Interacting with domino2 Objects
Source:vignettes/domino_object_vignette.Rmd
domino_object_vignette.RmdIn order to better implement some of the changes that have been made in domino2, the structure of the domino object has been modified. Here, we describe the data that is stored in the domino object, as well as how best to access this data.
Object contents
The contents of the domino object
class include a few broad groups: - input data, such as the L-R
database used, counts, and z-scores - calculations, such as correlations
or differential expression results - linkages, which show connections
between TFs, receptors, and ligands across the data set and within
clusters - signaling matrices, which show expression of ligands
signaling to active receptors in clusters (or a summary of overall
signaling) - build information, which includes the parameters used to
build the object in the build_domino() functions
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 2700 cells
#> Built with signaling between 9 clusters
print(dom)
#> A domino object of 2700 cells
#> Built with signaling between 9 clustersAccess functions
In order 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:domino2", 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] "naive_CD4_T_cell" "CD14_monocyte" "memory_CD4_T_cell"
#> [4] "B_cell" "CD8_T_cell" "CD16_monocyte"
#> [7] "NK_cell" "dendritic_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 |
| LINC00115 | 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 | -0.0574500 | -0.0574500 | -0.0574500 | -0.0574500 | -0.0574500 |
| AP006222.2 | -0.0331877 | -0.0331877 | -0.0331877 | -0.0331877 | -0.0331877 |
| RP11-206L10.2 | -0.0411864 | -0.0411864 | -0.0411864 | -0.0411864 | -0.0411864 |
| RP11-206L10.9 | -0.0332568 | -0.0332568 | -0.0332568 | -0.0332568 | -0.0332568 |
| LINC00115 | -0.0812841 | -0.0812841 | -0.0812841 | -0.0812841 | -0.0812841 |
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 | |
|---|---|---|---|---|---|
| ARID3A | 0.0000000 | 0.0000000 | 0.0000000 | 0.1825397 | 0.0000000 |
| ARNTL | 0.0000000 | 0.0338921 | 0.0000000 | 0.0276239 | 0.0131924 |
| ATF1 | 0.2017909 | 0.1039150 | 0.1261974 | 0.1303623 | 0.0000000 |
| ATF2 | 0.0754373 | 0.0822400 | 0.1471088 | 0.0693635 | 0.0710641 |
| ATF3 | 0.0678526 | 0.0763180 | 0.0774797 | 0.1279762 | 0.0527818 |
Information about the database referenced for ligand-receptor pairs
and composition of protien 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 |
|---|---|---|---|---|
| FGF4 & FGFR1 | FGF4 | P08620 | FGF4 | L |
| ADORA3 & ENTPD1 | ADORA3 | P0DMS8 | ADORA3 | R |
| EPHB3 & EFNB1 | EPHB3 | P54753 | EPHB3 | R |
| TNFSF18 & TNFRSF18 | TNFSF18 | Q9UNG2 | TNFSF18 | L |
| EFNA1 & EPHA1 | EFNA1 | P20827 | EFNA1 | L |
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 outside can be accessed with the
dom_de() function.
| naive_CD4_T_cell | CD14_monocyte | memory_CD4_T_cell | B_cell | CD8_T_cell | |
|---|---|---|---|---|---|
| ARID3A | 1.0000000 | 0.0000000 | 0.9999909 | 0.9999718 | 0.9999031 |
| ARNTL | 1.0000000 | 0.0000000 | 0.3287915 | 1.0000000 | 0.8408834 |
| ATF1 | 0.0942821 | 0.7919584 | 0.0000000 | 0.9999999 | 0.8473444 |
| ATF2 | 0.0000002 | 1.0000000 | 0.0000000 | 1.0000000 | 0.6222360 |
| ATF3 | 1.0000000 | 0.0000000 | 1.0000000 | 1.0000000 | 1.0000000 |
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.
cor_matrix <- dom_correlations(dom)
knitr::kable(cor_matrix[1:5, 1:5])| ARID3A | ARNTL | ATF1 | ATF2 | ATF3 | |
|---|---|---|---|---|---|
| FGFR1 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
| ADORA3 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
| EPHB3 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
| TNFRSF18 | -0.0415026 | 0.0292465 | 0.0411616 | 0.0595802 | -0.0560694 |
| EPHA1 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
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$NODAL_receptor
#> [1] "ACVR1B" "ACVR2B" "TDGF1" "character(0)"To view incoming ligands to each cluster:
incoming_links <- dom_linkages(dom, link_type = "incoming-ligand", by_cluster = TRUE)
# Check incoming signals to dendritic cells
incoming_links$dendritic_cell
#> [1] "MIF" "APP" "COPA" "CD99" "CD1D"
#> [6] "HLA-F" "INS" "ANXA1" "CXCL9" "BMP8B"
#> [11] "a4b1_complex" "aVb3_complex" "TG" "CSF3" "IL34"
#> [16] "CSF1" "CSF2" "TNF" "GRN" "LTA"
#> [21] "CTLA4" "CD28"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] "ZNF189" "KLF12" "ETV7" "IRF8" "KLF6" "FOS"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_naive_CD4_T_cell | L_CD14_monocyte | L_memory_CD4_T_cell | L_B_cell | L_CD8_T_cell | L_CD16_monocyte | L_NK_cell | L_dendritic_cell | L_platelet | |
|---|---|---|---|---|---|---|---|---|---|
| R_naive_CD4_T_cell | 0.0000000 | 0.0000000 | 0.1711738 | 0.1142025 | 0.1316175 | 0.2223212 | 0.2429505 | 0.000000 | 0.00000 |
| R_CD14_monocyte | 0.9054293 | 2.2595922 | 1.8484199 | 0.4113900 | 1.2793445 | 1.6668345 | 2.0653175 | 4.629239 | 3.38459 |
| R_memory_CD4_T_cell | 0.0000000 | 0.0000000 | 0.1711738 | 0.1142025 | 0.1316175 | 0.2223212 | 0.2429505 | 0.000000 | 0.00000 |
| R_B_cell | 0.0513875 | 0.1920610 | 0.2819420 | 0.0000000 | 0.0000000 | 0.2508179 | 0.1639465 | 1.052996 | 2.47526 |
| R_CD8_T_cell | 0.0000000 | 0.0000000 | 0.1711738 | 0.1142025 | 0.1316175 | 0.2223212 | 0.2429505 | 0.000000 | 0.00000 |
| R_CD16_monocyte | 0.9054293 | 2.2595922 | 1.8484199 | 0.4113900 | 1.2793445 | 1.6668345 | 2.0653175 | 4.629239 | 3.38459 |
| R_NK_cell | 0.1127173 | 1.5182660 | 0.6536131 | 0.0000000 | 0.2272370 | 1.0199925 | 0.4168540 | 2.582174 | 2.47526 |
| R_dendritic_cell | 0.2412594 | 2.2595922 | 1.4862873 | 0.0583676 | 1.2793445 | 1.0699215 | 2.0653175 | 4.629239 | 3.38459 |
| R_platelet | 0.4466970 | 0.7381964 | 0.5150958 | 0.4113900 | 0.3264903 | 1.0468853 | 0.5332812 | 2.317655 | 2.47526 |
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_naive_CD4_T_cell | dendritic_cell.L_CD14_monocyte | dendritic_cell.L_memory_CD4_T_cell | dendritic_cell.L_B_cell | dendritic_cell.L_CD8_T_cell | dendritic_cell.L_CD16_monocyte | dendritic_cell.L_NK_cell | dendritic_cell.L_dendritic_cell | dendritic_cell.L_platelet | |
|---|---|---|---|---|---|---|---|---|---|
| MIF | 0.0513875 | 0.0000000 | 0.2283356 | 0.0000000 | 0.0000000 | 0.0000000 | 0.1639465 | 0.3383730 | 0.0000000 |
| APP | 0.0000000 | 0.0910785 | 0.0478541 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.5651886 | 2.0283841 |
| COPA | 0.0000000 | 0.1009825 | 0.0057523 | 0.0000000 | 0.0000000 | 0.2508179 | 0.0000000 | 0.1494349 | 0.4468762 |
| CD99 | 0.0000000 | 0.0249929 | 0.1941752 | 0.0000000 | 0.5222139 | 0.0000000 | 0.9530171 | 0.1656705 | 0.9093296 |
| CD1D | 0.0000000 | 0.5461355 | 0.0000000 | 0.0583676 | 0.0000000 | 0.1733057 | 0.0000000 | 1.2646585 | 0.0000000 |
| HLA-F | 0.0000000 | 0.0000000 | 0.0133417 | 0.0000000 | 0.3264903 | 0.0258488 | 0.3693347 | 0.0000000 | 0.0000000 |
| ANXA1 | 0.0000000 | 0.1618442 | 0.4100748 | 0.0000000 | 0.2618538 | 0.0687405 | 0.5600050 | 0.6167362 | 0.0000000 |
| CXCL9 | 0.0000000 | 0.0083536 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
| CSF1 | 0.0000000 | 0.0000000 | 0.0487887 | 0.0000000 | 0.0000000 | 0.0043552 | 0.0090572 | 0.0000000 | 0.0000000 |
| TNF | 0.0000000 | 0.0000000 | 0.0543116 | 0.0000000 | 0.1574295 | 0.0589903 | 0.0000000 | 0.0061102 | 0.0000000 |
| GRN | 0.0000000 | 1.3262050 | 0.0000000 | 0.0000000 | 0.0000000 | 0.4878632 | 0.0000000 | 1.5230676 | 0.0000000 |
| LTA | 0.0613298 | 0.0000000 | 0.1746769 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0099570 | 0.0000000 | 0.0000000 |
| CTLA4 | 0.0000000 | 0.0000000 | 0.0638344 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 |
| CD28 | 0.1285421 | 0.0000000 | 0.2451419 | 0.0000000 | 0.0113571 | 0.0000000 | 0.0000000 | 0.0000000 | 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 domino2 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] "2024-02-02"
sessionInfo()
#> R version 4.3.2 (2023-10-31)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
#>
#> locale:
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#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] domino2_0.99.1
#>
#> loaded via a namespace (and not attached):
#> [1] DBI_1.2.1 bitops_1.0-7 formatR_1.14
#> [4] biomaRt_2.58.2 rlang_1.1.3 magrittr_2.0.3
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#> [28] xfun_0.41 zlibbioc_1.48.0 cachem_1.0.8
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