typos (19ba0ae7) · Commits · github_fork / HdWGCNA

R/WGCNA_functions.R

+6 −1

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		@@ -114,15 +114,20 @@ SelectNetworkGenes <- function(
		#' This function gets the expression matrix from the metacell object.
		#'
		#' @param seurat_obj A Seurat object
		#' @param wgcna_name name of the WGCNA experiment
		#' @param features list of features to use for WGCNA
		#' @param metacell_location name of the WGCNA experiment to copy the metacell object from
		#' @param ... additional parameters to pass to SelectNetworkGenes
		#' @param group (parameter not used anymore, can ignore)
		#' @keywords scRNA-seq
		#' @export
		#' @examples
		#' SetupForWGCNA(pbmc)
		SetupForWGCNA <- function(
		seurat_obj, wgcna_name,
		group=NULL,
		features = NULL,
		metacell_location = NULL,
		group=NULL,
		...
		){

R/plotting.R

+4 −3

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		@@ -2188,13 +2188,14 @@ ModuleTFNetwork <- function(
		# set up edge df
		edge_df <- data.frame(
		Var1 = tf_gene_name,
		Var2 = node_df$name,
		Var2 = as.character(node_df$name),
		value = node_df$odds_ratio,
		color = exp_cor$color
		)

		# set the edge color to grey if the overlap isn't significant:
		edge_df$color <- ifelse(cur_overlap$fdr <= 0.05, edge_df$color, 'grey')
		edge_df$color <- ifelse(node_df$fdr <= 0.05, edge_df$color, 'grey')
		edge_df <- subset(edge_df, color != 'grey')

		# set up node df
		node_df <- dplyr::bind_rows(node_df, tf_df) %>% as.data.frame()
		@@ -2229,7 +2230,7 @@ ModuleTFNetwork <- function(
		plot(
		g1,
		layout = as.matrix(node_df[,c('UMAP1', 'UMAP2')]),
		edge.color=adjustcolor(E(g1)$color, alpha.f=edge.alpha),
		edge.color=adjustcolor(E(g1)$color),
		vertex.size=V(g1)$size * size.scale,
		edge.curved=0,
		edge.width=edge_df$value*2,

README.md

+1 −1

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		@@ -7,7 +7,7 @@



		Note: hdWGCNA is under active development, so you will likely run into errors
		Note: hdWGCNA is under active development, so you will likely run into errors and small typos
		if you choose to use hdWGCNA before its first stable release.

		hdWGCNA, formerly known as scWGCNA, is an R package for performing weighted gene co-expression network analysis [(WGCNA)](https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/) in high dimensional

docs/articles/basic_tutorial.html

+10 −12

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		@@ -174,7 +174,7 @@

		<span class="co"># co-expression network analysis packages:</span>
		<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="http://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/" class="external-link">WGCNA</a></span><span class="op">)</span>
		<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://smorabit.github.io/hdWGCNA/" class="external-link">hdWGCNA</a></span><span class="op">)</span>
		<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">hdWGCNA</span><span class="op">)</span>

		<span class="co"># using the cowplot theme for ggplot</span>
		<span class="fu"><a href="https://ggplot2.tidyverse.org/reference/theme_get.html" class="external-link">theme_set</a></span><span class="op">(</span><span class="fu"><a href="https://wilkelab.org/cowplot/reference/theme_cowplot.html" class="external-link">theme_cowplot</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
		@@ -486,24 +486,22 @@ C1orf159 C1orf159 grey grey -0.015618737 0.002235229 -0.0003824554
		</h3>
		<p>The base Seurat plotting functions are also great for visualizing hdWGCNA outputs. Here we demonstrate plotting hMEs using <code>DotPlot</code> and <code>VlnPlot</code>. The key to using Seurat’s plotting functions to visualize the hdWGCNA data is to add it into the Seurat object’s <code>@meta.data</code> slot:</p>
		<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
		<code class="sourceCode R"><span class="co"># add hMEs to Seurat meta-data:</span>
		<span class="va">seurat_obj</span><span class="op">@</span><span class="va">meta.data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">cbind</a></span><span class="op">(</span>
		<span class="va">seurat_obj</span><span class="op">@</span><span class="va">meta.data</span>,
		<span class="fu"><a href="../reference/GetMEs.html">GetMEs</a></span><span class="op">(</span><span class="va">seurat_obj</span>, harmonized<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>
		<span class="op">)</span></code></pre></div>
		<code class="sourceCode R"><span class="co"># get hMEs from seurat object</span>
		<span class="va">MEs</span> <span class="op"><-</span> <span class="fu"><a href="../reference/GetMEs.html">GetMEs</a></span><span class="op">(</span><span class="va">seurat_obj</span>, harmonized<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span>
		<span class="va">mods</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">colnames</a></span><span class="op">(</span><span class="va">MEs</span><span class="op">)</span>; <span class="va">mods</span> <span class="op"><-</span> <span class="va">mods</span><span class="op">[</span><span class="va">mods</span> <span class="op">!=</span> <span class="st">'grey'</span><span class="op">]</span>

		<span class="co"># add hMEs to Seurat meta-data:</span>
		<span class="va">seurat_obj</span><span class="op">@</span><span class="va">meta.data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">cbind</a></span><span class="op">(</span><span class="va">seurat_obj</span><span class="op">@</span><span class="va">meta.data</span>, <span class="va">MEs</span><span class="op">)</span></code></pre></div>
		<p>Now we can easily use Seurat’s <code>DotPlot</code> function:</p>
		<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
		<code class="sourceCode R"><span class="co"># plot with Seurat's DotPlot function</span>
		<span class="va">p</span> <span class="op"><-</span> <span class="fu"><a href="https://satijalab.org/seurat/reference/DotPlot.html" class="external-link">DotPlot</a></span><span class="op">(</span>
		<span class="va">seurat_obj</span>,
		group.by <span class="op">=</span> <span class="st">'cell_type'</span>
		<span class="op">)</span>
		<span class="va">p</span> <span class="op"><-</span> <span class="fu"><a href="https://satijalab.org/seurat/reference/DotPlot.html" class="external-link">DotPlot</a></span><span class="op">(</span><span class="va">seurat_obj</span>, <span class="va">mods</span>, group.by <span class="op">=</span> <span class="st">'cell_type'</span><span class="op">)</span>

		<span class="co"># flip the x/y axes, rotate the axis labels, and change color scheme:</span>
		<span class="va">p</span> <span class="op"><-</span> <span class="va">p</span> <span class="op">+</span>
		<span class="fu"><a href="https://ggplot2.tidyverse.org/reference/coord_flip.html" class="external-link">coord_flip</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span>
		<span class="fu"><a href="https://satijalab.org/seurat/reference/SeuratTheme.html" class="external-link">RotatedAxis</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span>
		<span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_gradient.html" class="external-link">scale_color_gradient2</a></span><span class="op">(</span>high<span class="op">=</span><span class="st">'red'</span>, mid<span class="op">=</span><span class="st">'grey95'</span>, low<span class="op">=</span><span class="st">'blue'</span><span class="op">)</span> <span class="op">+</span>
		<span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_gradient.html" class="external-link">scale_color_gradient2</a></span><span class="op">(</span>high<span class="op">=</span><span class="st">'red'</span>, mid<span class="op">=</span><span class="st">'grey95'</span>, low<span class="op">=</span><span class="st">'blue'</span><span class="op">)</span>

		<span class="co"># plot output</span>
		<span class="va">p</span></code></pre></div>
		@@ -513,7 +511,7 @@ C1orf159 C1orf159 grey grey -0.015618737 0.002235229 -0.0003824554
		<code class="sourceCode R"><span class="co"># Plot INH-M4 hME using Seurat VlnPlot function</span>
		<span class="va">p</span> <span class="op"><-</span> <span class="fu"><a href="https://satijalab.org/seurat/reference/VlnPlot.html" class="external-link">VlnPlot</a></span><span class="op">(</span>
		<span class="va">seurat_obj</span>,
		features <span class="op">=</span> <span class="st">'INH-M3'</span>,
		features <span class="op">=</span> <span class="st">'INH-M12'</span>,
		group.by <span class="op">=</span> <span class="st">'cell_type'</span>,
		pt.size <span class="op">=</span> <span class="fl">0</span> <span class="co"># don't show actual data points</span>
		<span class="op">)</span>

docs/articles/network_visualizations.html

+3 −3

Original line number	Diff line number	Diff line
		@@ -158,7 +158,7 @@

		<span class="co"># co-expression network analysis packages:</span>
		<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="http://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/" class="external-link">WGCNA</a></span><span class="op">)</span>
		<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://smorabit.github.io/hdWGCNA/" class="external-link">hdWGCNA</a></span><span class="op">)</span>
		<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">hdWGCNA</span><span class="op">)</span>

		<span class="co"># network analysis & visualization package:</span>
		<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://igraph.org" class="external-link">igraph</a></span><span class="op">)</span>
		@@ -283,9 +283,9 @@ Code
		<span class="co"># plot with ggplot + gganimate</span>
		<span class="va">p</span> <span class="op"><-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">df</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x<span class="op">=</span><span class="va">UMAP1</span>, y<span class="op">=</span><span class="va">UMAP2</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span>
		<span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_point.html" class="external-link">geom_point</a></span><span class="op">(</span>color<span class="op">=</span><span class="va">df</span><span class="op">$</span><span class="va">color</span>, size<span class="op">=</span><span class="va">df</span><span class="op">$</span><span class="va">kME</span><span class="op">*</span><span class="fl">2</span> <span class="op">)</span> <span class="op">+</span>
		<span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">ggtitle</a></span><span class="op">(</span><span class="st">"Supervised weight: {closest_state}"</span><span class="op">)</span> <span class="op">+</span>
		<span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">ggtitle</a></span><span class="op">(</span><span class="st">"N hubs: {closest_state}"</span><span class="op">)</span> <span class="op">+</span>
		<span class="fu"><a href="https://gganimate.com/reference/transition_states.html" class="external-link">transition_states</a></span><span class="op">(</span>
		<span class="va">weight</span>,
		<span class="va">n_hubs</span>,
		transition_length <span class="op">=</span> <span class="fl">2</span>,
		state_length <span class="op">=</span> <span class="fl">2</span>,
		wrap <span class="op">=</span> <span class="cn">TRUE</span>

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