Combined or independent SuperCell runs for different samples

In SuperCell: Simplification of scRNA-Seq Data by Merging Together Similar Cells

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "figures/",
  fig.width = 6, fig.height = 6,
  eval = FALSE
)

Comparing a combined (i.e., processing samples together) and an independent (i.e., processing samples separately) construction of metacells with SuperCell (related to @daskelly question https://github.com/GfellerLab/SuperCell/issues/11#issuecomment-1090916447).

library(SuperCell)
library(Matrix)
data(cell_lines)

GE <- cell_lines$GE
cell.meta <- cell_lines$meta

gamma <- 20 # graining level
n.pc  <- 10 # number of PCs

To compare results, we use 2 samples that correspond to two different cancer cell lines (data from Tian et al., 2019)

cell.idx.HCC827 <- which(cell.meta == "HCC827")
cell.idx.H838   <- which(cell.meta == "H838")

A combined analysis -- construction of metacells processing two samples together

SC.HCC827.H838 <- SCimplify(
  GE[,c(cell.idx.HCC827, cell.idx.H838)],  # log-normalized gene expression matrix
  gamma = gamma, # graining level
  cell.split.condition = cell.meta[c(cell.idx.HCC827, cell.idx.H838)], # metacell do not mix cells from different cell lines
  n.pc = n.pc) # number of proncipal components to use

genes.use <- SC.HCC827.H838$genes.use

SC.HCC827.H838$cell.line <- supercell_assign(cell.meta[c(cell.idx.HCC827, cell.idx.H838)], 
                                             supercell_membership = SC.HCC827.H838$membership)

SC.GE.HCC827.H838 <- supercell_GE(GE[,c(cell.idx.HCC827, cell.idx.H838)], groups = SC.HCC827.H838$membership)

SC.HCC827.H838$SC_PCA <- supercell_prcomp(
  Matrix::t(SC.GE.HCC827.H838),
  supercell_size = SC.HCC827.H838$supercell_size, 
  genes.use = genes.use)

SC.HCC827.H838$SC_UMAP <- supercell_UMAP(
  SC.HCC827.H838, 
  n_neighbors = 10)

supercell_plot_UMAP(
    SC.HCC827.H838,
    group = "cell.line",
    title = paste0("Combined construction of HCC827 and H838 metacells")
  )

Independent analysis -- construction of metacells for each sample independently (applying the same granularity gamma and the same set of features genes.use)

SC.HCC827 <- SCimplify(GE[,cell.idx.HCC827],  # log-normalized gene expression matrix
                gamma = gamma, # graining level
                n.pc = n.pc, # number of proncipal components to use
                genes.use = genes.use) # using the same set of genes as for the combined analysis

SC.HCC827$cell.line <- supercell_assign(cell.meta[cell.idx.HCC827], supercell_membership = SC.HCC827$membership)

SC.H838 <- SCimplify(GE[,cell.idx.H838],  # log-normalized gene expression matrix
                gamma = gamma, # graining level
                n.pc = n.pc, # number of proncipal components to use
                genes.use = genes.use) # using the same set of genes as for the combined analysis
SC.H838$cell.line <- supercell_assign(cell.meta[cell.idx.H838], supercell_membership = SC.H838$membership)

SC.merged <- supercell_merge(list(SC.HCC827, SC.H838), fields = c("cell.line"))

# compute metacell gene expression for SC.HCC827
SC.GE.HCC827 <- supercell_GE(GE[, cell.idx.HCC827], groups = SC.HCC827$membership)
# compute metacell gene expression for SC.H838
SC.GE.H838 <- supercell_GE(GE[, cell.idx.H838], groups = SC.H838$membership)
# merge GE matricies
SC.GE.merged <- supercell_mergeGE(list(SC.GE.HCC827, SC.GE.H838))

SC.merged$SC_PCA <- supercell_prcomp(
  Matrix::t(SC.GE.merged),
  supercell_size = SC.merged$supercell_size, 
  genes.use = genes.use)

SC.merged$SC_UMAP <- supercell_UMAP(
  SC.merged, 
  n_neighbors = 10)

g <- supercell_plot_UMAP(
    SC.merged,
    group = "cell.line",
    title = paste0("Independent construction of HCC827 and H838 metacells")
  )

Combined and independent analyses do not result in the same metacell construction.

As the dimensionality reductions (even on the same set of features) are different for the combined (HCC827+H838) dataset and for the independent (HCC827 and H838 separately) datasets. The first PCA basen on global variability between two cell lines and the PCAs from the second approach represent local variability within each cell line. (sample).

heatmap(as.matrix(table(SC.merged$membership, SC.HCC827.H838$membership)), scale = "none")

Metacell size distribution

summary(SC.merged$supercell_size)
summary(SC.HCC827.H838$supercell_size)

Also, in the combined analysis, the graining level does not mean that each cell line (or sample) will have this particular graining level. For instance, in the combined analysis, the graining level for HCC827 is 18.6 and for H838 is 21.8, but this difference might be even more prominent if the heterogeneity and complexity of two samples are more different.

## Combined analysis
# actual graining level for H838 cell line
length(cell.idx.H838)/sum(SC.HCC827.H838$cell.line == "H838")
# actual graining level for H838 cell line
length(cell.idx.HCC827)/sum(SC.HCC827.H838$cell.line == "HCC827")

## Independent analysis 
# actual graining level for H838 cell line
length(cell.idx.H838)/sum(SC.merged$cell.line == "H838")
# actual graining level for HCC827 cell line
length(cell.idx.HCC827)/sum(SC.merged$cell.line == "HCC827")
# actual overall graining level in the combined analysis
length(SC.merged$membership)/max(SC.merged$membership)

SuperCell documentation built on Oct. 25, 2024, 5:07 p.m.