Hello everyone,
I'm currently working with a dataset comprising 496 cancer samples and my aim is to perform class discovery to identify potential clusters within the data. My plan involves constructing a graph using the igraph and subsequently employing Louvain clustering to uncover communities within the dataset.
To start, I've applied a nearest neighbors search using the RANN package to create an adjacency matrix. The outcome of this step is a list with two elements: nn.idx, which provides the nearest neighbor indices, and nn.dists, offering the corresponding Euclidean distances. Both matrices have dimensions of 496 x 10, representing the near neighbor information for each sample.
I'm seeking guidance on how to proceed from here to generate a square adjacency matrix suitable for building a graph object. I truly appreciate any assistance or insights you can provide.
Here's the script I've used for the nearest neighbors search:
neighbors <- RANN::nn2(df.dat.2)
This is a glimpse of how my data looks:
df.dat.2[1:6,1:5]
Cytotoxic_lymphocytes NK_cells IMMUNE_CD8MACRO_GALON IMMUNE_TREG_PASTILLE IMMUNE_TH1_GALON
DFR18201125_S2 1.0847794 -0.7873059 2.0384016 2.3400656 1.6245264
CMR18160811_S14 1.4107888 0.7359309 2.3173862 -0.6108772 1.1848030
AKNR18200612_S1 1.4906686 1.4865404 1.8352200 0.4802375 1.5588536
X211_S39 -0.2367320 -0.4840597 -1.4358730 0.5417923 -1.1066911
X213_S41 -0.6760709 -0.5416064 -2.3613827 0.1748922 -1.6711970
X214_S42 -1.0435179 -0.4920662 -0.8881929 -0.6243585 -0.8318914
Thank you for your insights and suggestions in advance.
1 answer
library(igraph)
#Create a graph adjacency based on correlation distances between genes in pairwise fashion.
g <- graph.adjacency(as.matrix(as.dist(cor(t(df.dat.2), method="pearson"))), mode="undirected", weighted=TRUE, diag=FALSE)
#If you want to use Euclidean distances instead of correlation,
g <- graph.adjacency(as.matrix(dist(MyData, method="euclidean")), mode="undirected", weighted=TRUE, diag=FALSE)
If you are working in single cell data, you could do something like below using pbmc3k data-
library(SingleCellExperiment)
library(Seurat)
library(scater)
library(scran)
library(igraph)
library(SeuratData)
library(pheatmap)
library(mclust)
#InstallData("pbmc3k")
pbmc <- LoadData("pbmc3k", type = "pbmc3k.final")
pbmc.sce <- as.SingleCellExperiment(pbmc.updated)
pbmc.sce
#create SNN graph
graph_k10 <- scran::buildSNNGraph(pbmc.sce, k = 10, use.dimred = "PCA", type = "rank")
#community detection in igraph using walktrap algorithm and Louvian method
clust_k10_walktrap <- igraph::cluster_walktrap(graph_k10)$membership
clust_k10_louvain <- igraph::cluster_louvain(graph_k10)$membership
table(clust_k10_walktrap)
table(clust_k10_louvain)
table(clust_k10_walktrap, clust_k10_louvain)
## Add cluster assignments to the SingleCellExperiment object and visualize in
## UMAP representation
pbmc.sce$cluster_walktrap_k10 <- factor(clust_k10_walktrap)
pbmc.sce$cluster_louvain_k10 <- factor(clust_k10_louvain)
scater::plotReducedDim(pbmc.sce, "UMAP", colour_by = "cluster_walktrap_k10")
scater::plotReducedDim(pbmc.sce, "UMAP", colour_by = "cluster_louvain_k10")
## Define a set of colors to use (must be at least as many as the number of
## communities)
cols <- RColorBrewer::brewer.pal(n = 12, name = "Paired")
## Plot the graph, color by cluster assignment
igraph::plot.igraph(
graph_k10, layout = layout_with_fr(graph_k10),
vertex.color = cols[clust_k10_walktrap],
vertex.size = 5, vertex.label = NA, main = "Walktrap"
)
igraph::plot.igraph(
graph_k10, layout = layout_with_fr(graph_k10),
vertex.color = cols[clust_k10_louvain],
vertex.size = 5, vertex.label = NA, main = "Louvain"
)
You need to use nn.idx element and define k (The maximum number of nearest neighbours to compute). And perform something like this-
rm(list=ls())
library(RANN)
library(igraph)
library(igraph.extensions)
library(leiden)
library(plot.igraph)
# Set the number of genes and samples
num_genes <- 5000
num_samples <- 40
# Generate random gene expression data
set.seed(123) # For reproducibility
gene_expression <- matrix(rnorm(num_genes * num_samples), ncol = num_samples)
# Add row and column names for clarity
rownames(gene_expression) <- paste("Gene", 1:num_genes, sep = "")
colnames(gene_expression) <- paste("S", 1:num_samples, sep = "")
head(gene_expression)
snn <- RANN::nn2(t(gene_expression), k=30)$nn.idx
adjacency_matrix <- matrix(0L, ncol(gene_expression), ncol(gene_expression))
rownames(adjacency_matrix) <- colnames(adjacency_matrix) <- colnames(gene_expression)
for(ii in 1:ncol(gene_expression)) {
adjacency_matrix[ii,colnames(gene_expression)[snn[ii,]]] <- 1L
}
graph_object <- graph_from_adjacency_matrix(adjacency_matrix, mode = "directed")
#plot_directed(graph_object, cex.arrow = 0.3, col.arrow = "grey50")
partition <- leiden(adjacency_matrix)
table(partition)
library("RColorBrewer")
node.cols <- brewer.pal(max(partition),"Pastel1")[partition]
plot_directed(graph_object, cex.arrow = 0.3, col.arrow = "grey50", fill.node = node.cols)
This is for your reference- https://github.com/TomKellyGenetics/leiden
Thanks so much bk11! Are there any specific criteria or guidelines that I should consider when selecting the k value for my gene expression data?
I also got this error when running this part of the script:
> partition <- leiden(adjacency_matrix)
Error: ImportError: DLL load failed while importing _igraph: The specified module could not be found.
Do you know how to resolve the issue?
I am working with R and this is my session info:
Sorry it doesn't let me to save the Session info here --- because of invalid characters!!
Are there any specific criteria or guidelines that I should consider when selecting the k value for my gene expression data?
The maximum number of nearest neighbours to compute. The default value is set to the smaller of the number of columnns in data
Error: ImportError: DLL load failed while importing _igraph: The specified module could not be found.
Did you install leiden? If not do this- install.packages("leiden") or devtools::install_github("TomKellyGenetics/leiden") and additionally you may have to do py_install("python-igraph") & py_install("leidenalg"). Please make sure that you have installed reticulate
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