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MetacellAnalysisToolkit_vignette.md

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Load data

library(MetacellAnalysisToolkit)
mc_data <- MetacellAnalysisToolkit::CD34_mc
sc_data <- MetacellAnalysisToolkit::CD34_sc

Compute quantitative metrics

Purity

After each metacell has been annotated to the most abundant cell category (e.g. cell type) composing the metacell, we can compute metacells purity. If the annotation considered is the cell type, the purity of a metacell is the proportion of the most abundant cell type within the metacell.

mc_data$purity <- mc_purity(membership = mc_data@misc$cell_membership$membership, annotation = sc_data$celltype)

## Loading required package: SeuratObject

## Loading required package: sp

## The legacy packages maptools, rgdal, and rgeos, underpinning this package
## will retire shortly. Please refer to R-spatial evolution reports on
## https://r-spatial.org/r/2023/05/15/evolution4.html for details.
## This package is now running under evolution status 0

## 
## Attaching package: 'SeuratObject'

## The following objects are masked from 'package:base':
## 
##     intersect, t

qc_boxplot(mc.obj = mc_data, qc.metrics = "purity")


qc_boxplot(mc.obj = mc_data, qc.metrics = "purity", split.by = "celltype")

Compactness

The compactness of a metacell is the variance of the components within the metacell. The lower the compactness value the better.

This metric, as well as the separation metric, are computed based on a low embedding of the single-cell data, e.g. PCA embedding which we generate in the next chunk.

library(Seurat)
sc_data <- NormalizeData(sc_data, normalization.method = "LogNormalize")
sc_data <- FindVariableFeatures(sc_data, nfeatures = 2500)
sc_data <- ScaleData(sc_data)

## Centering and scaling data matrix

sc_data <- RunPCA(sc_data, npcs = 50, verbose = F)
sc_data <- RunUMAP(sc_data, reduction = "pca", dims = c(1:50), n.neighbors = 15, verbose = F, min.dist = 0.5)

## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session

UMAPPlot(sc_data, group.by = "celltype", reduction = "umap")

We compute these metrics in a diffusion map obtained from the pca.

membership_df <- mc_data@misc$cell_membership
diffusion_comp <- get_diffusion_comp(sc.obj = sc_data, dims = 1:30)

## Computing diffusion maps ...

mc_data$compactness <- mc_compactness(cell.membership = membership_df,
                                      sc.obj = sc_data,
                                      sc.reduction = diffusion_comp)
qc_boxplot(mc.obj = mc_data, qc.metrics = "compactness")


qc_boxplot(mc.obj = mc_data, qc.metrics = "compactness", split.by = "celltype")

Separation

The separation of a metacell is the distance to the closest metacell [@SEACells]. The higher the separation value the better.

mc_data$separation <- mc_separation(cell.membership = membership_df, 
                                    sc.obj = sc_data, 
                                    sc.reduction = diffusion_comp)
qc_boxplot(mc.obj = mc_data, qc.metrics = "separation")


qc_boxplot(mc.obj = mc_data, qc.metrics = "separation", split.by = "celltype")

INV

The inner normalized variance (INV) of a metacell is the mean-normalized variance of gene expression within the metacell. The lower the INV value the better. Note that it is the only metric that is latent-space independent.

mc_data$INV <- mc_INV(cell.membership = membership_df, sc.obj = sc_data, group.label = "membership")

## Computing INV ...

## [1] "after get matrix"
## [1] "after do call"

qc_boxplot(mc.obj = mc_data, qc.metrics = "INV")


qc_boxplot(mc.obj = mc_data, qc.metrics = "INV", split.by = "celltype")

Representativeness of metacells

To visualize the metacells, we can project the metacells on the single-cell UMAP representation using the mc_projection() function (adapted from the plot.plot_2D() from the SEACells package). A good metacell partition should reproduce the overall structure of the single-cell data by uniformly representing the latent space. To use this function we need the data at the single-cell level (or at least an low-dimensional embedding of the data) and the single-cell membership to each the metacell.

mc_projection(
  sc.obj = sc_data,
  mc.obj = mc_data,
  cell.membership = membership_df,
  sc.reduction = "umap",
  sc.label = "celltype", # single cells will be colored according the sc.label
  metacell.label = "celltype" # metacells cell will be colored according the metacell.label
  )


# with custom colors:
colors <- c("HMP" = "#BC80BD", "DCPre" = "#66A61E", "HSC" = "#A6761D",
            "Ery" = "#E41A1C", "MEP" = "#B3B3B3", "cDC" = "#A6D854", "CLP" = "#1F78B4", "Mono" = "#E6AB02", "pDC" = "#B2DF8A" )
mc_projection(sc.obj = sc_data,
  mc.obj = mc_data,
  cell.membership = membership_df,
  sc.reduction = "umap",
  sc.label = "celltype", # single cells will be colored according the sc.label
  metacell.label = "celltype", # metacells cell will be colored according the metacell.label
  sc.color = colors,
  mc.color = colors)

By default the size of the metacells dots is proportionnal to the size of the metacells. Metacells can also be colored by a continuous variable such as one of the QC metrics computed in the previous chunks:

mc_projection(
  sc.obj = sc_data,
  mc.obj = mc_data,
  cell.membership = membership_df,
  sc.reduction = "umap",
  sc.label = "celltype", # single cells will be colored according the sc.label
  continuous_metric = TRUE,
  metric = "compactness"
  )