Normalization
Last updated: 2020-01-11
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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, vars.to.regress = "nFeature_RNA")Note: for comprehensive tutorials in the different options and workflow possibilities available within Seurat, we recommend looking at their website https://satijalab.org/seurat/. Special consideration should be put into if and how the user apply the vars.to.regress parameter, which specifies which variables to regress out. This can be used to correct for heterogeneity in the data that is of no interest.
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, color = "white") +
geom_density_2d(size = 0.3) +
geom_abline(intercept = 0, slope = 1, color = 'red') +
ggtitle("Mean-variance relationship") + DarkTheme()
# 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, color = "white") +
geom_line(data = poisson_model, color='red') +
ggtitle("Mean-detection-rate relationship") + DarkTheme()
cowplot::plot_grid(p1, p2, nrow = 2)
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 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_Sweden.1252 LC_CTYPE=English_Sweden.1252
[3] LC_MONETARY=English_Sweden.1252 LC_NUMERIC=C
[5] LC_TIME=English_Sweden.1252
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] dplyr_0.8.3 magrittr_1.5
[3] Matrix_1.2-17 STutility_0.1.0
[5] ggplot2_3.2.1 SingleCellExperiment_1.6.0
[7] SummarizedExperiment_1.14.1 DelayedArray_0.10.0
[9] BiocParallel_1.18.1 matrixStats_0.55.0
[11] Biobase_2.44.0 GenomicRanges_1.36.1
[13] GenomeInfoDb_1.20.0 IRanges_2.18.2
[15] S4Vectors_0.22.1 BiocGenerics_0.30.0
[17] Seurat_3.1.1.9023
loaded via a namespace (and not attached):
[1] reticulate_1.13 R.utils_2.9.0
[3] tidyselect_0.2.5 htmlwidgets_1.3
[5] grid_3.6.1 Rtsne_0.15
[7] munsell_0.5.0 codetools_0.2-16
[9] ica_1.0-2 future_1.15.0
[11] miniUI_0.1.1.1 withr_2.1.2
[13] colorspace_1.4-1 knitr_1.24
[15] ROCR_1.0-7 tensor_1.5
[17] gbRd_0.4-11 listenv_0.7.0
[19] labeling_0.3 Rdpack_0.11-0
[21] git2r_0.26.1 GenomeInfoDbData_1.2.1
[23] polyclip_1.10-0 rprojroot_1.3-2
[25] vctrs_0.2.0 xfun_0.9
[27] R6_2.4.0 doParallel_1.0.15
[29] rsvd_1.0.2 Morpho_2.7
[31] ggiraph_0.6.1 manipulateWidget_0.10.0
[33] bitops_1.0-6 spatstat.utils_1.13-0
[35] assertthat_0.2.1 promises_1.0.1
[37] SDMTools_1.1-221.1 scales_1.0.0
[39] imager_0.41.2 gtable_0.3.0
[41] npsurv_0.4-0 globals_0.12.4
[43] bmp_0.3 goftest_1.1-1
[45] workflowr_1.5.0 rlang_0.4.1
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[51] lazyeval_0.2.2 rgl_0.100.30
[53] yaml_2.2.0 reshape2_1.4.3
[55] abind_1.4-5 crosstalk_1.0.0
[57] backports_1.1.4 httpuv_1.5.2
[59] tools_3.6.1 gplots_3.0.1.1
[61] raster_3.0-7 RColorBrewer_1.1-2
[63] Rvcg_0.18 ggridges_0.5.1
[65] Rcpp_1.0.3 plyr_1.8.4
[67] zlibbioc_1.30.0 purrr_0.3.3
[69] RCurl_1.95-4.12 rpart_4.1-15
[71] deldir_0.1-23 viridis_0.5.1
[73] pbapply_1.4-2 cowplot_1.0.0
[75] zoo_1.8-6 ggrepel_0.8.1
[77] cluster_2.1.0 colorRamps_2.3
[79] fs_1.3.1 data.table_1.12.2
[81] magick_2.2 readbitmap_0.1.5
[83] lmtest_0.9-37 RANN_2.6.1
[85] fitdistrplus_1.0-14 shinyjs_1.0
[87] lsei_1.2-0 mime_0.7
[89] evaluate_0.14 xtable_1.8-4
[91] jpeg_0.1-8.1 gridExtra_2.3
[93] compiler_3.6.1 tibble_2.1.3
[95] KernSmooth_2.23-15 crayon_1.3.4
[97] R.oo_1.22.0 htmltools_0.3.6
[99] mgcv_1.8-28 later_0.8.0
[101] tiff_0.1-5 tidyr_1.0.0
[103] RcppParallel_4.4.3 MASS_7.3-51.4
[105] R.methodsS3_1.7.1 gdata_2.18.0
[107] metap_1.1 igraph_1.2.4.1
[109] pkgconfig_2.0.2 sp_1.3-2
[111] plotly_4.9.1 xml2_1.2.2
[113] foreach_1.4.7 webshot_0.5.1
[115] XVector_0.24.0 bibtex_0.4.2
[117] stringr_1.4.0 digest_0.6.22
[119] sctransform_0.2.0 RcppAnnoy_0.0.12
[121] tsne_0.1-3 spatstat.data_1.4-0
[123] rmarkdown_1.15 leiden_0.3.1
[125] uwot_0.1.4 gdtools_0.2.0
[127] shiny_1.3.2 gtools_3.8.1
[129] lifecycle_0.1.0 nlme_3.1-141
[131] jsonlite_1.6 viridisLite_0.3.0
[133] pillar_1.4.2 lattice_0.20-38
[135] httr_1.4.1 survival_2.44-1.1
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[141] stringi_1.4.3 caTools_1.17.1.2
[143] irlba_2.3.3 future.apply_1.3.0
[145] ape_5.3