Hi,
I want to make a heat map showing dissimilarities between clusters in single cell data. I retrieved this image from this article : 10.4049/jimmunol.2200365
In the article they describe how they did it like this : To quantify high-level differences in overall gene expression levels between clusters, Analysis of Similarities (ANOSIM) R statistics were computed in the vegan package in R .
But anosim doesnt give a output that can be used for constructing heat map. it only gives p value and R statistics. I couldnt figure it out.
•
written
by
carolofharvest •
1 answer
Difficult to know what was the calculation under the hood to plot the heatmap, the methods are not super detailed.
What I would do, is to select a number of gene markers (20,50,100, what you feel make the best distinction between your clusters) for each cluster (with FindAllMarkers in Seurat for example)
Then you create a matrix of normalized expression of gene markers per cluster by aggregating your counts for each clusters.
You scale your matrix by row and column, and you can call correlation between clusters and inverse it to get distances.
gene_markers <- FindAllMarkers(object, slot = "data", min.pct = 0.1, logfc.threshold = 0.5, only.pos = TRUE)
topDiffCluster <- gene_markers %>% filter(p_val_adj < 0.05) %>% filter(avg_log2FC > 0.5) %>% filter(pct.1 > 0.5) %>% arrange(cluster, desc(avg_log2FC)) %>% group_by(cluster) %>% top_n(n = 20, wt = avg_log2FC)
expression_matrix <- as.data.frame(AggregateExpression(object, assays = "RNA", group.by = "seurat_clusters", normalization.method = "LogNormalize", scale.factor = 10000, return.seurat = TRUE)[["RNA"]]$data[unique(topDiffCluster$gene),])
colnames(expression_matrix) <- gsub("g","",colnames(expression_matrix))
expression_matrix_scaled <- t(scale(t(expression_matrix)))
d=as.dist(1-cor(expression_matrix_scaled))
corrplot(d, method = 'shade', order = 'hclust')
Log in to answer this question.
