Issue with KMe Calculation : PlotKMEs and GetHubGenes Incomplete

[tetsy2]

Hello Sam,

I have just recently installed the package and I get the same problem as reported in #318

I follow the tutorial with my data until I get to the ModuleConnectivity.

compute eigengene-based connectivity (kME):

seurat_obj <- ModuleConnectivity(
seurat_obj,
group.by = 'orig.ident'
)

This gives a good results, BUT the next step is not working

p <- PlotKMEs(seurat_obj, ncol=3,wgcna_name='tutorial')

Indeed, this also works: GetHubGenes(seurat_obj, n_hubs=2, wgcna_name='tutorial',mods=NULL), but the results are wihout KMe (only the head):

gene_name module kME
1 TUBA1B turquoise NaN
2 MKI67 turquoise NaN
3 RRM2 turquoise NaN
4 TYMS turquoise NaN
5 UBE2C turquoise NaN
6 HIST1H1B turquoise NaN

Do you have any idea about what it could be?

Thanks!

[smorabit]

Hi, are you using the latest version of hdWGCNA? In #318 I was able to re-produce the issue but I pushed a fix for it already.

[tetsy2]

I used this:

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

Is there any recommended way yo install the package?

[smorabit]

What does it say when you run this:

packageVersion('hdWGCNA')

It could also help me better understand your issue if you include the other code that you have run before PlotKMEs.

[tetsy2]

Hello Sam!

This is the result: packageVersion('hdWGCNA')
[1] '0.4.0'

[tetsy2]

I am running this code:
seurat_obj <- SetupForWGCNA(
seurat_obj,
gene_select = "variable", # the gene selection approach
wgcna_name = "tutorial" # the name of the hdWGCNA experiment
)

construct metacells in each group

seurat_obj <- MetacellsByGroups(
seurat_obj = seurat_obj,
group.by = c("orig.ident"), # specify the columns in [email protected] 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 = 'orig.ident' # set the Idents of the metacell seurat object
)

seurat_obj <- NormalizeMetacells(seurat_obj)
seurat_obj <- ScaleMetacells(seurat_obj, features=VariableFeatures(seurat_obj))

seurat_obj <- SetDatExpr(
seurat_obj,
assay = 'RNA',
layer = 'data'
)

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)

seurat_obj <- ConstructNetwork(
seurat_obj,overwrite_tom = TRUE,
tom_name = 'Variable2' # name of the topoligical overlap matrix written to disk
)

PlotDendrogram(seurat_obj, main='INH hdWGCNA Dendrogram')
TOM <- GetTOM(seurat_obj)
pheatmap(TOM)

compute all MEs in the full single-cell dataset

seurat_obj <- ModuleEigengenes(
seurat_obj,
group.by.vars="orig.ident"
)

harmonized module eigengenes:

hMEs <- GetMEs(seurat_obj,wgcna_name='tutorial')

[smorabit]

Hi, I just ran through your code using the hdWGCNA tutorial dataset, and I am not able to reproduce your error on my end. I am not able to help resolve the issue until I am able to reproduce the behavior.