Last updated: 2019-10-31

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File Version Author Date Message
Rmd 7cdf8e1 Ludvig Larsson 2019-10-31 Changed font size
Rmd 786357d Ludvig Larsson 2019-10-31 Fixed warnings
html 786357d Ludvig Larsson 2019-10-31 Fixed warnings
Rmd f10ef37 Ludvig Larsson 2019-10-31 Added section noramlization and spatial faetures
html f10ef37 Ludvig Larsson 2019-10-31 Added section noramlization and spatial faetures

Normalization and scaling

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)

Version Author Date
f10ef37 Ludvig Larsson 2019-10-31

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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.

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 above, we transform the data according to the Seurat workflow. Note: for comprehensive tutorials in the different options and workflow possibilities available within Seurat, we recommend looking at their website https://satijalab.org/seurat/

se <- SCTransform(se, vars.to.regress = c("sample_id", "nFeature_RNA"))
 

A work by Joseph Bergenstråhle and Ludvig Larsson

 


sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] STutility_0.1.0             ggplot2_3.2.1              
 [3] SingleCellExperiment_1.6.0  SummarizedExperiment_1.14.1
 [5] DelayedArray_0.10.0         BiocParallel_1.18.1        
 [7] matrixStats_0.55.0          Biobase_2.44.0             
 [9] GenomicRanges_1.36.1        GenomeInfoDb_1.20.0        
[11] IRanges_2.18.3              S4Vectors_0.22.1           
[13] BiocGenerics_0.30.0         Seurat_3.1.1               

loaded via a namespace (and not attached):
  [1] backports_1.1.5         workflowr_1.3.0        
  [3] systemfonts_0.1.1       plyr_1.8.4             
  [5] igraph_1.2.4.1          lazyeval_0.2.2         
  [7] splines_3.6.1           crosstalk_1.0.0        
  [9] listenv_0.7.0           digest_0.6.22          
 [11] foreach_1.4.7           htmltools_0.4.0        
 [13] viridis_0.5.1           magick_2.2             
 [15] tiff_0.1-5              gdata_2.18.0           
 [17] magrittr_1.5            cluster_2.1.0          
 [19] doParallel_1.0.15       ROCR_1.0-7             
 [21] globals_0.12.4          RcppParallel_4.4.4     
 [23] R.utils_2.9.0           jpeg_0.1-8             
 [25] colorspace_1.4-1        ggrepel_0.8.1          
 [27] xfun_0.10               dplyr_0.8.3            
 [29] crayon_1.3.4            RCurl_1.95-4.12        
 [31] jsonlite_1.6            zeallot_0.1.0          
 [33] survival_2.44-1.1       zoo_1.8-6              
 [35] iterators_1.0.12        ape_5.3                
 [37] glue_1.3.1              gtable_0.3.0           
 [39] zlibbioc_1.30.0         XVector_0.24.0         
 [41] webshot_0.5.1           leiden_0.3.1           
 [43] future.apply_1.3.0      scales_1.0.0           
 [45] bibtex_0.4.2            miniUI_0.1.1.1         
 [47] Rcpp_1.0.2              metap_1.1              
 [49] viridisLite_0.3.0       xtable_1.8-4           
 [51] reticulate_1.13         rsvd_1.0.2             
 [53] SDMTools_1.1-221.1      tsne_0.1-3             
 [55] htmlwidgets_1.5.1       httr_1.4.1             
 [57] gplots_3.0.1.1          RColorBrewer_1.1-2     
 [59] ica_1.0-2               pkgconfig_2.0.3        
 [61] R.methodsS3_1.7.1       uwot_0.1.4             
 [63] tidyselect_0.2.5        rlang_0.4.1            
 [65] manipulateWidget_0.10.0 reshape2_1.4.3         
 [67] later_1.0.0             munsell_0.5.0          
 [69] tools_3.6.1             ggridges_0.5.1         
 [71] evaluate_0.14           stringr_1.4.0          
 [73] fastmap_1.0.1           yaml_2.2.0             
 [75] npsurv_0.4-0            knitr_1.25             
 [77] fs_1.3.1                fitdistrplus_1.0-14    
 [79] rgl_0.100.30            caTools_1.17.1.2       
 [81] purrr_0.3.2             RANN_2.6.1             
 [83] readbitmap_0.1.5        pbapply_1.4-2          
 [85] future_1.14.0           nlme_3.1-141           
 [87] whisker_0.4             mime_0.7               
 [89] R.oo_1.22.0             ggiraph_0.6.1          
 [91] xml2_1.2.2              compiler_3.6.1         
 [93] plotly_4.9.0            png_0.1-7              
 [95] lsei_1.2-0              Morpho_2.7             
 [97] tibble_2.1.3            stringi_1.4.3          
 [99] gdtools_0.2.0           lattice_0.20-38        
[101] Matrix_1.2-17           shinyjs_1.0            
[103] vctrs_0.2.0             pillar_1.4.2           
[105] lifecycle_0.1.0         Rdpack_0.11-0          
[107] lmtest_0.9-37           RcppAnnoy_0.0.13       
[109] data.table_1.12.2       cowplot_1.0.0          
[111] bitops_1.0-6            irlba_2.3.3            
[113] Rvcg_0.18               gbRd_0.4-11            
[115] httpuv_1.5.2            colorRamps_2.3         
[117] imager_0.41.2           R6_2.4.0               
[119] promises_1.1.0          bmp_0.3                
[121] KernSmooth_2.23-15      gridExtra_2.3          
[123] codetools_0.2-16        MASS_7.3-51.4          
[125] gtools_3.8.1            assertthat_0.2.1       
[127] rprojroot_1.3-2         withr_2.1.2            
[129] sctransform_0.2.0       GenomeInfoDbData_1.2.1 
[131] grid_3.6.1              tidyr_1.0.0            
[133] rmarkdown_1.16          Rtsne_0.15             
[135] git2r_0.26.1            shiny_1.4.0