OmniPath is a database of molecular signaling knowledge, combining data from more than 100 resources. It contains protein-protein and gene regulatory interactions, enzyme-PTM relationships, protein complexes, annotations about protein function, structure, localization and intercellular communication. OmniPath focuses on networks with directed interactions and effect signs (activation or inhibition) which are suitable inputs for many modeling techniques. OmniPath also features a large collection of proteins’ intercellular communication roles and interactions. OmniPath is distributed by a web service at https://omnipathdb.org/. The Bioconductor package OmnipathR is an R client with full support for all features of the OmniPath web server. Apart from OmniPath, it provides direct access to more than 15 further signaling databases (such as BioPlex, InBioMap, EVEX, Harmonizome, etc) and contains a number of convenience methods, such as igraph integration, and a close integration with the NicheNet pipeline for ligand activity prediction from transcriptomics data. In this demo we show the diverse data in OmniPath and the versatile and convenient ways to access this data by OmnipathR.
OmnipathR 3.2.0
Database knowledge is essential for omics data analysis and modeling. Despite being an important factor, contributing to the outcome of studies, often subject to little attention. With OmniPath our aim is to raise awarness of the diversity of available resources and facilitate access to these resources in a uniform and transparent way. OmniPath has been developed in a close contact to mechanistic modeling applications and functional omics analysis, hence it is especially suitable for these fields. OmniPath has been used for the analysis of various omics data. In the Saez-Rodriguez group we often use it in a pipeline with our footprint based methods DoRothEA and PROGENy and our causal reasoning method CARNIVAL to infer signaling mechanisms from transcriptomics data.
One recent novelty of OmniPath is a collection of intercellular communication interactions. Apart from simply merging data from existing resources, OmniPath defines a number of intercellular communication roles, such as ligand, receptor, adhesion, enzyme, matrix, etc, and generalizes the terms ligand and receptor by introducing the terms transmitter, receiver and mediator. This unique knowledge base is especially adequate for the emerging field of cell-cell communication analysis, typically from single cell transcriptomics, but also from other kinds of data.
No special pre-requisites apart from basic knowledge of R. OmniPath, the database resource in the focus of this workshop has been published in [1,2], however you don’t need to know anything about OmniPath to benefit from the workshop. In the workshop we will demonstrate the R/Bioconductor package OmnipathR. If you would like to try the examples yourself we recommend to install the latest version of the package before the workshop:
library(devtools)
install_github('saezlab/OmnipathR')In the workshop we will present the design and some important features of the OmniPath database, so can be confident you get the most out of it. Then we will demonstrate further useful features of the OmnipathR package, such as accessing other resources, building graphs. Participants are encouraged to experiment with the examples and shape the contents of the workshop by asking questions. We are happy to recieve questions and topic suggestions by email also before the workshop. These could help us to adjust the contents to the interests of the participants.
Total: 45 minutes
| Activity | Time |
|---|---|
| OmniPath database overview | 5m |
| Network datasets | 10m |
| Other OmniPath databases | 5m |
| Intercellular communication | 10m |
| Igraph integration | 5m |
| Further resources | 10m |
In this workshop you will get familiar with the design and features of the OmniPath databases. For example, to know some important details about the datasets and parameters which help you to query the database the most suitable way according to your purposes. You will also learn about functionalities of the OmnipathR package which might make your work easier.
library(OmnipathR)OmniPath consists of five major databases, each combining many original resources. The five databases are:
The parameters for each database (query type) are available in the web service, for example: https://omnipathdb.org/queries/interactions. The R package supports all features of the web service and the parameter names and values usually correspond to the web service parameters which you would use in a HTTP query string.
The network database contains protein-protein, gene regulatory and miRNA-mRNA interactions. Soon more interaction types will be added. Some of these categories can be further divided into datasets which are defined by the type of evidences. A full list of network datasets:
Not individual interactions but resource are classified into the datasets
above, so these can overlap. Each interaction type and dataset has its
dedicated function in OmnipathR, above we provide links to their help
pages. As an example, let’s see the gene regulatory interactions:
gri <- import_transcriptional_interactions()
gri## # A tibble: 73,802 x 17
## source target source_genesymbol target_genesymbol is_directed is_stimulation is_inhibition consensus_direc~
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 P35869 Q9ULH1 AHR ASAP1 1 0 0 0
## 2 P35869 P04798 AHR CYP1A1 1 1 0 1
## 3 P35869 P05177 AHR CYP1A2 1 0 0 0
## 4 P35869 Q16678 AHR CYP1B1 1 1 0 1
## 5 P35869 P11308 AHR ERG 1 0 0 0
## 6 P01100 P35869 FOS AHR 1 0 0 0
## 7 P35869 P01100 AHR FOS 1 0 0 0
## 8 P35869 P01106 AHR MYC 1 0 0 0
## 9 P35869 Q07889 AHR SOS1 1 0 0 0
## 10 P35869 P19224 AHR UGT1A6 1 0 0 0
## # ... with 73,792 more rows, and 9 more variables: consensus_stimulation <dbl>, consensus_inhibition <dbl>,
## # dip_url <lgl>, sources <chr>, references <chr>, curation_effort <dbl>, dorothea_level <chr>,
## # n_references <chr>, n_resources <int>The interaction data frame contains the UniProt IDs and Gene Symbols of the interacting partners, the list of resources and references (PubMed IDs) for each interaction, and whether the interaction is directed, stimulatory or inhibitory.
The network data frames can be converted to igraph graph objects, so you can make use of the graph and visualization methods of igraph:
gr_graph <- interaction_graph(gri)
gr_graph## IGRAPH fd0c62d DN-- 14991 73802 --
## + attr: name (v/c), up_ids (v/c), is_directed (e/n), is_stimulation (e/n), is_inhibition (e/n),
## | consensus_direction (e/n), consensus_stimulation (e/n), consensus_inhibition (e/n), dip_url
## | (e/l), sources (e/x), references (e/x), curation_effort (e/n), dorothea_level (e/c),
## | n_references (e/c), n_resources (e/n)
## + edges from fd0c62d (vertex names):
## [1] AHR->ASAP1 AHR->CYP1A1 AHR->CYP1A2 AHR->CYP1B1 AHR->ERG FOS->AHR AHR->FOS
## [8] AHR->MYC AHR->SOS1 AHR->UGT1A6 AHR->VGLL4 AR ->ABCC4 AR ->ABCE1 AR ->ABHD2
## [15] AR ->ABLIM1 AR ->ACAD10 AR ->ACOXL AR ->ACPP AR ->ACSL1 AR ->ACTA1 AR ->ADAMTS4
## [22] AR ->ADAMTSL1 AR ->ADGRG6 AR ->ADGRV1 AR ->ADIPOR1 AR ->AFDN AR ->AFF1 AR ->AFF3
## [29] AR ->AGAP1 AR ->AHSG AR ->CRYBG1 AR ->AKAP13 AR ->AKAP6 AR ->AKAP7 AR ->AKR1B1
## + ... omitted several edgesOn this network we can use OmnipathR’s find_all_paths function, which
is able to look up all paths up to a certain length between two set of
nodes:
paths <- find_all_paths(
graph = gr_graph,
start = c('EGFR', 'STAT3'),
end = c('AKT1', 'ULK1'),
attr = 'name'
)As this is a gene regulatory network, the paths are TFs regulating the transcription of other TFs.
Enzyme-substrate interactions are also available also in the interactions query, but the enzyme-substrate query type provides additional information about the PTM types and residues.
enz_sub <- import_omnipath_enzsub()
enz_sub## # A tibble: 39,042 x 12
## enzyme substrate enzyme_genesymbol substrate_genesymbol residue_type residue_offset modification sources
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 P06239 O14543 LCK SOCS3 Y 204 phosphorylation KEA;MI~
## 2 P06239 O14543 LCK SOCS3 Y 221 phosphorylation KEA;MI~
## 3 P12931 O14746 SRC TERT Y 707 phosphorylation BEL-La~
## 4 P06241 O15117 FYN FYB1 Y 651 phosphorylation HPRD;K~
## 5 P06241 O15117 FYN FYB1 Y 595 phosphorylation HPRD;K~
## 6 P06241 O15117 FYN FYB1 Y 697 phosphorylation HPRD;K~
## 7 P06241 O15117 FYN FYB1 Y 625 phosphorylation Phosph~
## 8 P06241 O15117 FYN FYB1 Y 771 phosphorylation Phosph~
## 9 P06241 O15117 FYN FYB1 Y 559 phosphorylation Phosph~
## 10 P06241 O15117 FYN FYB1 Y 755 phosphorylation Phosph~
## # ... with 39,032 more rows, and 4 more variables: references <chr>, curation_effort <dbl>,
## # n_references <chr>, n_resources <int>This data frame also can be converted to an igraph object:
es_graph <- enzsub_graph(enz_sub)
es_graph## IGRAPH ff7a925 DN-- 4397 39042 --
## + attr: name (v/c), up_ids (v/c), residue_type (e/c), residue_offset (e/n), modification (e/c),
## | sources (e/x), references (e/x), curation_effort (e/n), n_references (e/c), n_resources (e/n)
## + edges from ff7a925 (vertex names):
## [1] LCK ->SOCS3 LCK ->SOCS3 SRC ->TERT FYN ->FYB1 FYN ->FYB1 FYN ->FYB1 FYN ->FYB1
## [8] FYN ->FYB1 FYN ->FYB1 FYN ->FYB1 FYN ->FYB1 FYN ->FYB1 FYN ->FYB1 FYN ->FYB1
## [15] FYN ->FYB1 FYN ->FYB1 FYN ->FYB1 FYN ->FYB1 FYN ->FYB1 FYN ->FYB1 FYN ->FYB1
## [22] ABL1 ->PLSCR1 ABL1 ->PLSCR1 SRC ->PLSCR1 SRC ->PLSCR1 ABL1 ->TP73 CDK2 ->TP73 CHEK1->TP73
## [29] AURKB->BIRC5 AURKB->BIRC5 AURKB->BIRC5 CDK1 ->BIRC5 PDPK1->PDPK1 PDPK1->PDPK1 PDPK1->PDPK1
## [36] PDPK1->PDPK1 PDPK1->PDPK1 PDPK1->PDPK1 PDPK1->PDPK1 PDPK1->PDPK1 PDPK1->PDPK1 PDPK1->PDPK1
## [43] PDPK1->PDPK1 PDPK1->PDPK1 SRC ->PDPK1 SRC ->PDPK1 SRC ->PDPK1 SRC ->PDPK1 SRC ->PDPK1
## + ... omitted several edgesIt is also possible to add effect signs (stimulatory or inhibitory) to enzyme-PTM relationships:
es_signed <- get_signed_ptms(enz_sub)cplx <- import_omnipath_complexes()
cplx## # A tibble: 28,173 x 7
## name components components_genesym~ stoichiometry sources references identifiers
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 NFY P23511_P25208~ NFYA_NFYB_NFYC 1:1:1 CORUM;Comple~ 15243141;9372~ CORUM:4478;Comp~
## 2 mTORC2 P68104_P85299~ DEPTOR_EEF1A1_MLST~ 0:0:0:0:0 Signor <NA> Signor:SIGNOR-C2
## 3 mTORC1 P42345_Q8N122~ AKT1S1_DEPTOR_MLST~ 0:0:0:0:0 Signor <NA> Signor:SIGNOR-C3
## 4 SCF-betaTRCP P63208_Q13616~ BTRC_CUL1_SKP1 1:1:1 CORUM;Comple~ 9990852 CORUM:227;Compl~
## 5 CBP/p300 Q09472_Q92793 CREBBP_EP300 0:0 Signor <NA> Signor:SIGNOR-C6
## 6 P300/PCAF Q09472_Q92793~ CREBBP_EP300_KAT2B 0:0:0 Signor <NA> Signor:SIGNOR-C7
## 7 SMAD2/SMAD4 Q13485_Q15796 SMAD2_SMAD4 1:2 ComplexPorta~ 12923550;8061~ PDB:1u7v;Signor~
## 8 SMAD3/SMAD4 P84022_Q13485 SMAD3_SMAD4 2:1 ComplexPorta~ 12923550;8061~ PDB:1U7F;PDB:1u~
## 9 SMAD4/JUN P05412_Q13485 JUN_SMAD4 0:0 Signor <NA> Signor:SIGNOR-C~
## 10 SMAD2/SMURF2 Q15796_Q9HAU4 SMAD2_SMURF2 1:1 Compleat;Sig~ 11389444 Compleat:HC501;~
## # ... with 28,163 more rowsThe resulted data frame provides the constitution and stoichiometry of protein complexes, with references.
The annotations query type includes a diverse set of resources (about 60 of them), about protein function, localization, structure and expression. For most use cases it is better to convert the data into wide data frames, as these correspond to the original format of the resources. If you load more than one resources into wide data frames, the result will be a list of data frames, otherwise one data frame. See a few examples with localization data from UniProt, tissue expression from Human Protein Atlas and pathway information from SignaLink:
uniprot_loc <- import_omnipath_annotations(
resources = 'UniProt_location',
wide = TRUE
)
uniprot_loc## # A tibble: 55,470 x 5
## uniprot genesymbol entity_type location features
## <chr> <chr> <chr> <chr> <chr>
## 1 Q99470 SDF2 protein Secreted <NA>
## 2 O75494 SRSF10 protein Nucleus speckle <NA>
## 3 O75494 SRSF10 protein Cytoplasm <NA>
## 4 Q9NQ55 PPAN protein Nucleus <NA>
## 5 Q9NQ55 PPAN protein Nucleolus <NA>
## 6 Q8IW03 SIAH3 protein Mitochondrion <NA>
## 7 Q96L92 SNX27 protein Cytosol <NA>
## 8 Q96L92 SNX27 protein Early endosome membrane Peripheral membrane protein
## 9 Q96L92 SNX27 protein Cytoplasm <NA>
## 10 Q99598 TSNAX protein Nucleus <NA>
## # ... with 55,460 more rowsThe entity_type field can be protein, mirna or complex. Protein complexes
mostly annotated based on the consensus of their members, we should be aware
that this is an in silico inference.
In case of spelling mistake either in parameter names or values OmnipathR
either corrects the mistake or gives a warning or error:
uniprot_loc <- import_omnipath_annotations(
resources = 'Uniprot_location',
wide = TRUE
)## Warning in omnipath_check_param(param): The following resources are not available: Uniprot_location. Check the
## resource names for spelling mistakes.Above the name of the resource is wrong. If the parameter name is wrong, it throws an error:
uniprot_loc <- import_omnipath_annotations(
resuorces = 'UniProt_location',
wide = TRUE
)## Error in import_omnipath_annotations(resuorces = "UniProt_location", wide = TRUE): Downloading the entire annotations database is not allowed by default because of its huge size (>1GB). If you really want to do that, you find static files at https://archive.omnipathdb.org/. However we recommend to query a set of proteins or a few resources, depending on your interest.Singular vs. plural forms and a few synonyms are automatically corrected:
uniprot_loc <- import_omnipath_annotations(
resource = 'UniProt_location',
wide = TRUE
)Another example with tissue expression from Human Protein Atlas:
hpa_tissue <- import_omnipath_annotations(
resources = 'HPA_tissue',
wide = TRUE
)
hpa_tissue## # A tibble: 1,810,497 x 15
## uniprot genesymbol entity_type organ tissue level status prognostic favourable pathology n_not_detected
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 O43657 TSPAN6 protein skin 1 fibrobla~ Not ~ Appro~ False False False <NA>
## 2 O43657 TSPAN6 protein soft ~ peripher~ Not ~ Appro~ False False False <NA>
## 3 O43657 TSPAN6 protein endom~ glandula~ High Appro~ False False False <NA>
## 4 O43657 TSPAN6 protein lung pneumocy~ Low Appro~ False False False <NA>
## 5 O43657 TSPAN6 protein small~ glandula~ Medi~ Appro~ False False False <NA>
## 6 O43657 TSPAN6 protein hippo~ glial ce~ Not ~ Appro~ False False False <NA>
## 7 O43657 TSPAN6 protein spleen cells in~ Not ~ Appro~ False False False <NA>
## 8 O43657 TSPAN6 protein nasop~ respirat~ High Appro~ False False False <NA>
## 9 O43657 TSPAN6 protein lymph~ lymphoma Not ~ <NA> <NA> <NA> True 11
## 10 O43657 TSPAN6 protein bone ~ hematopo~ Not ~ Appro~ False False False <NA>
## # ... with 1,810,487 more rows, and 4 more variables: n_low <chr>, n_medium <chr>, n_high <chr>, score <chr>And pathway annotations from SignaLink:
slk_pathw <- import_omnipath_annotations(
resources = 'SignaLink_pathway',
wide = TRUE
)
slk_pathw## # A tibble: 2,372 x 4
## uniprot genesymbol entity_type pathway
## <chr> <chr> <chr> <chr>
## 1 P20963 CD247 protein T-cell receptor
## 2 P43403 ZAP70 protein Receptor tyrosine kinase
## 3 P43403 ZAP70 protein T-cell receptor
## 4 Q9NYJ8 TAB2 protein Receptor tyrosine kinase
## 5 Q9NYJ8 TAB2 protein Innate immune pathways
## 6 Q9NYJ8 TAB2 protein Toll-like receptor
## 7 Q9NYJ8 TAB2 protein JAK/STAT
## 8 O43318 MAP3K7 protein Receptor tyrosine kinase
## 9 O43318 MAP3K7 protein WNT
## 10 O43318 MAP3K7 protein Toll-like receptor
## # ... with 2,362 more rowsAnnotations can be easily added to network data frames, in this case both the source and target nodes will have their annotation data. This function accepts either the name of an annotation resource or an annotation data frame:
network <- import_omnipath_interactions()
network_slk_pw <- annotated_network(network, 'SignaLink_pathway')
network_slk_pw## # A tibble: 79,245 x 18
## source target source_genesymbol target_genesymbol is_directed is_stimulation is_inhibition consensus_direc~
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 P0DP25 P48995 CALM3 TRPC1 1 0 1 1
## 2 Q03135 P48995 CAV1 TRPC1 1 1 0 1
## 3 P14416 P48995 DRD2 TRPC1 1 1 0 1
## 4 Q02790 P48995 FKBP4 TRPC1 1 0 1 1
## 5 Q99750 P48995 MDFI TRPC1 1 0 1 1
## 6 Q14571 P48995 ITPR2 TRPC1 1 1 0 1
## 7 P29966 P48995 MARCKS TRPC1 1 0 1 1
## 8 Q13255 P48995 GRM1 TRPC1 1 1 0 1
## 9 Q13586 P48995 STIM1 TRPC1 1 1 0 1
## 10 P48995 Q13507 TRPC1 TRPC3 1 1 0 1
## # ... with 79,235 more rows, and 10 more variables: consensus_stimulation <dbl>, consensus_inhibition <dbl>,
## # dip_url <chr>, sources <chr>, references <chr>, curation_effort <dbl>, n_references <chr>,
## # n_resources <int>, pathway_source <chr>, pathway_target <chr>The intercell database assigns roles to proteins such as ligand, receptor,
adhesion, transporter, ECM, etc. The design of this database is far from
being simple, best is to check the description in the recent OmniPath paper
[1].
ic <- import_omnipath_intercell()
ic## # A tibble: 301,527 x 15
## category parent database scope aspect source uniprot genesymbol entity_type consensus_score transmitter
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <lgl>
## 1 transmem~ transm~ UniProt_~ gene~ locat~ resour~ Q9H6L2 TMEM231 protein 5 FALSE
## 2 transmem~ transm~ UniProt_~ gene~ locat~ resour~ Q8NFT8 DNER protein 8 FALSE
## 3 transmem~ transm~ UniProt_~ gene~ locat~ resour~ Q6NTF9 RHBDD2 protein 5 FALSE
## 4 transmem~ transm~ UniProt_~ gene~ locat~ resour~ Q9H1U9 SLC25A51 protein 5 FALSE
## 5 transmem~ transm~ UniProt_~ gene~ locat~ resour~ Q6V0L0 CYP26C1 protein 5 FALSE
## 6 transmem~ transm~ UniProt_~ gene~ locat~ resour~ Q5QGT7 RTP2 protein 6 FALSE
## 7 transmem~ transm~ UniProt_~ gene~ locat~ resour~ O60320 FAM189A1 protein 5 FALSE
## 8 transmem~ transm~ UniProt_~ gene~ locat~ resour~ A2RU48 SMCO3 protein 5 FALSE
## 9 transmem~ transm~ UniProt_~ gene~ locat~ resour~ Q9UHF4 IL20RA protein 8 FALSE
## 10 transmem~ transm~ UniProt_~ gene~ locat~ resour~ Q08722 CD47 protein 7 FALSE
## # ... with 301,517 more rows, and 4 more variables: receiver <lgl>, secreted <lgl>,
## # plasma_membrane_transmembrane <lgl>, plasma_membrane_peripheral <lgl>This data frame is about individual proteins. To create a network of
intercellular communication, we provide the import_intercell_network
function:
icn <- import_intercell_network(high_confidence = TRUE)
icn## # A tibble: 6,544 x 46
## category_intercell_source parent_intercel~ source target category_interc~ parent_intercel~ target_genesymb~
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 activating_cofactor receptor_regula~ O14786 P35968 receptor receptor KDR
## 2 activating_cofactor receptor_regula~ O14786 Q9UIW2 receptor receptor PLXNA1
## 3 activating_cofactor receptor_regula~ P08138 P04629 receptor receptor NTRK1
## 4 activating_cofactor receptor_regula~ P34925 Q9H461 receptor receptor FZD8
## 5 activating_cofactor receptor_regula~ Q01973 P21860 receptor receptor ERBB3
## 6 activating_cofactor receptor_regula~ Q01974 O00238 receptor receptor BMPR1B
## 7 activating_cofactor receptor_regula~ Q01974 Q13467 receptor receptor FZD5
## 8 activating_cofactor receptor_regula~ Q01974 Q13467 tight_junction tight_junction FZD5
## 9 activating_cofactor receptor_regula~ Q01974 Q14332 receptor receptor FZD2
## 10 activating_cofactor receptor_regula~ Q13145 O00238 receptor receptor BMPR1B
## # ... with 6,534 more rows, and 39 more variables: source_genesymbol <chr>, is_directed <dbl>,
## # is_stimulation <dbl>, is_inhibition <dbl>, consensus_direction <dbl>, consensus_stimulation <dbl>,
## # consensus_inhibition <dbl>, dip_url <chr>, ligrecextra <lgl>, omnipath <lgl>, sources <chr>,
## # references <chr>, curation_effort <dbl>, n_references <chr>, n_resources <int>,
## # database_intercell_source <chr>, scope_intercell_source <chr>, aspect_intercell_source <chr>,
## # category_source_intercell_source <chr>, genesymbol_intercell_source <chr>,
## # entity_type_intercell_source <chr>, consensus_score_intercell_source <dbl>, ...The result is similar to the annotated_network, each interacting partner
has its intercell annotations. In the intercell database, OmniPath aims to
ship all available information, which means it might contain quite some
false positives. The high_confidence option is a shortcut to stringent
filter settings based on the number and consensus of provenances. Using
instead the filter_intercell_network function, you can have a fine control
over the quality filters. It has many options which are described in the
manual.
icn <- import_intercell_network()
icn_hc <- filter_intercell_network(
icn,
ligand_receptor = TRUE,
consensus_percentile = 30,
loc_consensus_percentile = 50,
simplify = TRUE
)The filter_intecell function does a similar procedure on an intercell
annotation data frame.
The list of available resources for each query type can be retrieved
by the get_..._resources function. For example, the annotation resources
are:
get_annotation_resources()## [1] "Adhesome" "Almen2009" "Baccin2019" "CORUM_Funcat"
## [5] "CORUM_GO" "CSPA" "CSPA_celltype" "CancerGeneCensus"
## [9] "CancerSEA" "CellCellInteractions" "CellChatDB" "CellChatDB_complex"
## [13] "CellPhoneDB" "CellPhoneDB_complex" "CellTalkDB" "ComPPI"
## [17] "DGIdb" "DisGeNet" "EMBRACE" "Exocarta"
## [21] "GO_Intercell" "GPCRdb" "Guide2Pharma" "HGNC"
## [25] "HPA_secretome" "HPA_subcellular" "HPA_tissue" "HPMR"
## [29] "HPMR_complex" "HumanCellMap" "ICELLNET" "ICELLNET_complex"
## [33] "IntOGen" "Integrins" "KEGG-PC" "Kirouac2010"
## [37] "LOCATE" "LRdb" "MCAM" "MSigDB"
## [41] "Matrisome" "MatrixDB" "Membranome" "NetPath"
## [45] "OPM" "Phobius" "Phosphatome" "Ramilowski2015"
## [49] "Ramilowski_location" "SIGNOR" "SignaLink_function" "SignaLink_pathway"
## [53] "Surfaceome" "TCDB" "TFcensus" "TopDB"
## [57] "UniProt_family" "UniProt_keyword" "UniProt_location" "UniProt_tissue"
## [61] "UniProt_topology" "Vesiclepedia" "Zhong2015" "connectomeDB2020"
## [65] "iTALK" "kinase.com" "talklr"Categories in the intercell query also can be listed:
get_intercell_generic_categories()## [1] "transmembrane" "transmembrane_predicted"
## [3] "peripheral" "plasma_membrane"
## [5] "plasma_membrane_transmembrane" "plasma_membrane_regulator"
## [7] "plasma_membrane_peripheral" "secreted"
## [9] "cell_surface" "ecm"
## [11] "ligand" "receptor"
## [13] "secreted_enzyme" "secreted_peptidase"
## [15] "extracellular" "intracellular"
## [17] "receptor_regulator" "secreted_receptor"
## [19] "sparc_ecm_regulator" "ecm_regulator"
## [21] "ligand_regulator" "cell_surface_ligand"
## [23] "cell_adhesion" "matrix_adhesion"
## [25] "adhesion" "matrix_adhesion_regulator"
## [27] "cell_surface_enzyme" "cell_surface_peptidase"
## [29] "secreted_enyzme" "extracellular_peptidase"
## [31] "secreted_peptidase_inhibitor" "transporter"
## [33] "ion_channel" "ion_channel_regulator"
## [35] "gap_junction" "tight_junction"
## [37] "adherens_junction" "desmosome"
## [39] "intracellular_intercellular_related"# get_intercell_categories() # this would show also the specific categoriesAn increasing number of other resources (currently around 20) can be directly
accessed by OmnipathR (not from the omnipathdb.org domain, but from their
original providers). As an example,
OmnipathR uses UniProt data to translate identifiers. You may find a list
of the available identifiers in the manual page of translate_ids function.
The evaluation of the parameters is tidyverse style, and both UniProt’s
notation and a simple internal notation can be used. Furthermore, it can
handle vectors, data frames or list of vectors.
d <- data.frame(uniprot_id = c('P00533', 'Q9ULV1', 'P43897', 'Q9Y2P5'))
d <- translate_ids(
d,
uniprot_id = uniprot, # the source ID type and column name
genesymbol # the target ID type using OmniPath's notation
)
d## uniprot_id genesymbol
## 1 P00533 EGFR
## 2 Q9ULV1 FZD4
## 3 P43897 TSFM
## 4 Q9Y2P5 SLC27A5It is possible to have one source ID type and column in one call, but multiple target ID types and columns: to translate a network, two calls are necessary. Note: certain functionality fails recently due to changes in other packages, will be fixed in a few days.
network <- import_omnipath_interactions()
network <- translate_ids(
network,
source = uniprot_id,
source_entrez = entrez
)
network <- translate_ids(
network,
target = uniprot_id,
target_entrez = entrez
)OmnipathR is able to look up ancestors and descendants in ontology trees,
and also exposes the ontology tree in three different formats: as a
data frame, as a list of lists or as an igraph graph object. All these
can have two directions: child-to-parent (c2p) or parent-to-child (p2c).
go <- go_ontology_download()
go$rel_tbl_c2p## # A tibble: 59,207 x 3
## term relation parents
## <fct> <chr> <list>
## 1 GO:0000001 is_a <chr [2]>
## 2 GO:0000002 is_a <chr [1]>
## 3 GO:0000003 is_a <chr [1]>
## 4 GO:0000006 is_a <chr [1]>
## 5 GO:0000007 is_a <chr [1]>
## 6 GO:0000009 is_a <chr [1]>
## 7 GO:0000010 is_a <chr [1]>
## 8 GO:0000011 is_a <chr [2]>
## 9 GO:0000012 is_a <chr [1]>
## 10 GO:0000014 is_a <chr [1]>
## # ... with 59,197 more rowsTo convert the relations to list or graph format, use the
relations_table_to_list or relations_table_to_graph functions. To
swap between c2p and p2c use the swap_relations function.
go_graph <- relations_table_to_graph(go$rel_tbl_c2p)
go_graph## IGRAPH b1e4b45 DN-- 43850 86697 --
## + attr: name (v/c), relation (e/c)
## + edges from b1e4b45 (vertex names):
## [1] GO:0000001->GO:0048308 GO:0000001->GO:0048311 GO:0000002->GO:0007005 GO:0000003->GO:0008150
## [5] GO:0000006->GO:0005385 GO:0000007->GO:0005385 GO:0000009->GO:0000030 GO:0000010->GO:0004659
## [9] GO:0000011->GO:0007033 GO:0000011->GO:0048308 GO:0000012->GO:0006281 GO:0000014->GO:0004520
## [13] GO:0000015->GO:1902494 GO:0000015->GO:0005829 GO:0000016->GO:0004553 GO:0000017->GO:0042946
## [17] GO:0000018->GO:0051052 GO:0000018->GO:0006310 GO:0000019->GO:0000018 GO:0000019->GO:0006312
## [21] GO:0000022->GO:0051231 GO:0000022->GO:1903047 GO:0000022->GO:0000070 GO:0000022->GO:0007052
## [25] GO:0000023->GO:0005984 GO:0000024->GO:0000023 GO:0000024->GO:0046351 GO:0000025->GO:0000023
## [29] GO:0000025->GO:0046352 GO:0000026->GO:0000030 GO:0000027->GO:0022618 GO:0000027->GO:0042255
## + ... omitted several edgesIt can also translate term IDs to term names:
ontology_ensure_name('GO:0000022')## [1] "mitotic spindle elongation"The first call takes a few seconds as it loads the database, subsequent calls are faster.
OmnipathR features a logging facility, a YML configuration file and
a cache directory. By default the highest level messages are printed to
the console, and you can browse the full log from R by calling
omnipath_log(). The cache can be controlled by a number of functions,
for example you can search for cache files by omnipath_cache_search(),
and delete them by omnipath_cache_remove:
omnipath_cache_search('dorothea')## $c80a1943db40b7d3e23dcaa2afb68d3c37342458
## $c80a1943db40b7d3e23dcaa2afb68d3c37342458$key
## [1] "c80a1943db40b7d3e23dcaa2afb68d3c37342458"
##
## $c80a1943db40b7d3e23dcaa2afb68d3c37342458$url
## [1] "https://github.com/saezlab/dorothea/raw/master/inst/extdata/networks/chip_seq/remap/network.rds"
##
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## list()
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## [1] ""
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## [1] "C:\\Users\\biocbuild\\AppData\\Local\\Cache\\OmnipathR/c80a1943db40b7d3e23dcaa2afb68d3c37342458-1"
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## $`04562e577c465d67da1d5636644c939fc0850b15`$key
## [1] "04562e577c465d67da1d5636644c939fc0850b15"
##
## $`04562e577c465d67da1d5636644c939fc0850b15`$url
## [1] "https://omnipathdb.org/interactions?genesymbols=yes&resources=ABS,ARACNe-GTEx_DoRothEA,DoRothEA-reviews_DoRothEA,ENCODE-distal,ENCODE-proximal&datasets=dorothea&organisms=9606&dorothea_levels=A,B&fields=sources,references,curation_effort,dorothea_level&license=academic"
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## list()
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## list()
##
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## [1] "rds"
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## [1] "1"
##
## $`04562e577c465d67da1d5636644c939fc0850b15`$versions$`1`$path
## [1] "C:\\Users\\biocbuild\\AppData\\Local\\Cache\\OmnipathR/04562e577c465d67da1d5636644c939fc0850b15-1.rds"
##
## $`04562e577c465d67da1d5636644c939fc0850b15`$versions$`1`$dl_started
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## [1] "ready"
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## $f4de1f63d60f35d18bb262dd6de84d1cccd9cc97$key
## [1] "f4de1f63d60f35d18bb262dd6de84d1cccd9cc97"
##
## $f4de1f63d60f35d18bb262dd6de84d1cccd9cc97$url
## [1] "https://omnipathdb.org/interactions?genesymbols=yes&datasets=dorothea,tf_target&organisms=9606&dorothea_levels=A,B&fields=sources,references,curation_effort,dorothea_level&license=academic"
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## list()
##
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## list()
##
## $f4de1f63d60f35d18bb262dd6de84d1cccd9cc97$ext
## [1] "rds"
##
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## $f4de1f63d60f35d18bb262dd6de84d1cccd9cc97$versions$`1`$dl_finished
## [1] "2021-10-26 23:00:05 EDT"The configuration can be set by options, all options are prefixed with
omnipath., and can be saved by omnipath_save_config. For example, to
exclude all OmniPath resources which don’t allow for-profit use:
options(omnipath.license = 'commercial')The internal state is contained by the omnipath.env environment.
Find more examples in the other vignettes and the manual. For example, the
NicheNet vignette presents the integratation between OmnipathR and
nichenetr, a method for prediction of ligand-target gene connections.
Another Bioconductor package wppi is able to add context specific scores
to networks, based on genes of interest, functional annotations and network
proximity (random walks with restart). The new paths vignette presents
some approaches to construct pathways from networks. The design of the
OmniPath database is described in our recent paper [1], while an in depth
analysis of the pathway resources is available in the first OmniPath
paper [2].
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 17763)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=C LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] OmnipathR_3.2.0 BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] progress_1.2.2 tidyselect_1.1.1 xfun_0.27 bslib_0.3.1 purrr_0.3.4
## [6] vctrs_0.3.8 generics_0.1.1 htmltools_0.5.2 yaml_2.2.1 utf8_1.2.2
## [11] rlang_0.4.12 jquerylib_0.1.4 pillar_1.6.4 later_1.3.0 glue_1.4.2
## [16] DBI_1.1.1 rappdirs_0.3.3 bit64_4.0.5 readxl_1.3.1 lifecycle_1.0.1
## [21] stringr_1.4.0 cellranger_1.1.0 evaluate_0.14 knitr_1.36 tzdb_0.1.2
## [26] fastmap_1.1.0 parallel_4.1.1 curl_4.3.2 fansi_0.5.0 Rcpp_1.0.7
## [31] readr_2.0.2 backports_1.2.1 checkmate_2.0.0 BiocManager_1.30.16 vroom_1.5.5
## [36] jsonlite_1.7.2 bit_4.0.4 hms_1.1.1 digest_0.6.28 stringi_1.7.5
## [41] bookdown_0.24 dplyr_1.0.7 cli_3.0.1 tools_4.1.1 magrittr_2.0.1
## [46] logger_0.2.2 sass_0.4.0 tibble_3.1.5 crayon_1.4.1 tidyr_1.1.4
## [51] pkgconfig_2.0.3 ellipsis_0.3.2 xml2_1.3.2 prettyunits_1.1.1 assertthat_0.2.1
## [56] rmarkdown_2.11 httr_1.4.2 R6_2.5.1 igraph_1.2.7 compiler_4.1.1[1] D Turei, A Valdeolivas, L Gul, N Palacio-Escat, M Klein, O Ivanova, M Olbei, A Gabor, F Theis, D Modos, T Korcsmaros and J Saez-Rodriguez (2021) Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Molecular Systems Biology 17:e9923
[2] D Turei, T Korcsmaros and J Saez-Rodriguez (2016) OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nature Methods 13(12)