Last updated: 2021-05-04

Checks: 7 0

Knit directory: STUtility_web_site/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20191031) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version caf5937. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .DS_Store
    Ignored:    analysis/.DS_Store
    Ignored:    analysis/manual_annotation.png
    Ignored:    pre_data/

Unstaged changes:
    Modified:   analysis/_site.yml

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Spatial_Features.Rmd) and HTML (docs/spatial_features.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html da131a6 Ludvig Larsson 2021-05-04 Build site.
html 3ef1b70 Ludvig Larsson 2021-05-04 Build site.
html 9fe84d9 Ludvig Larsson 2021-05-04 Build site.
html bfe87d1 Ludvig Larsson 2021-05-04 Build site.
html 1dc000c Ludvig Larsson 2021-04-28 Build site.
html 1e9c615 Ludvig Larsson 2021-04-28 Build site.
html 34884bd Ludvig Larsson 2020-10-06 Build site.
html d13dabd Ludvig Larsson 2020-06-05 Build site.
html 60407bb Ludvig Larsson 2020-06-05 Build site.
html e339ebc Ludvig Larsson 2020-06-05 Build site.
html 6606127 Ludvig Larsson 2020-06-05 Build site.
html f0ea9c1 Ludvig Larsson 2020-06-05 Build site.
html 651f315 Ludvig Larsson 2020-06-05 Build site.
html a6a0c51 Ludvig Larsson 2020-06-05 Build site.
html f3c5cb4 Ludvig Larsson 2020-06-05 Build site.
html f89df7e Ludvig Larsson 2020-06-05 Build site.
html 1e67a7c Ludvig Larsson 2020-06-04 Build site.
html f71b0c0 Ludvig Larsson 2020-06-04 Build site.
html 8a54a4d Ludvig Larsson 2020-06-04 Build site.
html 5e466eb Ludvig Larsson 2020-06-04 Build site.
html 377408d Ludvig Larsson 2020-06-04 Build site.
html ed54ffb Ludvig Larsson 2020-06-04 Build site.
html f14518c Ludvig Larsson 2020-06-04 Build site.
html efd885b Ludvig Larsson 2020-06-04 Build site.
html 4f42429 Ludvig Larsson 2020-06-04 Build site.
html f677bf1 jbergenstrahle 2020-04-01 Build site.
html a024bce jbergenstrahle 2020-01-12 Build site.
html 0b6d8e8 jbergenstrahle 2020-01-11 Build site.
html fb06450 jbergenstrahle 2020-01-11 Build site.
html a3a1f1f Ludvig Larsson 2019-10-31 Changed font
html 786357d Ludvig Larsson 2019-10-31 Fixed warnings
html f10ef37 Ludvig Larsson 2019-10-31 Added section noramlization and spatial faetures

Spatial Auto-correlation


STutility includes a method for finding genes with spatially conserved patterns across the tissue. The ranking method makes use neighborhood networks to compute the spatial lag for each gene, here defined as the summed expression of that gene in neighboring spots. Each gene is then ranked by the correlation between the lag vector and the original expression vector. The output is a data.frame with gene names ranked by decreasing spatial auto-correlation.

This method is partly inspired by work from the Giotto team and we reccomend you to check out their R package Giotto and the related publication; [“Giotto: a toolbox for integrative analysis and visualization of spatial expression data”](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02286-2.


library(spdep)
spatgenes <- CorSpatialGenes(se)

By default, the saptial-auto-correlation scores are only calculated for the variable genes in the Seurat object, here we have 3000. Among the top most variable features in our Seurat object, we find genes coding for hemoglobin; “Hbb-bs” “Hba-a1” “Hba-a2”. These are typically expressed in blood vessels which are more randomly distributed across the tissue compared to larger tissue structures. Knowing the spatial auto-correlation can therefore be useful to distinguish genes expressed in larger tissue compartments. One way to make use of this is to restrict the selection of features used for dimensionality reduction and clustering to include ony gene that are highly variable and spatially auto-correlated, and that way avoid clustering based on structures such as blood vessels.

head(VariableFeatures(se))
[1] "Hbb-bs" "Hba-a1" "Hba-a2" "Plp1"   "Mbp"    "Ptgds" 
spatgenes[c("Hbb-bs", "Hba-a1", "Hba-a2"), ]
         gene       cor
Hbb-bs Hbb-bs 0.3864246
Hba-a1 Hba-a1 0.3285132
Hba-a2 Hba-a2 0.3152952

Let’s plot some of the genes with highest spatial auto-correlation.

head(spatgenes) %>%
  kbl() %>%
  kable_styling()
gene cor
Mbp Mbp 0.9203725
Camk2n1 Camk2n1 0.9054777
Slc6a3 Slc6a3 0.8917767
Th Th 0.8762578
Nrgn Nrgn 0.8744128
Tmsb4x Tmsb4x 0.8715944
FeatureOverlay(se, features = c("Mbp", "Camk2n1", "Slc6a3", "Th"), 
              sampleids = 1,
              cols = c("lightgray", "mistyrose", "red", "darkred", "black"),
              pt.size = 1.5, 
              add.alpha = TRUE,
              ncol = 2)

Matrix factorization


The strength of untargeted whole transcriptome capture is the ability to perform unsupervised analysis and the ability to find spatial gene expression patterns. We’ve found good use of using non-negative matrix factorization (NNMF or NMF) to find underlying patterns of transcriptomic profiles. This factor analysis, along with various dimensionality reduction techniques, can all be ran via “RunXXX()”, where X = the method of choice, e.g.:

se <- RunNMF(se, nfactors = 40) # Specificy nfactors to choose the number of factors, default=20.


While RunNMF() is an STutility add-on, others are supported via Seurat (RunPCA(), RunTSNE, RunICA(), runUMAP() ) and for all of them, the output are stored in the Seurat object.

We can then plot a variable number of dimensions across the samples using ST.DimPlot or as an overlay using DimOverlay. These two functions are similar to the ST.FeaturePlot and FeatureOverlay but have been adapted to specifically draw dimensionality reduction vectors instead of features.

NOTE: by default, the colorscale of dimensionality reduction vectors will be centered at 0. If we have a dimensionality reduction vector x this means that the range of colors will go from -max(abs(x)) to max(abs(x)). This behaviour is typically desired when plotting e.g. PCA vectors, but for NMF all values are strictly positive so you can disable this centering by setting center.zero = FALSE.

cscale <- c("lightgray", "mistyrose", "red", "darkred", "black")

ST.DimPlot(se, 
           dims = 1:10,
           ncol = 2, # Sets the number of columns at dimensions level
           grid.ncol = 2, # Sets the number of columns at sample level
           reduction = "NMF", 
           pt.size = 1, 
           center.zero = F, 
           cols = cscale, 
           show.sb = FALSE)

ST.DimPlot(se, 
           dims = 11:20,
           ncol = 2, 
           grid.ncol = 2, 
           reduction = "NMF", 
           pt.size = 1, 
           center.zero = F, 
           cols = cscale, 
           show.sb = FALSE)

ST.DimPlot(se, 
           dims = 21:30,
           ncol = 2, 
           grid.ncol = 2, 
           reduction = "NMF", 
           pt.size = 1, 
           center.zero = F, 
           cols = cscale, 
           show.sb = FALSE)

ST.DimPlot(se, 
           dims = 31:40,
           ncol = 2, 
           grid.ncol = 2, 
           reduction = "NMF", 
           pt.size = 1, 
           center.zero = F, 
           cols = cscale, 
           show.sb = FALSE)


We can also print a summary of the genes that contribute most to the dimensionality reduction vectors.

For NMF output which is not centered at 0 looking at the “negative” side of the distribution doesn’t really add any valuable information, instead you can aget a barplot summarizing the top most contributing genes using FactorGeneLoadingPlot.

print(se[["NMF"]])
factor_ 1 
Positive:  Pvalb, Gad1, Spp1, Slc32a1, Six3, Sparc, Lsm6, Ndufb8, Nsg1, Agt 
       Enho, Ldhb, Gabra1, Cst3, Cox6c, Ndrg2, Cox5a, Ckb, Lgi2, Nefh 
Negative:  Rgs20, C1qtnf2, Lyn, Nid2, St6galnac2, Rbm20, Pdzd2, Mndal, Emx2, Sp9 
       Gcnt2, Gm19410, Irak2, Tbx2, Gm11627, Nfatc1, Bambi, Sorcs1, Pam, Arhgap45 
factor_ 2 
Positive:  Vamp1, Pvalb, Bend6, Nat8l, Cox5a, Pcp4l1, Stmn3, Nefm, Syt2, Snrpn 
       Pcsk1n, Ndufa4, Ctxn3, Slc17a6, Cend1, Kcnab3, Cox4i1, Mdh1, Scn1b, Camk2n2 
Negative:  Plxnc1, Cdhr1, C1qtnf2, Evc, Klf11, Nid2, Ly6g6f, Hist1h4h, Ppp1r18, St6galnac2 
       Fxyd2, Blnk, Fam89a, Dock8, Emx2, Cyp20a1, Sp9, Ginm1, Acacb, Gm10561 
factor_ 3 
Positive:  mt-Nd3, Rps29, Rps21, Rpl39, Rplp1, mt-Co3, Cnot3, Rpl37, Rps19, Rps27 
       mt-Nd5, Rpl35a, Rps28, mt-Nd4l, Rpl37a, Tatdn1, Rpl26, Nnat, Uba52, mt-Co1 
Negative:  Otx1, Rgs20, Evc, Pkd2, Nid2, Ppp1r18, St6galnac2, Tnxb, Atf4, Rbm20 
       Slc25a45, Pdzd2, Mndal, Amt, Blnk, Agtrap, Grik1, Fam89a, Dock8, Pcdh18 
factor_ 4 
Positive:  Th, Slc6a3, Slc18a2, Ddc, Sncg, Slc10a4, Ret, Dlk1, En1, Chrna6 
       Drd2, Cplx1, Sv2c, Aldh1a1, Gch1, Uchl1, Gap43, Chrnb3, Tagln3, Foxa1 
Negative:  Cdhr1, Evc, Nid2, St6galnac2, Slc25a45, Blnk, C530008M17Rik, Dock8, Emx2, Sp9 
       Bin2, Wnt5b, Nfatc1, Bambi, Sorcs1, Phactr4, Masp1, Rapgef3, Kdr, Epha8 
factor_ 5 
Positive:  Spink8, Tmsb4x, Fibcd1, Hpca, Fkbp1a, Itpka, Cnih2, Calm2, Tspan13, Lefty1 
       Crym, Prnp, Dynll1, Rprml, Cpne6, Arpc5, Mpped1, Cpne7, Neurod6, Ociad2 
Negative:  Cdh9, Otx1, Spsb1, Cdhr1, Rgs20, Nid2, Ly6g6f, Ppp1r18, Rbm20, Slc25a45 
       Pdzd2, Mndal, Atp2a3, Grik1, Fam89a, Cux1, Tex9, Dock8, Msi1, Sp9 
FactorGeneLoadingPlot(se, factor = 1)

Clustering


Clustering is a standard procedure in genomic analysis, and the methods for doing so are numerous. Here we demonstrate an example where we use the result of the factor analysis the previous section. Going through the list of factors (e.g. via ST:DimPlot(se, dims = [dims you want to look at])), we can notice dimensions that are “spatially active”, i.e. that seems to confer a spatial pattern along their axis. We can extract these dimensions and use as input to e.g. clustering functions. Here, we use all dimensions from the NMF and construct a Shared Nearest Neighbor (SSN) Graph.

se <- FindNeighbors(object = se, verbose = FALSE, reduction = "NMF", dims = 1:40)


Followed by clustering using a modularity optimizer

se <- FindClusters(object = se, verbose = FALSE)


And plotting of the clusters spatially

library(RColorBrewer)
n <- 19
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))

ST.FeaturePlot(object = se, features = "seurat_clusters", cols = col_vector, pt.size = 1, ncol = 2)


If you think that the distribution of clusters gets too cluttered, you can also split the view so that only one cluster at the time gets colored, just note that you can only do this for one section at the time (set ìndex).

ST.FeaturePlot(object = se, features = "seurat_clusters", pt.size = 1, split.labels = T, indices = 1, show.sb = FALSE, ncol = 5)

ST.FeaturePlot(object = se, features = "seurat_clusters", pt.size = 1, split.labels = T, indices = 2, show.sb = FALSE, ncol = 5)


Most variable features


We can take a specific look at some of the most variable features defined during the normalization step.

head(se@assays$SCT@var.features, 20)
 [1] "Hbb-bs"  "Hba-a1"  "Hba-a2"  "Plp1"    "Mbp"     "Ptgds"   "Hbb-bt" 
 [8] "Slc6a3"  "Sst"     "Th"      "Ddc"     "Npy"     "Slc18a2" "Mobp"   
[15] "Nrgn"    "Mal"     "Pcp4"    "Prkcd"   "Apod"    "Myoc"   
top <- se@assays$SCT@var.features

fts <- c("Th", "Mbp", "Nrgn")
for (ftr in fts) {
  p <- FeatureOverlay(se, 
                  features = ftr, 
                  sampleids = 1:2,
                  cols = c("lightgray", "mistyrose", "red", "darkred", "black"),
                  pt.size = 1.5, 
                  pt.alpha = 0.5, 
                 ncols = 2)
  print(p)
}


Spatial vs. UMAP visualziation


Another useful feature is that you can now compare the spatial distribution of a gene with the typical “graph embeddings” s.a. UMAP and t-SNE.

# Run UMAP
se <- RunUMAP(se, reduction = "NMF", dims = 1:40, n.neighbors = 10)
# Define colors for heatmap
heatmap.colors <- c("lightgray", "mistyrose", "red", "darkred", "black")
fts <- c("Prkcd", "Opalin", "Lamp5")

# plot transformed features expression on UMAP embedding
p.fts <- lapply(fts, function(ftr) {
  FeaturePlot(se, features = ftr, reduction = "umap", order = TRUE, cols = heatmap.colors)
})

# plot transformed features expression on Visium coordinates
p3 <- ST.FeaturePlot(se, features = fts, ncol = 2, grid.ncol = 1, cols = heatmap.colors, pt.size = 1, show.sb = FALSE)

# Construct final plot
cowplot::plot_grid(cowplot::plot_grid(plotlist = p.fts, ncol = 1), p3, ncol = 2, rel_widths = c(1, 1.3))

RGB dimensionality reduction plots


One approach to visualize the result of dimensionality reduction is to use the first three dimensions and transform the values into RGB color space. This 3 dimensional space can then be utilized for spatial visualization. We demonstrate the technique with UMAP, using our factors as input:

se <- RunUMAP(object = se, dims = 1:40, verbose = FALSE, n.components = 3, reduction = "NMF", reduction.name = "umap.3d")


We use the first three dimensions for plotting:

ST.DimPlot(object = se, dims = 1:3, reduction = "umap.3d", blend = T, pt.size = 1.8)


DEA


Lets try this out by an example. Looking at , lets say we are interested in cluster 19, and want to answer - “Which genes are significantly up-regulated in this region compared to the overall expression across the tissue?”

markers <- FindMarkers(se, ident.1 = "19")
head(markers) %>%
  kbl() %>%
  kable_styling()
p_val avg_logFC pct.1 pct.2 p_val_adj
Dsp 0 1.2427413 0.818 0.015 0
Lct 0 0.9157160 0.768 0.021 0
Tdo2 0 0.7143539 0.707 0.026 0
Capn3 0 1.1263876 0.859 0.084 0
Prox1 0 1.1988020 0.919 0.117 0
C1ql2 0 1.7382051 1.000 0.169 0

Note that the clusters were already set as the Seurat objects levels. Type levels(se) to see the current levels of your object. If other clusters, annotations etc are of interest, set this before by specifying Idents(se) <-

Note also, if we are interested in comparing two levels against each other, and not just “one against the rest”, we simply add a ident.2 = parameter to the above.

FeatureOverlay(se, features = "Dsp", 
              sampleids = 1:2,
              cols = c("lightgray", "mistyrose", "red", "darkred", "black"),
              pt.size = 1.5, 
              pt.alpha = 0.5,
              ncol = 2)


 

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] RColorBrewer_1.1-2 magrittr_2.0.1     kableExtra_1.3.4   STutility_0.1.0   
[5] ggplot2_3.3.3      Seurat_3.1.5       workflowr_1.6.2   

loaded via a namespace (and not attached):
  [1] uuid_0.1-4              backports_1.1.6         systemfonts_0.2.1      
  [4] plyr_1.8.6              igraph_1.2.6            lazyeval_0.2.2         
  [7] sp_1.4-1                splines_4.0.5           crosstalk_1.1.1        
 [10] listenv_0.8.0           digest_0.6.27           foreach_1.5.0          
 [13] htmltools_0.5.1.1       viridis_0.5.1           magick_2.3             
 [16] tiff_0.1-5              gdata_2.18.0            fansi_0.4.2            
 [19] cluster_2.1.1           doParallel_1.0.15       ROCR_1.0-11            
 [22] limma_3.44.1            globals_0.14.0          gmodels_2.18.1         
 [25] svglite_1.2.3           jpeg_0.1-8.1            colorspace_2.0-0       
 [28] rvest_0.3.6             blob_1.2.1              ggrepel_0.9.1          
 [31] xfun_0.13               dplyr_1.0.5             crayon_1.4.1           
 [34] jsonlite_1.7.2          zeallot_0.1.0           survival_3.2-10        
 [37] zoo_1.8-9               iterators_1.0.12        ape_5.4-1              
 [40] glue_1.4.2              gtable_0.3.0            webshot_0.5.2          
 [43] leiden_0.3.7            future.apply_1.7.0      scales_1.1.1           
 [46] DBI_1.1.0               miniUI_0.1.1.1          Rcpp_1.0.6             
 [49] viridisLite_0.3.0       xtable_1.8-4            spData_0.3.5           
 [52] units_0.6-6             reticulate_1.18         spdep_1.1-3            
 [55] rsvd_1.0.3              akima_0.6-2             tsne_0.1-3             
 [58] htmlwidgets_1.5.3       httr_1.4.2              ellipsis_0.3.1         
 [61] ica_1.0-2               farver_2.1.0            pkgconfig_2.0.3        
 [64] uwot_0.1.10             deldir_0.1-25           utf8_1.2.1             
 [67] labeling_0.4.2          tidyselect_1.1.0        rlang_0.4.10           
 [70] manipulateWidget_0.10.1 reshape2_1.4.4          later_1.1.0.1          
 [73] munsell_0.5.0           tools_4.0.5             dbscan_1.1-5           
 [76] generics_0.1.0          ggridges_0.5.3          evaluate_0.14          
 [79] stringr_1.4.0           fastmap_1.0.1           yaml_2.2.1             
 [82] knitr_1.28              fs_1.5.0                fitdistrplus_1.1-3     
 [85] rgl_0.100.54            purrr_0.3.4             RANN_2.6.1             
 [88] readbitmap_0.1.5        pbapply_1.4-3           future_1.21.0          
 [91] nlme_3.1-152            whisker_0.4             mime_0.10              
 [94] ggiraph_0.7.7           xml2_1.3.2              rstudioapi_0.13        
 [97] compiler_4.0.5          plotly_4.9.3            png_0.1-7              
[100] e1071_1.7-3             Morpho_2.8              tibble_3.1.0           
[103] stringi_1.5.3           highr_0.8               gdtools_0.2.2          
[106] lattice_0.20-41         Matrix_1.3-2            classInt_0.4-3         
[109] shinyjs_1.1             vctrs_0.3.7             LearnBayes_2.15.1      
[112] pillar_1.5.1            lifecycle_1.0.0         lmtest_0.9-38          
[115] RcppAnnoy_0.0.18        data.table_1.14.0       cowplot_1.1.1          
[118] irlba_2.3.3             Rvcg_0.19.1             raster_3.1-5           
[121] httpuv_1.5.2            patchwork_1.1.1         colorRamps_2.3         
[124] R6_2.5.0                imager_0.42.1           promises_1.2.0.1       
[127] KernSmooth_2.23-18      gridExtra_2.3           bmp_0.3                
[130] parallelly_1.24.0       codetools_0.2-18        gtools_3.8.2           
[133] boot_1.3-27             MASS_7.3-53.1           assertthat_0.2.1       
[136] rprojroot_1.3-2         withr_2.4.1             sctransform_0.2.1      
[139] expm_0.999-4            parallel_4.0.5          grid_4.0.5             
[142] tidyr_1.1.3             coda_0.19-3             class_7.3-18           
[145] rmarkdown_2.1           Rtsne_0.15              git2r_0.27.1           
[148] sf_0.9-7                shiny_1.4.0.2