Package 'hdWGCNA'

Description

Define the set of likely target genes (Regulons) for each transcrition factor

Usage

AssignTFRegulons(
  seurat_obj,
  strategy = "A",
  reg_thresh = 0.01,
  n_tfs = 10,
  n_genes = 50,
  wgcna_name = NULL
)

Arguments

Details

AssignTFRegulons uses the TF network information from ConstructTFNetwork to define sets of confident TF-gene pairs. A "regulon" is the set of target genes for a given TF, and this function provides three different strategies to define TF regulons. Strategy "A" selects the top TFs for each gene, strategy "B" selects the top genes for each TF, and strategy "C" retains all TF-gene pairs above a certain regulatory score (reg_thresh).

Value

seurat_obj with the TF Regulon information added

Description

Computes module eigengenes for all WGCNA co-expression modules

Usage

AvgModuleExpr(seurat_obj, n_genes = 25, wgcna_name = NULL, ...)

Arguments

Description

Makes evenly-spaced bins of cells along a pseudotime trajectory.

Usage

BinPseudotime(seurat_obj, n_bins = 50, pseudotime_col = "pseudotime")

Arguments

Description

Check inputs prior to running to_pseudobulk

Usage

check_inputs(
  input,
  meta = meta,
  replicate_col = "replicate",
  cell_type_col = "cell_type",
  label_col = "label"
)

Arguments

Value

a cleaned up expression matrix and meta data object

Description

Function to check if Seurat version is v5

Usage

CheckSeurat5()

Description

CheckWGCNAName

Usage

CheckWGCNAName(seurat_obj, wgcna_name)

Arguments

Description

Internal helper function that computes module eigengene for a single module.

Usage

ComputeModuleEigengene(
  seurat_obj,
  cur_mod,
  modules,
  group.by.vars = NULL,
  verbose = TRUE,
  vars.to.regress = NULL,
  scale.model.use = "linear",
  pc_dim = 1,
  assay = NULL,
  wgcna_name = NULL,
  ...
)

Arguments

Description

This function takes a Seurat object and constructs averaged 'metacells' based on neighboring cells.

Usage

ConstructMetacells(
  seurat_obj,
  name = "agg",
  ident.group = "seurat_clusters",
  k = 25,
  reduction = "pca",
  dims = NULL,
  assay = "RNA",
  cells.use = NULL,
  slot = "counts",
  layer = "counts",
  meta = NULL,
  return_metacell = FALSE,
  mode = "average",
  max_shared = 15,
  target_metacells = 1000,
  max_iter = 5000,
  verbose = FALSE,
  wgcna_name = NULL
)

Arguments

Description

Computes metaspots in a given Seurat object containing spatial transcriptomics data. This function is called by MetaspotsByGroups and should NOT be run directly!

Usage

ConstructMetaspots(
  cur_seurat,
  mode = "sum",
  assay = "Spatial",
  slot = "counts",
  layer = "counts"
)

Arguments

Description

Constructs a co-expression network and groups genes into modules given a Seurat object that has been prepared for network analysis.

Usage

ConstructNetwork(
  seurat_obj,
  soft_power = NULL,
  min_power = 3,
  tom_outdir = "TOM",
  tom_name = NULL,
  consensus = FALSE,
  overwrite_tom = FALSE,
  wgcna_name = NULL,
  blocks = NULL,
  maxBlockSize = 30000,
  randomSeed = 12345,
  corType = "pearson",
  consensusQuantile = 0.3,
  networkType = "signed",
  TOMType = "signed",
  TOMDenom = "min",
  scaleTOMs = TRUE,
  calibrationQuantile = 0.95,
  sampleForCalibration = TRUE,
  sampleForCalibrationFactor = 1000,
  useDiskCache = TRUE,
  chunkSize = NULL,
  deepSplit = 4,
  pamStage = FALSE,
  detectCutHeight = 0.995,
  minModuleSize = 50,
  mergeCutHeight = 0.2,
  saveConsensusTOMs = TRUE,
  ...
)

Arguments

Details

ConstructNetwork builds a co-expression network and identifies clusters of highly co-expressed genes (modules) from the metacell or metaspot expression matrix stored in the Seurat object. Before running this function, the following functions must be run on the input Seurat object:

1. SetupForWGCNA

2. MetacellsByGroups or MetaspotsByGroups, and NormalizeMetacells

3. SetDatExpr or SetMultiExpr

4. TestSoftPowers or TestSoftPowersConsensus

This function can also be used to perform consensus network analysis if consensus=TRUE is selected. ConstructNetwork calls the WGCNA function blockwiseConsensusModules to compute the adjacency matrix, topological overlap matrix, and to run the Dynamic Tree Cut algorithm to identify gene modules. blockwiseConsensusModules has numerous parameters but here we have selected default parameters that we have found to provide reasonable results on a variety of single-cell and spatial transcriptomic datasets.

Value

seurat_obj with the co-expression network and gene modules computed for the selected wgcna experiment

Description

Constructs a "pseudobulk" gene expression matrix summarizing the expression levels of each gene across a grouping variable (cell types for example) in each biological replicate.

Usage

ConstructPseudobulk(
  seurat_obj,
  group.by,
  replicate_col,
  label_col = NULL,
  assay = "RNA",
  slot = "counts",
  layer = "counts",
  min_reps = 20,
  wgcna_name = NULL
)

Arguments

Details

This function constructs pseudobulk gene expression profiles across the provided cell groups and the provided biological replicates. We note that low numbers of replicates are typical in single-cell and spatial transcriptomics due to the large monetary cost of running these experiments, and pseudobulk-ing your data for hdWGCNA is only recommended in the case where you have a sufficient number of replicates. Here we have set the minimum recommended number to 20. Using fewer than 20 replicates risks the results not being reproducible or robust, and therefore are not biologically meaningful due to spurious noisy correlations.

Value

a matrix containing pseudobulk expression profiles

Description

Construct a network of transcription factors and target genes based on gene co-expression

Usage

ConstructTFNetwork(
  seurat_obj,
  model_params,
  nfold = 5,
  callbacks = NULL,
  wgcna_name = NULL
)

Arguments

Details

ConstructTFNetwork uses motif-gene information to build a directed network of transcription factors (TFs) and target genes. XGBoost regression is leveraged to model the expression of each gene based on its candidate TF regulators. This analysis gives us information about how important each TF is for predicting each gene, allowing us to prioritize the most likely regulators of each gene. This process is done on the gene expression matrix stored with SetDatExpr, which is typically the hdWGCNA metacell gene expression matrix.

Value

seurat_obj with the TF network added

Description

Compute the Pearson correlation matrix between columns of two sparse matrices.

Usage

corSparse(X, Y = NULL, cov = FALSE)

Arguments

Details

Originally from http://stackoverflow.com/questions/5888287/running-cor-or-any-variant-over-a-sparse-matrix-in-r and the qlcMatrix & Signac packages.

Author(s)

Michael Cysouw, Karsten Looschen

Description

Wrapper function to run Seurat's DimPlot function on the metacell object.

Usage

DimPlotMetacells(seurat_obj, ...)

Arguments

Description

This function makes an expression heatmap of the top n hub genes per module using Seurat's DoHeatmap, and then assembles them all into one big heatmap.

Usage

DoHubGeneHeatmap(
  seurat_obj,
  n_hubs = 10,
  n_cells = 200,
  group.by = NULL,
  module_names = NULL,
  combine = TRUE,
  draw.lines = TRUE,
  disp.min = -2.5,
  disp.max = 2.5,
  wgcna_name = NULL
)

Arguments

Examples

DoHubGeneHeatmap

Description

Generates bar plots from Enrichr data to visualize enriched terms for hdWGCNA modules in a Seurat object. The function outputs a PDF file for each module, with separate bar plots for each database.

Usage

EnrichrBarPlot(
  seurat_obj,
  outdir = "enrichr_plots",
  n_terms = 25,
  p_cutoff = 0.05,
  p_adj = TRUE,
  plot_size = c(6, 15),
  logscale = FALSE,
  plot_bar_color = NULL,
  plot_text_color = NULL,
  wgcna_name = NULL
)

Arguments

Details

This function processes the Enrichr output stored in a Seurat object, filters enriched terms by significance, and generates bar plots for each WGCNA module. Separate plots are created for each database included in the Enrichr results. The top enriched terms for each module and database are ordered by their combined enrichment score. If there are ties in the enrichment scores, the function uses all tied terms.

The bar plot text and bar colors can be customized, and plots can be saved with log-transformed enrichment scores if specified. Text wrapping is applied to long term names for better readability.

Value

Generates PDF files in the specified output directory, with one file per WGCNA module. Each file contains bar plots for all databases associated with that module.

Description

Generate a dot plot visualizing enrichment results from Enrichr for hdWGCNA modules.

Usage

EnrichrDotPlot(
  seurat_obj,
  database,
  mods = "all",
  n_terms = 3,
  p_cutoff = 0.05,
  p_adj = TRUE,
  break_ties = TRUE,
  term_size = 10,
  wgcna_name = NULL
)

Arguments

Details

This function creates dot plots from Enrichr results associated with hdWGCNA modules. Each module is represented by its most enriched terms from the specified Enrichr database. The size of the dots indicates the enrichment score, and the color indicates the statistical significance (-log10 transformed p-value).

• The function first retrieves WGCNA module and Enrichr data from the specified Seurat object.

• Modules are filtered based on the mods parameter, and enriched terms are filtered by significance using p_cutoff and p_adj.

• The top n_terms terms for each module are selected based on the Combined Score. If ties occur, the break_ties parameter determines how they are resolved.

• A dot plot is generated where each dot represents an enriched term, its size corresponds to the Combined Score (log-transformed), and its color indicates the significance (-log10 transformed p-value).

Value

A ggplot2 object representing the dot plot of enriched terms for the specified modules and database.

Description

Function to compare expression levels of co-expression modules between two sets of cell barcodes.

Usage

FindAllDMEs(
  seurat_obj,
  group.by,
  features = "MEs",
  harmonized = TRUE,
  add_missing = FALSE,
  wgcna_name = NULL,
  test.use = "wilcox",
  only.pos = FALSE,
  logfc.threshold = 0,
  min.pct = 0,
  verbose = FALSE,
  pseudocount.use = 0,
  ...
)

Arguments

Value

A dataframe contaning differential ME results

Examples

FindAllDMEs

Description

Function to compare TF regulon scores between two sets of cell barcodes.

Usage

FindDifferentialRegulons(
  seurat_obj,
  barcodes1,
  barcodes2,
  assay = "RNA",
  slot = "data",
  layer = "data",
  wgcna_name = NULL,
  test.use = "wilcox",
  only.pos = FALSE,
  logfc.threshold = 0,
  min.pct = 0,
  verbose = FALSE,
  pseudocount.use = 0,
  ...
)

Arguments

Details

FindDifferentialRegulons compares two groups based on their TF regulon scores. Three comparisons are made for different sets of features: positive regulon scores, negative regulon scores, and gene expression. The same settings for the test will be used for all three tests.

Value

A dataframe contaning differential regulon results

Description

Function to compare expression levels of co-expression modules between two sets of cell barcodes.

Usage

FindDMEs(
  seurat_obj,
  barcodes1,
  barcodes2,
  features = "MEs",
  harmonized = TRUE,
  wgcna_name = NULL,
  add_missing = FALSE,
  test.use = "wilcox",
  only.pos = FALSE,
  logfc.threshold = 0,
  min.pct = 0,
  verbose = FALSE,
  pseudocount.use = 0,
  ...
)

Arguments

Value

A dataframe contaning differential ME results

Examples

FindDMEs

Description

Finds the set of "major isoforms" for each gene in each cell population. The set of major isoforms consists of the isoforms accounting for a desired proportion of a gene's expression. Pseudobulk replicates in each sample and cell population are used for this calculation.

Usage

FindMajorIsoforms(
  seurat_obj,
  group.by,
  replicate_col,
  isoform_delim = "[.]",
  proportion_thresh = 0.8,
  low_thresh = 25,
  assay = "iso",
  slot = "counts",
  cluster_markers = NULL,
  wgcna_name = NULL
)

Arguments

Details

FindMajorIsoforms computes the set of major isoforms in a given Seurat object that contains isoform-level expression information. First, pseudobulk replicates are computed for the given cell populations and samples present in the Seurat object. For each gene in each cell population, we rank the gene's isoforms by expression level and take the top expressing isoforms that make up the desired proportion of the gene's total expression, making sure to exclude any very lowly expressed isoforms.

Optionally, the user may supply a marker gene table for each cell population (formatted like the output of Seurat FindAllMarkers), and then the algorithm will only return major isoforms of the given marker genes.

Value

a list of major isoforms for each cell population

Description

GetActiveWGCNA

Usage

GetActiveWGCNA(seurat_obj)

Arguments

Description

GetActiveWGCNAName

Usage

GetActiveWGCNAName(seurat_obj)

Arguments

Description

GetAvgModuleExpr

Usage

GetAvgModuleExpr(seurat_obj, wgcna_name = NULL)

Arguments

Description

This function gets the WGCNA expression matrix.

Usage

GetDatExpr(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetDegrees

Usage

GetDegrees(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetEnrichrRegulonTable

Usage

GetEnrichrRegulonTable(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetEnrichrTable

Usage

GetEnrichrTable(seurat_obj, wgcna_name = NULL)

Arguments

Description

Extract the top N hub genes for a given set of modules. This function outputs a table with the gene name, the module, and the kME for that module for the top N hub genes.

Usage

GetHubGenes(seurat_obj, n_hubs = 10, mods = NULL, wgcna_name = NULL)

Arguments

Description

Function to retrieve module eigengens from Seurat object.

Usage

GetMELoadings(seurat_obj, harmonized = TRUE, wgcna_name = NULL)

Arguments

Description

Function to retrieve module eigengens from Seurat object.

Usage

GetMEs(seurat_obj, harmonized = TRUE, wgcna_name = NULL)

Arguments

Description

GetMetacellObject

Usage

GetMetacellObject(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetMetacellParams

Usage

GetMetacellParams(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetModulePreservation

Usage

GetModulePreservation(seurat_obj, mod_name, wgcna_name = NULL)

Arguments

Description

GetModules

Usage

GetModules(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetModuleScores

Usage

GetModuleScores(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetModuleTraitCorrelation

Usage

GetModuleTraitCorrelation(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetModuleUMAP

Usage

GetModuleUMAP(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetMotifMatrix

Usage

GetMotifMatrix(seurat_obj)

Arguments

Examples

GetMotifMatrix

Description

GetMotifOverlap

Usage

GetMotifOverlap(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetMotifs

Usage

GetMotifs(seurat_obj)

Arguments

Examples

GetMotifs

Description

GetMotifScores

Usage

GetMotifScores(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetMotifTargets

Usage

GetMotifTargets(seurat_obj)

Arguments

Examples

GetMotifTargets

Description

This function gets the expression matrix from the metacell object.

Usage

GetMultiExpr(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetNetworkData

Usage

GetNetworkData(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetPFMList

Usage

GetPFMList(seurat_obj)

Arguments

Examples

GetPFMList

Description

GetPowerTable

Usage

GetPowerTable(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetRegulonScores

Usage

GetRegulonScores(seurat_obj, target_type, wgcna_name = NULL)

Arguments

Description

GetTFEval

Usage

GetTFEval(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetTFNetwork

Usage

GetTFNetwork(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetTFRegulons

Usage

GetTFRegulons(seurat_obj, wgcna_name = NULL)

Arguments

Description

Retrieve target genes for a set of transcription factors (TFs) from a Seurat object, based on either regulons or the full TF network. The function allows exploration of direct targets or extending the network by adding target genes of TFs that are downstream in the network.

Usage

GetTFTargetGenes(
  seurat_obj,
  selected_tfs,
  depth = 1,
  target_type = "both",
  use_regulons = TRUE,
  wgcna_name = NULL
)

Arguments

Details

This function retrieves direct and extended targets of a set of TFs based on the regulatory network stored in the Seurat object. The depth parameter controls how many layers of TF-target interactions to explore, while the target_type parameter allows filtering based on correlation direction. The use_regulons flag specifies whether to use regulon data, which may provide a more confident set of interactions, or the full TF network.

Value

A data frame containing the TF-target interactions at each specified depth level, with additional information such as regulatory score, correlation, and depth.

Description

GetTOM

Usage

GetTOM(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetWGCNAGenes

Usage

GetWGCNAGenes(seurat_obj, wgcna_name = NULL)

Arguments

Description

GetWGCNAParams

Usage

GetWGCNAParams(seurat_obj, wgcna_name = NULL)

Arguments

Description

Construct a unified network plot comprising hub genes for multiple modules.

Usage

HubGeneNetworkPlot(
  seurat_obj,
  mods = "all",
  n_hubs = 6,
  n_other = 3,
  sample_edges = TRUE,
  edge_prop = 0.5,
  return_graph = FALSE,
  edge.alpha = 0.25,
  vertex.label.cex = 0.5,
  hub.vertex.size = 4,
  other.vertex.size = 1,
  wgcna_name = NULL,
  ...
)

Arguments

Examples

HubGeneNetworkPlot

Description

This function takes a Seurat object and constructs averaged 'metacells' based on neighboring cells in provided groupings, such as cluster or cell type.

Usage

MetacellsByGroups(
  seurat_obj,
  group.by = c("seurat_clusters"),
  ident.group = "seurat_clusters",
  k = 25,
  reduction = "pca",
  dims = NULL,
  assay = NULL,
  slot = "counts",
  layer = "counts",
  mode = "average",
  cells.use = NULL,
  min_cells = 100,
  max_shared = 15,
  target_metacells = 1000,
  max_iter = 5000,
  verbose = FALSE,
  wgcna_name = NULL
)

Arguments

Details

MetacellsByGroups merges transcriptomically similar cells into "metacells". Given a dimensionally-reduced representation of the input dataset, this algorithm first uses KNN to identify similar cells. A bootstrapped sampling procedure is then used to group together similar cells until convergence is reached. Importantly, this procedure is done in a context-specific manner based on the provided group.by parameters. Typically this means that metacells will be constructed separately for each biological replicate, cell type or cell state, disease condition, etc. The metacell representation is considerably less sparse than the original single-cell dataset, which is preferable for co-expression network analysis or othter analyses that rely on correlations.

Value

seurat_obj with a metacell seurat_obj stored in the specified WGCNA experiment

Description

Computes metaspots in a given Seurat object containing spatial transcriptomics data.

Usage

MetaspotsByGroups(
  seurat_obj,
  group.by = c("seurat_clusters"),
  ident.group = "seurat_clusters",
  assay = "Spatial",
  slot = "counts",
  layer = "counts",
  mode = "sum",
  min_spots = 50,
  wgcna_name = NULL
)

Arguments

Description

Computes eigengene-based connectivity (kME) based on module eigengenes and the feature expression in selected cell populations / spatial regions.

Usage

ModuleConnectivity(
  seurat_obj,
  group.by = NULL,
  group_name = NULL,
  corFnc = "bicor",
  corOptions = "use='p'",
  harmonized = TRUE,
  assay = NULL,
  slot = "data",
  layer = "data",
  sparse = TRUE,
  reassign_modules = TRUE,
  TOM_use = NULL,
  wgcna_name = NULL,
  ...
)

Arguments

Details

ModuleConnectivity computes the eigengene-based connectivity (kME) of each feature and each module. This is done by correlating the gene expression signal with the module eigengenes for each module. Features with high kME values have greater network connectivity within that module, and we can identify module hub genes by taking the top genes for each module by kME values.

We recommend using the group.by and group_name parameters that were previously used in the SetDatExpr function, so only the relevant cell populations / spatial regions are considered for computing kMEs.

Depending on the parameters for network analysis, sometimes a feature has a negative kME in the module that it was originally assigned. This discrepancy arises since the network construction is done on the metacell/metaspot matrix but the kME calculation is done on the cell/spot matrix. By default, we account for this by reassigning features with negative kMEs in their assigned module to the module that had the highest kME for that feature.

Value

seurat_obj with the kMEs computed for the selected wgcna experiment

Description

Plot Module Eigengene correlogram

Usage

ModuleCorrelogram(
  seurat_obj,
  MEs2 = NULL,
  features = "hMEs",
  order = "original",
  method = "ellipse",
  exclude_grey = TRUE,
  type = "upper",
  tl.col = "black",
  tl.srt = 45,
  sig.level = c(1e-04, 0.001, 0.01, 0.05),
  pch.cex = 0.7,
  col = NULL,
  ncolors = 200,
  wgcna_name = NULL,
  wgcna_name2 = NULL,
  ...
)

Arguments

Examples

MECorrelogram

Description

Plot Module Eigengene correlogram

Usage

ModuleCorrNetwork(
  seurat_obj,
  cluster_col = NULL,
  exclude_grey = TRUE,
  features = "hMEs",
  reduction = "umap",
  cor_cutoff = 0.2,
  label_vertices = FALSE,
  edge_scale = 5,
  vertex_size = 15,
  niter = 100,
  vertex_frame = FALSE,
  wgcna_name = NULL
)

Arguments

Examples

ModuleCorrNetwork

Description

Computes module eigengenes for co-expression modules

Usage

ModuleEigengenes(
  seurat_obj,
  group.by.vars = NULL,
  modules = NULL,
  vars.to.regress = NULL,
  scale.model.use = "linear",
  verbose = TRUE,
  assay = NULL,
  pc_dim = 1,
  wgcna_name = NULL,
  ...
)

Arguments

Details

ModuleEigengenes summarizes the gene expression signatures of entire co-expression modules. This is done by performing singular value decomposition (SVD) on a subset of the scaled expression matrix containing only features assigned to each module. The module eigengene (ME), defined as the first dimension of the SVD matrix, retains the most variation, and we use this vector as a summary of gene expression for the whole module.

The module gene expression matrix is first scaled using the Seurat ScaleData function. The user can optionally adjust for covariates of interest in this step using the vars.to.regress parameter. Additionally, the module eigengenes themselves can be adjusted for technical biases such as sequencing batch, dataset of origin, or other factors using the Harmony algorithm with the group.by.vars parameter.

Value

seurat_obj with the module eigengenes computed for the selected wgcna experiment

Description

Computes a module score for each co-expression module using Seurat AddModuleScore or UCell.

Usage

ModuleExprScore(
  seurat_obj,
  n_genes = 25,
  method = "Seurat",
  wgcna_name = NULL,
  ...
)

Arguments

Details

ModuleExprScore provides an alternative function to ModuleEigengenes for summarizing the expression level of each module. The user can choose between Seurat AddModuleScore or UCell using the method parameter.

Value

seurat_obj with module scores computed for the selected wgcna experiment

Description

Plot module eigengenes as a FeaturePlot

Usage

ModuleFeaturePlot(
  seurat_obj,
  module_names = NULL,
  wgcna_name = NULL,
  reduction = "umap",
  features = "hMEs",
  order_points = TRUE,
  restrict_range = TRUE,
  point_size = 0.5,
  alpha = 1,
  label_legend = FALSE,
  ucell = FALSE,
  raster = FALSE,
  raster_dpi = 500,
  raster_scale = 1,
  plot_ratio = 1,
  title = TRUE
)

Arguments

Examples

ModuleFeaturePlot

Description

Visualizes the top hub genes for selected modules as a circular network plot with one inner circle and one outer circle.

Usage

ModuleNetworkPlot(
  seurat_obj,
  n_inner = 10,
  n_outer = 15,
  n_conns = 500,
  mods = "all",
  outdir = "ModuleNetworks",
  wgcna_name = NULL,
  plot_size = c(6, 6),
  edge.alpha = 0.25,
  edge.width = 1,
  vertex.label.cex = 1,
  vertex.size = 6,
  ...
)

Arguments

Description

Computes module preservation statistics in a query dataset for a given reference dataset

Usage

ModulePreservation(
  seurat_obj,
  seurat_ref,
  name,
  n_permutations = 500,
  parallel = FALSE,
  seed = 12345,
  gene_mapping = NULL,
  genome_ref_col = NULL,
  genome_query_col = NULL,
  return_raw = FALSE,
  wgcna_name = NULL,
  wgcna_name_ref = NULL,
  ...
)

Arguments

Details

ModulePreservation performs a statistical test to assess the preservation of co-expression modules identified in one dataset in an independent dataset. This method is originally described by Langfelder et al. in the 2011 paper "Is My Network Module Preserved and Reproducible?". This method can be used to assess biological differences in networks, as well as technical differences / reproducibility across different batches.

Description

Computes module preservation statistics in a query dataset for a given reference dataset

Usage

ModulePreservationNetRep(
  seurat_query,
  seurat_ref,
  name,
  n_permutations = 10000,
  n_threads = 8,
  gene_mapping = NULL,
  genome_ref_col = NULL,
  genome_query_col = NULL,
  TOM_use = NULL,
  ref_power = 1,
  query_power = 1,
  wgcna_name = NULL,
  wgcna_name_ref = NULL,
  ...
)

Arguments

Details

ModulePreservationNetRep performs a statistical test to assess the preservation of co-expression modules identified in one dataset in an independent dataset using the NetRep implementation rather than the WGCNA implementation. This method is originally described by Langfelder et al. in the 2011 paper "Is My Network Module Preserved and Reproducible?". The NetRep implementation of the module preservation test is described by Ritchie et al. in the 2016 paper "A Scalable Permutation Approach Reveals Replication and Preservation Patterns of Networks Modules in Large Datasets" This method can be used to assess biological differences in networks, as well as technical differences / reproducibility across different batches. Note that the outputs of ModulePreservation and ModulePreservationNetRep are not identical. This function requires separate installation of the NetRep package, which is not directly included as a dependency in hdWGCNA.

Value

A Seurat Object containing the module preservation statistics from NetRep.

Description

Plots the expression level (module eigengene) of each co-expression module for different groups as a radar plot.

Usage

ModuleRadarPlot(
  seurat_obj,
  group.by = NULL,
  barcodes = NULL,
  combine = TRUE,
  ncol = 4,
  features = "hMEs",
  wgcna_name = NULL,
  fill = TRUE,
  draw.points = FALSE,
  ...
)

Arguments

Details

ModuleRadarPlot visualizes the expression level (module eigengene, ME) of each co-expression module for different groups in a radial coordinate system. The ME for each module is averaged for each group, scaled, and then is plotted radially. The resulting plots help us to interpret which cell groups (clusters, cell types, etc) are expressing each module.

Value

ggplot object containing the ModuleRadarPlot

Description

This function visualizes the regulatory network between gene modules as a heatmap, where each cell represents the regulatory score between a source and target module. The heatmap can display either positive, negative, or delta (positive minus negative) regulatory scores. Optionally, a dendrogram can be plotted to cluster modules based on their regulatory interactions.

Usage

ModuleRegulatoryHeatmap(
  seurat_obj,
  feature = "delta",
  TFs_only = TRUE,
  dendrogram = TRUE,
  coord_fixed = TRUE,
  max_val = 1,
  min_val_label = 3,
  high_color = "orange2",
  mid_color = "white",
  low_color = "dodgerblue",
  wgcna_name = NULL
)

Arguments

Details

The function visualizes the regulatory network between gene modules by plotting regulatory scores as a heatmap. Each cell in the heatmap represents the regulatory interaction between a source module (columns) and a target module (rows). The color of the cell represents the magnitude and direction (positive or negative) of the regulatory score. Users can choose to plot positive, negative, or delta scores and can adjust the color gradient and score thresholds. A dendrogram can be added to cluster modules based on their regulatory patterns, and labels can be shown for cells with a sufficient number of interactions.

Value

A ggplot2 object visualizing the module regulatory network as a heatmap, which can be further customized or displayed using plot().

Description

Summarizes Transcription Factor regulatory networks across co-expression modules.

Usage

ModuleRegulatoryNetwork(seurat_obj, TFs_only = TRUE, wgcna_name = NULL)

Arguments

Details

This function summarizes transcription factor regulatory networks across modules to infer module-module relationships. The number of directed TF links across modules are counted and normalized. The strength of these links from the XGBoost model are also tracked.

Value

A data frame with the following columns:

Description

This function visualizes the regulatory network between gene modules from a Seurat object. The plot displays transcription factor (TF) regulatory interactions between modules, with edges representing positive or negative regulatory links.

Usage

ModuleRegulatoryNetworkPlot(
  seurat_obj,
  feature = "delta",
  TFs_only = TRUE,
  layout = "umap",
  umap_background = FALSE,
  max_val = 1,
  cutoff = 0,
  focus_source = NULL,
  focus_target = NULL,
  loops = TRUE,
  label_modules = TRUE,
  high_color = "orange2",
  mid_color = "white",
  low_color = "dodgerblue",
  wgcna_name = NULL,
  ...
)

Arguments

Details

The function visualizes the regulatory network between gene modules by plotting transcription factor regulatory interactions. Positive and negative regulatory scores are displayed as edges connecting nodes (modules), with node sizes proportional to the number of genes in each module. Users can customize the network layout (e.g., UMAP or other graph layouts), filter edges based on score thresholds, and highlight specific modules by using the focus parameters. Edge colors represent the strength and direction (positive or negative) of regulatory interactions, and loops can optionally be plotted to indicate self-regulation.

Value

A ggplot2 object visualizing the regulatory network, which can be further customized or displayed using plot().

Description

Plots a ranked barplot of genes in a co-expression module by intramodular connectivity

Usage

ModuleTopologyBarplot(
  seurat_obj,
  mod,
  features = "kME",
  plot_color = NULL,
  alpha = TRUE,
  genes_order = NULL,
  return_genes = FALSE,
  wgcna_name = NULL
)

Arguments

Details

ModuleTopologyBarplot generates a barplot showing the intramodular connectivity of each gene in a specific co-expression module. Each bar in this plot represents a single gene, and they are ranked based on the strength of their connections within that particular module. A custom gene ordering can be supplied, which is helpful when comparing the module topologies side by side with more than one dataset.

Value

ggplot object containing the ModuleTopologyBarplot

Description

Plots a heatmap of the co-expression network topology of a given module.

Usage

ModuleTopologyHeatmap(
  seurat_obj,
  mod,
  matrix = "TOM",
  matrix_name = NULL,
  order_by = "kME",
  high_color = NULL,
  low_color = "white",
  raster = TRUE,
  raster_dpi = 200,
  plot_max = "q99",
  plot_min = 0,
  return_genes = FALSE,
  genes_order = NULL,
  TOM_use = NULL,
  datExpr_use = NULL,
  wgcna_name = NULL,
  ...
)

Arguments

Details

ModuleTopologyHeatmap generates a triangular heatmap plot showing the network "topology" of a specific co-expression module. Each cell in the heatmap represents a gene-gene pair, and the the heatmap is colored by the strength of the connection between these two genes. By default the genes in this heatmap are ordered in both the rows and the columns based on their importance in the module, ranked either by eigengene-based connectivity (kME) or by network degree.

Value

ggplot object containing the ModuleTopologyHeatmap

Description

Correlates categorical and numeric variables with Module Eigengenes or hub-gene scores.

Usage

ModuleTraitCorrelation(
  seurat_obj,
  traits,
  group.by = NULL,
  features = "hMEs",
  cor_method = "pearson",
  subset_by = NULL,
  subset_groups = NULL,
  wgcna_name = NULL,
  ...
)

Arguments

Examples

ModuleTraitCorrelation

Description

Makes a igraph network plot using the module UMAP

Usage

ModuleUMAPPlot(
  seurat_obj,
  sample_edges = TRUE,
  edge_prop = 0.2,
  label_hubs = 5,
  edge.alpha = 0.25,
  vertex.label.cex = 0.5,
  label_genes = NULL,
  return_graph = FALSE,
  keep_grey_edges = TRUE,
  wgcna_name = NULL,
  ...
)

Arguments

Examples

ModuleUMAPPlot

Description

Displays the top n TFs in a set of modules as a bar plot

Usage

MotifOverlapBarPlot(
  seurat_obj,
  n_tfs = 10,
  plot_size = c(5, 6),
  outdir = "MotifOverlaps/",
  motif_font = "helvetica_regular",
  module_names = NULL,
  wgcna_name = NULL
)

Arguments

Examples

MotifOverlapBarPlot

Description

This function scans the promoter regions of protein-coding genes for transcription factor (TF) motifs. It extracts promoter sequences using an Ensembl database (EnsDb) and then searches these sequences for TF binding motifs using position frequency matrices (PFMs). The Seurat object is updated with a matrix of motif-gene matches, a list of target genes for each TF, and additional motif information.

Usage

MotifScan(seurat_obj, pfm, EnsDb, species_genome, wgcna_name = NULL)

Arguments

Details

The MotifScan function performs the following steps:

• It extracts promoter regions (typically 2 kb upstream of the transcription start site) of protein-coding genes from the provided EnsDb.

• It uses the motifmatchr package to search these promoters for TF motifs, using the input PFMs.

• The function returns the Seurat object updated with several key pieces of information:

  • A motif-gene match matrix indicating the presence or absence of each motif in the promoter of each gene.

  • A list of target genes for each TF based on motif presence.

  • A summary of motifs, including the number of target genes for each TF.

• This data is stored in the seurat_obj's metadata and can be used for downstream analysis, such as regulatory network inference.

Value

A modified Seurat object containing the results of the motif scan, including:

• seurat_obj@misc$motif_matrix: A binary matrix indicating motif matches for each gene.

• seurat_obj@misc$motif_info: A data frame containing motif names, IDs, and the number of target genes.

• seurat_obj@misc$motif_targets: A list of genes targeted by each motif.

• seurat_obj@misc$pfm: The original PFMs used for the motif scan.

Description

Wrapper function to run Seurat's NormalizeData function on the metacell object.

Usage

NormalizeMetacells(seurat_obj, wgcna_name = NULL, ...)

Arguments

Description

Plots the results from OverlapModulesDEGs as a bar plot

Usage

OverlapBarPlot(
  overlap_df,
  plot_var = "odds_ratio",
  logscale = FALSE,
  neglog = FALSE,
  label_size = 2,
  ...
)

Arguments

Examples

OverlapBarPlot

Description

Makes barplots from Enrichr data

Usage

OverlapDotPlot(
  overlap_df,
  plot_var = "odds_ratio",
  logscale = TRUE,
  neglog = FALSE,
  plot_significance = TRUE,
  ...
)

Arguments

Examples

OverlapDotPlot

Description

Performs Fisher's Exact Test for overlap between DEGs and hdWGCNA modules.

Usage

OverlapModulesDEGs(
  seurat_obj,
  deg_df,
  fc_cutoff = 0.5,
  group_col = "cluster",
  wgcna_name = NULL,
  ...
)

Arguments

Description

Overlap modules with TF target genes

Usage

OverlapModulesMotifs(seurat_obj, wgcna_name = NULL)

Arguments

Examples

OverlapModulesDEGs

Description

Plot WGCNA dendrogram

Usage

PlotDendrogram(
  seurat_obj,
  groupLabels = "Module colors",
  wgcna_name = NULL,
  dendroLabels = FALSE,
  hang = 0.03,
  addGuide = TRUE,
  guideHang = 0.05,
  main = "",
  ...
)

Arguments

Examples

PlotDendrogram

Description

Function to visualize differential TF regulon activity between two groups of cells based on regulon scores. The plot shows the average log fold-change (logFC) for positive and negative regulon scores in a scatter plot, with the points colored by their associated module and optionally labeled with the most differentially active regulons.

Usage

PlotDifferentialRegulons(
  seurat_obj,
  dregs,
  n_label = 10,
  logfc_thresh = 0.1,
  lm = TRUE,
  wgcna_name = NULL
)

Arguments

Details

This function generates a scatter plot where each point represents a TF regulon, with the x-axis showing the average log fold-change for positive regulon scores and the y-axis showing the average log fold-change for negative regulon scores. Points are colored by their module assignment, and the size of the points reflects the module eigengene correlation (kME) value of the TF.

Differentially expressed regulons can be highlighted, and the most significantly up- and down-regulated regulons can be labeled. Additional options allow adding a linear regression line and controlling label density based on logFC thresholds.

Value

A ggplot object representing the differential regulon scatter plot.

Description

Plotting function for the results of FindDMEs and FindAllDMEs

Usage

PlotDMEsLollipop(
  seurat_obj,
  DMEs,
  wgcna_name,
  group.by = NULL,
  comparison = NULL,
  pvalue,
  avg_log2FC = "avg_log2FC"
)

Arguments

Value

A ggplot object PlotDMEsLollipop

Description

Plotting function for the results of FindDMEs and FindAllDMEs.

Usage

PlotDMEsVolcano(
  seurat_obj,
  DMEs,
  plot_labels = TRUE,
  mod_point_size = 4,
  label_size = 4,
  show_cutoff = TRUE,
  wgcna_name = NULL,
  xlim_range = NULL,
  ylim_range = NULL
)

Arguments

Details

This function generates a volcano plot for differential module expression (DME) analysis results. It can handle both two-group comparisons (using the output of FindDMEs) and one-vs-all comparisons (using the output of FindAllDMEs).

Value

A ggplot object containing the volcano plot for the DME results.

Examples

# Example usage:
# Assuming `seurat_obj` is your Seurat object and `DMEs` is the output from FindDMEs
PlotDMEsVolcano(seurat_obj, DMEs, wgcna_name = "MG")

Description

Plotting function to show genes by kME value in each module

Usage

PlotKMEs(
  seurat_obj,
  n_hubs = 10,
  text_size = 2,
  ncol = 5,
  plot_widths = c(3, 2),
  wgcna_name = NULL
)

Arguments

Description

An Internal function for PlotDMEsLollipop to Plotting function for the results of FindDMEs and FindAllDMEs

Usage

PlotLollipop(modules, cur_DMEs, pvalue, avg_log2FC = "avg_log2FC")

Arguments

Value

A ggplot object

Examples

plot_list <- PlotLollipop(modules, DMEs, cur_title= c("Group1_vs_Group2"), pvalue, avg_log2FC = 'avg_log2FC')

Description

Plotting function for Module Preservation statistics

Usage

PlotModulePreservation(
  seurat_obj,
  name,
  statistics = "summary",
  plot_labels = TRUE,
  label_size = 4,
  mod_point_size = 4,
  wgcna_name = NULL
)

Arguments

Details

This function creates a scatter plot showing the module preservation statistics for each module compared to the size of the module (number of genes).

Description

Plots a ranked lollipop plot of co-expression modules based on the results of module preservation analysis.

Usage

PlotModulePreservationLollipop(
  seurat_obj,
  name,
  features = NULL,
  fdr = TRUE,
  wgcna_name = NULL
)

Arguments

Details

PlotModulePreservationLollipop generates a lollipop plot showing module preservation results. If the module preservation test was performed using the WGCNA method, the statistic that will be shown is the Z-summary preservation statistic. If the analysis was performed using NetRep, then the statistic that will be shown is the FDR corrected averaged p-values from the module preservation permutation test.

Value

ggplot object containing the PlotModulePreservationLollipop

Description

Plotting function for Module Preservation statistics

Usage

PlotModuleTraitCorrelation(
  seurat_obj,
  high_color = "red",
  mid_color = "grey90",
  low_color = "blue",
  label = NULL,
  label_symbol = "stars",
  plot_max = NULL,
  text_size = 2,
  text_color = "black",
  text_digits = 3,
  combine = TRUE,
  wgcna_name = NULL
)

Arguments

Description

Plots module eigengene dynamics over a pseudotime trajectory.

Usage

PlotModuleTrajectory(
  seurat_obj,
  pseudotime_col = "pseudotime",
  n_bins = 20,
  harmonized = TRUE,
  ncol = 4,
  point_size = 1,
  line_size = 1,
  se = TRUE,
  group_colors = NULL,
  wgcna_name = NULL
)

Arguments

Description

Plot Soft Power Threshold results

Usage

PlotSoftPowers(
  seurat_obj,
  selected_power = NULL,
  point_size = 5,
  text_size = 3,
  plot_connectivity = TRUE,
  wgcna_name = NULL
)

Arguments

Examples

PlotSoftPowers

Description

Project a set of co-expression modules from a reference to a query dataset

Usage

ProjectModules(
  seurat_obj,
  seurat_ref = NULL,
  modules = NULL,
  group.by.vars = NULL,
  gene_mapping = NULL,
  genome1_col = NULL,
  genome2_col = NULL,
  overlap_proportion = 0.5,
  vars.to.regress = NULL,
  scale.model.use = "linear",
  wgcna_name = NULL,
  wgcna_name_proj = NULL,
  ...
)

Arguments

Examples

ProjectModules

Description

Reassigns features to different modules

Usage

ReassignModules(
  seurat_obj,
  harmonized = TRUE,
  features = NULL,
  new_modules = NULL,
  ignore = FALSE,
  auto_reassign = FALSE,
  wgcna_name = NULL
)

Arguments

Details

ReassignModules reassigns features with negative kMEs in their assigned module to the module that had the highest kME for that feature. Alternatively, this function can manually assign features to different modules, which can be helpful if certain genes of interest are assigned to the grey module. We generally do not advise reassigning features to new modules if they are in non-grey modules, but the user can do so at their own risk by setting ignore=TRUE.

Value

seurat_obj with the an updated modules table for the selected wgcna experiment

Description

Plots the top target genes within a specific TF regulon

Usage

RegulonBarPlot(
  seurat_obj,
  selected_tf,
  cutoff = 0.2,
  top_n = Inf,
  TFs_only = FALSE,
  high_color = "orange2",
  mid_color = "white",
  low_color = "dodgerblue",
  wgcna_name = NULL
)

Arguments

Details

RegulonBarPlot creates a bar plot showing the top target genes for a particular TF based on the TF regulatory network analysis. Target genes are ranked by the predicted interaction strength from XGBoost (Gain) multiplied by the sign of the correlation between the TF and the target gene.

Value

ggplot object containing the RegulonBarPlot

Description

Calculate expression scores for TF Regulons

Usage

RegulonScores(
  seurat_obj,
  target_type = "positive",
  cor_thresh = 0.05,
  exclude_grey_genes = TRUE,
  wgcna_name = NULL,
  ...
)

Arguments

Details

RegulonScores calculates expression signatures for each TF regulon using the UCell algorithm This function can calculate separate scores for TF regulons for target genes that are positively or negatively correlated with the TF, representing putative acivated or repressed genes. These scores conveniently summarize the expression levels of the entire TF regulon, similar to module eigengenes for the co-expression network analyssis.

Value

seurat_obj with the TF Regulon Scores added

Description

Reset the unique color for each hdWGCNA module

Usage

ResetModuleColors(seurat_obj, new_colors, wgcna_name = NULL)

Arguments

Description

Reset the uname of each hdWGCNA module

Usage

ResetModuleNames(
  seurat_obj,
  new_name = "M",
  reset_levels = FALSE,
  wgcna_name = NULL
)

Arguments

Description

Run Enrichr gene set enrichment tests on hdWGCNA modules

Usage

RunEnrichr(
  seurat_obj,
  dbs = c("GO_Biological_Process_2021", "GO_Cellular_Component_2021",
    "GO_Molecular_Function_2021"),
  max_genes = 100,
  wait = TRUE,
  wait_time = 5,
  wgcna_name = NULL,
  ...
)

Arguments

Description

Run Enrichr gene set enrichment tests on hdWGCNA modules

Usage

RunEnrichrRegulons(
  seurat_obj,
  dbs = c("GO_Biological_Process_2021", "GO_Cellular_Component_2021",
    "GO_Molecular_Function_2021"),
  depth = 1,
  use_regulons = TRUE,
  min_genes = 5,
  wait = TRUE,
  wait_time = 5,
  wgcna_name = NULL,
  ...
)

Arguments

Description

Wrapper function to run harmony's RunHarmony function on the metacell object.

Usage

RunHarmonyMetacells(seurat_obj, ...)

Arguments

Description

Run UMAP on co-expression matrix using hub genes as features.

Usage

RunModuleUMAP(
  seurat_obj,
  n_hubs = 10,
  exclude_grey = TRUE,
  genes_use = NULL,
  wgcna_name = NULL,
  n_neighbors = 25,
  metric = "cosine",
  spread = 1,
  min_dist = 0.4,
  supervised = FALSE,
  ...
)

Arguments

Description

Wrapper function to run Seurat's RunPCA function on the metacell object.

Usage

RunPCAMetacells(seurat_obj, wgcna_name = NULL, ...)

Arguments

Description

Wrapper function to run Seurat's RunUMAP function on the metacell object.

Usage

RunUMAPMetacells(seurat_obj, ...)

Arguments

Description

Function to scale a numeric vector between 0 and 1.

Usage

scale01(x)

Arguments

Description

Wrapper function to run Seurat's ScaleData function on the metacell object.

Usage

ScaleMetacells(seurat_obj, wgcna_name = NULL, ...)

Arguments

Description

Select genes that will be used for co-expression network analysis

Usage

SelectNetworkGenes(
  seurat_obj,
  gene_select = "variable",
  fraction = 0.05,
  group.by = NULL,
  gene_list = NULL,
  assay = NULL,
  wgcna_name = NULL
)

Arguments

Details

SelectNetworkGenes allows us to specify the genes that will be used for co-expression network analysis. This function is called by SetupForWGCNA. By default, the variable features in VariableFeatures(seurat_obj) are used. A custom gene list can also be used with the gene_list parameter and setting gene_select="custom".

We can also identify genes that are expressed above 0 in a certain proportion of the dataset by settting gene_select='fraction'. For example, by setting fraction=0.1 and group.by='seurat_clusters', this function will identify the set of genes that are expressed in 10% of cells in at least one of the clusters.

Description

SetActiveWGCNA

Usage

SetActiveWGCNA(seurat_obj, wgcna_name)

Arguments

Description

SetAvgModuleExpr

Usage

SetAvgModuleExpr(seurat_obj, avg_mods, wgcna_name = NULL)

Arguments

Description

This function specifies the gene expression matrix for co-expression network analysis.

Usage

SetDatExpr(
  seurat_obj,
  group_name,
  use_metacells = TRUE,
  group.by = NULL,
  multi.group.by = NULL,
  multi_group_name = NULL,
  return_seurat = TRUE,
  assay = NULL,
  slot = "data",
  layer = "data",
  mat = NULL,
  features = NULL,
  wgcna_name = NULL,
  ...
)

Arguments

Details

SetDatExpr is a critical function of the hdWGCNA pipeline that determines the gene expession matrix that will be used for network analysis. We typically use this function to select a cell type or group of cell types for network analysis using the group.by parameter, but we provide additional parameters for further customization.

Description

SetDegrees

Usage

SetDegrees(seurat_obj, degree_df, wgcna_name = NULL)

Arguments

Description

SetEnrichRegulonTable

Usage

SetEnrichrRegulonTable(seurat_obj, enrich_table, wgcna_name = NULL)

Arguments

Description

SetEnrichrTable

Usage

SetEnrichrTable(seurat_obj, enrich_table, wgcna_name = NULL)

Arguments

Description

SetMELoadings

Usage

SetMELoadings(seurat_obj, loadings, harmonized = TRUE, wgcna_name = NULL)

Arguments

Description

SetMEs

Usage

SetMEs(seurat_obj, MEs, harmonized = TRUE, wgcna_name = NULL)

Arguments

Description

SetMetacellObject

Usage

SetMetacellObject(seurat_obj, metacell_obj, wgcna_name = NULL)

Arguments

Description

SetMetacellParams

Usage

SetMetacellParams(seurat_obj, params, wgcna_name = NULL)

Arguments

Description

SetModulePreservation

Usage

SetModulePreservation(seurat_obj, mod_pres, mod_name, wgcna_name = NULL)

Arguments

Description

SetModules

Usage

SetModules(seurat_obj, modules, wgcna_name = NULL)

Arguments

Description

SetModuleScores

Usage

SetModuleScores(seurat_obj, mod_scores, wgcna_name = NULL)

Arguments

Description

SetModuleTraitCorrelation

Usage

SetModuleTraitCorrelation(seurat_obj, mt_cor, wgcna_name = NULL)

Arguments

Description

SetModuleUMAP

Usage

SetModuleUMAP(seurat_obj, umap_df, wgcna_name = NULL)

Arguments

Description

SetMotifMatrix

Usage

SetMotifMatrix(seurat_obj, tf_match)

Arguments

Examples

SetMotifMatrix

Description

SetMotifOverlap

Usage

SetMotifOverlap(seurat_obj, overlap_df, wgcna_name = NULL)

Arguments

Description

SetMotifs

Usage

SetMotifs(seurat_obj, motif_df)

Arguments

Examples

SetMotifs

Description

SetMotifScores

Usage

SetMotifScores(seurat_obj, tf_scores, wgcna_name = NULL)

Arguments

Description

SetMotifTargets

Usage

SetMotifTargets(seurat_obj, motif_targets)

Arguments

Examples

SetMotifTargets

Description

This function sets up the expression matrix input for consensus WGCNA based on the metacell expression matrix, the full expression matrix, or a provided pseudobulk expression matrix.

Usage

SetMultiExpr(
  seurat_obj,
  group_name,
  use_metacells = TRUE,
  group.by = NULL,
  multi.group.by = NULL,
  multi_groups = NULL,
  assay = NULL,
  slot = "data",
  layer = "data",
  mat = NULL,
  mat_group_delim = 3,
  wgcna_name = NULL,
  ...
)

Arguments

Description

SetNetworkData

Usage

SetNetworkData(seurat_obj, net, wgcna_name = NULL)

Arguments

Description

SetPFMList

Usage

SetPFMList(seurat_obj, pfm_list)

Arguments

Examples

SetPFMList

Description

SetPowerTable

Usage

SetPowerTable(seurat_obj, power_table, wgcna_name = NULL)

Arguments

Description

SetRegulonScores

Usage

SetRegulonScores(seurat_obj, regulon_scores, target_type, wgcna_name = NULL)

Arguments

Description

SetTFEval

Usage

SetTFEval(seurat_obj, tf_eval, wgcna_name = NULL)

Arguments

Description

SetTFNetwork

Usage

SetTFNetwork(seurat_obj, tf_net, wgcna_name = NULL)

Arguments

Description

SetTFRegulons

Usage

SetTFRegulons(seurat_obj, tf_regulons, wgcna_name = NULL)

Arguments

Description

Create a slot in a Seurat object to store hdWGCNA data

Usage

SetupForWGCNA(
  seurat_obj,
  wgcna_name,
  features = NULL,
  metacell_location = NULL,
  ...
)

Arguments