Stereo-Seq MOSTA E12.5_E1S3 analysis#

Date

2022-11-15

Dataset explanation#

Stereo-seq is a sequencing-based spatial transcriptomics technology that was developed and used by Chen et al. in 2022 (https://doi.org/10.1016/j.cell.2022.04.003) to generate the Mouse Organogenesis Spatiotemporal Transcriptomic Atlas (MOSTA). This tutorial demonstrates how to interactively filter and analyze the third sagittal section of a mouse embryo at embryonic day 12.5 (“E12.5_E1S3”) from MOSTA.

Pre-processing#

The necessary input files for sample E12.5_E1S3 are publicly available and were downloaded from the following original sources:

  1. FASTQ files (https://db.cngb.org/search/sample/?q=CNP0001543)

  2. barcodeToPos.h5 file (https://db.cngb.org/stomics/mosta/download.html)

The STOMICS Analysis Workflow (SAW) pipeline (https://github.com/BGIResearch/SAW) was used to process these files. The output of the SAW pipeline is an *.h5ad file at a specific bin size. One bin of size n represents an n x n square of aggregated spatial barcodes. In this tutorial, a sample with a bin size of 200 was used.

Start Giotto#

# Ensure Giotto Suite is installed
if(!"Giotto" %in% installed.packages()) {
  devtools::install_github("drieslab/Giotto@Suite")
}

library(Giotto)
# Ensure the Python environment for Giotto has been installed
genv_exists = checkGiottoEnvironment()
if(!genv_exists){
  # The following command need only be run once to install the Giotto environment.
  installGiottoEnvironment()
}

1. Create a Giotto object#

# download E12.5_E1S3_bin200.h5ad output from SAW pipeline (509.4 MB)
# alternatively, specify path to *.h5ad output of SAW pipeline
anndata_download = "https://zenodo.org/record/7323947/files/E12.5_E1S3_bin200.h5ad?download=1"
anndata_file = "E12.5_E1S3_bin_200.h5ad"
download.file(anndata_download, anndata_file)

# convert anndata file to giotto object
stereo_go <- Giotto::anndataToGiotto(anndata_file)

2. Process Giotto object#

# filter number of genes
# important to discard bins (aggregated barcodes) outside of embryo
stereo_go <- stereo_go %>% filterGiotto(expression_threshold = 1,
                                         feat_det_in_min_cells = 5,
                                         min_det_feats_per_cell = 750)
# normalize
stereo_go <- stereo_go %>% normalizeGiotto(scalefactor = 5000, verbose = T)

# add statistics
stereo_go <- stereo_go %>% addStatistics()

# make plot
# each dot here represents a 200x200 aggregation of spatial barcodes (bin size 200)
spatPlot2D(gobject = stereo_go, cell_color = "nr_feats", color_as_factor = F, point_size = 1.5, show_plot = T, save_plot = F)
../../_images/1.png

3. Dimension reduction#

  • identify highly variable features (HVF)

stereo_go <- stereo_go %>% calculateHVF(zscore_threshold = 1, show_plot = F)
  • perform PCA

  • identify number of significant principal components (PCs)

stereo_go <- stereo_go %>% runPCA(expression_values = 'scaled', feats_to_use = 'hvf')
screePlot(stereo_go, ncp = 30)
plotPCA(stereo_go)
../../_images/2.png ../../_images/3.png
  • run UMAP and TSNE on PCs (or directly on matrix)

stereo_go <- stereo_go %>% runUMAP(dimensions_to_use = 1:30, n_threads = 4)

# plot UMAP, coloring cells/points based on nr_feats
plotUMAP(gobject = stereo_go,
         cell_color = 'nr_feats', color_as_factor = F, point_size = 2)
../../_images/4.png
stereo_go = stereo_go %>% runtSNE(dimensions_to_use = 1:30)
plotTSNE(gobject = stereo_go)
../../_images/5.png

4. Clustering#

  • create a shared (default) nearest network in PCA space (or directly on matrix)

  • cluster on nearest network with Leiden or Louvan (kmeans and hclust are alternatives)

# sNN network (default)
stereo_go <- stereo_go %>% createNearestNetwork(dimensions_to_use = 1:30, k = 12)

# leiden clustering
stereo_go <- stereo_go %>% doLeidenCluster(resolution = 1, n_iterations = 1000)

plotUMAP(gobject = stereo_go, cell_color = 'leiden_clus', point_size = 2.5,
         show_NN_network = F, edge_alpha = 0.05)

# merge small groups based on similarity
leiden_similarities = stereo_go %>% getClusterSimilarity(expression_values = 'scaled',
                                                          cluster_column = 'leiden_clus')

stereo_go = stereo_go %>% mergeClusters(expression_values = 'scaled',
                                         cluster_column = 'leiden_clus',
                                         new_cluster_name = 'leiden_clus_m',
                                         max_group_size = 100,
                                         force_min_group_size = 25,
                                         max_sim_clusters = 10,
                                         min_cor_score = 0.7)

plotUMAP(gobject = stereo_go, cell_color = 'leiden_clus_m', point_size = 2.5,
         show_NN_network = F, edge_alpha = 0.05)
../../_images/6.png ../../_images/7.png

5. Co-visualization#

  • co-visualize expression UMAP and spatial data clusters

spatDimPlot2D(gobject = stereo_go, cell_color = 'leiden_clus_m',
              dim_point_size = 1.5, spat_point_size = 1.5,
              show_plot = T, return_plot = F)
../../_images/8.png

6. Spatial Genes#

  • find genes with spatially coherent expression patterns

# create knn
stereo_go <- stereo_go %>% createSpatialNetwork(method = "kNN", k = 8)

# select 100 random genes
set.seed(144)
featureMetadata = fDataDT(stereo_go)
gene_list = featureMetadata[sample(length(featureMetadata$feat_ID), 100), "feat_ID"]

# use binSpect method to find spatial genes
spat_genes <- stereo_go %>% binSpect(expression_values = "scaled",
                                     subset_feats = gene_list$feat_ID,
                                     spatial_network_name = "kNN_network")

7. Subsetting/Filtering#

  • perform these steps to select an ROI using an interactive polygon selection tool

  • to draw a polygon on the interactive plot, click the mouse to start a line segment. Click again to draw the endpoint of the segment, which becomes the startpoint of the following segment. Click “Done” to close the app and save the polygon coordinates.

  • See our tutorial on interactive selection/filtering in “Getting started” to learn more.

my_spatPlot <- spatPlot2D(gobject = stereo_go,
                          cell_color = 'leiden_clus',
                          color_as_factor = T,
                          show_plot = FALSE,
                          point_size = 2,
                          save_plot = FALSE)

# create a polygon mask around a ROI, coordinates will be saved after clicking 'Done'
library(shiny)
library(miniUI)
my_polygon_coordinates <- plotInteractivePolygons(my_spatPlot, height = 500)

# create new giotto object from polygon coordinates
lasso_polygons <- createGiottoPolygonsFromDfr(my_polygon_coordinates,
                                              name = "cell",
                                              calc_centroids = FALSE)

# store the polygons info within the giotto object
stereo_go <- addGiottoPolygons(gobject = stereo_go,
                               gpolygons = list(lasso_polygons))

# find intersection between original giotto object and polygon subset
my_intersect <- getCellsFromPolygon(stereo_go)

# create new giotto roi subset
stereo_go_subset <- stereo_go %>% subsetGiotto(cell_ids = my_intersect$cell_ID)

# visualize filtered ROI
# Your plot below will reflect the polygon(s) you constructed above in my_polygon_coordinates
spatPlot2D(gobject = stereo_go_subset, cell_color = 'leiden_clus',
           color_as_factor = T, show_plot = FALSE,
           point_size = 2,save_plot = FALSE)
../../_images/9.png
sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /share/pkg.7/r/4.2.1/install/lib64/R/lib/libRblas.so
LAPACK: /share/pkg.7/r/4.2.1/install/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] miniUI_0.1.1.1 shiny_1.7.2    Giotto_2.1

loaded via a namespace (and not attached):
  [1] systemfonts_1.0.4     plyr_1.8.8            igraph_1.3.5
  [4] lazyeval_0.2.2        sp_1.5-1              splines_4.2.1
  [7] BiocParallel_1.32.1   listenv_0.8.0         scattermore_0.8
 [10] ggplot2_3.4.0         digest_0.6.30         htmltools_0.5.3
 [13] fansi_1.0.3           memoise_2.0.1         magrittr_2.0.3
 [16] ScaledMatrix_1.6.0    tensor_1.5            cluster_2.1.3
 [19] ROCR_1.0-11           tzdb_0.3.0            remotes_2.4.2
 [22] globals_0.16.1        readr_2.1.2           matrixStats_0.62.0
 [25] spatstat.sparse_2.1-1 colorspace_2.1-0      rappdirs_0.3.3
 [28] ggrepel_0.9.1         textshaping_0.3.6     xfun_0.34
 [31] dplyr_1.0.10          crayon_1.5.2          jsonlite_1.8.3
 [34] progressr_0.10.1      spatstat.data_2.2-0   survival_3.3-1
 [37] zoo_1.8-10            glue_1.6.2            polyclip_1.10-0
 [40] gtable_0.3.1          leiden_0.4.2          DelayedArray_0.24.0
 [43] BiocSingular_1.14.0   future.apply_1.10.0   BiocGenerics_0.44.0
 [46] abind_1.4-7           scales_1.2.1          DBI_1.1.3
 [49] spatstat.random_2.2-0 Rcpp_1.0.9            viridisLite_0.4.1
 [52] xtable_1.8-6          rsthemes_0.3.1        reticulate_1.26
 [55] spatstat.core_2.4-4   rsvd_1.0.5            bit_4.0.4
 [58] stats4_4.2.1          htmlwidgets_1.5.4     httr_1.4.4
 [61] FNN_1.1.3.1           RColorBrewer_1.1-3    ellipsis_0.3.2
 [64] Seurat_4.1.1          ica_1.0-3             pkgconfig_2.0.3
 [67] farver_2.1.1          sass_0.4.2.9000       uwot_0.1.14
 [70] deldir_1.0-6          utf8_1.2.2            here_1.0.1
 [73] tidyselect_1.2.0      labeling_0.4.2        rlang_1.0.6
 [76] reshape2_1.4.4        later_1.3.0           cachem_1.0.6
 [79] munsell_0.5.0         tools_4.2.1           cli_3.4.1
 [82] dbscan_1.1-11         generics_0.1.3        ggridges_0.5.3
 [85] evaluate_0.18         stringr_1.4.1         fastmap_1.1.0
 [88] ragg_1.2.2            yaml_2.3.6            goftest_1.2-3
 [91] knitr_1.40            bit64_4.0.5           fitdistrplus_1.1-8
 [94] purrr_0.3.5           RANN_2.6.1            pbapply_1.5-0
 [97] future_1.29.0         nlme_3.1-158          mime_0.12
[100] arrow_9.0.0           hdf5r_1.3.5           compiler_4.2.1
[103] rstudioapi_0.14       plotly_4.10.1         png_0.1-7
[106] spatstat.utils_2.3-1  tibble_3.1.8          bslib_0.4.1
[109] stringi_1.7.8         rgeos_0.5-9           lattice_0.20-45
[112] Matrix_1.5-1          SeuratDisk_0.0.0.9020 vctrs_0.5.0
[115] pillar_1.8.1          lifecycle_1.0.3       jquerylib_0.1.4
[118] spatstat.geom_2.4-0   lmtest_0.9-40         RcppAnnoy_0.0.20
[121] data.table_1.14.4     cowplot_1.1.1         irlba_2.3.5.1
[124] httpuv_1.6.6          patchwork_1.1.0.9000  R6_2.5.1
[127] promises_1.2.0.1      KernSmooth_2.23-20    gridExtra_2.3
[130] IRanges_2.32.0        parallelly_1.32.1     codetools_0.2-18
[133] MASS_7.3-57           gtools_3.9.3          assertthat_0.2.1
[136] rprojroot_2.0.3       withr_2.5.0           SeuratObject_4.1.0
[139] sctransform_0.3.3     S4Vectors_0.36.0      mgcv_1.8-40
[142] parallel_4.2.1        hms_1.1.1             terra_1.5-34
[145] beachmat_2.14.0       grid_4.2.1            rpart_4.1.16
[148] tidyr_1.2.1           rmarkdown_2.18        MatrixGenerics_1.10.0
[151] Rtsne_0.16