Clustering and Phenotyping
FlowSOM Clustering
r
library(FlowSOM)
# Prepare data
expr <- exprs(fcs)
marker_cols <- grep('CD|HLA', colnames(fcs), value = TRUE)
# Build SOM
fsom <- FlowSOM(fcs,
colsToUse = marker_cols,
xdim = 10, ydim = 10,
nClus = 20,
seed = 42)
# Get cluster assignments
clusters <- GetMetaclusters(fsom)
# Add to flowFrame
exprs(fcs) <- cbind(exprs(fcs), cluster = clusters)
CATALYST Workflow (Full Pipeline)
r
library(CATALYST)
library(SingleCellExperiment)
# Create SCE from flowSet
sce <- prepData(fs, panel, md, transform = TRUE, cofactor = 5)
# Clustering
sce <- cluster(sce,
features = 'type', # Use 'type' markers from panel
xdim = 10, ydim = 10,
maxK = 20,
seed = 42)
# View cluster assignments
table(cluster_ids(sce, 'meta20'))
Phenograph Clustering
r
library(Rphenograph) # Extract expression matrix expr <- assay(sce, 'exprs') # Run Phenograph pheno_result <- Rphenograph(t(expr[rowData(sce)$marker_class == 'type', ]), k = 30) # Get clusters sce$phenograph <- factor(membership(pheno_result[[2]]))
Dimensionality Reduction
r
# UMAP sce <- runDR(sce, dr = 'UMAP', features = 'type') # tSNE sce <- runDR(sce, dr = 'TSNE', features = 'type') # Plot plotDR(sce, 'UMAP', color_by = 'meta20')
Cluster Annotation
r
# Heatmap of marker expression by cluster
plotExprHeatmap(sce, features = 'type',
by = 'cluster_id', k = 'meta20',
scale = 'first', row_anno = FALSE)
# Manual annotation
cluster_annotation <- c(
'1' = 'CD4 T cells',
'2' = 'CD8 T cells',
'3' = 'B cells',
'4' = 'NK cells',
'5' = 'Monocytes'
)
sce$cell_type <- cluster_annotation[as.character(cluster_ids(sce, 'meta20'))]
Cluster Merging
r
# Merge similar clusters
merging_table <- data.frame(
original = 1:20,
merged = c(1, 1, 2, 2, 3, 3, 4, 4, 5, 5,
6, 6, 7, 7, 8, 8, 9, 9, 10, 10)
)
sce <- mergeClusters(sce, k = 'meta20', table = merging_table, id = 'merged')
Abundance Analysis (per sample)
r
# Cluster frequencies per sample abundances <- table(cluster_ids(sce, 'meta20'), sce$sample_id) freq <- prop.table(abundances, margin = 2) # Plot plotAbundances(sce, k = 'meta20', by = 'cluster_id', group_by = 'condition')
Marker Expression Summary
r
# Median expression per cluster plotClusterExprs(sce, k = 'meta20', features = 'type') # Expression by cluster and condition plotPbExprs(sce, k = 'meta20', features = 'type', facet_by = 'cluster_id')
Export Results
r
# Add cluster info to metadata colData(sce)$cluster <- cluster_ids(sce, 'meta20') # Export to CSV results <- as.data.frame(colData(sce)) write.csv(results, 'clustering_results.csv', row.names = FALSE) # Save SCE saveRDS(sce, 'sce_clustered.rds')
Choosing Number of Clusters
r
# Delta area plot plotNRS(sce, features = 'type') # Or visual inspection of heatmap at different K plotExprHeatmap(sce, features = 'type', by = 'cluster_id', k = 'meta10') plotExprHeatmap(sce, features = 'type', by = 'cluster_id', k = 'meta20')
Batch Integration
r
# If batch effects present library(batchelor) sce <- runDR(sce, dr = 'UMAP', features = 'type') # Check for batch effects plotDR(sce, 'UMAP', color_by = 'batch') # MNN correction if needed sce_corrected <- fastMNN(sce, batch = sce$batch)
Related Skills
- •gating-analysis - Manual alternative
- •differential-analysis - Compare clusters between conditions
- •single-cell/clustering - Similar concepts for scRNA-seq