Error in ModuleEigengenes: "invalid argument type" when using default parameters

[sunzhendong123]

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Describe the bug
When following the official documentation of hdWGCNA (using the recommended example data and code workflow), the ModuleEigengenes function throws an error when running the following code snippet:

packageVersion("hdWGCNA")
[1] ‘0.3.0’

seurat_obj <- ModuleEigengenes(
seurat_obj,
group.by.vars = "Sample"
)
Error in !CheckWGCNAName(seurat_obj, wgcna_name) : invalid argument type

Steps to reproduce
using the recommended example data and code workflow

# Put code in this box

setwd("~/Desktop/RStudio/单细胞")

# single-cell analysis package
library(Seurat)

# plotting and data science packages
library(tidyverse)
library(cowplot)
library(patchwork)

# co-expression network analysis packages:
library(WGCNA)
library(hdWGCNA)

# using the cowplot theme for ggplot
theme_set(theme_cowplot())

# set random seed for reproducibility
set.seed(12345)

# optionally enable multithreading
enableWGCNAThreads(nThreads = 8)

# load the Zhou et al snRNA-seq dataset
seurat_obj <- readRDS('示例数据/hdWGCNA/Zhou_2020_control.rds')


seurat_obj <- SeuratObject::UpdateSeuratObject(seurat_obj)


p <- DimPlot(seurat_obj, group.by='cell_type', label=TRUE) +
  umap_theme() + ggtitle('Zhou et al Control Cortex') + NoLegend()

p

seurat_obj <- SetupForWGCNA(
  seurat_obj,
  gene_select = "fraction", # the gene selection approach
  fraction = 0.05, # fraction of cells that a gene needs to be expressed in order to be included
  wgcna_name = "tutorial" # the name of the hdWGCNA experiment
)

# construct metacells  in each group
seurat_obj <- MetacellsByGroups(
  seurat_obj = seurat_obj,
  group.by = c("cell_type", "Sample"), # specify the columns in seurat_obj@meta.data to group by
  reduction = 'harmony', # select the dimensionality reduction to perform KNN on
  k = 25, # nearest-neighbors parameter
  max_shared = 10, # maximum number of shared cells between two metacells
  ident.group = 'cell_type' # set the Idents of the metacell seurat object
)

# normalize metacell expression matrix:
seurat_obj <- NormalizeMetacells(seurat_obj)

seurat_obj <- SetDatExpr(
  seurat_obj,
  group_name = "INH", # the name of the group of interest in the group.by column
  group.by='cell_type', # the metadata column containing the cell type info. This same column should have also been used in MetacellsByGroups
  assay = 'RNA', # using RNA assay
  layer = 'data' # using normalized data
)

# Test different soft powers:
seurat_obj <- TestSoftPowers(
  seurat_obj,
  networkType = 'signed' # you can also use "unsigned" or "signed hybrid"
)

# plot the results:
plot_list <- PlotSoftPowers(seurat_obj)

# assemble with patchwork
wrap_plots(plot_list, ncol=2)

power_table <- GetPowerTable(seurat_obj)
head(power_table)

# construct co-expression network:
seurat_obj <- ConstructNetwork(
  seurat_obj,
  tom_name = 'INH' # name of the topoligical overlap matrix written to disk
)

PlotDendrogram(seurat_obj, main='INH hdWGCNA Dendrogram')

TOM <- GetTOM(seurat_obj)

# need to run ScaleData first or else harmony throws an error:
#seurat_obj <- ScaleData(seurat_obj, features=VariableFeatures(seurat_obj))

# compute all MEs in the full single-cell dataset
seurat_obj <- ModuleEigengenes(
  seurat_obj,
  group.by.vars="Sample"
)


**R session info**
> packageVersion("hdWGCNA")
[1] ‘0.3.0’
> R.version
               _                           
platform       aarch64-apple-darwin20      
arch           aarch64                     
os             darwin20                    
system         aarch64, darwin20           
status                                     
major          4                           
minor          5.1                         
year           2025                        
month          06                          
day            13                          
svn rev        88306                       
language       R                           
version.string R version 4.5.1 (2025-06-13)
nickname       Great Square Root    

**Screenshots**
If applicable, add screenshots to help explain your problem.

**More than one problem?** 
hdWGCNA has been updated to version 0.4 in the documentation, but the installable R package remains at version 0.3. Could you please release the new version of the R package?

[smorabit]

Hi,

hdWGCNA has been updated to version 0.4 in the documentation, but the installable R package remains at version 0.3. Could you please release the new version of the R package?

Please install the latest version of the hdWGCNA as described in the installation instructions:

devtools::install_github('smorabit/hdWGCNA', ref='dev')

[smorabit]

@sunzhendong123 I created a release of v0.4.08, please let me know whether it helps you.

[sunzhendong123]

Thank you for your reply. I will attempt to run the code using the new version of the R package.