# Network comparison: consensus modules and module preservation

# Installation

    if(!requireNamespace('BiocManager', quietly = TRUE))
      install.packages('BiocManager')

    BiocManager::install("BioNERO")

    # Load package after installation
    library(BioNERO)
    set.seed(12) # for reproducibility

# Introduction

Comparing different coexpression networks can reveal relevant biological patterns. For instance, seeking for consensus modules can identify coexpression modules that occur in all data sets regardless of natural variation and, hence, are core players of the studied phenotype. Additionally, module preservation within and across species can reveal patterns of conservation and divergence between transcriptomes. In this vignette, we will explore consensus modules and module preservation analyses with `BioNERO`. Although they seem similar, their goals are opposite: while consensus modules identification focuses on the commonalities, module preservation focuses on the divergences.

# Data loading and description

We will use RNA-seq data of maize (Zea mays) and rice (Oryza sativa) obtained from Shin et al. (2020).

    data(zma.se)
    zma.se
    ## class: SummarizedExperiment
    ## dim: 10802 28
    ## metadata(0):
    ## assays(1): ''
    ## rownames(10802): ZeamMp030 ZeamMp044 ... Zm00001d054106 Zm00001d054107
    ## rowData names(0):
    ## colnames(28): SRX339756 SRX339757 ... SRX2792103 SRX2792104
    ## colData names(1): Tissue

    data(osa.se)
    osa.se
    ## class: SummarizedExperiment
    ## dim: 7647 27
    ## metadata(0):
    ## assays(1): ''
    ## rownames(7647): Os01g0100700 Os01g0100900 ... Os12g0641400 Os12g0641500
    ## rowData names(0):
    ## colnames(27): SRX831140 SRX831141 ... SRX263041 SRX1544234
    ## colData names(1): Tissue

All `BioNERO`’s functions for consensus modules and module preservation analyses require the expression data to be in a list. Each element of the list can be a SummarizedExperiment object (recommended) or an expression data frame with genes in row names and samples in column names.

# Consensus modules

The most common objective in consensus modules identification is to find core modules across different tissues or treatments for the same species. For instance, one can infer GCNs for different types of cancer in human tissues (say prostate and liver) and identify modules that occur in all sets, which are likely core components of cancer biology. Likewise, one can also identify consensus modules across samples from different geographical origins to find modules that are not affected by population structure or kinship.

## Data preprocessing

Here, we will subset 22 random samples from the maize data twice and find consensus modules between the two sets.

    # Preprocess data and keep top 2000 genes with highest variances
    filt_zma <- exp_preprocess(zma.se, variance_filter = TRUE, n = 2000)
    ## Number of removed samples: 1

    # Create different subsets by resampling data
    zma_set1 <- filt_zma[, sample(colnames(filt_zma), size=22, replace=FALSE)]
    zma_set2 <- filt_zma[, sample(colnames(filt_zma), size=22, replace=FALSE)]
    colnames(zma_set1)
    ##  [1] "SRX3804716" "SRX2792102" "SRX2527287" "SRX2792108" "SRX3804723"
    ##  [6] "SRX2792107" "SRX2792103" "SRX2792104" "SRX3804715" "SRX339808"
    ## [11] "SRX339758"  "SRX339756"  "SRX339809"  "SRX2792105" "SRX339757"
    ## [16] "SRX2641029" "SRX339762"  "SRX2792110" "SRX339807"  "SRX3804718"
    ## [21] "ERX2154032" "SRX339764"
    colnames(zma_set2)
    ##  [1] "SRX2792111" "SRX339756"  "SRX3804715" "SRX2792108" "SRX2792103"
    ##  [6] "SRX339762"  "SRX339807"  "ERX2154030" "SRX2792105" "SRX2792107"
    ## [11] "SRX2527287" "ERX2154032" "SRX2792104" "SRX2527288" "SRX339809"
    ## [16] "SRX3804718" "SRX3804716" "SRX2792109" "SRX3804723" "SRX2792102"
    ## [21] "SRX339808"  "SRX339758"

    # Create list
    zma_list <- list(set1 = zma_set1, set2 = zma_set2)
    length(zma_list)
    ## [1] 2

## Identification of consensus modules

As described in the first vignette, before inferring the GCNs, we need to identify the optimal \(\beta\) power that makes the network closer to a scale-free topology. We can do that with `consensus_SFT_fit()`.

    cons_sft <- consensus_SFT_fit(zma_list, setLabels = c("Maize 1", "Maize 2"),
                                  cor_method = "pearson")
    ##    Power SFT.R.sq  slope truncated.R.sq mean.k. median.k. max.k.
    ## 1      5    0.658 -0.650          0.656  107.00     90.50  304.0
    ## 2      6    0.761 -0.706          0.774   79.70     62.90  244.0
    ## 3      7    0.833 -0.757          0.863   61.30     45.10  200.0
    ## 4      8    0.825 -0.830          0.868   48.30     32.80  169.0
    ## 5      9    0.822 -0.916          0.887   38.70     24.50  145.0
    ## 6     10    0.838 -0.975          0.910   31.50     18.90  126.0
    ## 7     11    0.824 -1.040          0.914   26.00     15.00  111.0
    ## 8     12    0.837 -1.090          0.932   21.70     12.20   97.6
    ## 9     13    0.851 -1.130          0.944   18.30     10.10   86.6
    ## 10    14    0.842 -1.190          0.941   15.50      8.42   77.3
    ## 11    15    0.844 -1.230          0.947   13.30      7.17   69.4
    ## 12    16    0.842 -1.270          0.951   11.50      6.13   62.5
    ## 13    17    0.855 -1.300          0.962    9.99      5.24   56.5
    ## 14    18    0.864 -1.320          0.967    8.73      4.54   51.3
    ## 15    19    0.869 -1.330          0.970    7.67      3.92   46.7
    ## 16    20    0.875 -1.340          0.977    6.78      3.38   42.7
    ##    Power SFT.R.sq  slope truncated.R.sq mean.k. median.k. max.k.
    ## 1      5    0.592 -0.621          0.574   94.70     82.40  265.0
    ## 2      6    0.702 -0.666          0.710   69.90     56.80  209.0
    ## 3      7    0.757 -0.713          0.771   53.20     40.30  167.0
    ## 4      8    0.829 -0.729          0.853   41.50     29.50  136.0
    ## 5      9    0.855 -0.782          0.884   32.90     22.10  113.0
    ## 6     10    0.868 -0.826          0.911   26.60     17.20   96.6
    ## 7     11    0.873 -0.868          0.931   21.80     13.70   83.2
    ## 8     12    0.837 -0.943          0.914   18.10     11.30   73.0
    ## 9     13    0.826 -1.000          0.917   15.20      9.30   64.7
    ## 10    14    0.815 -1.070          0.917   12.80      7.73   57.6
    ## 11    15    0.810 -1.120          0.926   11.00      6.53   51.5
    ## 12    16    0.823 -1.150          0.943    9.42      5.45   46.3
    ## 13    17    0.815 -1.210          0.942    8.16      4.57   41.8
    ## 14    18    0.827 -1.240          0.954    7.11      3.92   37.8
    ## 15    19    0.833 -1.260          0.964    6.23      3.42   34.3
    ## 16    20    0.831 -1.280          0.965    5.49      2.99   31.2

This function returns a list with the optimal powers and a summary plot, exactly as `SFT_fit()` does.

    powers <- cons_sft$power
    powers
    ## set1 set2
    ##    7    8
    cons_sft$plot

Now, we can infer GCNs and identify consensus modules across data sets.

    consensus <- consensus_modules(zma_list, power = powers, cor_method = "pearson")
    ## ..connectivity..
    ## ..matrix multiplication (system BLAS)..
    ## ..normalization..
    ## ..done.
    ## ..connectivity..
    ## ..matrix multiplication (system BLAS)..
    ## ..normalization..
    ## ..done.
    ##  ..done.
    ##  multiSetMEs: Calculating module MEs.
    ##    Working on set 1 ...
    ##    Working on set 2 ...
    names(consensus)
    ## [1] "consMEs"             "exprSize"            "sampleInfo"
    ## [4] "genes_cmodules"      "dendro_plot_objects"
    head(consensus$genes_cmodules)
    ##       Genes Cons_modules
    ## 1 ZeamMp030    lightcyan
    ## 2 ZeamMp044       grey60
    ## 3 ZeamMp092       grey60
    ## 4 ZeamMp108         blue
    ## 5 ZeamMp116       grey60
    ## 6 ZeamMp158         blue

Finally, we can correlate consensus module eigengenes to sample metadata (here, plant tissues).1

    consensus_trait <- consensus_trait_cor(consensus, cor_method = "pearson")
    head(consensus_trait)
    ##       trait              ME         cor     pvalue  group
    ## 1 endosperm         MEblack          NA         NA Tissue
    ## 2 endosperm        MEgrey60  0.05143433 0.82244990 Tissue
    ## 3 endosperm       MEdarkred  0.03438255 0.88082052 Tissue
    ## 4 endosperm          MEblue -0.09885816 0.66550539 Tissue
    ## 5 endosperm     MElightcyan  0.09297754 0.68440932 Tissue
    ## 6 endosperm MEdarkturquoise -0.45285214 0.03330611 Tissue

As with the output of `module_trait_cor()`, users can visualize the output of `consensus_trait_cor()` with the function `plot_module_trait_cor()`.

    plot_module_trait_cor(consensus_trait)

# Module preservation

Module preservation is often used to study patterns of evolutionary conservation and divergence across transcriptomes, an approach named phylotranscriptomics. This way, one can investigate how evolution shaped the expression profiles for particular gene families across taxa.

## Data preprocessing

To calculate module preservation statistics, gene IDs must be shared by the expression sets. For intraspecies comparisons, this is an easy task, as gene IDs are the same. However, for interspecies comparisons, users need to identify orthogroups between the different species and collapse the gene-level expression values to orthogroup-level expression values. This way, all expression sets will have common row names. We recommend identifying orthogroups with OrthoFinder (Emms and Kelly 2015), as it is simple to use and widely used.2 Here, we will compare maize and rice expression profiles. The orthogroups between these species were downloaded from the PLAZA 4.0 Monocots database (Van Bel et al. 2018).

    data(og.zma.osa)
    head(og.zma.osa)
    ##              Family Species           Gene
    ## 1548 ORTHO04M000001     osa   Os01g0100700
    ## 1549 ORTHO04M000001     osa   Os01g0100900
    ## 4824 ORTHO04M000001     zma Zm00001d009743
    ## 4854 ORTHO04M000001     zma Zm00001d020834
    ## 4874 ORTHO04M000001     zma Zm00001d026672
    ## 4921 ORTHO04M000001     zma Zm00001d039873

As you can see, the orthogroup object for `BioNERO` must be a data frame with orthogroups, species IDs and gene IDs, respectively. Let’s collapse gene-level expression to orthogroup-level with `exp_genes2orthogroups()`. By default, if there is more than one gene in the same orthogroup for a given species, their expression levels are summarized to the median. Users can also summarize to the mean.

    # Store SummarizedExperiment objects in a list
    zma_osa_list <- list(osa = osa.se, zma = zma.se)

    # Collapse gene-level expression to orthogroup-level
    ortho_exp <- exp_genes2orthogroups(zma_osa_list, og.zma.osa, summarize = "mean")

    # Inspect new expression data
    ortho_exp$osa[1:5, 1:5]
    ##                SRX831140 SRX831141 SRX831137 SRX831138 SRX831134
    ## ORTHO04M000001  6.909420  7.258330  94.20870  92.85195 123.01060
    ## ORTHO04M000002  9.203498  8.709974  66.45512  44.97913  33.86936
    ## ORTHO04M000003  9.417930  9.444861  42.57513  66.02237  55.37741
    ## ORTHO04M000004  9.019436  8.920091  96.22074  62.56506 109.32262
    ## ORTHO04M000005 40.845040 41.844234  52.33474  31.31474  22.42236
    ortho_exp$zma[1:5, 1:5]
    ##                SRX339756 SRX339757 SRX339758 SRX339762 SRX339763
    ## ORTHO04M000001  26.02510  15.07917  14.91571  13.82989  8.080476
    ## ORTHO04M000002  19.28281  13.94254  13.57854  12.96032 13.522525
    ## ORTHO04M000003  45.17294  48.63796  54.22404  42.12135 10.779117
    ## ORTHO04M000004  28.05475  38.53734  39.48070  27.13272  2.978207
    ## ORTHO04M000005  67.58868  34.87009  21.46280  12.79565  7.452068

Now, we will preprocess both expression sets and keep only the top 1000 orthogroups with the highest variances for demonstration purposes.

    # Preprocess data and keep top 1000 genes with highest variances
    ortho_exp <- lapply(ortho_exp, exp_preprocess, variance_filter=TRUE, n=1000)
    ## Number of removed samples: 2
    ## Number of removed samples: 2

    # Check orthogroup number
    sapply(ortho_exp, nrow)
    ##  osa  zma
    ## 1000 1000

## Calculating module preservation statistics

Now that row names are comparable, we can infer GCNs for each set. We will do that iteratively with lapply.

    # Calculate SFT power
    power_ortho <- lapply(ortho_exp, SFT_fit, cor_method="pearson")
    ##    Power SFT.R.sq  slope truncated.R.sq mean.k. median.k. max.k.
    ## 1      3  0.15600  1.740          0.846  191.00    192.00  249.0
    ## 2      4  0.12400  1.080          0.844  129.00    130.00  178.0
    ## 3      5  0.10300  0.784          0.817   91.70     92.90  134.0
    ## 4      6  0.06140  0.528          0.818   67.80     68.90  106.0
    ## 5      7  0.04550  0.397          0.803   51.80     52.60   85.5
    ## 6      8  0.02660  0.234          0.840   40.70     40.80   70.7
    ## 7      9  0.00989  0.121          0.868   32.60     32.50   59.8
    ## 8     10  0.04090 -0.274          0.820   26.70     26.10   53.2
    ## 9     11  0.12200 -0.458          0.871   22.20     21.40   47.8
    ## 10    12  0.20600 -0.600          0.908   18.80     18.00   43.4
    ## 11    13  0.27900 -0.688          0.928   16.00     15.30   39.7
    ## 12    14  0.39900 -0.839          0.952   13.80     13.10   36.4
    ## 13    15  0.48600 -0.949          0.951   12.10     11.30   33.6
    ## 14    16  0.55500 -1.010          0.959   10.60      9.71   31.2
    ## 15    17  0.59600 -1.080          0.959    9.39      8.34   29.0
    ## 16    18  0.64700 -1.130          0.965    8.37      7.20   27.1
    ## 17    19  0.68300 -1.120          0.969    7.50      6.28   25.3
    ## 18    20  0.72300 -1.120          0.979    6.76      5.52   23.8
    ## No power reached R-squared cut-off, now choosing max R-squared based power
    ##    Power SFT.R.sq   slope truncated.R.sq mean.k. median.k. max.k.
    ## 1      3   0.6910  0.7290         0.6260   269.0     284.0  407.0
    ## 2      4   0.4540  0.3460         0.3270   199.0     209.0  329.0
    ## 3      5   0.0802  0.1070        -0.0486   153.0     158.0  273.0
    ## 4      6   0.0459 -0.0746        -0.0302   121.0     123.0  233.0
    ## 5      7   0.2620 -0.2140         0.3480    98.1      97.1  201.0
    ## 6      8   0.4070 -0.3270         0.4820    80.7      78.1  176.0
    ## 7      9   0.5030 -0.4180         0.5960    67.4      63.4  155.0
    ## 8     10   0.4930 -0.4950         0.5950    57.0      52.5  138.0
    ## 9     11   0.5730 -0.5520         0.6790    48.8      43.8  124.0
    ## 10    12   0.5770 -0.6090         0.6860    42.1      37.2  112.0
    ## 11    13   0.6560 -0.6500         0.7490    36.7      31.6  101.0
    ## 12    14   0.6740 -0.7010         0.7630    32.2      27.5   92.1
    ## 13    15   0.7240 -0.7330         0.7930    28.4      23.8   84.2
    ## 14    16   0.7800 -0.7460         0.8470    25.3      20.6   77.3
    ## 15    17   0.8330 -0.7540         0.9060    22.6      18.0   71.2
    ## 16    18   0.8560 -0.7740         0.9200    20.3      15.8   65.7
    ## 17    19   0.8690 -0.7870         0.9320    18.3      13.9   60.9
    ## 18    20   0.8810 -0.8120         0.9240    16.6      12.3   56.6

    # Infer GCNs
    gcns <- lapply(seq_along(power_ortho), function(n)
      exp2gcn(ortho_exp[[n]], SFTpower = power_ortho[[n]]$power,
              cor_method = "pearson")
      )
    ## ..connectivity..
    ## ..matrix multiplication (system BLAS)..
    ## ..normalization..
    ## ..done.
    ## ..connectivity..
    ## ..matrix multiplication (system BLAS)..
    ## ..normalization..
    ## ..done.

    length(gcns)
    ## [1] 2

Initially, module preservation analyses were performed with WGCNA’s algorithm (Langfelder and Horvath 2008). However, the summary preservation statistics used by WGCNA rely on parametric assumptions that are often not met. For this purpose, the NetRep algorithm (Ritchie et al. 2016) is more accurate than WGCNA, as it uses non-parametric permutation analyses. Both algorithms are implemented in `BioNERO` for comparison purposes, but we strongly recommend using the NetRep algorithm. Module preservation analysis can be performed with a single function: `module_preservation()`.

    # Using rice as reference and maize as test
    pres <- module_preservation(ortho_exp,
                                ref_net = gcns[[1]],
                                test_net = gcns[[2]],
                                algorithm = "netrep")
    ## 1 modules in osa were preserved in zma:
    ## black

None of the modules in rice were preserved in maize. This can be either due to the small number of orthogroups we have chosen or to the natural biological variation between species and sampled tissues. You can (and should) include more orthogroups in your analyses for a better view of transcriptional conservation between species.

# Identifying singletons and duplicated genes

Finally, `BioNERO` can identify singletons and duplicated genes with `is_singleton()`. This function returns logical vectors indicating if each of the input genes is a singleton or not.

    # Sample 50 random genes
    genes <- sample(rownames(zma.se), size = 50)
    is_singleton(genes, og.zma.osa)
    ## Zm00001d001841 Zm00001d002104 Zm00001d004006 Zm00001d005944 Zm00001d006390
    ##           TRUE           TRUE           TRUE           TRUE           TRUE
    ## Zm00001d008725 Zm00001d008874 Zm00001d009669 Zm00001d011096 Zm00001d011507
    ##           TRUE           TRUE           TRUE           TRUE           TRUE
    ## Zm00001d012816 Zm00001d014124 Zm00001d014890 Zm00001d015497 Zm00001d017085
    ##           TRUE           TRUE           TRUE           TRUE           TRUE
    ## Zm00001d017275 Zm00001d017654 Zm00001d019718 Zm00001d020807 Zm00001d025222
    ##           TRUE           TRUE           TRUE           TRUE           TRUE
    ## Zm00001d028731 Zm00001d030626 Zm00001d031041 Zm00001d031963 Zm00001d032111
    ##           TRUE           TRUE           TRUE           TRUE           TRUE
    ## Zm00001d032186 Zm00001d032231 Zm00001d032671 Zm00001d032843 Zm00001d034004
    ##           TRUE           TRUE           TRUE           TRUE           TRUE
    ## Zm00001d034713 Zm00001d036482 Zm00001d036651 Zm00001d037613 Zm00001d038937
    ##           TRUE           TRUE           TRUE           TRUE           TRUE
    ## Zm00001d038960 Zm00001d039619 Zm00001d042213 Zm00001d043146 Zm00001d043914
    ##           TRUE           TRUE           TRUE           TRUE           TRUE
    ## Zm00001d044251 Zm00001d044274 Zm00001d047303 Zm00001d048893 Zm00001d049399
    ##           TRUE           TRUE           TRUE           TRUE           TRUE
    ## Zm00001d049617 Zm00001d052078 Zm00001d052888 Zm00001d053016 Zm00001d053962
    ##           TRUE           TRUE           TRUE           TRUE           TRUE

# Session info

This vignette was created under the following conditions:

    ## R version 4.5.2 (2025-10-31)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## time zone: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] BioNERO_1.18.0   BiocStyle_2.38.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] RColorBrewer_1.1-3          ggdendro_0.2.0
    ##   [3] sys_3.4.3                   rstudioapi_0.18.0
    ##   [5] jsonlite_2.0.0              shape_1.4.6.1
    ##   [7] NetRep_1.2.9                magrittr_2.0.4
    ##   [9] farver_2.1.2                rmarkdown_2.30
    ##  [11] GlobalOptions_0.1.3         vctrs_0.7.1
    ##  [13] memoise_2.0.1               base64enc_0.1-3
    ##  [15] htmltools_0.5.9             S4Arrays_1.10.1
    ##  [17] dynamicTreeCut_1.63-1       SparseArray_1.10.8
    ##  [19] Formula_1.2-5               sass_0.4.10
    ##  [21] bslib_0.9.0                 htmlwidgets_1.6.4
    ##  [23] plyr_1.8.9                  impute_1.84.0
    ##  [25] cachem_1.1.0                networkD3_0.4.1
    ##  [27] buildtools_1.0.0            igraph_2.2.1
    ##  [29] lifecycle_1.0.5             ggnetwork_0.5.14
    ##  [31] iterators_1.0.14            pkgconfig_2.0.3
    ##  [33] Matrix_1.7-4                R6_2.6.1
    ##  [35] fastmap_1.2.0               MatrixGenerics_1.22.0
    ##  [37] clue_0.3-66                 digest_0.6.39
    ##  [39] colorspace_2.1-2            patchwork_1.3.2
    ##  [41] AnnotationDbi_1.72.0        S4Vectors_0.48.0
    ##  [43] GENIE3_1.32.0               Hmisc_5.2-5
    ##  [45] GenomicRanges_1.62.1        RSQLite_2.4.5
    ##  [47] labeling_0.4.3              mgcv_1.9-4
    ##  [49] httr_1.4.7                  abind_1.4-8
    ##  [51] compiler_4.5.2              withr_3.0.2
    ##  [53] bit64_4.6.0-1               doParallel_1.0.17
    ##  [55] htmlTable_2.4.3             S7_0.2.1
    ##  [57] backports_1.5.0             BiocParallel_1.44.0
    ##  [59] DBI_1.2.3                   intergraph_2.0-4
    ##  [61] MASS_7.3-65                 DelayedArray_0.36.0
    ##  [63] rjson_0.2.23                tools_4.5.2
    ##  [65] foreign_0.8-90              nnet_7.3-20
    ##  [67] glue_1.8.0                  nlme_3.1-168
    ##  [69] grid_4.5.2                  checkmate_2.3.3
    ##  [71] cluster_2.1.8.1             reshape2_1.4.5
    ##  [73] generics_0.1.4              sva_3.58.0
    ##  [75] gtable_0.3.6                preprocessCore_1.72.0
    ##  [77] sna_2.8                     data.table_1.18.0
    ##  [79] WGCNA_1.73                  XVector_0.50.0
    ##  [81] BiocGenerics_0.56.0         ggrepel_0.9.6
    ##  [83] foreach_1.5.2               pillar_1.11.1
    ##  [85] stringr_1.6.0               limma_3.66.0
    ##  [87] genefilter_1.92.0           circlize_0.4.17
    ##  [89] splines_4.5.2               dplyr_1.1.4
    ##  [91] lattice_0.22-7              survival_3.8-6
    ##  [93] bit_4.6.0                   annotate_1.88.0
    ##  [95] tidyselect_1.2.1            locfit_1.5-9.12
    ##  [97] GO.db_3.22.0                ComplexHeatmap_2.26.0
    ##  [99] maketools_1.3.2             Biostrings_2.78.0
    ## [101] knitr_1.51                  gridExtra_2.3
    ## [103] IRanges_2.44.0              Seqinfo_1.0.0
    ## [105] edgeR_4.8.2                 SummarizedExperiment_1.40.0
    ## [107] RhpcBLASctl_0.23-42         stats4_4.5.2
    ## [109] xfun_0.56                   Biobase_2.70.0
    ## [111] statmod_1.5.1               matrixStats_1.5.0
    ## [113] stringi_1.8.7               statnet.common_4.13.0
    ## [115] yaml_2.3.12                 minet_3.68.0
    ## [117] evaluate_1.0.5              codetools_0.2-20
    ## [119] data.tree_1.2.0             tibble_3.3.1
    ## [121] BiocManager_1.30.27         cli_3.6.5
    ## [123] rpart_4.1.24                xtable_1.8-4
    ## [125] jquerylib_0.1.4             network_1.19.0
    ## [127] Rcpp_1.1.1                  coda_0.19-4.1
    ## [129] png_0.1-8                   fastcluster_1.3.0
    ## [131] XML_3.99-0.20               parallel_4.5.2
    ## [133] ggplot2_4.0.1               blob_1.3.0
    ## [135] scales_1.4.0                crayon_1.5.3
    ## [137] GetoptLong_1.1.0            rlang_1.1.7
    ## [139] KEGGREST_1.50.0

# References

Emms, David M., and Steven Kelly. 2015. “OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy.” Genome Biology 16 (1): 1–14. https://doi.org/10.1186/s13059-015-0721-2.
Langfelder, Peter, and Steve Horvath. 2008. “WGCNA: an R package for weighted correlation network analysis.” BMC Bioinformatics 9 (1): 559. https://doi.org/10.1186/1471-2105-9-559.
Ritchie, Scott C., Stephen Watts, Liam G. Fearnley, Kathryn E. Holt, Gad Abraham, and Michael Inouye. 2016. “A Scalable Permutation Approach Reveals Replication and Preservation Patterns of Network Modules in Large Datasets.” Cell Systems 3 (1): 71–82. https://doi.org/10.1016/j.cels.2016.06.012.
Shin, Junha, Harald Marx, Alicia Richards, Dries Vaneechoutte, Dhileepkumar Jayaraman, Junko Maeda, Sanhita Chakraborty, et al. 2020. “A network-based comparative framework to study conservation and divergence of proteomes in plant phylogenies.” Nucleic Acids Research, 1–23. https://doi.org/10.1093/nar/gkaa1041.
Van Bel, Michiel, Tim Diels, Emmelien Vancaester, Lukasz Kreft, Alexander Botzki, Yves Van De Peer, Frederik Coppens, and Klaas Vandepoele. 2018. “PLAZA 4.0: An integrative resource for functional, evolutionary and comparative plant genomics.” Nucleic Acids Research 46 (D1): D1190–96. https://doi.org/10.1093/nar/gkx1002.

1. NOTE: Blank grey cells in the heatmap represent correlation values that have opposite sign in the expression sets. For each correlation pair, consensus correlations are calculated by selecting the minimum value across matrices of consensus module-trait correlations.↩︎

2. PRO TIP: If you identify orthogroups with OrthoFinder, `BioNERO` has a helper function named `parse_orthofinder()` that parses the Orthogroups.tsv file generated by OrthoFinder into a suitable data frame for module preservation analysis. See `?parse_orthofinder` for more details.↩︎