rev_gene()

Revise genes with rev_gene(). For scRNA-seq data, we suggest to revise the gene symbols with rev_gene(). geneinfo is the system data.frame containing the information of human and mouse from NCBI gene(updated in June. 19, 2022). To use your own geneinfo data.frame, please refer to demo_geneinfo() to build a new one, e.g., rat, zebrafish, Drosophila, C. elegans, etc.

library(scCrossTalk)
load(paste0(system.file(package = "scCrossTalk"), "/extdata/example.rda"))

# demo_geneinfo
demo_geneinfo()
#>   symbol synonyms species
#> 1   A1BG      A1B   Human
#> 2   A1BG      ABG   Human
#> 3  A2MP1     A2MP   Human
#> 4   Aco1      Aco   Mouse

# demo_sc_data
demo_sc_data()
#> 6 x 6 sparse Matrix of class "dgCMatrix"
#>      cell1 cell2 cell3 cell4 cell5 cell6
#> A1BG    41     .     .     4     .    29
#> A2M      .     .    42    12     .     .
#> A2MP    13     .     .    50     .     .
#> NAT1     .    21     .     .    23     .
#> NAT2    45     .    48     .     .     .
#> NATP     .    38     8    50     .    14

# revise gene symbols
sc_data <- rev_gene(data = sc_data,data_type = "count",species = "Human",geneinfo = geneinfo)

create_scCrossTalk()

Create scCrossTalk object with create_scCrossTalk(). Users need to provide the raw count data and the cell type for create_scCrossTalk(). scCrossTalk will use NormalizeData() in Seurat to normalize data by default. If your data has been normalized by other methods, you can set the parameter if_normalize = FALSE

obj <- create_scCrossTalk(sc_data = sc_data, sc_celltype = sc_celltype, species = "Human", if_normalize = TRUE)
#> Warning: Feature names cannot have underscores ('_'), replacing with dashes
#> ('-')

# If your data has been normalized by other methods, you can set the parameter `if_normalize = FALSE`

# obj <- create_scCrossTalk(sc_data = sc_data, sc_celltype = sc_celltype,species = "Human", if_normalize = FALSE)

find_lrpairs()

Find highly expressed LR pairs with find_lrpairs(). Users need to provided the speices and lr pairs database for find_lrpairs(). lrpairs is the system data.frame containing the known LR pairs of human and mouse from CellTalkDB. To use your own lrpairs data.frame, please refer to demo_lrpairs() to build a new one, e.g., rat, zebrafish, Drosophila, C. elegans, etc.

# demo_lrpairs
demo_lrpairs()
#>   ligand receptor species
#> 1 CX3CL1   CX3CR1   Human
#> 2  TGFB1   TGFBR2   Human
#> 3   CCL2     CCR2   Human
#> 4    Sst    Sstr1   Mouse

# find highly expressed LR pairs
obj <- find_lrpairs(object = obj, lrpairs = lrpairs, min_cell_num = 10, cell_min_pct = 0.1, p_value = 0.05)
#> Finding highly expressed LR pairs

# results is stored in obj@cci
cci <- obj@cci
head(cci)
#>   celltype_sender celltype_receiver ligand receptor ligand_pct ligand_exp_avg
#> 1     acinar_cell       acinar_cell    VIM     CD44  0.6155718      0.8339240
#> 2     acinar_cell       acinar_cell     C3     CD46  0.5255474      0.8740566
#> 3     acinar_cell       acinar_cell CXCL12    ITGB1  0.6496350      0.6158164
#> 4     acinar_cell       acinar_cell  LMAN1    MCFD2  0.8199513      0.7800645
#> 5     acinar_cell       acinar_cell CXCL12     SDC4  0.6496350      0.6158164
#> 6     acinar_cell        alpha_cell     C3     CD46  0.5255474      0.8740566
#>   ligand_z_score ligand_p_value receptor_pct receptor_exp_avg receptor_z_score
#> 1       2.611653    0.004505282    0.9075426        2.2410825         7.678032
#> 2       2.756148    0.002924328    0.8077859        0.8377001         2.625249
#> 3       1.826372    0.033897157    0.7883212        0.6926912         2.103154
#> 4       2.417736    0.007808710    0.8223844        0.9291400         2.954472
#> 5       1.826372    0.033897157    0.7737226        0.7283321         2.231477
#> 6       2.756148    0.002924328    0.9227683        1.6624160         5.250688
#>       score
#> 1 1.8688924
#> 2 0.7321973
#> 3 0.4265706
#> 4 0.7247891
#> 5 0.4485189
#> 6 1.4530456

plot_cci_chord()

Parameters of plot_cci_chord() see below:

• object scCrossTalk object after find_lrpairs
• celltype which cell types to plot by order. Default is to plot all cell types
• celltype_color Colors for the cell types, whose length must be equal to celltype
• ligand which ligand to use. Default is to plot all inferred ligands
• receptor which receptor to use. Default is to plot all inferred receptors
• edge_color Colors for the edges from the sender cell type, whose length must be equal to celltype
• edge_type Types for the edges from the sender cell type. Default is “big.arrow”. “ellipse” for ellipse, “triangle” for triangle, “curved” for curved. Details see circlize::chordDiagram
• show_type which type to show, and for sum of inferred LR number and score, respectively. Default is
• if_show_autocrine Whether to show autocrine. Default is
• text_size Size of text labels. Default is
• y_scale y_scale to adjust the text. Default is
• ... parameters pass to circlize::chordDiagram, e.g., link.arr.width, link.arr.length, link.arr.col

plot_cci_chord(object = obj)

plot_cci_circle()

Parameters of plot_cci_circle() see below:

• object scCrossTalk object after find_lrpairs
• celltype which cell types to plot. Default is to plot all cell types
• ligand which ligand to use. Default is to plot all inferred ligands
• receptor which receptor to use. Default is to plot all inferred receptors
• celltype_color Colors for the cell types, whose length must be equal to celltype
• edge_color Colors for the edges from the sender cell type, whose length must be equal to celltype
• edge_type Types for the edges. “fan” by default, “link”, “hive”
• show_type which type to show, “number” and “score” for sum of inferred LR number and score, respectively. Default is “number”
• if_show_autocrine Whether to show autocrine. Default is FALSE
• edge_alpha Transparency of edge. Default is 0.5
• node_size Size of node. Default is 10
• text_size Size of text. Default is 5

plot_cci_circle(object = obj)

plot_cci_circle_simple()

# as sender
plot_cci_circle_simple(object = obj, celltype = "ductal_cell", celltype_dir = "sender")

# as receiver
plot_cci_circle_simple(object = obj, celltype = "ductal_cell", celltype_dir = "reciver")

plot_cci_heatmap()

Parameters of plot_cci_heatmap() see below:

• object scCrossTalk object after find_lrpairs
• celltype which cell types to plot. Default is to plot all cell types
• ligand which ligand to use. Default is to plot all inferred ligands
• receptor which receptor to use. Default is to plot all inferred receptors
• celltype_color Colors for the cell types, whose length must be equal to celltype
• edge_color Colors for the edges from the sender cell type, whose length must be equal to celltype, or use “NO” to cancel it
• show_type which type to show, “number” and “score” for sum of inferred LR number and score, respectively. Default is “number”
• if_show_autocrine Whether to show autocrine. Default is FALSE
• edge_alpha Transparency of edge. Default is 0.5
• node_size Size of node. Default is 40
• text_size Size of text. Default is 15
• node_pad Size of node padding. Numeric essentially influences the width height. Default is 20
• ... parameters pass to networkD3::sankeyNetwork

plot_cci_heatmap(object = obj)

plot_cci_sankey()

Parameters of plot_cci_sankey() see below:

• object scCrossTalk object after find_lrpairs
• celltype which cell types to plot by order. Default is to plot all cell types
• ligand which ligand to use. Default is to plot all inferred ligands
• receptor which receptor to use. Default is to plot all inferred receptors
• show_type which type to show, “number” and “score” for sum of inferred LR number and score, respectively. Default is “number”
• text_size Size of text labels. Default is 10
• viridis_option option in viridis::scale_color_viridis, can be “A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”. Default is “D”.
• ... parameters pass to heatmaply::heatmaply, e.g., grid_color

plot_cci_sankey(object = obj)

plot_cci_lrpairs_heatmap()

Parameters of plot_cci_lrpairs_heatmap()see below:

• object scCrossTalk object after find_lrpairs
• celltype which cell types to plot. Default is to plot all cell types
• ligand which ligand to use. Default is to plot all inferred ligands
• receptor which receptor to use. Default is to plot all inferred receptors
• if_show_autocrine Whether to show autocrine. Default is FALSE
• if_horizontal Whether to plot with the horizontal direction. Default is TRUE
• text_size Size of text labels. Default is 10
• viridis_option option in viridis::scale_color_viridis, can be “A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”. Default is “D”.
• ... parameters pass to heatmaply::heatmaply, e.g., grid_color

plot_cci_lrpairs_heatmap(object = obj)

plot_cci_lrpairs_bubble()

Parameters of plot_cci_lrpairs_bubble() see below:

• object scCrossTalk object after find_lrpairs
• celltype which cell types to plot. Default is to plot all cell types
• ligand which ligand to use. Default is to plot all inferred ligands
• receptor which receptor to use. Default is to plot all inferred receptors
• if_show_autocrine Whether to show autocrine. Default is FALSE
• if_horizontal Whether to plot with the horizontal direction. Default is TRUE
• size Size of the bubble. Default is 2
• viridis_option option in viridis::scale_color_viridis, can be “A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”. Default is “D”.

plot_cci_lrpairs_bubble(object = obj)

get_top_lrpairs()

obj_filter <- obj
obj_filter@cci <- get_top_lrpairs(object = obj_filter, top_n = 5)
plot_cci_lrpairs_bubble(object = obj_filter, size = 2, if_horizontal = FALSE)

plot_cci_lrpairs_heatmap(object = obj_filter, celltype = c("alpha_cell","beta_cell", "delta_cell", "ductal_cell"))

plot_lrpairs_chord()

Parameters of plot_lrpairs_chord() see below:

• object scCrossTalk object after find_lrpairs
• celltype_sender Name of celltype_sender. One or more cell types
• celltype_receiver Name of celltype_receiver. One or more cell types
• celltype_color Colors for the celltype_sender nodes and celltype_receiver nodes, or use “NO” to make it simple
• ligand which ligand to use. Default is to plot all inferred ligands
• receptor which receptor to use. Default is to plot all inferred receptors
• edge_color Colors for the edges from the sender cell type
• edge_type Types for the edges from the sender cell type. Default is “circle”, “big.arrow” for big arrow, “triangle” for triangle, “ellipse” for ellipse, “curved” for curved. Details see circlize::chordDiagram
• text_size Size of text labels. Default is 0.5
• y_scale y_scale to adjust the text. Default is 1
• ... parameters pass to circlize::chordDiagram, e.g., link.arr.width, link.arr.length, link.arr.col

plot_lrpairs_chord(obj,celltype_sender = "alpha_cell",celltype_receiver = "beta_cell",link.arr.width = 0.5, link.arr.length = 0.2, text_size = 0.8)

plot_lrpairs_heatmap()

Parameters of plot_lrpairs_heatmap() see below:

• object scCrossTalk object after find_lrpairs
• celltype_sender Name of celltype_sender. One or more cell types
• celltype_receiver Name of celltype_receiver. One or more cell types
• ligand which ligand to use. Default is to plot all inferred ligands
• receptor which receptor to use. Default is to plot all inferred receptors
• text_size Size of text labels. Default is 3
• if_horizontal Whether to plot with the horizontal direction. Default is TRUE
• viridis_option option in viridis::scale_color_viridis, can be “A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”. Default is “D”.
• ... parameters pass to heatmaply::heatmaply, e.g., grid_color

plot_lrpairs_heatmap(obj,celltype_sender = "alpha_cell",celltype_receiver = "beta_cell")

sessionInfo()
#> R version 4.1.1 (2021-08-10)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19045)
#> 
#> 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] scCrossTalk_1.0
#> 
#> loaded via a namespace (and not attached):
#>   [1] circlize_0.4.13       plyr_1.8.6            igraph_1.2.7         
#>   [4] lazyeval_0.2.2        sp_1.4-5              splines_4.1.1        
#>   [7] crosstalk_1.2.0       listenv_0.8.0         scattermore_0.7      
#>  [10] ggplot2_3.3.6         digest_0.6.28         foreach_1.5.1        
#>  [13] ca_0.71.1             htmltools_0.5.2       viridis_0.6.2        
#>  [16] fansi_0.5.0           magrittr_2.0.1        tensor_1.5           
#>  [19] cluster_2.1.2         ROCR_1.0-11           globals_0.14.0       
#>  [22] graphlayouts_0.7.1    matrixStats_0.61.0    spatstat.sparse_3.0-0
#>  [25] rmdformats_1.0.3      prettyunits_1.1.1     colorspace_2.0-2     
#>  [28] ggrepel_0.9.1         xfun_0.30             dplyr_1.0.7          
#>  [31] crayon_1.4.2          jsonlite_1.7.2        progressr_0.9.0      
#>  [34] spatstat.data_3.0-0   survival_3.2-11       zoo_1.8-9            
#>  [37] iterators_1.0.13      glue_1.4.2            polyclip_1.10-0      
#>  [40] registry_0.5-1        gtable_0.3.0          webshot_0.5.4        
#>  [43] leiden_0.3.9          future.apply_1.8.1    shape_1.4.6          
#>  [46] abind_1.4-5           scales_1.1.1          DBI_1.1.1            
#>  [49] miniUI_0.1.1.1        Rcpp_1.0.7            viridisLite_0.4.0    
#>  [52] xtable_1.8-4          progress_1.2.2        reticulate_1.22      
#>  [55] spatstat.core_2.3-0   htmlwidgets_1.5.4     httr_1.4.2           
#>  [58] RColorBrewer_1.1-2    ellipsis_0.3.2        Seurat_4.1.1         
#>  [61] ica_1.0-2             pkgconfig_2.0.3       farver_2.1.0         
#>  [64] sass_0.4.0            uwot_0.1.10           deldir_1.0-6         
#>  [67] utf8_1.2.2            labeling_0.4.2        tidyselect_1.1.1     
#>  [70] rlang_0.4.12          reshape2_1.4.4        later_1.3.0          
#>  [73] munsell_0.5.0         tools_4.1.1           generics_0.1.1       
#>  [76] ggridges_0.5.3        evaluate_0.14         stringr_1.4.0        
#>  [79] fastmap_1.1.0         heatmaply_1.4.0       yaml_2.2.1           
#>  [82] goftest_1.2-3         knitr_1.36            fitdistrplus_1.1-6   
#>  [85] tidygraph_1.2.0       purrr_0.3.4           RANN_2.6.1           
#>  [88] dendextend_1.16.0     ggraph_2.0.5          pbapply_1.5-0        
#>  [91] future_1.23.0         nlme_3.1-152          mime_0.12            
#>  [94] compiler_4.1.1        plotly_4.10.0         png_0.1-7            
#>  [97] spatstat.utils_3.0-1  tibble_3.1.5          tweenr_1.0.2         
#> [100] bslib_0.3.1           stringi_1.7.5         highr_0.9            
#> [103] rgeos_0.5-9           lattice_0.20-44       Matrix_1.3-4         
#> [106] vctrs_0.3.8           pillar_1.6.4          lifecycle_1.0.1      
#> [109] networkD3_0.4         spatstat.geom_3.0-3   lmtest_0.9-38        
#> [112] jquerylib_0.1.4       GlobalOptions_0.1.2   RcppAnnoy_0.0.19     
#> [115] data.table_1.14.2     cowplot_1.1.1         irlba_2.3.3          
#> [118] seriation_1.4.0       httpuv_1.6.3          patchwork_1.1.1      
#> [121] R6_2.5.1              bookdown_0.25         promises_1.2.0.1     
#> [124] TSP_1.2-1             KernSmooth_2.23-20    gridExtra_2.3        
#> [127] parallelly_1.28.1     codetools_0.2-18      MASS_7.3-54          
#> [130] assertthat_0.2.1      SeuratObject_4.1.0    sctransform_0.3.3    
#> [133] mgcv_1.8-36           parallel_4.1.1        hms_1.1.1            
#> [136] grid_4.1.1            rpart_4.1-15          tidyr_1.1.4          
#> [139] rmarkdown_2.13        Rtsne_0.15            ggforce_0.3.3        
#> [142] shiny_1.7.1