Interacting with domino Objects
Source:vignettes/domino_object_vignette.Rmd
domino_object_vignette.RmdIn this tutorial, we will go into more detail on the structure of a domino object and the ways in which to access the data stored within. We will be using the domino object we built on the Getting Started page. If you have not yet built a domino object, you can do so by following the instructions on the Getting Started page.
Instructions to load data
set.seed(42)
library(dominoSignal)
# BiocFileCache helps with file management across sessions
bfc <- BiocFileCache::BiocFileCache(ask = FALSE)
data_url <- "https://zenodo.org/records/10951634/files/pbmc_domino_built.rds"
tmp_path <- BiocFileCache::bfcrpath(bfc, data_url)
dom <- readRDS(tmp_path)Object contents
There is a great deal of information stored with the domino object class. The domino object is an S4 class object that contains a variety of information about the data set used to build the object, the calculated values, and the linkages between receptors, ligands, and transcription factors. The object is structured as follows (with some examples of the information stored within each slot:
-
Input Data
Information about the database used to construct the rl_map
Inputted counts matrix
Inputted z-scored counts matrix
Inputted cluster labels
Inputted transcription factor activation scores
-
Calculated values
Differential expression p-values of transcription factors in each cluster
Correlation values between ligands and receptors
Median correlation between components of receptor complexes
-
Linkages
Complexes show the component genes of any complexes in the rl map
Receptor - ligand linkages as determined from the rl map
Transcription factor - target linkages as determined from the SCENIC analysis (or other regulon inference method)
Transcription factors that are differentially expressed in each cluster
Transcription factors that are correlated with receptors
Transcription factors that are correlated with receptors in each cluster
Receptors which are active in each cluster
Ligands that may activate a receptor in a given cluster (so-called incoming ligands; these may include ligands from outside the data set)
-
Signaling matrices
For each cluster, incoming ligands and the clusters within the data set that they are coming from
A summary of signaling between all clusters
-
Miscellaneous Information
Build information, which includes the parameters used to build the object in the
build_domino()functionsThe pared down receptor ligand map information used in building the object
The percent expression of receptors within each cluster
For commonly accessed information (the number of cells, clusters, and some build information), the show and print methods for domino objects can be used.
dom
#> A domino object of 2607 cells
#> Built with signaling between 9 clusters
print(dom)Access functions
To facilitate access to the information stored in the domino object,
we have provided a collection of functions to retrieve specific items.
These functions begin with “dom_” and can be listed using
ls().
ls("package:dominoSignal", pattern = "^dom_")
#> [1] "dom_clusters" "dom_correlations" "dom_counts"
#> [4] "dom_database" "dom_de" "dom_info"
#> [7] "dom_linkages" "dom_network_items" "dom_signaling"
#> [10] "dom_tf_activation" "dom_zscores"Input data
When creating a domino object with the create_domino()
function, several inputs are required which are then stored in the
domino object itself. These include cluster labels, the counts matrix,
z-scored counts matrix, transcription factor activation scores, and the
R-L database used in create_rl_map_cellphonedb().
For example, to access the cluster names in the domino object:
dom_clusters(dom)
#> [1] "B_cell" "CD14_monocyte" "CD16_monocyte"
#> [4] "CD8_T_cell" "dendritic_cell" "memory_CD4_T_cell"
#> [7] "naive_CD4_T_cell" "NK_cell" "Platelet"Setting an argument labels = TRUE will return the vector
of cluster labels for each cell rather than the unique cluster
names.
To access the counts:
count_matrix <- dom_counts(dom)
knitr::kable(count_matrix[1:5, 1:5])| AAACATACAACCAC-1 | AAACATTGAGCTAC-1 | AAACATTGATCAGC-1 | AAACCGTGCTTCCG-1 | AAACCGTGTATGCG-1 | |
|---|---|---|---|---|---|
| AL627309.1 | 0 | 0 | 0 | 0 | 0 |
| AP006222.2 | 0 | 0 | 0 | 0 | 0 |
| RP11-206L10.2 | 0 | 0 | 0 | 0 | 0 |
| RP11-206L10.9 | 0 | 0 | 0 | 0 | 0 |
| FAM87B | 0 | 0 | 0 | 0 | 0 |
Or z-scored counts:
z_matrix <- dom_zscores(dom)
knitr::kable(z_matrix[1:5, 1:5])| AAACATACAACCAC-1 | AAACATTGAGCTAC-1 | AAACATTGATCAGC-1 | AAACCGTGCTTCCG-1 | AAACCGTGTATGCG-1 | |
|---|---|---|---|---|---|
| AL627309.1 | -0.0581122 | -0.0581122 | -0.0581122 | -0.0581122 | -0.0581122 |
| AP006222.2 | -0.0335151 | -0.0335151 | -0.0335151 | -0.0335151 | -0.0335151 |
| RP11-206L10.2 | -0.0399375 | -0.0399375 | -0.0399375 | -0.0399375 | -0.0399375 |
| RP11-206L10.9 | -0.0337574 | -0.0337574 | -0.0337574 | -0.0337574 | -0.0337574 |
| FAM87B | -0.0274227 | -0.0274227 | -0.0274227 | -0.0274227 | -0.0274227 |
The transcription factor activation scores can be similarly accessed:
activation_matrix <- dom_tf_activation(dom)
knitr::kable(activation_matrix[1:5, 1:5])| AAACATACAACCAC-1 | AAACATTGAGCTAC-1 | AAACATTGATCAGC-1 | AAACCGTGCTTCCG-1 | AAACCGTGTATGCG-1 | |
|---|---|---|---|---|---|
| ARNTL | 0.0446386 | 0.0413253 | 0.0441566 | 0.0437952 | 0.1087952 |
| ATF3 | 0.0582029 | 0.0870623 | 0.0509289 | 0.1172953 | 0.0638104 |
| ATF4 | 0.0295733 | 0.0437047 | 0.0542889 | 0.0427879 | 0.0436855 |
| ATF6 | 0.0000000 | 0.0211198 | 0.1151665 | 0.0850461 | 0.0460666 |
| BCL3 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
Information about the database referenced for ligand - receptor pairs
and composition of protein complexes can be extracted from the
dom_database() function. By default, the function returns
the name(s) of the database(s) used:
dom_database(dom)
#> [1] "CellPhoneDB_v4.0"If you would like to view the entire ligand - receptor map, set
name_only = FALSE:
db_matrix <- dom_database(dom, name_only = FALSE)
knitr::kable(db_matrix[1:5, 1:5])| int_pair | name_A | uniprot_A | gene_A | type_A | |
|---|---|---|---|---|---|
| 4 | RAreceptor_RXRG & atRetinoicAcid_byALDH1A3 | RAreceptor_RXRG | P48443,P29373 | RXRG,CRABP2 | R |
| 5 | RAreceptor_RXRG & atRetinoicAcid_byALDH1A2 | RAreceptor_RXRG | P48443,P29373 | RXRG,CRABP2 | R |
| 6 | RAreceptor_RXRG & atRetinoicAcid_byALDH1A1 | RAreceptor_RXRG | P48443,P29373 | RXRG,CRABP2 | R |
| 7 | RAreceptor_RXRB & atRetinoicAcid_byALDH1A3 | RAreceptor_RXRB | P28702,P29373 | RXRB,CRABP2 | R |
| 8 | RAreceptor_RXRB & atRetinoicAcid_byALDH1A2 | RAreceptor_RXRB | P28702,P29373 | RXRB,CRABP2 | R |
Calculations
Active transcription factors in each cluster are determined by
conducting Wilcoxon rank sum tests for each transcription factor where
the transcription factor activity scores amongst all cells in a cluster
are tested against the activity scores of all cells outside of the
cluster. The p-values for the one-sided test for greater activity within
the cluster compared to other cells can be accessed with the
dom_de() function.
| B_cell | CD14_monocyte | CD16_monocyte | CD8_T_cell | dendritic_cell | |
|---|---|---|---|---|---|
| ARNTL | 0.0000019 | 1 | 0.9947780 | 0.9865518 | 0.8359381 |
| ATF3 | 0.0006972 | 0 | 0.0000000 | 1.0000000 | 0.0000000 |
| ATF4 | 0.8399818 | 1 | 1.0000000 | 0.9631934 | 0.1748338 |
| ATF6 | 0.5988625 | 1 | 0.9893142 | 0.0365779 | 0.9549489 |
| BCL3 | 0.9299696 | 0 | 0.6007326 | 0.1482247 | 0.0757460 |
Linkage between receptors and transcription factors is assessed by
Spearman correlation between transcription factor activity scores and
scaled expression of receptor-encoding genes across all cells in the
data set. Spearman coefficients can be accessed with the
dom_correlations() function. Setting type to
“complex” will return the median correlation between components of
receptor complexes; the default (“rl”) will return receptor - ligand
correlations.
cor_matrix <- dom_correlations(dom)
knitr::kable(cor_matrix[1:5, 1:5])| ARNTL | ATF3 | ATF4 | ATF6 | BCL3 | |
|---|---|---|---|---|---|
| TNFRSF18 | 0.0057014 | -0.0297120 | 0.0043825 | 0.0063055 | -0.0029035 |
| TNFRSF4 | 0.0373481 | -0.1012705 | 0.0344042 | 0.0508720 | 0.0282500 |
| TNFRSF14 | -0.0356892 | 0.0541435 | 0.0015811 | 0.0093978 | 0.0206700 |
| TNFRSF25 | 0.0310362 | -0.1171502 | 0.0701541 | 0.0458227 | 0.0047186 |
| TNFRSF1B | -0.0933019 | 0.2690328 | -0.0625319 | -0.0457366 | 0.0239629 |
Linkages
Linkages between ligands, receptors, and transcription factors can be
accessed in several different ways, depending on the specific link and
the scope desired. The dom_linkages() function has three
arguments - the first, like all of our access functions, is for the
domino object. The second, link_type, is used to specify
which linkages are desired (options are complexes, receptor - ligand, tf
- target, or tf - receptor). The third argument,
by_cluster, determines whether the linkages returned are
arranged by cluster (though this does change the available linkage types
to tf - receptor, receptor, or incoming-ligand). For example, to access
the complexes used across the dataset:
complex_links <- dom_linkages(dom, link_type = "complexes")
# Look for components of NODAL receptor complex
complex_links$NODAL_receptor
#> NULLTo view incoming ligands to each cluster:
incoming_links <- dom_linkages(dom, link_type = "incoming-ligand", by_cluster = TRUE)
# Check incoming signals to dendritic cells
incoming_links$dendritic_cell
#> [1] "COPA" "MIF" "APP"
#> [4] "FAM19A4" "TAFA4" "ANXA1"
#> [7] "CD99" "integrin_aVb3_complex" "integrin_a4b1_complex"
#> [10] "BMP8B" "PLAU" "CSF3"
#> [13] "CXCL9" "HLA-F" "CD1D"
#> [16] "INS" "IL34" "CSF1"
#> [19] "CSF2" "CTLA4" "CD28"
#> [22] "GRN" "TNF" "LTA"
#> [25] "CXCL12" "CXCL14" "CD58"If, for some reason, you find yourself in need of the entire linkage structure (not recommended), it can be accessed through its slot name; domino objects are S4 objects.
all_linkages <- slot(dom, "linkages")
# Names of all sub-structures:
names(all_linkages)
#> [1] "complexes" "rec_lig" "tf_targets"
#> [4] "clust_tf" "tf_rec" "clust_tf_rec"
#> [7] "clust_rec" "clust_incoming_lig"Alternately, to obtain a simplified list of receptors, ligands,
and/or features in the domino object, use the
dom_network_items() function. To pull all transcription
factors associated with the dendritic cell cluster:
dc_tfs <- dom_network_items(dom, "dendritic_cell", return = "features")
head(dc_tfs)
#> [1] "ATF3" "CEBPD" "FOSB" "STAT6" "KLF4" "CEBPA"Signaling Matrices
The averaged z-scored expression of ligands and receptors between different clusters can be accessed in matrix form.
signal_matrix <- dom_signaling(dom)
knitr::kable(signal_matrix)| L_B_cell | L_CD14_monocyte | L_CD16_monocyte | L_CD8_T_cell | L_dendritic_cell | L_memory_CD4_T_cell | L_naive_CD4_T_cell | L_NK_cell | L_Platelet | |
|---|---|---|---|---|---|---|---|---|---|
| R_B_cell | 0.0494729 | 0.1976263 | 0.1686996 | 0.0000000 | 0.3040989 | 0.2459618 | 0.1367747 | 0.0000000 | 4.013299 |
| R_CD14_monocyte | 0.8060665 | 2.5378490 | 1.4429140 | 1.1787148 | 3.4961650 | 2.2611486 | 1.2608492 | 1.4485708 | 10.559449 |
| R_CD16_monocyte | 0.8060665 | 2.5378490 | 1.1473216 | 1.1787148 | 3.4961650 | 2.2611486 | 0.8757246 | 1.4055805 | 7.258572 |
| R_CD8_T_cell | 0.3759111 | 0.0000000 | 0.1263349 | 0.2120444 | 0.0000000 | 0.2772703 | 0.0000000 | 0.1604005 | 0.000000 |
| R_dendritic_cell | 0.3176857 | 2.5041485 | 0.7368126 | 1.3152485 | 3.4961650 | 2.3809976 | 0.3061580 | 1.5659811 | 7.258572 |
| R_memory_CD4_T_cell | 0.3759111 | 0.0000000 | 0.1263349 | 0.2120444 | 0.0000000 | 0.3801282 | 0.0000000 | 0.1604005 | 0.000000 |
| R_naive_CD4_T_cell | 0.3759111 | 0.0000000 | 0.1263349 | 0.2120444 | 0.0000000 | 0.3801282 | 0.0000000 | 0.1604005 | 0.000000 |
| R_NK_cell | 0.2930600 | 1.5760509 | 0.6086536 | 0.2556741 | 1.5025648 | 0.7620921 | 0.1772729 | 0.1604005 | 4.013299 |
| R_Platelet | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.000000 |
To view signaling to a specific cluster from the other clusters, set
the cluster argument to the cluster name.
dc_matrix <- dom_signaling(dom, "dendritic_cell")
knitr::kable(dc_matrix)| dendritic_cell.L_B_cell | dendritic_cell.L_CD14_monocyte | dendritic_cell.L_CD16_monocyte | dendritic_cell.L_CD8_T_cell | dendritic_cell.L_dendritic_cell | dendritic_cell.L_memory_CD4_T_cell | dendritic_cell.L_naive_CD4_T_cell | dendritic_cell.L_NK_cell | dendritic_cell.L_Platelet | |
|---|---|---|---|---|---|---|---|---|---|
| COPA | 0.0000000 | 0.1352458 | 0.1686996 | 0.0000000 | 0.1706852 | 0.0243003 | 0.0000000 | 0.0000000 | 0.1634935 |
| MIF | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0810828 | 0.1637432 | 0.1367747 | 0.0000000 | 0.0000000 |
| APP | 0.0494729 | 0.0623805 | 0.0000000 | 0.0000000 | 0.0523309 | 0.0579182 | 0.0000000 | 0.0000000 | 3.8498060 |
| ANXA1 | 0.0000000 | 0.1864958 | 0.0000000 | 0.1682595 | 0.6876552 | 0.5065038 | 0.0000000 | 0.3542401 | 0.0000000 |
| CD99 | 0.0000000 | 0.0626658 | 0.0000000 | 0.4718174 | 0.0000000 | 0.1952307 | 0.0000000 | 0.7924524 | 1.9983051 |
| integrin_a4b1_complex | 0.0775377 | 0.0000000 | 0.0274081 | 0.0128595 | 0.0933263 | 0.1408531 | 0.0000000 | 0.0000000 | 1.2469674 |
| BMP8B | 0.1291568 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
| PLAU | 0.0000000 | 0.0455798 | 0.0000000 | 0.0711901 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
| CXCL9 | 0.0000000 | 0.0663249 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0582573 | 0.0000000 | 0.0000000 | 0.0000000 |
| HLA-F | 0.0000000 | 0.0000000 | 0.0000000 | 0.2918047 | 0.0000000 | 0.0000000 | 0.0000000 | 0.2588880 | 0.0000000 |
| CD1D | 0.0615183 | 0.5670314 | 0.1007509 | 0.0000000 | 1.2126187 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
| CSF1 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0067252 | 0.0000000 | 0.1724684 | 0.0000000 | 0.0000000 | 0.0000000 |
| CTLA4 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0369179 | 0.0000000 | 0.2085753 | 0.0000000 | 0.0000000 | 0.0000000 |
| CD28 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.2771802 | 0.1288850 | 0.0000000 | 0.0000000 |
| GRN | 0.0000000 | 1.3784246 | 0.2818176 | 0.0000000 | 1.1878101 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
| TNF | 0.0000000 | 0.0000000 | 0.0318015 | 0.1191403 | 0.0106558 | 0.1381279 | 0.0000000 | 0.0000000 | 0.0000000 |
| LTA | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.1828448 | 0.0404983 | 0.0000000 | 0.0000000 |
| CXCL12 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.1028579 | 0.0000000 | 0.0000000 | 0.0000000 |
| CD58 | 0.0000000 | 0.0000000 | 0.1263349 | 0.1365337 | 0.0000000 | 0.1521363 | 0.0000000 | 0.1604005 | 0.0000000 |
Build information
To keep track of the options set when running
build_domino(), they are stored within the domino object
itself. To view these options, use the dom_info()
function.
dom_info(dom)
#> $create
#> [1] TRUE
#>
#> $build
#> [1] TRUE
#>
#> $build_variables
#> max_tf_per_clust min_tf_pval max_rec_per_tf
#> 25.000 0.001 25.000
#> rec_tf_cor_threshold min_rec_percentage
#> 0.250 0.100Continued Development
Since dominoSignal is a package still being developed, there are new functions and features that will be implemented in future versions. In the meantime, we have put together further information on plotting and an example analysis can be viewed on our Getting Started page. Additionally, if you find any bugs, have further questions, or want to share an idea, please let us know here.
Vignette Build Information
Date last built and session information:
Sys.Date()
#> [1] "2024-12-18"
sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.5 LTS
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#>
#> time zone: UTC
#> tzcode source: system (glibc)
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#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
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#> other attached packages:
#> [1] dominoSignal_1.0.3
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