Main
After pre-processing, we create a SpaTalk object with st_data and st_meta for single-cell ST data by setting if_st_is_sc and spot_max_cell as TRUE and 1
library(SpaTalk)# load starmap data
load(paste0(system.file(package = 'SpaTalk'), "/extdata/starmap_data.rda"))
load(paste0(system.file(package = 'SpaTalk'), "/extdata/starmap_meta.rda"))
# create SpaTalk data
obj <- createSpaTalk(st_data = as.matrix(starmap_data),
st_meta = starmap_meta[, -4],
species = "Mouse",
if_st_is_sc = T,
spot_max_cell = 1)Cell-type decomposition
First, we apply NNLM and spatial mapping to reconstruct the ST data at single-cell resolution using dec_celltype(). For single-cell ST data with known cell types, you can directly infer cell cell communication skiping deconvolution. Please refer to the tutorial
# decode the cell-type composition
obj <- dec_celltype(object = obj,
sc_data = as.matrix(starmap_data),
sc_celltype = starmap_meta$celltype)## Performing Non-negative regression for each cell
## ***Done***Use plot_st_celltype_percent() to view cell-type percent
# plot cell-type percent across spatial cells
plot_st_celltype_percent(object = obj, celltype = 'Oligo',size = 2)
Use plot_st_gene() to view gene expression
# plot marker gene expression across spatial cells
plot_st_gene(object = obj, gene = 'Plp1',size = 2, if_use_newmeta = F)
Use plot_st_cor_heatmap() to view correlation heatmap
# correlation between marker gene expression and cell type percent across spatial cells
plot_st_cor_heatmap(object = obj,
marker_genes = c("Plp1","Vip","Sst","Lamp5","Pcp4","Mfge8","Pvalb"),
celltypes = c("Oligo","VIP","SST","eL2_3","eL6","Astro","PVALB"),
scale = "none",
color_low = 'blue',
color_high = 'yellow',
color_mid = 'yellow')
ST at single-cell resolution
Use plot_st_celltype() to view cell type in space
# plot cell type in reconstructed ST atlas
plot_st_celltype(object = obj, celltype = 'Oligo', size = 2)
Use plot_st_gene() to view gene expression in space
# plot marker gene expression in single-cell ST data
plot_st_gene(object = obj,gene = 'Plp1', if_use_newmeta = T, size = 2)
Use plot_st_celltype_density() to view cell-type density in space
# plot cell-type density in single-cell ST data
plot_st_celltype_density(object = obj,
celltype = 'Oligo',
type = 'raster',
color_low = 'purple',
color_high = 'yellow')
plot_st_celltype_density(object = obj,
celltype = 'Oligo',
type = 'contour',
color_low = 'purple',
color_high = 'yellow')
Use plot_st_celltype_all() to view all cell types in space
# plot all cell types in single-cell ST data
plot_st_celltype_all(object = obj, size = 2)
Infer cell-cell communications
Based on the single-cell ST data, we use find_lr_path() and dec_cci() to infer cell-cell communications mediated by ligand-receptor interactions (LRIs) in space using LRIs from CellTalkDB, pathways from KEGG and Reactome, and transcriptional factors (TFs) from AnimalTFDB. In practise, we use the real cell type of STARmap
obj@meta$rawmeta$celltype <- starmap_meta$celltype
# Filter LRIs with downstream targets
obj <- find_lr_path(object = obj, lrpairs = lrpairs, pathways = pathways)## Checking input data
## Begin to filter lrpairs and pathways
## ***Done***# Infer cell-cell communications from SST to PVALB neurons
obj <- dec_cci(object = obj,
celltype_sender = 'eL5',
celltype_receiver = 'Astro')## Begin to find LR pairs# Get LR and downstream pathways
obj_lr_path <- get_lr_path(object = obj,
celltype_sender = 'eL5',
celltype_receiver = 'Astro',
ligand = 'Cort',
receptor = 'Sstr2')
obj_lr_path$tf_path## src dest src_tf dest_tf hop co_ratio tf
## 909510 Sstr2 Gnai1 NO NO 1 0.1037736 Smad3
## 1079710 Gnai1 Gnb5 NO NO 2 0.1886792 Smad3
## 108053 Gnai1 Gng7 NO NO 2 0.2641509 Smad3
## 4594212 Gnb5 Mapk3 NO NO 3 0.1320755 Smad3
## 461983 Gng7 Mapk3 NO NO 3 0.1886792 Smad3
## 3005311 Mapk3 Smad3 NO YES 4 0.1037736 Smad3
## 316521 Smad3 Acta2 YES NO 5 0.1509434 Smad3
## 3272071 Smad3 Serpine1 YES NO 5 0.1509434 Smad3
## 9095101 Sstr2 Gnai1 NO NO 1 0.1037736 Egr1
## 10797101 Gnai1 Gnb5 NO NO 2 0.1886792 Egr1
## 1080531 Gnai1 Gng7 NO NO 2 0.2641509 Egr1
## 45942121 Gnb5 Mapk3 NO NO 3 0.1320755 Egr1
## 4619831 Gng7 Mapk3 NO NO 3 0.1886792 Egr1
## 318001 Mapk3 Egr1 NO YES 4 0.1698113 Egr1
## 3107421 Egr1 Spp1 YES NO 5 0.1415094 Egr1obj_lr_path$path_pvalue## celltype_sender celltype_receiver ligand receptor
## 1 eL5 Astro Cort Sstr2
## 2 eL5 Astro Cort Sstr2
## 3 eL5 Astro Cort Sstr2
## receptor_pathways pvalue gene_count
## 1 cAMP signaling pathway 3.830075e-03 2
## 2 Neuroactive ligand-receptor interaction 2.077683e-02 2
## 3 Growth hormone synthesis, secretion and action 3.433205e-05 3
## gene
## 1 Sstr2,Gnai1
## 2 Cort,Sstr2
## 3 Sstr2,Gnai1,Mapk3Use plot_ccdist() to view distribution of senders and receivers
# Point plot with spatial distribution of celltype_sender and celltype_receiver
plot_ccdist(object = obj,
celltype_sender = 'eL5',
celltype_receiver = 'Astro',
size = 2,
arrow_length = 0.1)
Use plot_cci_lrpairs() to view all LRIs of senders and receivers
# Heatmap with LR pairs of celltype_sender and celltype_receiver
plot_cci_lrpairs(object = obj, celltype_sender = 'eL5', celltype_receiver = 'Astro')
Use plot_lrpair() to view the specific LRI
# Point plot with LR pair from celltype_sender to celltype_receiver
plot_lrpair(object = obj,
celltype_sender = 'eL5',
ligand = 'Cort',
celltype_receiver = 'Astro',
receptor = 'Sstr2',
if_plot_density = F,
size = 2,
arrow_length = 0.1)
Use plot_lrpair_vln() to view violin plot with spatial distance of LRI
# Violin plot with spatial distance of LR pair between senders and receivers and between all cell-cell pairs
plot_lrpair_vln(object = obj,
celltype_sender = 'eL5',
ligand = 'Cort',
celltype_receiver = 'Astro',
receptor = 'Sstr2')##
## Wilcoxon rank sum test with continuity correction
##
## data: celltype_dist1 and celltype_dist2
## W = 108823510, p-value < 2.2e-16
## alternative hypothesis: true location shift is greater than 0
Use plot_lr_path() to view network with LR and downstream pathways
# Plot network with LR and downstream pathways
plot_lr_path(object = obj,
celltype_sender = 'eL5',
ligand = 'Cort',
celltype_receiver = 'Astro',
receptor = 'Sstr2')
Use plot_path2gene() to view river plot of significantly activated pathways
# River plot of significantly activated pathways and related downstream genes of receptors
plot_path2gene(object = obj,
celltype_sender = 'eL5',
ligand = 'Cort',
celltype_receiver = 'Astro',
receptor = 'Sstr2')## Warning: The `.dots` argument of `group_by()` is deprecated as of dplyr 1.0.0.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
Note
To infer all paired cell-cell communications, use dec_cci_all() instead of dec_cci()
# Infer all cell-cell communications
# obj <- dec_cci_all(object = obj)sessionInfo()## R version 4.1.1 (2021-08-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Chinese (Simplified)_China.936
## [2] LC_CTYPE=Chinese (Simplified)_China.936
## [3] LC_MONETARY=Chinese (Simplified)_China.936
## [4] LC_NUMERIC=C
## [5] LC_TIME=Chinese (Simplified)_China.936
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] SpaTalk_1.0 ggalluvial_0.12.3 ggplot2_3.3.5
##
## loaded via a namespace (and not attached):
## [1] Seurat_4.0.5 Rtsne_0.15 colorspace_2.0-2
## [4] ggsignif_0.6.3 deldir_1.0-6 ellipsis_0.3.2
## [7] ggridges_0.5.3 spatstat.data_2.1-0 ggpubr_0.4.0
## [10] leiden_0.3.9 listenv_0.8.0 farver_2.1.0
## [13] ggrepel_0.9.1 fansi_0.5.0 scatterpie_0.1.7
## [16] codetools_0.2-18 splines_4.1.1 knitr_1.36
## [19] polyclip_1.10-0 jsonlite_1.7.2 broom_0.7.10
## [22] ica_1.0-2 cluster_2.1.2 png_0.1-7
## [25] uwot_0.1.10 pheatmap_1.0.12 spatstat.sparse_2.0-0
## [28] ggforce_0.3.3 shiny_1.7.1 sctransform_0.3.2
## [31] compiler_4.1.1 httr_1.4.2 backports_1.3.0
## [34] assertthat_0.2.1 SeuratObject_4.0.2 Matrix_1.3-4
## [37] fastmap_1.1.0 lazyeval_0.2.2 later_1.3.0
## [40] tweenr_1.0.2 htmltools_0.5.2 prettyunits_1.1.1
## [43] tools_4.1.1 igraph_1.2.7 gtable_0.3.0
## [46] glue_1.4.2 RANN_2.6.1 reshape2_1.4.4
## [49] dplyr_1.0.7 Rcpp_1.0.7 carData_3.0-4
## [52] scattermore_0.7 jquerylib_0.1.4 vctrs_0.3.8
## [55] nlme_3.1-152 lmtest_0.9-38 xfun_0.30
## [58] stringr_1.4.0 globals_0.14.0 mime_0.12
## [61] miniUI_0.1.1.1 lifecycle_1.0.1 irlba_2.3.3
## [64] rstatix_0.7.0 goftest_1.2-3 NNLM_0.4.4
## [67] future_1.23.0 MASS_7.3-54 zoo_1.8-9
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## [85] stringi_1.7.5 highr_0.9 rlang_0.4.12
## [88] pkgconfig_2.0.3 matrixStats_0.61.0 evaluate_0.14
## [91] lattice_0.20-44 tensor_1.5 ROCR_1.0-11
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## [109] pillar_1.6.4 withr_2.4.2 fitdistrplus_1.1-6
## [112] prettydoc_0.4.1 abind_1.4-5 survival_3.2-11
## [115] tibble_3.1.5 future.apply_1.8.1 car_3.0-12
## [118] crayon_1.4.2 KernSmooth_2.23-20 utf8_1.2.2
## [121] spatstat.geom_2.3-0 plotly_4.10.0 rmarkdown_2.13
## [124] progress_1.2.2 isoband_0.2.5 grid_4.1.1
## [127] data.table_1.14.2 digest_0.6.28 xtable_1.8-4
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