  }

  # check mode

  if(!(mode %in% c('sum', 'average'))){

    stop('Invalid choice for mode. Mode can be either sum or average.')

  }

    }

  }

  # check that k > min_cells 

  if(min_cells < k ){

    warning("min_cells is smaller than k, this may result in downstream errors if very small groups are allowed.")

  }

  # check that max_shared is valid:

  if(max_shared < 0){

    max_shared <- 0

    warning(paste0("max_shared specified (", max_shared, ') is too low, setting max_shared <- 0'))

  }

  # subset seurat object by seleted cells:

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

    seurat_full <- seurat_obj

    seurat_obj = seurat_list,

    name = groupings,

    meta = meta_list,

    MoreArgs = list(k=k, reduction=reduction, assay=assay, slot=slot, return_metacell=TRUE, mode=mode, max_shared=max_shared, max_iter=max_iter, target_metacells=target_metacells, verbose=verbose, wgcna_name=wgcna_name)

    MoreArgs = list(

      k=k, 

      reduction=reduction, 

      assay=assay, 

      slot=slot, 

      return_metacell=TRUE, 

      mode=mode,

       max_shared=max_shared, 

       max_iter=max_iter, 

       target_metacells=target_metacells, 

       verbose=verbose, 

       wgcna_name=wgcna_name

    )

  )

  names(metacell_list) <- groupings