Normalization
Last updated: 2021-05-05
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Knit directory: STUtility_web_site/
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SCTransform (Seurat)
In order to normalize the data we recommend using variance stabilized transformation available in the SCTransform function in Seurat as of v3.0.
Following the rationale expressed below, we transform the data according to the Seurat workflow.
se <- SCTransform(se)Regressing out batch effects
When deciding on a normalization strategy using SCTransform it is important to consider potential batch effects that could confound downstream analysis. Such batch effects could for example arise between different sample specimens, storage times, array slides etc. and even between consecutive sections prepared on the same slide. The experimental setup is crucial to make it possible to combat such batch effects and it is important to carefully think through if and how they should be removed to make the biological variation in your data more meaningful. Some of these effects can be effectively removed during normalization with SCTransform by specifying your batches with the vars.to.regress option.
For example if you want to regress out the “section number” from the data you need to make sure that you have a variable in your meta.data giving a unique ID for each section. If you don’t have this information in your meta.data you can get it from the “Staffli” object stored inside the Seurat object. Then you can simply set vars.to.regress = "section" when running SCTransform to correct for potential technical effects separating the two sections.
# Add a section column to your meta.data
se$section <- paste0("section_", GetStaffli(se)[[, "sample", drop = T]])
# Run normalization with "vars.to.regress" option
se.batch.cor <- SCTransform(se, vars.to.regress = "section")Note: for comprehensive tutorials in the different options and workflow possibilities available within Seurat, we recommend looking at their website https://satijalab.org/seurat/.
Rationale of approach
Each spot in a Spatial Transcriptomics dataset typically contains RNA from a mixture of cells so why would we apply a workflow that was developed for single-cell RNAseq data? We can calculate some properties to visually inspect the data to see that ST data have similar properties to that of scRNAseq data.
library(Matrix)
library(magrittr)
library(dplyr)
library(ggplot2)
# Get raw count data
umi_data <- GetAssayData(object = se, slot = "counts", assay = "RNA")
dim(umi_data)
# Calculate gene attributes
gene_attr <- data.frame(mean = rowMeans(umi_data),
detection_rate = rowMeans(umi_data > 0),
var = apply(umi_data, 1, var),
row.names = rownames(umi_data)) %>%
mutate(log_mean = log10(mean), log_var = log10(var))
# Obtain spot attributes from Seurat meta.data slot
spot_attr <- se[[c("nFeature_RNA", "nCount_RNA")]]
p1 <- ggplot(gene_attr, aes(log_mean, log_var)) +
geom_point(alpha = 0.3, shape = 16) +
geom_density_2d(size = 0.3) +
geom_abline(intercept = 0, slope = 1, color = 'red') +
ggtitle("Mean-variance relationship")
# add the expected detection rate under Poisson model
x = seq(from = -2, to = 2, length.out = 1000)
poisson_model <- data.frame(log_mean = x, detection_rate = 1 - dpois(0, lambda = 10^x))
p2 <- ggplot(gene_attr, aes(log_mean, detection_rate)) +
geom_point(alpha = 0.3, shape = 16) +
geom_line(data = poisson_model, color='red') +
ggtitle("Mean-detection-rate relationship")
p1 - p2
We can see from the mean-variance and Mean-detection-rate scatter plots that genes show overdispersion compared to what would be expected under a Poisson model. Because these properties are shared between ST and scRNAseq data we have reasoned that the workflow presented in the Seurat package should be applicable for ST data as well. It is important however to keep in mind that each spots contains a mixture of cell types and should be interpreted as a morphological unit in the context of a tissue section.
A work by Joseph Bergenstråhle and Ludvig Larsson
sessionInfo()R version 4.0.5 (2021-03-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dplyr_1.0.5 magrittr_2.0.1 Matrix_1.3-2 STutility_0.1.0
[5] ggplot2_3.3.3 Seurat_3.1.5
loaded via a namespace (and not attached):
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[4] systemfonts_0.2.1 plyr_1.8.6 igraph_1.2.6
[7] lazyeval_0.2.2 sp_1.4-1 splines_4.0.5
[10] crosstalk_1.1.1 listenv_0.8.0 digest_0.6.27
[13] foreach_1.5.0 htmltools_0.5.1.1 viridis_0.5.1
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[118] R6_2.5.0 imager_0.42.1 promises_1.2.0.1
[121] KernSmooth_2.23-18 gridExtra_2.3 bmp_0.3
[124] parallelly_1.24.0 codetools_0.2-18 gtools_3.8.2
[127] boot_1.3-27 MASS_7.3-53.1 assertthat_0.2.1
[130] rprojroot_1.3-2 withr_2.4.1 sctransform_0.2.1
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