Spatial Experiments raster (SEraster)

SEraster is a pre-processing tool to enable scalable and accurate analysis of large-scale spatial omics datasets with existing tools.

Overview

Installation

To install SEraster, we recommend using remotes:

require(remotes)
remotes::install_github('JEFworks-Lab/SEraster')

Tutorials

Input data format

In the examples below, we assume the input data is provided as a SpatialExperiment Bioconductor object. Please refer to the SpatialExperiment package and the merfish_mousePOA dataset in the package to see how you would format your data into a SpatialExperiment object.

Example

A short example workflow is shown below.

Load packages

library(SpatialExperiment)
library(SEraster)

Load example dataset

data("merfish_mousePOA")
dim(merfish_mousePOA)

[1]  155 6509

Getting started

Rasterize gene expression

rastGexp <- SEraster::rasterizeGeneExpression(merfish_mousePOA, assay_name="volnorm", resolution = 50)

# plot total rasterized gene expression
SEraster::plotRaster(rastGexp, name = "Total rasterized gene expression")

# plot specific gene
SEraster::plotRaster(rastGexp, feature_name = "Esr1", name = "Esr1")

Rasterize cell-type labels

rastCt <- SEraster::rasterizeCellType(merfish_mousePOA, col_name = "celltype", resolution = 50)

# plot total cell counts
SEraster::plotRaster(rastCt, name = "cell counts", option = "inferno")

# plot specific cell-type
SEraster::plotRaster(rastCt, feature_name = "Inhibitory", name = "Inhibitory neuron counts", option = "inferno")

Downstream Analysis

We will highlight spatial variable gene (SVG) and cell-type cooccurrence analyses as examples of downstream analysis that benefit from SEraster.

Spatial variable gene (SVG) analysis

Here, we use a previously developed tool called nnSVG. Please refer to nnSVG for more details about the package. We can directly input rasterized gene expression SpatialExperiment object from SEraster into nnSVG.

library(nnSVG)

# run nnSVG
set.seed(0)
rastGexp <- nnSVG(rastGexp, assay_name = "pixelval")

# number of significant SVGs
table(rowData(rastGexp)$padj <= 0.05)

## 
## FALSE  TRUE
##    17   138

# plot rasterized gene expression of top-ranked SVG
top_svg <- which(rowData(rastGexp)$rank == 1)
top_svg_name <- rownames(rowData(rastGexp))[top_svg]
SEraster::plotRaster(rastGexp, feature_name = top_svg_name, name = top_svg_name)

We can also perform cell-type specific SVG analysis by subsetting the dataset prior to applying SEraster.

# subset data
ct_interest <- "Excitatory"
spe_sub <- merfish_mousePOA[,merfish_mousePOA$celltype == ct_interest]

# run SEraster
rastGexp_sub <- SEraster::rasterizeGeneExpression(spe_sub, assay_name="volnorm", resolution = 50)

# run nnSVG
set.seed(0)
rastGexp_sub <- nnSVG(rastGexp_sub, assay_name = "pixelval")

# number of significant SVGs
table(rowData(rastGexp_sub)$padj <= 0.05)

## 
## FALSE  TRUE 
##    45   110

# plot rasterized gene expression of top-ranked SVG
top_svg <- which(rowData(rastGexp_sub)$rank == 1)
top_svg_name <- rownames(rowData(rastGexp_sub))[top_svg]
SEraster::plotRaster(rastGexp_sub, feature_name = top_svg_name, name = top_svg_name)

Cell-type cooccurrence analysis

Rasterized cell-type labels can be used to analyze pair-wise cell-type cooccurrence. To do so, we binarize the rasterized cell-type labels using a relative enrichment metric and a previously developed tool called CooccurrenceAffinity. Please refer to our paper for more details about the methodology and CooccurrenceAffinity for more details about the package.

library(CooccurrenceAffinity)

# extract cell-type labels
ct_labels <- as.factor(colData(merfish_mousePOA)$celltype)

# compute relative enrichment (RE) metric
mat <- assay(rastCt, "pixelval")
mat_re <- do.call(rbind, lapply(rownames(rastCt), function(ct_label) {
    mat[ct_label,] / (sum(mat[ct_label,]) / sum(mat) * colSums(mat))
}))
rownames(mat_re) <- rownames(mat)

# binarize
mat_bin <- ifelse(mat_re >= 1, 1, 0)

# add RE and binarized layers to SpatialExperiment object
assays(rastCt) <- list(pixelval = assay(rastCt, "pixelval"), re = mat_re, bin = mat_bin)

ct_interest <- "Ependymal"

# plot pixel value for a cell-type of interest
plotRaster(rastCt, assay_name = "pixelval", feature_name = ct_interest, name = "cell-type counts", option = "inferno")

# plot RE value for a cell-type of interest
plotRaster(rastCt, assay_name = "re", feature_name = ct_interest, name = "RE", option = "inferno")

# plot binarized value for a cell-type of interest
plotRaster(rastCt, assay_name = "bin", feature_name = ct_interest, factor_levels = c(0,1), name = "binarized", option = "inferno")

# run CooccurrenceAffinity
ct_coocc <- CooccurrenceAffinity::affinity(data = mat_bin, row.or.col = "row", squarematrix = c("all"))

# plot maximum likelihood estimates of affinity metric (alpha MLE)
CooccurrenceAffinity::plotgg(data = ct_coocc, variable = "alpha_mle", legendlimit = "datarange")

LylaAtta123/SEraster