hdWGCNA (trisomy)

Author: Carlo Zanetti

Performs hdWGCNA co-expression network analysis on the trisomy microglia dataset. Constructs metacells, selects soft power threshold, builds a TOM-based network, computes harmony-corrected module eigengenes (hMEs), and runs GO enrichment via EnrichrR. Follows the hdWGCNA basic tutorial.

Inputs: output/run1/objects/prelabelled_integrated_rpca.qs2 Outputs: WGCNA Seurat object, module graphs, enrichment PDFs written to output/run1/wgcna/

Workflow overview

  1. Loading in data: Load libraries, seurat object and select expressed genes (>5% of cells).
  2. Metacell Construction:
    • Aggregates k-Nearest Neighbors (k=25) into dense “metacells”.
    • Crucial for robust correlation calculation
  3. Network parameter selection
  4. Network Construction:
    • Soft Power Selection: Finds the threshold for a scale-free network.
    • Topological Overlap Matrix (TOM): Clusters genes into co-expression modules.
  5. Module Eigengenes (MEs):
    • Calculates a summary score for each module per cell.
    • Applies Harmony to correct MEs for batch effects (hMEs).
  6. Visualization & Biological Annotation:
    • Radar plots of module activity.
    • GO Enrichment (Enrichr) to determine module function.
  7. Network visualisation - connectivity plot
  8. Module enrichment - enrichR
  9. Saving final object
Code 
rm(list = ls())
Code 
library(Seurat)
library(dplyr)
library(tidyr)
library(WGCNA)
library(hdWGCNA)
Warning: replacing previous import 'GenomicRanges::intersect' by
'SeuratObject::intersect' when loading 'hdWGCNA'
Warning: replacing previous import 'GenomicRanges::setdiff' by 'dplyr::setdiff'
when loading 'hdWGCNA'
Warning: replacing previous import 'GenomicRanges::union' by 'dplyr::union'
when loading 'hdWGCNA'
Warning: replacing previous import 'dplyr::as_data_frame' by
'igraph::as_data_frame' when loading 'hdWGCNA'
Warning: replacing previous import 'Seurat::components' by 'igraph::components'
when loading 'hdWGCNA'
Warning: replacing previous import 'dplyr::groups' by 'igraph::groups' when
loading 'hdWGCNA'
Warning: replacing previous import 'dplyr::union' by 'igraph::union' when
loading 'hdWGCNA'
Warning: replacing previous import 'GenomicRanges::subtract' by
'magrittr::subtract' when loading 'hdWGCNA'
Warning: replacing previous import 'Matrix::as.matrix' by 'proxy::as.matrix'
when loading 'hdWGCNA'
Warning: replacing previous import 'igraph::groups' by 'tidygraph::groups' when
loading 'hdWGCNA'
Code 
library(cowplot)
library(patchwork)
library(qs2)
library(igraph)
library(magrittr)
library(glue)
library(enrichR)
library(GeneOverlap)

disableWGCNAThreads()

theme_set(theme_cowplot())

set.seed(42)

WGCNA::allowWGCNAThreads(nThreads = 2)
Allowing multi-threading with up to 2 threads.

File set-up

Code 
run_num <- "run1"
out_dir <- file.path("output", run_num, "wgcna")
graphs_dir <- file.path(out_dir, "graphs")
objects_dir <- file.path(out_dir, "objects")
wgcna_name <- glue("emily_jan_{run_num}")

dir.create(graphs_dir, recursive = TRUE, showWarnings = FALSE)
dir.create(objects_dir, recursive = TRUE, showWarnings = FALSE)

1. Loading in data

Code 
labelled_path <- file.path("output", run_num, "objects", "prelabelled_integrated_rpca.qs2")
if (!file.exists(labelled_path)) {
  message("Required input not found: ", labelled_path, "\nRun seurat_trisomy_v1.qmd and seurat_trisomy_v2.qmd first.")
  knitr::knit_exit()
}
seurat_obj <- qs_read(labelled_path)

Ensure ID columns are factors for downstream grouping

Code 
seurat_obj$cluster_id <- factor(Idents(seurat_obj))
seurat_obj$sample_id <- factor(seurat_obj$orig.ident)

Plot to check clusters are correct before starting

Code 
p <- DimPlot(seurat_obj, group.by='cluster_id', label=TRUE) +
   umap_theme() + ggtitle('') + NoLegend()

p

hdWGCNA works on normalised RNA counts

Code 
seurat_obj[["RNA"]] <- CreateAssayObject(counts = seurat_obj[["SCT"]]@counts)
Warning: Assay RNA changing from Assay5 to Assay
Warning: Different cells and/or features from existing assay RNA
Code 
DefaultAssay(seurat_obj) <- "RNA"
seurat_obj <- NormalizeData(seurat_obj)

Gene selection (>5% of cells)

Code 
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 = wgcna_name
)

2. Metacell construction

Group by sample so metacells are created from same sample of origin

Code 
seurat_obj <- MetacellsByGroups(
  seurat_obj = seurat_obj,
  group.by = "sample_id", # specify the columns in seurat_obj@meta.data to group by
  reduction = 'umap.rpca', # 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 = 'sample_id' # set the Idents of the metacell seurat object
  )
Idents(seurat_obj) <- "microglia"
seurat_obj <- NormalizeMetacells(seurat_obj)
Normalizing layer: counts

3. Network parameter selection

WGCNA requires a soft power threshold to ensure the gene network has a scale-free topology.

SFT -> most genes have few connections, while a few hubs have many.

Code 
seurat_obj <- SetDatExpr(
  seurat_obj, assay = "RNA")
Warning in asMethod(object): sparse->dense coercion: allocating vector of size
1.1 GiB
Code 
seurat_obj <- TestSoftPowers(
  seurat_obj,
  networkType = 'signed' # you can also use "unsigned" or "signed hybrid"
)
pickSoftThreshold: will use block size 3972.
 pickSoftThreshold: calculating connectivity for given powers...
   ..working on genes 1 through 3972 of 11263
Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
"all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used for
individual columns with zero (or missing) MAD.
Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
"all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used for
individual columns with zero (or missing) MAD.
Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
"all.obs", : bicor: zero MAD in variable 'y'. Pearson correlation was used for
individual columns with zero (or missing) MAD.
   ..working on genes 3973 through 7944 of 11263
Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
"all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used for
individual columns with zero (or missing) MAD.
Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
"all.obs", : bicor: zero MAD in variable 'y'. Pearson correlation was used for
individual columns with zero (or missing) MAD.
Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
"all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used for
individual columns with zero (or missing) MAD.
Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
"all.obs", : bicor: zero MAD in variable 'y'. Pearson correlation was used for
individual columns with zero (or missing) MAD.
   ..working on genes 7945 through 11263 of 11263
Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
"all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used for
individual columns with zero (or missing) MAD.
Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
"all.obs", : bicor: zero MAD in variable 'y'. Pearson correlation was used for
individual columns with zero (or missing) MAD.
Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
"all.obs", : bicor: zero MAD in variable 'x'. Pearson correlation was used for
individual columns with zero (or missing) MAD.
Warning in (function (x, y = NULL, robustX = TRUE, robustY = TRUE, use =
"all.obs", : bicor: zero MAD in variable 'y'. Pearson correlation was used for
individual columns with zero (or missing) MAD.
   Power SFT.R.sq slope truncated.R.sq  mean.k. median.k. max.k.
1      1    0.267 18.20          0.458 5750.000  5.78e+03 6020.0
2      2    0.111  7.23          0.970 2990.000  3.00e+03 3380.0
3      3    0.024 -1.89          0.916 1580.000  1.57e+03 2050.0
4      4    0.241 -3.86          0.780  851.000  8.29e+02 1310.0
5      5    0.497 -4.03          0.780  466.000  4.43e+02  876.0
6      6    0.769 -4.01          0.899  260.000  2.39e+02  613.0
7      7    0.913 -3.79          0.976  148.000  1.30e+02  444.0
8      8    0.947 -3.37          0.988   86.400  7.13e+01  331.0
9      9    0.949 -2.93          0.980   51.700  3.95e+01  252.0
10    10    0.949 -2.57          0.966   31.800  2.21e+01  196.0
11    12    0.984 -1.96          0.980   13.200  7.05e+00  127.0
12    14    0.971 -1.76          0.963    6.280  2.32e+00  102.0
13    16    0.937 -1.62          0.920    3.390  7.90e-01   86.2
14    18    0.879 -1.54          0.847    2.050  2.76e-01   75.5
15    20    0.887 -1.43          0.873    1.360  9.82e-02   67.6
16    22    0.864 -1.36          0.865    0.969  3.60e-02   61.5
17    24    0.827 -1.31          0.840    0.733  1.34e-02   56.7
18    26    0.857 -1.23          0.906    0.580  5.10e-03   52.6
19    28    0.820 -1.20          0.897    0.475  1.98e-03   49.1
20    30    0.770 -1.18          0.837    0.400  7.73e-04   46.0
Code 
plot_list <- PlotSoftPowers(seurat_obj)
  Power   SFT.R.sq     slope truncated.R.sq   mean.k. median.k.    max.k.
1     1 0.26682740 18.165878      0.4575717 5754.9847 5783.2121 6022.3576
2     2 0.11053483  7.233747      0.9702855 2993.0794 2998.0197 3380.1032
3     3 0.02404144 -1.889392      0.9164226 1582.9425 1565.3427 2046.1957
4     4 0.24128267 -3.863393      0.7796488  851.3891  829.1830 1307.2296
5     5 0.49657839 -4.032561      0.7797495  466.0815  442.8920  876.3305
6     6 0.76914201 -4.011946      0.8989230  260.0613  238.9068  612.9242
Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.
ℹ The deprecated feature was likely used in the hdWGCNA package.
  Please report the issue to the authors.
Code 
combined_soft <- wrap_plots(plot_list, ncol = 2)

print(combined_soft)

Code 
ggsave(
  filename = file.path(graphs_dir,"soft_power_selection.pdf"),
  plot = combined_soft,
  width = 12,       # increase width if labels overlap
  height = 7,       # increase height for more clarity
)
Code 
power_table <- GetPowerTable(seurat_obj)

4. Network construction

Builds correlation matrix using metacells. Uses dendrogram to cluster genes into modules.

Code 
seurat_obj <- ConstructNetwork(
  seurat_obj,
  tom_name = wgcna_name, # name of the topoligical overlap matrix written to disk
  tom_outdir = objects_dir,
  overwrite_tom = TRUE
)
Soft power not provided. Automatically using the lowest power that meets 0.8 scale-free topology fit. Using soft_power = 7
 Calculating consensus modules and module eigengenes block-wise from all genes
 Calculating topological overlaps block-wise from all genes
   Flagging genes and samples with too many missing values...
    ..step 1
    TOM calculation: adjacency..
    ..will use 2 parallel threads.
     Fraction of slow calculations: 0.000000
    ..connectivity..
    ..matrix multiplication (system BLAS)..
    ..normalization..
    ..done.
 ..Working on block 1 .
 ..Working on block 1 .
 ..merging consensus modules that are too close..

Plots dendrogram showing gene clustering

Code 
pdf(file.path(graphs_dir,"dendrogram.pdf"), width = 12, height = 7)
PlotDendrogram(seurat_obj, main='hdWGCNA Dendrogram')
dev.off()
png 
  2

5. Module eigengenes (hME)

Harmony batch correction to the MEs - gives harmonised module eigengenes.

Calculate the ME which acts as a representative expression profile for the whole module. Due to multiple sequencing pools -> Harmony batch correction to these scores.

Code 
seurat_obj <- ScaleData(seurat_obj, features=VariableFeatures(seurat_obj))
Centering and scaling data matrix
Code 
seurat_obj <- ModuleEigengenes(
 seurat_obj,
 group.by.vars="pool_id"
)
[1] "blue"
Centering and scaling data matrix
Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from pcablue to pcablue_
pcablue_ 1 
Positive:  CCDC200, TEX14, ENSG00000262202, FOSB, ENSG00000285646, IER2, NR4A1, SERTAD1, BTG2, MIR142HG 
       ENSG00000287979, MCL1, SRGN, NFKBID, PPP1R10, MEPCE, MIR23AHG, ENSG00000289865, NR4A2, EGR1 
       KDM6B, CREM, HOOK2, LINC00910, PPP1R15A, FOS, DDX5, FAM53C, CD83, ENSG00000270103 
Negative:  KLF3, SLTM, MPHOSPH8, HNRNPA3, PRPF40A, ZC3H13, TOP1, SEMA6D, SRRM1, HCG25 
       CD53, ENSG00000290073, RAP1B, SNRNP70, SNW1, ZFAND6, ZNF90, NOL8, CDC40, TBC1D17 
       E4F1, PLPP7, PUM3, CASC9, ENSG00000290937, ZNF347, ATG4D, GRAP, ZSCAN32, PLCXD1 
pcablue_ 2 
Positive:  DUSP1, FOS, KLF2, KLF6, JUN, RGS1, SAT1, RHOB, CD83, NR4A2 
       GADD45B, IER3, CH25H, JUNB, CEBPD, CCL3, DNAJB1, PHACTR1, CCL3L3, CCL4L2 
       JUND, ZFP36L1, C9orf72, SGK1, CCL4, SRGN, BTG2, TALAM1, OLR1, IER2 
Negative:  GSTM3, H2AZ1, ENSG00000285646, LINC00910, CROCC, PLEKHO2, SNHG3, TUBB4B, HNRNPAB, ALDH16A1 
       MEPCE, STX5-DT, TUBA1C, SAE1, NASP, ENSG00000289543, MAP3K11, NOSIP, CSRNP2, ENSG00000290117 
       ENSG00000289267, PYM1, SNHG1, ASL, ENSG00000288909, ENSG00000287296, GPR84, C14orf119, ZC3H12A, TRPT1 
pcablue_ 3 
Positive:  RGS1, RAB20, LPCAT1, TBC1D7, PHACTR1, ENSG00000262810, KLHL6, TMEM156, ENSG00000285280, LINC01480 
       SPATA13, PLA2G4A, PRKCH, GLA, NFAM1, ENSG00000284999, PRMT9, GRAMD4, APOC4, MICAL1 
       NGLY1, LINC03002, MAEA, EXD3, OPRM1, GPR183, ELL2, ENSG00000236069, OXSM, PEBP4 
Negative:  EIF1, SRSF5, SERTAD1, PNRC1, EIF4A1, H3-3B, CCDC200, ENSG00000262202, ENSG00000285646, TEX14 
       ARF4, SNHG5, EGR1, ATF4, RPL22L1, SRSF7, RBM8A, H2AZ1, LINC00910, ENSG00000287979 
       IER2, JUND, IL1B, SDCBP, DDX3X, MEPCE, EIF4G2, BZW1, JUNB, STX5-DT 
pcablue_ 4 
Positive:  ENSG00000262202, ENSG00000270022, ATP2B1-AS1, LINC00910, ENSG00000287979, ENSG00000288900, ENSG00000290039, CROCC, CLASP1-AS1, ENSG00000288755 
       PPP1R10, ENSG00000285646, ENSG00000276216, PTCH2, TRA2B, WSB1, ENSG00000289341, IKBKB-DT, ENSG00000287697, TEX14 
       SLC38A2, ENSG00000270103, ENSG00000259805, JUN, ENSG00000253736, ENSG00000289928, ENSG00000258820, WDR74, ENSG00000287190, TMEM107 
Negative:  EIF4A1, EIF1, H3-3B, CCL3, JUND, SLC25A3, IL1B, RBM8A, YWHAZ, BZW1 
       ZNF331, SLC2A3, PLEK, CCL3L3, SRGN, ATF4, HSP90AB1, UBE2J1, C5AR1, TPM4 
       ZFP36, ARMCX3, ATF3, PNRC1, SELENOK, SDCBP, CD69, HNRNPAB, PLAUR, JUNB 
pcablue_ 5 
Positive:  DDX5, HNRNPA2B1, RHOB, RSRP1, HSPA5, HNRNPDL, SFPQ, PDK4, XBP1, FUS 
       SLC38A2, WSB1, SAT1, GOLGA8A, CD53, IER3, RSRC2, FPR1, SKIL, TAF1D 
       HP1BP3, TALAM1, CGAS, TRA2B, GLUL, FCGR1BP, HHEX, DNAJB1, IPCEF1, TOP1 
Negative:  CCL3, CCL4, SDCBP, CCL3L3, ATF3, LINC00963, FOSL2, CCL4L2, CXCL8, NR4A1 
       PRKCH, EGR2, ENSG00000287216, EIF1, C5AR1, STX11, RAB20, IL4I1, ENSG00000289959, TOM1 
       ENSG00000289259, CCL2, GRAMD4, NR4A3, NEAT1, STX17-DT, TTYH3, LINC02929, FOSL2-AS1, LINC01344 
Transposing data matrix
Initializing state using k-means centroids initialization
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Harmony 1/10
Harmony 2/10
Harmony 3/10
Harmony converged after 3 iterations
[1] "grey"
Centering and scaling data matrix
Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from pcagrey to pcagrey_
pcagrey_ 1 
Positive:  GLIPR1, RNF130, CTSB, CREG1, JAK1, CD302, MAF, ARL5A, LAPTM4A, GRB2 
       TCF25, EVI2B, RNF13, STAG2, TNFRSF1A, MICOS10, HPGDS, N4BP2L2, CPVL, LAIR1 
       CTSH, TMEM123, TGFB1, SERINC1, PTTG1IP, MILR1, RTN4, PCBP2, RAB31, MSL3 
Negative:  ZNF714, ELOVL5, AMOT, ZNF107, CEP295, PCNT, INSIG2, FUT8, MINDY2, MPHOSPH9 
       POGLUT3, ATL3, EIF4G1, VKORC1L1, CDK5RAP2, ZNF430, ZNF791, CEP43, SMC6, FIRRE 
       VPS13A, HK2, ITGB3, PMS1, VCL, TRIM24, PAQR5, DHTKD1, EFCAB11, POLA2 
pcagrey_ 2 
Positive:  RTTN, DSCAM, SOCS6, XACT, SLC1A3, ITGAX, KCNMA1, TGFBR1, XYLT1, LINC01909 
       CCDC26, FNIP2, ACER3, HEG1, SYT17, GPD2, ITPR1, CYTH3, LPCAT2, TMEM154 
       FRRS1, PLPP1, BCL2, ENSG00000288857, PCLO, LINC01088, ADGRE2, SMAD3, PLAU, GRAMD1B 
Negative:  MAF, ABCG2, CCND1, GLIPR1, GATM, PTTG1IP, APLP2, CD302, GIMAP1, TMEM123 
       PDIA4, SERPINB6, CYSLTR1, MANF, FZD2, ADAP2, TNFRSF1A, NAGA, TMEM50B, LAPTM4A 
       SSR1, LMAN1, EBI3, IFT74, ATP6AP2, CCDC50, GIMAP6, CRELD2, RPN1, SPCS3 
pcagrey_ 3 
Positive:  DNASE2, ADGRE2, ITGAX, TMEM154, PLAC8, C1orf54, MANF, HEG1, ATP6AP2, SHISAL2A 
       HPSE, CTSH, CTSB, RTTN, TOR3A, SPTBN1, HPRT1, HM13, SLC7A1, LCP1 
       SQOR, BHLHE41, SOCS6, HAMP, XYLT1, PCLO, TMEM70, XACT, P2RY11, QPCT 
Negative:  MAF, N4BP2L2, ADAP2, GLIPR1, NMRK1, SON, HPGDS, MTUS1, IL10RA, MDM4 
       CYSLTR1, MEF2C-AS1, ZFYVE16, ABCG2, ENSG00000288932, PHF3, PCM1, APLP2, PAXBP1, CCNL2 
       ZRANB2, APAF1, PRPF4B, NPHP3, ZMAT1, CD302, CREBRF, DELE1, DUSP16, CLK4 
pcagrey_ 4 
Positive:  SLC1A3, CTSB, SOCS6, BHLHE41, RTTN, ITGAX, DNASE2, CTSH, HAVCR2, TGFBR1 
       DSCAM, GALNT1, LPCAT2, PLPP1, RTN4, MMP2, SON, ID2, SHTN1, TMED10 
       CTTNBP2NL, CYTH3, SPCS3, HM13, RPN1, ENSG00000257258, RDH11, KCNMA1, ATP6AP2, TMED7 
Negative:  PTGS2, KIRREL3, CFAP210, PLAC8, ITGB7, GPAT3, PLXNC1, PTPN13, ADGRE2, P2RX1 
       PRKG1, FUT8, NECTIN3, PIEZO2, PDE4DIP, DYNLT5, ACOT11, CCDC26, BICC1, LTB4R 
       PPP1R16B, NHSL1-AS1, GNAI1, GPR35, NIN, RAB11FIP1, SOCS2, RNF130, CABP4, ESYT1 
pcagrey_ 5 
Positive:  SON, ENSG00000189229, PDIA4, CHCHD2, DNAJB11, TTC3, TMEM50B, HSPH1, PAXBP1, HERC2P3 
       MANF, PTTG1IP, MORC3, DNAJC3, ATP6AP2, ENSG00000287335, UBE2G2, FUT8, FANCC, ELOVL5 
       LINC00511, HAMP, ZC3HAV1, ITGB3, PCBP3, GNAI1, LTN1, SPCS3, MEST, MAGT1 
Negative:  POU6F2, YPEL3-DT, ENSG00000288857, ENSG00000286339, ENSG00000289168, LINC01088, CCDC92, ENSG00000254710, KLHL24, DSCAM 
       HBP1, STAG2, CREBRF, FAM199X, JAK1, MMP2, SOCS6, GUCY1A1, SMIM14, CPVL 
       LINC02245, MTURN, TNFAIP8L1, BHLHE41, MMP24OS, PRKAR1A, TCP11L2, EVI2B, CREG1, ARL5A 
Transposing data matrix
Initializing state using k-means centroids initialization
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Harmony 1/10
Harmony 2/10
Harmony 3/10
Harmony converged after 3 iterations
Warning: Key 'harmony_' taken, using 'bziua_' instead
[1] "red"
Centering and scaling data matrix
Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from pcared to pcared_
pcared_ 1 
Positive:  IFI44L, IFIT3, MX1, IFIT1, IFI6, MX2, ISG15, RSAD2, XAF1, IFI44 
       EPSTI1, OAS2, IFITM3, SAMD9L, IFITM1, STAT1, IFIT2, EIF2AK2, PARP9, LY6E 
       PARP14, HERC5, OAS3, SP110, USP18, B2M, CMPK2, IRF7, HERC6, OASL 
Negative:  MRPL39, PWP2, ZNF793, TMEM187, IFNGR2, RRP1B, HSPA13, UBE2F, RENBP, LIMA1 
       POTEF, NDUFV3, CCND3, BATF, SCIMP, SNX10, NUDCD1, POTEE, GART, NBN 
       PDXK, OGFR, MVP, RCN1, RRBP1, PTPRN2, GIMAP2, BTN3A1, APOL2, STX17 
pcared_ 2 
Positive:  HLA-B, HLA-C, B2M, HLA-A, HLA-F, BST2, IFI27, C2, IFITM3, LGALS3BP 
       IFI6, LY6E, PSME2, SERPING1, S100A9, PSMB9, BTN3A2, CD48, PSMB8-AS1, IFNGR2 
       SHISA5, PDXK, ANKRD22, PPA1, SNX10, SCIMP, SIGLEC1, RENBP, PTPRN2, ODF3B 
Negative:  RIGI, PNPT1, DDX60L, HESX1, PPM1K, OASL, IFIT2, RNF213, RSAD2, CMPK2 
       HERC5, TRIM22, USP18, ZCCHC2, TNFSF10, PARP14, TRIM5, IRF1-AS1, HERC6, ZNFX1 
       STAT2, IFIT3, IFI16, IFIT1, PARP12, TRIM25, TRIM56, IRF1, SAMD9, IFIH1 
pcared_ 3 
Positive:  WARS1, ANKRD22, PPA1, S100A9, LIMK2, TAP1, PSME2, IRF1, IL1RN, PSMB9 
       IFITM1, JAK3, TAP2, APOL6, IL15RA, SCO2, TYMP, CD48, NUDCD1, APOL2 
       MVP, APOL1, ZNFX1, LIMA1, RCN1, BAZ1A, SCIMP, GPR180, NBN, ADPRS 
Negative:  XAF1, IFI44L, EPSTI1, EIF2AK2, MX1, SP110, IFI44, OAS1, PARP14, BST2 
       IRF9, IFI16, IFI6, PARP9, SAMD9L, RNF213, STAT1, TRIM22, ADAR, UNC93B1 
       B2M, PLSCR1, OAS2, HERC5, PPM1K, SIGLEC1, IFI27, DDX60L, MX2, IFIT1 
pcared_ 4 
Positive:  IFITM3, IFITM1, SIGLEC1, S100A9, LY6E, BST2, ISG15, SECTM1, IFI6, SERPING1 
       OAS3, IL1RN, PSME2, PPA1, LGALS3BP, LAP3, TYMP, MX2, SHISA5, UBE2L6 
       IFI35, IFI27, LIMK2, BAZ1A, RRBP1, USP18, FBXO6, RSAD2, JAK3, BATF 
Negative:  TLR3, HLA-B, RRP1B, TMEM187, PATL2, PWP2, HLA-C, GART, HLA-F, SCIMP 
       BTN3A2, POTEE, IRF9, HSPA13, HLA-A, TRIM22, GIMAP2, MRPL39, SAMD9, NDUFV3 
       POTEF, ZNF793, BTN3A1, TNFSF10, CCR1, TRIM21, SLFN12, PSMB8-AS1, STX17, ZCCHC2 
pcared_ 5 
Positive:  PDXK, RENBP, NDUFV3, HSPA13, POTEF, IFNGR2, RRP1B, GART, PTPRN2, SIGLEC1 
       POTEE, SCIMP, PWP2, MRPL39, IFI27, AXL, LIMA1, TMEM187, ZNF793, NRIR 
       RCN1, IFI44L, PPA1, RRBP1, SHISA5, IFITM3, IFI6, XAF1, NUDCD1, LGALS3BP 
Negative:  TNFSF10, IFI16, HLA-C, HLA-A, HLA-B, IFIT2, IRF1, B2M, HESX1, OAS1 
       OASL, HERC5, RIGI, TAP1, USP18, RTP4, APOL6, PSMB9, APOBEC3G, PPM1K 
       IL15RA, SAMD9, HLA-F, IFIT1, RBM43, TNFSF13B, IFIT3, IRF2, UNC93B1, GIMAP2 
Transposing data matrix
Initializing state using k-means centroids initialization
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Harmony 1/10
Harmony 2/10
Harmony converged after 2 iterations
[1] "brown"
Centering and scaling data matrix
Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from pcabrown to pcabrown_
pcabrown_ 1 
Positive:  MORC4, INO80B, ITGB1, EIF3A, CDC5L, OMA1, UPK3BL1, HEXIM2, GHDC, DDRGK1 
       TRMT61A, FUZ, C15orf40, SMUG1, ENSG00000260077, DMAC2, XRN2, NR2F6, GTPBP6, IFT46 
       SPART, RRP9, NSDHL, LTB, HNRNPD, HRAS, RUFY1, SLC41A3, ATP6AP1-DT, PJA1 
Negative:  EEF1A1, RPLP1, RPL13, TPT1, RPL41, RPS8, RPS2, RPL13A, RPL10, FTL 
       RPS24, RPS6, RPL19, RPS14, RPS23, RPL37, RPL11, RPS3, RPS3A, RPS12 
       RPS18, RPLP2, TMSB4X, RPL34, RPL21, RPS28, RPL39, RPL28, RPS27A, RPL7A 
pcabrown_ 2 
Positive:  SDF2L1, CDK2AP2, PSMA5, PSMC4, RANBP1, ADRM1, SNRPD1, PSMC2, PSMC5, NOLC1 
       PSMD2, PSMB2, SF3A3, PSMD6, SEC13, PSMD13, SF3B4, TWF2, ALYREF, MAPRE1 
       PGAM1, ARHGDIA, ADCK2, PSMA3, NDUFAB1, PSMB6, CNDP2, ENO1, EIF6, ARPC5L 
Negative:  RPS23, TPT1, RPL39, RPS14, RPL30, RPS15A, RPL32, RPL34, RPL37, RPL26 
       RPS28, RPS24, RPLP2, RPS18, RPS3A, RPL13, RPL23, RPL13A, RPS8, RPL31 
       RPS27, RPS29, RPL37A, EEF1A1, RPL11, RPL35A, RPL38, RPS3, RPL27A, RPS21 
pcabrown_ 3 
Positive:  S100A4, DBI, GYPC, NAP1L1, S100A13, FXYD5, RFLNB, S100A6, ZYX, EMP3 
       YBX3, VAT1, RPL14, HSD17B14, TAGLN2, RBFOX2, DUSP3, PABPC1, CPNE2, GAS5 
       RAC2, TXN, DYNLT1, RAB13, PTMS, EPB41L4A-AS1, SNHG16, MPP1, IGBP1, IMPDH2 
Negative:  C1QC, C1QA, CD81, CYBA, CST3, FCGRT, C1QB, FOLR2, PSAP, RNASET2 
       RAC1, ITGB2, TYROBP, ACTB, PLD3, HLA-E, TLN1, ATP6V0B, SPCS2, AIF1 
       TREM2, CD99, CD68, LAMP1, SYNGR2, SSR4, FSCN1, BCAP31, RNASE6, CNPY3 
pcabrown_ 4 
Positive:  NCL, RGS10, MNDA, DHRS9, HMGB1, GAS5, PTMA, RAC1, RPS26, GPX1 
       HNRNPK, UBB, PABPC1, MORF4L1, HNRNPA1, ARRB2, SERP1, CALM2, PTGES3, RPL5 
       EIF1B, DYNLL1, H1-0, GNAS, IK, HNRNPD, C11orf58, U2AF1, H3-3A, RPL24 
Negative:  MT-CO3, MT-CO1, MT-CO2, MT-ND4L, MT-ND4, MT-ND5, MT-ATP6, MT-CYB, MT-ND1, MT-ND2 
       MT-ND3, MT-ATP8, NPC2, APOE, PSAP, APOC1, FCER1G, CD63, C1QB, C1QC 
       CYBA, FCGRT, GRN, FOLR2, SERPINA1, CD68, CD59, MYDGF, CD81, GM2A 
pcabrown_ 5 
Positive:  CST3, C1QA, GGTA1, AIF1, MT-CYB, AGR2, MT-CO3, MT-ND3, MT-ATP6, MT-CO1 
       MT-ND4, MT-CO2, FCGRT, MT-ND2, RGS10, MT-ND5, MT-ND4L, GPBAR1, HMGB1, MNDA 
       MT-ND1, ENSG00000289474, FSCN1, LST1, RNASE6, PIH1D1, EFHD2, MT-ATP8, CRYBB1, TLE5 
Negative:  APOC1, APOE, CD68, TREM2, IFI30, CD63, CSTB, FTH1, GYPC, NAP1L1 
       LGALS9, S100A11, FTL, NPC2, ATP6V1F, DBI, FCER1G, VAT1, VEGFB, S100A13 
       ANXA5, NAGK, SMS, LILRA4, CXCL16, GPX1, GRN, CD81, ITGB2, ACTG1 
Transposing data matrix
Initializing state using k-means centroids initialization
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Harmony 1/10
Harmony 2/10
Harmony 3/10
Harmony converged after 3 iterations
Warning: Key 'harmony_' taken, using 'prouy_' instead
[1] "turquoise"
Centering and scaling data matrix
Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from pcaturquoise to pcaturquoise_
pcaturquoise_ 1 
Positive:  CMTM7, LYPLA1, LINC03070, DCP2, RAP2A, DOCK6, ENSG00000289901, TNFRSF25, ENSG00000258561, ZBTB47-AS1 
       CTNNA3, FRMD3, HULC, RNLS, MIRLET7BHG, OSTF1, LINC01376, SUN1, TM9SF3, FAM13A-AS1 
       ENSG00000289228, ENSG00000291067, KCNMB4, SCAP, LINC02964, PTPRA, STXBP4, ENSG00000289463, PPP2R5C, RAD23B 
Negative:  DOCK4, FRMD4A, ITPR2, MEF2A, PLXDC2, QKI, SFMBT2, FCHSD2, ELMO1, INPP5D 
       FOXN3, DOCK8, DIAPH2, DOCK10, ANKRD44, AOAH, ST6GAL1, LRMDA, SLC8A1, PRKCA 
       ATP8B4, SLC9A9, SRGAP2, KCNQ3, DIP2B, ARHGAP22, PICALM, ENSG00000249738, TCF12, DISC1 
pcaturquoise_ 2 
Positive:  CERS6, CENPP, UTRN, EFR3A, NLK, HS2ST1, MYO9A, OSBPL3, AGTPBP1, POLA1 
       MTM1, PDE8A, ARHGAP6, CLEC2D, ANKS1A, LARGE1, LINC00342, BICD1, AGAP1, OPHN1 
       PIK3CG, DENND1A, SCMH1, INPP4A, ENOX2, KIF13A, GPRIN3, MMS22L, SND1, NUBPL 
Negative:  APBB1IP, LINC02712, SIPA1L2, FRMD4A, C3, IL6ST, OTULINL, FYB1, ADAM28, MERTK 
       TIAM1, ENSG00000250604, AOAH, SRGAP2, ANKRD44, SLCO2B1, SLC4A7, DLEU1, ENSG00000249738, PLXDC2 
       RASGEF1C, MEF2A, DOCK4, SPECC1, SORL1, ARHGAP12, DOCK8, MAP4K4, ENTPD1, ST6GAL1 
pcaturquoise_ 3 
Positive:  PLXDC2, APBB1IP, C3, SORL1, EPB41L2, GNB4, ENSG00000291144, ANOS1, CYFIP1, ADAM28 
       LHFPL2, SYNDIG1, DOCK4, RASGEF1C, ZEB1, TENM4, CACNA1D, CYRIB, DLEU1, TMC7 
       AZIN2, KCNIP1, TLN2, DOCK10, RASSF8, EML4, MARCHF3, ENTPD1, JAZF1, ENSG00000250604 
Negative:  NAV2, SLC9A9, LPAR6, SSBP2, ITPR2, FAM13A, FGL1, SPRED1, CYBB, TCF12 
       ANKRD12, ZEB2, ENSG00000287684, FLI1, FCHSD2, LINC03070, ARHGAP6, WDFY3, TBC1D9, TBC1D4 
       AOAH, DNAJC13, TLE4, SRGAP1, JMJD1C, CSGALNACT1, MGAT5, ELMO1, TBC1D5, EPB41L3 
pcaturquoise_ 4 
Positive:  OTULINL, LINC02712, IL6ST, CYBB, TNFRSF21, IGSF21, LINC01768, WNT5A, ITGA9, GIMAP8 
       ADGRG5, LINC01645, ITPR2, C3, LPAR6, ZNF608, STYXL2, AOAH, CD4, SLC4A7 
       PLEKHG5, SSBP2, SLCO2B1, CYRIA, CMKLR1, SYCP2L, SPECC1, RB1, STON2, SMIM35 
Negative:  ARHGAP24, FMNL2, ADK, PLXDC2, PAG1, PDE3B, RALGAPA2, LRRK1, SNX29, CACNA1D 
       BMP2K, CELF2, FGD4, ARHGAP26, LNCAROD, ANOS1, NR3C1, GNAQ, MAP4K3, RBM47 
       MYO9B, LRCH1, TMEM117, LRMDA, FNIP1, ADAM28, ABR, DENND4C, MGAT4A, USP6NL 
pcaturquoise_ 5 
Positive:  MALAT1, A2M, ADAM28, HSPA1B, ADAM7-AS1, LPAR6, CD84, MIR503HG, LRRK1, RBM39 
       SORL1, TLR7, IL6ST, TAGAP, APBB1IP, HECTD2, HSPA1A, ARRDC3, DDX17, H2AC6 
       VPS13C, P2RX7, ADAM10, LRRK2, CREBZF, CAPN7, HPS3, RAPGEF6, ATP10D, TGFBR2 
Negative:  ZNF710, CMIP, CHST11, NXN, PTPRE, CAMK1D, LRMDA, ABR, ZDHHC14, LDLRAD4 
       MAML3, PALD1, WWOX, ZSWIM6, SKI, JAZF1, ZNF609, ITGA9, LINC01768, FLI1 
       SH3RF3, PRKCA, SLC39A11, PRKCB, UBASH3B, BNC2, TNRC18, CENPP, MAML2, PACSIN2 
Transposing data matrix
Initializing state using k-means centroids initialization
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Harmony 1/10
Harmony 2/10
Harmony converged after 2 iterations
[1] "magenta"
Centering and scaling data matrix
Warning in svd.function(A = t(x = object), nv = npcs, ...): You're computing
too large a percentage of total singular values, use a standard svd instead.
Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from pcamagenta to pcamagenta_
pcamagenta_ 1 
Positive:  CD9, PLA2G7, LGALS3, LGALS1, LINC01235, HLA-DRA, HLA-DRB1, LRRC39, SCIN, FABP3 
       HLA-DPA1, CXCR4, HLA-DPB1, FABP5, ASAH1, NIBAN1, LITAF, MYO1E, PTCRA, PADI2 
       CD74, LIPA, SLC11A1, SPP1, MPDZ, LPL, HLA-DMA, HLA-DRB6, HBEGF, PLIN2 
Negative:  ADAMTS17, UAP1L1, LONRF1, SIRPA, DPEP2, AMDHD2, ATG16L2, MORC1, SLC15A3, ROR2 
       PI4K2A, APOC1P1, FAM107B, RAP2B, DHRS7, SLC43A3, LILRA2, KCNAB2, MAP4K3-DT, HIVEP1 
       CCRL2, CYSTM1, HEXB, NFIL3, ADCY3, KLHDC8B, CEBPA, CTSL, CALHM2, KCNJ2 
pcamagenta_ 2 
Positive:  SPP1, LRRC39, PDPN, FABP3, CTSL, CD9, LIPA, RAMP1, EDN1, TMEM144 
       PTCRA, ASAH1, MYO1E, CTSD, LINC01235, PDGFA, FAM20C, LGALS3, TIGD4, HBEGF 
       RAB42, ALAS1, NIBAN1, ADAMTS17, MPDZ, LPL, UGCG, KCNJ5, CMYA5, FCMR 
Negative:  HLA-DPA1, HLA-DPB1, HLA-DRB1, HLA-DRA, HLA-DQA1, HLA-DRB6, HLA-DQA2, CD74, HLA-DRB5, HLA-DMA 
       CIITA, SCPEP1, ATG16L2, TNFAIP2, KCNAB2, MS4A7, PLA2G7, LILRA2, NFIL3, BHLHE40 
       DPEP2, DHRS7, ROR2, CALHM2, LTA4H, BCL2A1, SYTL3, ALCAM, DENND2D, MAP4K3-DT 
pcamagenta_ 3 
Positive:  SPP1, PDPN, NIBAN1, RGS16, PADI2, SLC11A1, SYTL3, PTGER4, WIPF3, SCIN 
       EDN1, ALCAM, CMYA5, ALAS1, BCL2A1, FAM107B, NFIL3, MORC1, ADAMTS17, LINC01235 
       CLEC19A, PTCRA, KCNJ2, DHRS7, PDGFA, TIGD4, CD74, SCPEP1, MPDZ, ACSL1 
Negative:  MS4A7, CTSD, TNFAIP2, RAB42, KCNJ5, LGALS3, CTSL, RRAGD, RAP2B, DENND2D 
       ASAH1, AMDHD2, LRRC39, KCNAB2, LITAF, KLHDC8B, CXCR4, UAP1L1, SLC43A3, MSR1 
       SLC15A3, DPYD, CYSTM1, PLIN2, PLA2G7, HEXB, LGALS1, SIRPA, HBEGF, CALHM2 
pcamagenta_ 4 
Positive:  MSR1, RASGEF1B, HBEGF, MS4A7, HIVEP1, DPYD, PTGER4, BCL2A1, ACSL1, LONRF1 
       ATG16L2, PLIN2, LITAF, FAM107B, EDN1, SLC43A3, ALAS1, TNFAIP2, KCNJ2, PDGFA 
       NFIL3, NAB2, PI4K2A, RGS16, CXCR4, MYO1E, UGCG, FCMR, RAMP1, MIR155HG 
Negative:  CTSD, ASAH1, LIPA, SPP1, LGALS3, LPL, LGALS1, PLA2G7, CEBPA, FABP3 
       SCPEP1, DHRS7, GSDME, LRRC39, HEXB, MPDZ, TIGD4, PDPN, SIRPA, LINC01235 
       WIPF3, CD74, FABP5, APOC1P1, LTA4H, RRAGD, CD9, CTSL, SLC15A3, TMEM144 
pcamagenta_ 5 
Positive:  ALCAM, GSDME, FABP5, SYTL3, DHRS7, DPYD, LGALS1, LINC01235, MSR1, ACSL1 
       PADI2, WIPF3, LILRA2, LGALS3, SLC43A3, SLC11A1, SCIN, RAB42, LTA4H, FAM20C 
       CD9, KCNJ5, IER5L, HBEGF, RGS16, BHLHE40, MIR155HG, APOC1P1, CTSL, RAP2B 
Negative:  EDN1, TIGD4, ASAH1, TMEM144, FCMR, MYO1E, PDPN, FAM107B, PLA2G7, MORC1 
       ALAS1, ADAMTS17, NIBAN1, SCPEP1, LRRC39, HIVEP1, CTSD, NAB2, CXCR4, KLHDC8B 
       CD74, PLIN2, PTGER4, HEXB, LITAF, CLEC19A, CMYA5, RASGEF1B, HLA-DQA1, LONRF1 
Transposing data matrix
Initializing state using k-means centroids initialization
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Harmony 1/10
Harmony converged after 1 iterations
Warning: Key 'harmony_' taken, using 'qblcp_' instead
[1] "green"
Centering and scaling data matrix
Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from pcagreen to pcagreen_
pcagreen_ 1 
Positive:  TRAF2, TPR, EXO5, CCHCR1, RTN3, DCLRE1B, ADAM15, SRR, ENSG00000289047, FAM72B 
       GUCY1B1, PPAT, KLHL23, DYNLT2B, ERLIN1, THOP1, PPP5C, MAGOHB, PSRC1, GUSB 
       GOLM1, FN3KRP, CLN6, TPGS2, HLTF, CEP44, CCDC77, MANEA, MGME1, ENSG00000291130 
Negative:  CLSPN, CENPK, CENPU, HELLS, ZWINT, NUSAP1, RAD51AP1, ORC6, KIF11, ATAD2 
       FANCI, LMNB1, GGH, MCM4, CENPF, SHCBP1, WEE1, FEN1, NCAPG2, BRCA1 
       BRCA2, H1-5, EZH2, BRIP1, WDR76, ASF1B, C21orf58, KNTC1, STIL, SMC4 
pcagreen_ 2 
Positive:  MCM6, HELLS, MCM4, MCM3, MCM5, WDHD1, RBL1, MCM7, CLSPN, WDR76 
       MSH6, POLE2, DNMT1, CDC7, KNTC1, BRCA1, UNG, SLF1, RFC4, ORC6 
       PCNA, DNAJC9, ATAD5, PRIM1, LIG1, CHAF1A, ZGRF1, TIMELESS, FEN1, TMEM97 
Negative:  CENPF, PTTG1, CENPE, H1-5, AURKA, NUSAP1, KIF11, CENPA, GPSM2, CDKN2C 
       H2AC17, APOLD1, H3C4, RACGAP1, HMGB2, ARHGAP11B, H2AC11, SGO2, CKAP2, NCAPD2 
       H2AX, ECT2, TACC3, CEP112, INCENP, H2AC12, PARPBP, PRR11, CDC25B, FBXO5 
pcagreen_ 3 
Positive:  CKS1B, SMC2, MCM5, TUBB, KNSTRN, PTTG1, CKAP2, AURKA, NUCB2, PRR11 
       RNASE2, BRI3BP, PGP, CBX5, MAGOHB, ACTL6A, HAT1, HACD3, CENPE, DTYMK 
       MSH6, ZNF85, SPDL1, DBF4, DNMT1, PAICS, FADS1, HLTF, SGO2, GOLM1 
Negative:  H2AC11, H1-5, H3C4, H2AC12, H2AC17, FANCI, MASTL, EZH2, CLSPN, CENPU 
       BRIP1, NCAPG2, SLFN13, FANCA, DHFR, ATAD2, C21orf58, BTG3, WDR76, CDCA4 
       STIL, NUP210, MCM8, FBXO5, POLE2, RBBP8, NSD2, ACYP1, ENSG00000227598, FAM111A 
pcagreen_ 4 
Positive:  ENSG00000224905, CEP112, CCDC18, GEN1, C5orf34, CENPE, PARPBP, BRCA2, CENPJ, KNTC1 
       RBL1, RFC3, ARHGAP11B, ATAD5, ZNF85, CIP2A, LIN9, ZGRF1, UBL7-DT, ZNF724 
       SGO2, CDC7, ECT2, AURKA, CEP152, BRIP1, DDX11, NSD2, TTF2, SMC4 
Negative:  PCNA, TUBB, H2AC17, TMEM106C, H3C4, FEN1, H1-5, H2AX, H2AC12, H2AC11 
       TUBG1, BTG3, DHFR, PAICS, NRM, HMGB2, RTN3, DTYMK, RBBP8, RRM1 
       LMNB2, GMNN, NUCB2, USP1, CDK2, UBE2T, HACD3, FBXO5, UNG, C9orf40 
pcagreen_ 5 
Positive:  TUBB, CENPK, PTTG1, MCM4, CKS1B, GGH, CENPH, ZWINT, CENPF, LMNB1 
       MCM6, ASF1B, RAD51AP1, AURKA, CLSPN, NRM, ORC6, CENPE, HELLS, PARPBP 
       FEN1, MCM5, PHF19, ENSG00000267383, BRCA1, DHFR, SHCBP1, TMEM106C, CENPU, DTYMK 
Negative:  CCP110, FAM111A, H2AC12, H3C4, CASP8AP2, H2AC11, HMGXB4, NCAPD3, H2AC17, NFATC2IP 
       ERLIN1, DCK, CHEK2, PGP, MSH6, MGME1, RMI1, NEDD1, NSD2, MANEA 
       H1-5, CEP44, TTF2, CDCA4, ICMT, POLD3, CTPS1, GMPS, SLFN13, HLTF 
Transposing data matrix
Initializing state using k-means centroids initialization
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Harmony 1/10
Harmony converged after 1 iterations
[1] "black"
Centering and scaling data matrix
Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from pcablack to pcablack_
pcablack_ 1 
Positive:  ENSG00000291063, ZRANB1, PANK2, SCAF11, ENSG00000290858, MIA2, SF3B1, WIPI1, TMX1, DPP4 
       C6orf62, KIF20B, GSTM2, ELAVL4, RTCB, HCK, EFNB2, ENSG00000254040, SLC2A5, ENC1 
       JAM2, ADAMTSL2, IRF8, ENSG00000287826, RASAL3, GNG7, LUC7L3, ELK3, SESN3, WASF2 
Negative:  P2RY12, CX3CR1, P2RY13, HTRA1, IFNGR1, FCGR3A, OLFML3, VSIR, GAL3ST4, SELPLG 
       LAPTM5, ENSG00000286403, SIGLEC10, TLR10, CSF1R, CSF2RA, MEF2C, GPR34, ZFP36L2, BIN2 
       JAG1, USP53, TMEM119, LPAR5, SAMSN1, PTAFR, PRPF38B, IGF1, LINC00996, HCG22 
pcablack_ 2 
Positive:  LAPTM5, SERPINF1, HTRA1, OLFML3, FCGR3A, RPN2, GAL3ST4, ADGRG1, TMEM119, SELPLG 
       SIGLEC10, STING1, SLC2A5, CD300A, TM6SF1, PTGS1, ADORA3, PMEPA1, LY86, PAQR8 
       TMX1, LPAR5, SUCNR1, TLR10, MRC2, MTDH, EMID1, P2RY12, PLXDC1, ENC1 
Negative:  MEF2C, USP53, ARGLU1, PNISR, B3GNT5, LUC7L3, SAMSN1, PTPRC, ZFP36L2, ADRB2 
       CSF2RA, IFNGR1, SF3B1, IRAG2, MCF2L2, TRAF3IP3, PRPF38B, TLR1, OFD1, MED12L 
       FGF20, SCAF11, MROCKI, RGS18, ENSG00000291063, C6orf62, PANK2, ENSG00000286403, BOD1L1, P2RY13 
pcablack_ 3 
Positive:  SAMSN1, IFNGR1, LILRB4, SLC2A5, HTRA1, GPR34, JAM2, PTPRC, ADRB2, PTAFR 
       ZFP36L2, SELPLG, METTL7A, MTDH, SERPINF1, FCGR3A, CX3CR1, LY86, MROCKI, LAPTM5 
       IL6R, IL18, TM6SF1, CSF2RA, OLFML3, P2RY12, B3GNT5, LPAR5, ADORA3, TMX1 
Negative:  LINC00996, JAG1, SLC7A8, PLXDC1, IGF1, ADGRG1, STING1, HCK, SERPINB9, SPNS2 
       ENSG00000286403, RGS18, DPP4, PCED1B, MKNK1, RASAL3, RBM19, ENSG00000254040, SYT6, ENSG00000287826 
       EMID1, VASH1, SIGLEC10, DNAJC10, ENSG00000287958, TLR1, MEF2C, SLC29A3, FFAR4, WIPI1 
pcablack_ 4 
Positive:  CECR2, VASH1, MRC2, GNG7, ADAMTSL2, DAGLB, SERPINB9, ENC1, SESN3, SYT6 
       MROCKI, SLC2A5, RBM19, PMEPA1, SAMSN1, WIPI1, EFNB2, ELAVL4, SPNS2, IL6R 
       C6orf62, HTRA1, ENSG00000290858, GSTM2, RASAL3, MCF2L2, ENSG00000291063, EMID1, ZRANB1, PCED1B 
Negative:  CSF1R, GPR34, FGL2, ZFP36L2, OFD1, LAPTM5, STING1, RPN2, IL13RA1, IGF1 
       SF3B1, METTL7A, P2RY13, PLXDC1, LINC00996, MTDH, WASF2, TMX1, RGS18, VSIR 
       PNISR, KIF20B, LUC7L3, PTPRC, ARGLU1, CX3CR1, DNAJC10, PRPF38B, RTCB, P2RY12 
pcablack_ 5 
Positive:  PNISR, LUC7L3, ARGLU1, SF3B1, HCG22, ELK3, SCAF11, GAL3ST4, LPIN2, BOD1L1 
       MTDH, PRPF38B, PTAFR, CSF1R, ENC1, GPR34, ENSG00000291063, CSF3R, SELPLG, PMEPA1 
       MROCKI, LPAR5, EMID1, LY86, TMEM119, GNG7, WIPI1, SYT6, TM6SF1, ENSG00000286403 
Negative:  ELAVL4, FGL2, MCF2L2, RGS18, B3GNT5, IFNGR1, IRF8, TLR10, PTPRC, SERPINB9 
       SUCNR1, METTL7A, MED12L, ADRB2, DPP4, ENSG00000287958, LILRB4, OFD1, ADGRG1, BIN2 
       TNFRSF13C, CD300A, JAM2, HCK, ZFP36L2, PANK2, KIF20B, ENSG00000254040, ADAMTSL2, SAMSN1 
Transposing data matrix
Initializing state using k-means centroids initialization
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Harmony 1/10
Harmony 2/10
Harmony converged after 2 iterations
Warning: Key 'harmony_' taken, using 'zjhab_' instead
[1] "yellow"
Centering and scaling data matrix
Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from pcayellow to pcayellow_
pcayellow_ 1 
Positive:  SNCA, AGAP3, CDS2, LINC00173, TRMT5, CYBRD1, CAPN2, ENSG00000285877, OSGIN2, NEDD4 
       SEPTIN10, MXI1, SQLE, ZNF106, P2RY1, FAM151B, BACE2, TMEM94, EEA1, CLDN7 
       HFE, DCAF12, WWC3, DGKH, VIPR1, RHOU, NORAD, CHST7, IRAG1, HPCAL1 
Negative:  COLEC12, F13A1, CD163, PID1, TGFBI, SELENOP, IGFBP4, RNASE1, CD163L1, CD36 
       CD28, DAB2, IQGAP2, LYVE1, ITSN1, SLC38A6, TNFRSF11A, MS4A4A, WWP1, MAMDC2 
       MTSS1, NRP1, MAN1A1, KCNAB1, EDA, DRAM1, IQGAP1, MRC1, RBPJ, EYA2 
pcayellow_ 2 
Positive:  MRC1, LYVE1, EGFL7, WLS, TPM1, EMB, F13A1, IGFBP4, SLC40A1, CD163 
       DAB2, STAB1, LINC00671, SELENOP, PCDH12, COLEC12, RNASE1, PLTP, TGFBI, SNX6 
       BLVRA, COCH, PMP22, BLVRB, TXNIP, AFF3, CD163L1, PID1, ADGRG6, NRP1 
Negative:  DTNA, ATG7, PPARG, MITF, SAMD4A, MAFB, ARID5B, SGPP1, GAS7, RASGRP3 
       TLR2, CHSY3, HDAC9, RASA1, STARD13, SH3RF1, ASAP1, PECAM1, DMXL2, SNX24 
       CYTH1, IGF2R, ZC3H12C, ENSG00000250777, CPM, SNCA, SPIRE1, NEK6, FAM110B, FOXP1 
pcayellow_ 3 
Positive:  AP1S2, BLVRB, METRNL, LGMN, TSPAN4, IQGAP2, CD36, FARP1, IGFBP4, SNX6 
       F13A1, TMEM176A, TMEM37, IQGAP1, GAS6, BLVRA, DAB2, TMEM176B, RPP25, PPARG 
       SELENOP, CD163, HSPB1, TGFBI, MGLL, COLEC12, MAFB, ATP1B3, MFHAS1, LY96 
Negative:  AFF3, FRMD4B, SGMS1, NRP1, FMN1, HDAC9, CASK, MRC1, STAB1, CBLB 
       ENSG00000287616, MYO5A, CABLES1, TRPS1, DOCK7, DNAAF9, EPS15, LINC00671, SOX5, RASA1 
       TMCC1, DUSP6, ARAP2, EMB, RASA2, ZFPM2-AS1, TMEM236, TENT5A, ENSG00000287100, SLC40A1 
pcayellow_ 4 
Positive:  MS4A6A, TXNIP, HACD4, TLR2, KYNU, PYGL, CD14, FOXP1, HS3ST4, TNFAIP8 
       AHR, CYTL1, PTPN12, PECAM1, LRRFIP1, TNFRSF11A, DTNA, TEC, IQGAP1, LINC00472 
       DOC2A, DUSP6, HNMT, TGFBI, ANTXR1, SNCA, DSE, CLDN7, EPS8, SLC25A24 
Negative:  LGMN, HRH1, SPIRE1, MITF, ABCA1, GAS6, TIMP2, FRMD4B, MRC1, NRP2 
       CTSC, ATP1B1, ATG7, MTSS1, SAMD4A, PPARG, STARD13, ABCC5, MCOLN1, NPAS3 
       ENSG00000251034, PLEKHA1, FAM20A, MAN1A1, SORBS3, NRP1, SGMS1, PDGFC, GAS7, DAB2 
pcayellow_ 5 
Positive:  MS4A6A, CTSC, CD14, MS4A4A, SLC40A1, CYTL1, TMEM176B, ATP1B1, MAFB, MFSD1 
       ABCA1, NRP1, TXNIP, ENSG00000250777, PLTP, MRC1, TMEM176A, FRMD4B, SCARB2, PECAM1 
       HSPB1, DRAM2, MPEG1, SMPDL3A, HNMT, ARL4C, SELENOP, DSC2, STAB1, TLR2 
Negative:  JAM3, LDLR, ENSG00000287616, USP36, SEPTIN11, SQLE, POLE, ASAP1, HIP1, CCDC14 
       BACE2, EAF2, GTDC1, CTDSPL, DNM1, NEDD4, RPH3AL, RIMKLB, STX2, ENSG00000253968 
       LINC00472, LIMD1, MCUR1, ITSN1, MCC, TCF12-DT, PARVB, LINC00671, DRAM1, TESK2 
Transposing data matrix
Initializing state using k-means centroids initialization
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Harmony 1/10
Harmony 2/10
Harmony converged after 2 iterations
[1] "pink"
Centering and scaling data matrix
Warning in svd.function(A = t(x = object), nv = npcs, ...): You're computing
too large a percentage of total singular values, use a standard svd instead.
Warning: Keys should be one or more alphanumeric characters followed by an
underscore, setting key from pcapink to pcapink_
pcapink_ 1 
Positive:  ITM2B, PLD4, ITM2C, PPIB, ALOX5AP, MARCKS, CKB, LTC4S, PDIA3, HSP90B1 
       ARL6IP5, YWHAH, ACY3, IGSF6, BIN1, C12orf75, VDAC1, CALR, GSN, PDIA6 
       TMBIM6, CAPZB, CANX, TMBIM4, PPT1, SEC11C, MLEC, LINC02953, SCAMP2, IL3RA 
Negative:  TRIM34, SMANTIS, LARP7, BRD3, BROX, SNX22, PABPN1, SEPTIN2, NSL1, NT5C 
       LINC02381, DPYSL2, FEZ2, SLC7A7, GDI2, CXXC5, UBXN4, AHCYL1, GSTM4, AK2 
       CCR5AS, KTN1, SYS1, FYTTD1, IL16, SIPA1, TARS1, NAA20, SLC39A7, C19orf38 
pcapink_ 2 
Positive:  SMAP2, CKB, MARCKS, ALDH2, GIMAP4, PLEKHO1, LINC01736, ACY3, SVBP, YWHAH 
       C12orf75, CAPZB, FEZ2, CCR5AS, ABI3, RASSF2, IVD, NDRG2, SNX22, BRD3 
       GSN, LINC02381, SIPA1, BROX, ENSG00000263305, MOB1A, IL16, AHCYL1, ENSG00000260370, IL3RA 
Negative:  HSP90B1, CALR, PDIA3, PDIA6, CANX, PPIB, MLEC, TMBIM6, TMED2, KDELR2 
       DDOST, SEC11C, PRDX4, SLC3A2, SARAF, CCDC47, MGST2, SLC39A7, TECR, SCAMP2 
       TMBIM4, TSPAN3, KTN1, G6PC3, OCIAD1, ALOX5AP, PPT1, UBXN4, SPINT2, ITM2C 
pcapink_ 3 
Positive:  CALR, AK2, VDAC1, TARS1, HSP90B1, TMIGD2, C12orf75, ENSG00000263305, YWHAH, LINC01736 
       ABI3, RASSF2, LINC02953, CXXC5, PLVAP, SEC11C, PDIA3, GPN3, FYTTD1, SNX22 
       AGMAT, NDRG2, GSTM4, ENSG00000260370, BIN1, SLC39A7, PDIA6, ENSG00000291250, NBL1, IVD 
Negative:  ITM2B, SARAF, TMBIM6, ARL6IP5, TMBIM4, IGSF6, PLD4, SMAP2, SCAMP2, ALOX5AP 
       PPT1, LTC4S, FEZ2, SPINT2, ITM2C, KTN1, MGST2, OCIAD1, GIMAP4, MARCKS 
       TSPAN3, ALDH2, PRDX4, PABPN1, UBXN4, TECR, CCDC47, GSN, LAT2, PLEKHO1 
pcapink_ 4 
Positive:  SIGLEC8, PLVAP, IL3RA, REEP4, SYS1, C19orf38, NT5C, SLC3A2, SLC7A7, IL16 
       ITM2C, SMANTIS, ALOX5AP, ABI3, RASSF2, ENSG00000260370, G6PC3, BRD3, ITM2B, TMIGD2 
       PRDX4, NDRG2, SLC39A7, SIPA1, SPINT2, CKB, LAT2, GSTM4, ENSG00000263305, TECR 
Negative:  BIN1, CAPZB, DPYSL2, GPN3, MARCKS, SMAP2, MOB1A, SEPTIN2, VDAC1, FEZ2 
       GSN, GDI2, PLEKHO1, XRCC5, NSL1, NBL1, C12orf75, NAA20, PPIB, CALR 
       FYTTD1, GIMAP4, IGSF6, PDIA3, LINC01736, SEC11C, PDIA6, KDELR2, ACY3, HSP90B1 
pcapink_ 5 
Positive:  YWHAH, DPYSL2, SUSD3, PRDX4, MOB1A, IDH2, LAT2, SLC7A7, TMBIM6, ALDH2 
       PGM2, ABI3, LINC01736, ALOX5AP, PPT1, SVBP, TMED2, GDI2, GSN, GSTM4 
       KDELR2, DDOST, SEPTIN2, SYS1, CXXC5, NT5C, AGMAT, LINC02381, G6PC3, IGSF6 
Negative:  ENSG00000291250, SMAP2, IL3RA, NBL1, SMANTIS, PLEKHO1, PLVAP, BIN1, FEZ2, HSP90B1 
       TMIGD2, ITM2C, ACY3, CAPZB, LTC4S, ENSG00000260370, ITM2B, SLC3A2, ENSG00000263305, SNX22 
       UBXN4, CKB, CCR5AS, TRIM34, CALR, PLD4, PABPN1, PDIA6, PDIA3, TECR 
Transposing data matrix
Initializing state using k-means centroids initialization
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Warning: Quick-TRANSfer stage steps exceeded maximum (= 4325200)
Harmony 1/10
Harmony 2/10
Harmony converged after 2 iterations
Warning: Key 'harmony_' taken, using 'qblcp_' instead
Code 
hMEs <- GetMEs(seurat_obj)

Module connectivity

How strongly each gene correlates with its module eigengene - high kME = hub gene.

Code 
seurat_obj <- ModuleConnectivity(
  seurat_obj
)
[1] brown
Levels: blue grey red brown turquoise magenta green black yellow pink
Code 
p <- PlotKMEs(seurat_obj, ncol=5)
Warning in annotate("label", x = 0, y = 0, label = paste0(top_genes, collapse = "\n"), : Ignoring unknown parameters: `label.size`
Ignoring unknown parameters: `label.size`
Ignoring unknown parameters: `label.size`
Ignoring unknown parameters: `label.size`
Ignoring unknown parameters: `label.size`
Ignoring unknown parameters: `label.size`
Ignoring unknown parameters: `label.size`
Ignoring unknown parameters: `label.size`
Ignoring unknown parameters: `label.size`
Code 
p

Code 
ggsave(
   filename = file.path(graphs_dir,"KMEs.pdf"),
   plot = p,
   width = 12,       # increase width if labels overlap
   height = 7,       # increase height for more clarity
)
Code 
hub_df <- GetHubGenes(seurat_obj, n_hubs = 10)

6. Visualisation

UMAP plots

Code 
#UMAP visualisation - colour cells by their ME score 
plot_list <- ModuleFeaturePlot(
  seurat_obj,
  features='hMEs', # plot the hMEs
  order=TRUE, # order so the points with highest hMEs are on top
  reduction = "umap.rpca"
)
[1] "blue"
Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.
ℹ The deprecated feature was likely used in the hdWGCNA package.
  Please report the issue to the authors.
[1] "red"
[1] "brown"
[1] "turquoise"
[1] "magenta"
[1] "green"
[1] "black"
[1] "yellow"
[1] "pink"
Code 
p <- wrap_plots(plot_list, ncol=4)
p

Code 
ggsave(
  filename = file.path(graphs_dir,"umap_modules.pdf"),
  plot = p,
  width = 18,       # try 18–20 for 3 columns
  height = 18
)

Radar plots

Code 
plots <- ModuleRadarPlot(
  seurat_obj,
  group.by = "cluster_id",
  barcodes = rownames(seurat_obj@meta.data),
  axis.label.size = 3,
  grid.label.size = 3
)
Loading required package: ggradar
Warning: 'plot.data' contains value(s) > grid.max, data scaled to grid.max
Warning: 'plot.data' contains value(s) > grid.max, data scaled to grid.max
Warning: 'plot.data' contains value(s) > grid.max, data scaled to grid.max
Warning: 'plot.data' contains value(s) > grid.max, data scaled to grid.max
Warning: 'plot.data' contains value(s) > grid.max, data scaled to grid.max
Warning: 'plot.data' contains value(s) > grid.max, data scaled to grid.max
Warning: 'plot.data' contains value(s) > grid.max, data scaled to grid.max
Warning: 'plot.data' contains value(s) > grid.max, data scaled to grid.max
Warning: 'plot.data' contains value(s) > grid.max, data scaled to grid.max
Code 
plots <- lapply(
  plots,
  \(p) p +
    coord_cartesian(clip = "off") +                     # allow text outside circle
    theme(
      plot.margin = margin(25, 25, 25, 25),             # more whitespace
      axis.text.x = element_text(size = 9, vjust = 1),  # tweak label placement
      axis.title.x = element_blank(),
      axis.title.y = element_blank()
    )
)
Coordinate system already present.
ℹ Adding new coordinate system, which will replace the existing one.
Coordinate system already present.
ℹ Adding new coordinate system, which will replace the existing one.
Coordinate system already present.
ℹ Adding new coordinate system, which will replace the existing one.
Coordinate system already present.
ℹ Adding new coordinate system, which will replace the existing one.
Coordinate system already present.
ℹ Adding new coordinate system, which will replace the existing one.
Coordinate system already present.
ℹ Adding new coordinate system, which will replace the existing one.
Coordinate system already present.
ℹ Adding new coordinate system, which will replace the existing one.
Coordinate system already present.
ℹ Adding new coordinate system, which will replace the existing one.
Coordinate system already present.
ℹ Adding new coordinate system, which will replace the existing one.
Code 
combined <- wrap_plots(plots, ncol = 3)
combined

Code 
ggsave(
  filename = file.path(graphs_dir,"module_radarplot.pdf"),
  plot = combined,
  width = 18,       # try 18–20 for 3 columns
  height = 12
)

Add modules to metadata

Code 
MEs <- GetMEs(seurat_obj, harmonized=TRUE)
modules <- GetModules(seurat_obj)
mods <- levels(modules$module); mods <- mods[mods != 'grey']

seurat_obj@meta.data <- cbind(seurat_obj@meta.data, MEs)
Code 
qs_save(seurat_obj, file.path(objects_dir, "seurat_obj_checkpoint.qs2"))

7. Network visualisation

Code 
ModuleNetworkPlot(
  seurat_obj,
  outdir = file.path(graphs_dir,'ModuleNetworks')
)
Writing output files to output/run1/wgcna/graphs/ModuleNetworks[1] "blue"
[1] "red"
[1] "brown"
[1] "turquoise"
[1] "magenta"
[1] "green"
[1] "black"
[1] "yellow"
[1] "pink"

8. Module enrichment (enrichR)

Code 
dbs <- c('GO_Biological_Process_2025','GO_Cellular_Component_2025','GO_Molecular_Function_2025')

# perform enrichment tests
seurat_obj <- RunEnrichr(
  seurat_obj,
  dbs=dbs,
  max_genes = 100 # use max_genes = Inf to choose all genes
)
Uploading data to Enrichr... Done.
  Querying GO_Biological_Process_2025... Done.
  Querying GO_Cellular_Component_2025... Done.
  Querying GO_Molecular_Function_2025... Done.
Parsing results... Done.
Uploading data to Enrichr... Done.
  Querying GO_Biological_Process_2025... Done.
  Querying GO_Cellular_Component_2025... Done.
  Querying GO_Molecular_Function_2025... Done.
Parsing results... Done.
Uploading data to Enrichr... Done.
  Querying GO_Biological_Process_2025... Done.
  Querying GO_Cellular_Component_2025... Done.
  Querying GO_Molecular_Function_2025... Done.
Parsing results... Done.
Uploading data to Enrichr... Done.
  Querying GO_Biological_Process_2025... Done.
  Querying GO_Cellular_Component_2025... Done.
  Querying GO_Molecular_Function_2025... Done.
Parsing results... Done.
Uploading data to Enrichr... Done.
  Querying GO_Biological_Process_2025... Done.
  Querying GO_Cellular_Component_2025... Done.
  Querying GO_Molecular_Function_2025... Done.
Parsing results... Done.
Uploading data to Enrichr... Done.
  Querying GO_Biological_Process_2025... Done.
  Querying GO_Cellular_Component_2025... Done.
  Querying GO_Molecular_Function_2025... Done.
Parsing results... Done.
Uploading data to Enrichr... Done.
  Querying GO_Biological_Process_2025... Done.
  Querying GO_Cellular_Component_2025... Done.
  Querying GO_Molecular_Function_2025... Done.
Parsing results... Done.
Uploading data to Enrichr... Done.
  Querying GO_Biological_Process_2025... Done.
  Querying GO_Cellular_Component_2025... Done.
  Querying GO_Molecular_Function_2025... Done.
Parsing results... Done.
Uploading data to Enrichr... Done.
  Querying GO_Biological_Process_2025... Done.
  Querying GO_Cellular_Component_2025... Done.
  Querying GO_Molecular_Function_2025... Done.
Parsing results... Done.
Code 
enrich_df <- GetEnrichrTable(seurat_obj)

EnrichrBarPlot(
  seurat_obj,
  outdir = file.path(graphs_dir,"enrichr_plots"), # name of output directory
  n_terms = 10, # number of enriched terms to show (sometimes more are shown if there are ties)
  plot_size = c(5,7), # width, height of the output .pdfs
  logscale=TRUE # do you want to show the enrichment as a log scale?
)
[1] "blue"
[1] "red"
[1] "brown"
[1] "turquoise"
[1] "magenta"
[1] "green"
[1] "black"
[1] "yellow"
[1] "pink"

9. Saving final WGCNA object

Code 
qs_save(seurat_obj, file.path(objects_dir, "seurat_wgcna_basic.qs2"))