In saezlab/OmnipathR: OmniPath web service client and more

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Introduction

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.

Overview

Pre-requisites

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')

Participation

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.

R / Bioconductor packages used

• OmnipathR
• igraph
• dplyr

Time outline

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 |

Workshop goals and objectives

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.

Learning goals

• Learn about the OmniPath database, its contents and how it can be useful
• Get a picture about the OmnipathR package capabilities
• Learn about the datasets and parameters of various OmniPath query types

Learning objectives

• Try examples of each OmniPath query type with various parameters
• Build igraph networks, search for paths
• Access some further interesting resources

Workshop

library(OmnipathR)

Data from OmniPath

OmniPath consists of five major databases, each combining many original resources. The five databases are:

• Network (interactions)
• Enzyme-substrate relationships (enzsub)
• Protein complexes (complexes)
• Annotations (annotations)
• Intercellular communication roles (intercell)

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.

Networks

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:

• Protein-protein interactions (post_translational) import_post_translational_interactions
• omnipath: literature curated, directed interactions with effect signs; corresponds to the first edition of OmniPath, hence the confusing name is due to historical reasons import_omnipath_interactions
• pathwayextra: directed and signed interactions, without literature references (might be literature curated, but references are not available) import_pathwayextra_interactions
• kinaseextra: enzyme-PTM interactions without literature references import_kinaseextra_interactions
• ligrecextra: ligand-receptor interactions without literature references import_ligrecextra_interactions
• Gene regulatory interactions (transcriptional) import_transcriptional_interactions
• dorothea: a comprehensive collection built out of 18 resources, contains literature curated, ChIP-Seq, gene expression derived and TF binding site predicted data, with 5 confidence levels (A-E) import_dorothea_interactions
• tf_target: additional literature curated interactions import_tf_target_interactions
• miRNA interactions (post_transcriptional and mirna_transcriptional)
• mirnatarget: literature curated miRNA-mRNA interactions import_mirnatarget_interactions
• tf_mirna: literature curated TF-miRNA interactions (transcriptional regulations of miRNA) import_tf_mirna_interactions
• lncRNA interactions (lncrna_post_transcriptional)
• lncrna_mrna: literature curated lncRNA-mRNA interactions import_lncrna_mrna_interactions
• Small molecule-protein interactions (small_molecule_protein)
• small_molecule: metabolites, intrinsic ligands or drug compounds targeting human proteins import_small_molecule_protein_interactions

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

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.

Igraph integration

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

On 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 relationships

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

This data frame also can be converted to an igraph object:

es_graph <- enzsub_graph(enz_sub)
es_graph

It is also possible to add effect signs (stimulatory or inhibitory) to enzyme-PTM relationships:

es_signed <- get_signed_ptms(enz_sub)

Protein complexes

cplx <- import_omnipath_complexes()
cplx

The resulted data frame provides the constitution and stoichiometry of protein complexes, with references.

Annotations

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

The 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
)

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
)

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,
    # Limiting to a handful of proteins for a faster vignette build:
    proteins = c('DLL1', 'MEIS2', 'PHOX2A', 'BACH1', 'KLF11', 'FOXO3', 'MEFV')
)
hpa_tissue

And pathway annotations from SignaLink:

slk_pathw <- import_omnipath_annotations(
    resources = 'SignaLink_pathway',
    wide = TRUE
)
slk_pathw

Combining networks with annotations

Annotations 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

Intercellular communication roles

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

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

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.

Metadata

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()

Categories in the intercell query also can be listed:

get_intercell_generic_categories()
# get_intercell_categories() # this would show also the specific categories

Data from other resources

An 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,

General purpose functionalities

Identifier translation

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

It 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
)

Gene Ontology

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

To 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

It can also translate term IDs to term names:

ontology_ensure_name('GO:0000022')

The first call takes a few seconds as it loads the database, subsequent calls are faster.

Useful tips

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')

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.

Further information

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].

Session info {.unnumbered}

sessionInfo()

References {.unnumbered}

[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)

saezlab/OmnipathR documentation built on May 18, 2024, 2:17 a.m.