Usage Examples
In the following paragraphs, we provide some examples to describe how to use
the OmnipathR package to retrieve different types of
information from Omnipath webserver. In addition, we play around with the
data aiming at obtaining some biological relevant information.
Noteworthy, the sections complexes, annotations and intercell are
linked. We explore the annotations and roles in inter-cellular communications
of the proteins involved in a given complex. This basic example shows the
usefulness of integrating the information available in the different
Omnipath resources.
Interactions
Proteins interact among them and with other biological molecules to perform
cellular functions. Proteins also participates in pathways, linked series of
reactions occurring inter/intra cells to transform products or to transmit
signals inducing specific cellular responses. Protein interactions are
therefore a very valuable source of information to understand cellular
functioning.
We here download the original OmniPath human interactions (Türei, Korcsmáros, and Saez-Rodriguez 2016).
To do so, we first check the different source databases and select some of
them. Then, we print some of the downloaded interactions (“+” means
activation, “-” means inhibition and “?” means undirected interactions or
inconclusive data).
## We check some of the different interaction databases
interaction_resources()
## [1] "ABS" "ACSN" "ACSN_SignaLink3"
## [4] "ARACNe-GTEx_DoRothEA" "ARN" "Adhesome"
## [7] "AlzPathway" "BEL-Large-Corpus_ProtMapper" "Baccin2019"
## [10] "BioGRID" "CA1" "CancerCellMap"
## [13] "CancerDrugsDB" "CellCall" "CellChatDB"
## [16] "CellChatDB-cofactors" "CellPhoneDB" "CellPhoneDB_Cellinker"
## [19] "CellTalkDB" "Cellinker" "CollecTRI"
## [22] "Cui2007" "CytReg_CollecTRI" "DEPOD"
## [25] "DIP" "DLRP_Cellinker" "DLRP_talklr"
## [28] "DOMINO" "DeathDomain" "DoRothEA"
## [31] "DoRothEA-A_CollecTRI" "DoRothEA-reviews_DoRothEA" "ELM"
## [34] "EMBRACE" "ENCODE-distal" "ENCODE-proximal"
## [37] "ENCODE_tf-mirna" "ExTRI_CollecTRI" "FANTOM4_DoRothEA"
## [40] "Fantom5_LRdb" "GEREDB_CollecTRI" "GOA_CollecTRI"
## [43] "Guide2Pharma" "Guide2Pharma_Cellinker" "Guide2Pharma_LRdb"
## [46] "Guide2Pharma_talklr" "HOCOMOCO_DoRothEA" "HPMR"
## [49] "HPMR_Cellinker" "HPMR_LRdb" "HPMR_talklr"
## [52] "HPRD" "HPRD-phos" "HPRD_KEA"
## [55] "HPRD_LRdb" "HPRD_MIMP" "HPRD_talklr"
## [58] "HTRI_CollecTRI" "HTRIdb" "HTRIdb_DoRothEA"
## [61] "HuRI" "ICELLNET" "InnateDB"
## [64] "InnateDB_SignaLink3" "IntAct" "IntAct_CollecTRI"
## [67] "IntAct_DoRothEA" "JASPAR_DoRothEA" "KEA"
## [70] "KEGG-MEDICUS" "Kinexus_KEA" "Kirouac2010"
## [73] "LRdb" "Li2012" "Lit-BM-17"
## [76] "LncRNADisease" "MIMP" "MPPI"
## [79] "Macrophage" "MatrixDB" "NCI-PID_ProtMapper"
## [82] "NFIRegulomeDB_DoRothEA" "NRF2ome" "NTNU.Curated_CollecTRI"
## [85] "NetPath" "NetworKIN_KEA" "ORegAnno"
## [88] "ORegAnno_DoRothEA" "PAZAR_DoRothEA" "Pavlidis2021_CollecTRI"
## [91] "PhosphoNetworks" "PhosphoPoint" "PhosphoSite"
## [94] "PhosphoSite_KEA" "PhosphoSite_MIMP" "PhosphoSite_ProtMapper"
## [97] "PhosphoSite_noref" "ProtMapper" "REACH_ProtMapper"
## [100] "RLIMS-P_ProtMapper" "Ramilowski2015" "Ramilowski2015_Baccin2019"
## [103] "ReMap_DoRothEA" "Reactome_LRdb" "Reactome_ProtMapper"
## [106] "Reactome_SignaLink3" "RegNetwork_DoRothEA" "SIGNOR"
## [109] "SIGNOR_CollecTRI" "SIGNOR_ProtMapper" "SPIKE"
## [112] "SPIKE_LC" "STRING_talklr" "SignaLink3"
## [115] "Sparser_ProtMapper" "TCRcuration_SignaLink3" "TFactS_CollecTRI"
## [118] "TFactS_DoRothEA" "TFe_DoRothEA" "TRED_DoRothEA"
## [121] "TRIP" "TRRD_DoRothEA" "TRRUST_CollecTRI"
## [124] "TRRUST_DoRothEA" "TransmiR" "UniProt_LRdb"
## [127] "Wang" "Wojtowicz2020" "connectomeDB2020"
## [130] "dbPTM" "iPTMnet" "iTALK"
## [133] "miR2Disease" "miRDeathDB" "miRTarBase"
## [136] "miRecords" "ncRDeathDB" "phosphoELM"
## [139] "phosphoELM_KEA" "phosphoELM_MIMP" "scConnect"
## [142] "talklr"
## The interactions are stored into a data frame.
pathways <- omnipath(resources = c("SignaLink3", "PhosphoSite", "SIGNOR"))
## We visualize the first interactions in the data frame.
print_interactions(head(pathways))
## # A tibble: 6 × 5
## source interaction target n_resources n_references
## <chr> <chr> <chr> <int> <int>
## 1 SRC (P12931) ==( + )==> TRPV1 (Q8NER1) 5 6
## 2 PRKACA (P17612) ==( ? )==> TRPC6 (Q9Y210) 2 3
## 3 PRKG1 (Q13976) ==( - )==> TRPC3 (Q13507) 8 2
## 4 PTPN1 (P18031) ==( - )==> TRPV6 (Q9H1D0) 6 2
## 5 PRKG1 (Q13976) ==( + )==> TRPC7 (Q9HCX4) 3 1
## 6 OS9 (Q13438) ==(+/-)==> TRPV4 (Q9HBA0) 3 1
Protein-protein interaction networks
Protein-protein interactions are usually converted into networks. Describing
protein interactions as networks not only provides a convenient format for
visualization, but also allows applying graph theory methods to mine the
biological information they contain.
We convert here our set of interactions to a network/graph
(igraphobject). Then, we apply two very common approaches to
extract information from a biological network:
- Shortest Paths: finding a path between two nodes (proteins) going
through the minimum number of edges. This can be very useful to track
consecutive reactions within a given pathway. We display below the shortest
path between two given proteins and all the possible shortests paths between
two other proteins. It is to note that the functions
print_path\_es and
print_path\_vs display very similar results, but the first one takes as an
input an edge sequence and the second one a node sequence.
## We transform the interactions data frame into a graph
pathways_g <- interaction_graph(pathways)
## Find and print shortest paths on the directed network between proteins
## of interest:
print_path_es(
igraph::shortest_paths(
pathways_g,
from = "TYRO3",
to = "STAT3",
output = "epath"
)$epath[[1]],
pathways_g
)
## source interaction target n_resources n_references
## 1 TYRO3 (Q06418) ==( + )==> PIK3R1 (P27986) 4 3
## 2 PIK3R1 (P27986) ==(+/-)==> PIK3CG (P48736) 4 3
## 3 PIK3CG (P48736) ==( + )==> RAC1 (P63000) 2 2
## 4 RAC1 (P63000) ==( + )==> STAT3 (P40763) 9 4
## Find and print all shortest paths between proteins of interest:
print_path_vs(
igraph::all_shortest_paths(
pathways_g,
from = "DYRK2",
to = "MAPKAPK2"
)$res,
pathways_g
)
## Pathway 1: DYRK2 -> NOTCH1 -> RBPJ -> DUSP1 -> MAPK3 -> MAPKAPK2
## Pathway 2: DYRK2 -> TBK1 -> AKT1 -> PEA15 -> MAPK3 -> MAPKAPK2
## Pathway 3: DYRK2 -> NOTCH1 -> RBPJ -> DUSP1 -> MAPK14 -> MAPKAPK2
## Pathway 4: DYRK2 -> TBK1 -> AKT1 -> GSK3B -> MAPK14 -> MAPKAPK2
## Pathway 5: DYRK2 -> TBK1 -> AKT1 -> MAP2K4 -> MAPK14 -> MAPKAPK2
## Pathway 6: DYRK2 -> TBK1 -> AKT1 -> MAP3K5 -> MAPK14 -> MAPKAPK2
## Pathway 7: DYRK2 -> NOTCH1 -> RBPJ -> DUSP1 -> MAPK1 -> MAPKAPK2
## Pathway 8: DYRK2 -> TBK1 -> AKT1 -> PEA15 -> MAPK1 -> MAPKAPK2
- Clustering: grouping nodes (proteins) in such a way that nodes belonging
to the same group (called cluster) are more connected in the network to each
other than to those in other groups (clusters). Since proteins interact to
perform their functions, proteins within the same cluster are likely to be
implicated in similar biological tasks. Figure 2 shows the
subgraph containing the proteins and interactions of a specifc protein, ERBB2
The igraph package contains functions to apply sevaral
different cluster methods on graphs (visit https://igraph.org/r/doc/ for
detailed information.)
## We apply a clustering algorithm (Louvain) to group proteins in
## our network. We apply here Louvain which is fast but can only run
## on undirected graphs. Other clustering algorithms can deal with
## directed networks but with longer computational times,
## such as cluster_edge_betweenness. These cluster methods are directly
## available in the igraph package.
pathways_g_u <- igraph::as.undirected(pathways_g, mode = "mutual")
## Warning: `as.undirected()` was deprecated in igraph 2.1.0.
## ℹ Please use `as_undirected()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
pathways_g_u <- igraph::simplify(pathways_g_u)
clusters <- igraph::cluster_fast_greedy(pathways_g_u)
## We extract the cluster where a protein of interest is contained
erbb2_cluster_id <- clusters$membership[which(clusters$names == "ERBB2")]
erbb2_cluster_g <- igraph::induced_subgraph(
pathways_g_u,
igraph::V(pathways_g)$name[which(clusters$membership == erbb2_cluster_id)]
)
Other interaction datasets
We used above the interactions from the dataset described in the original
OmniPath publication (Türei, Korcsmáros, and Saez-Rodriguez 2016). In this section, we provide
examples on how to retry and deal with interactions from the remaining
datasets. The same functions can been applied to every interaction dataset.
Ligand-receptor Extra
In the following example we are going to work with the ligrecextra
dataset, which contains ligand-receptor interactions without literature
reference. Our goal is to find the potential receptors associated to a given
ligand, CDH1 (Figure 3).
## We query and store the interactions into a dataframe
ligrec_netw <- ligrecextra(
resources = c("iTALK", "Baccin2019"),
organism = 9606 # human
)
## Receptors of the CDH1 ligand.
downstream_cdh1 <- dplyr::filter(
ligrec_netw,
source_genesymbol == "CDH1"
)
## We transform the interactions data frame into a graph
downstream_cdh1_g <- interaction_graph(downstream_cdh1)
## We induce a network with these genes
downstream_cdh1_g <- igraph::induced_subgraph(
interaction_graph(omnipath()),
igraph::V(downstream_cdh1_g)$name
)
DoRothEA Regulons
Another very interesting interaction dataset also available in OmniPath is
DoRothEA (Garcia-Alonso et al. 2019). It contains transcription
factor (TF)-target interactions with confidence score, ranging from A-E,
being A the most confident interactions. In the code chunk shown below, we
select and print the most confident interactions for a given TF.
## We query and store the interactions into a dataframe
dorothea_netw <- dorothea(
dorothea_levels = "A",
organism = 9606
)
## We select the most confident interactions for a given TF and we print
## the interactions to check the way it regulates its different targets
downstream_gli1 <- dplyr::filter(
dorothea_netw,
source_genesymbol == "GLI1"
)
print_interactions(downstream_gli1)
## # A tibble: 7 × 5
## source interaction target n_resources n_references
## <chr> <chr> <chr> <int> <dbl>
## 1 GLI1 (P08151) ==( + )==> BCL2 (P10415) 2 7
## 2 GLI1 (P08151) ==( + )==> PTCH1 (Q13635) 3 5
## 3 GLI1 (P08151) ==( + )==> SFRP1 (Q8N474) 2 3
## 4 GLI1 (P08151) ==( + )==> IGFBP6 (P24592) 2 3
## 5 GLI1 (P08151) ==( - )==> EGR2 (P11161) 2 1
## 6 GLI1 (P08151) ==( + )==> CCND2 (P30279) 2 1
## 7 GLI1 (P08151) ==( - )==> SLIT2 (O94813) 2 1
miRNA-target dataset
The last dataset describing interactions is mirnatarget. It stores
miRNA-mRNA and TF-miRNA interactions. These interactions are only available
for human so far. We next select the miRNA interacting with the TF selected in
the previous code chunk, GLI1. The main function of miRNAs seems to
be related with gene regulation. It is therefore interesting to see how some
miRNA can regulate the expression of a TF which in turn regulates the
expression of other genes. Figure 4 shows a schematic network of
the miRNA targeting GLI1 and the genes regulated by this TF.
## We query and store the interactions into a dataframe
mirna_targets <- mirna_target(
resources = c("miR2Disease", "miRDeathDB")
)
## We select the interactions where a miRNA is interacting with the TF
## used in the previous code chunk and we print these interactions.
upstream_gli1 <- dplyr::filter(
mirna_targets,
target_genesymbol == "GLI1"
)
print_interactions(upstream_gli1)
## # A tibble: 3 × 5
## source interaction target n_resources n_references
## <chr> <chr> <chr> <int> <dbl>
## 1 hsa-miR-324-5p (MIMAT0000761) ==( ? )==> GLI1 (P08151) 3 2
## 2 hsa-miR-125b (MIMAT0000423) ==( ? )==> GLI1 (P08151) 2 1
## 3 hsa-miR-326 (MIMAT0000756) ==( ? )==> GLI1 (P08151) 2 1
## We transform the previous selections to graphs (igraph objects)
downstream_gli1_g <- interaction_graph(downstream_gli1)
upstream_gli1_g <- interaction_graph(upstream_gli1)
Small molecule-protein dataset
This new dataset has been first added to OmniPath in January 2022. It is still
quite small: 3.5k interactions from three resources (SIGNOR, CancerDrugsDB
and Cellinker), but has prospects of a great growth in the future. As an
example, lets look for targets of a cancer drug, the MEK inhibitor Trametinib:
trametinib_targets <- small_molecule(sources = "TRAMETINIB")
print_interactions(trametinib_targets)
## # A tibble: 32 × 5
## source interaction target n_resources n_references
## <chr> <chr> <chr> <int> <dbl>
## 1 TRAMETINIB (11707110) ==( - )==> MAP2K1 (Q02750) 2 1
## 2 TRAMETINIB (11707110) ==( - )==> MAP2K2 (P36507) 2 1
## 3 TRAMETINIB (11707110) ==( ? )==> MAP2K3 (P46734) 1 0
## 4 TRAMETINIB (11707110) ==( ? )==> CDKN2A (Q8N726) 1 0
## 5 TRAMETINIB (11707110) ==( ? )==> STAG2 (Q8N3U4) 1 0
## 6 TRAMETINIB (11707110) ==( ? )==> ERBB3 (P21860) 1 0
## 7 TRAMETINIB (11707110) ==( ? )==> ARAF (P10398) 1 0
## 8 TRAMETINIB (11707110) ==( ? )==> DDX43 (Q9NXZ2) 1 0
## 9 TRAMETINIB (11707110) ==( ? )==> GNAQ (P50148) 1 0
## 10 TRAMETINIB (11707110) ==( ? )==> ETV5 (P41161) 1 0
## # ℹ 22 more rows
Note, the human readable compound names are not reliable, use PubChem CIDs
instead.
Post-translational modifications (PTMs)
Another query type available is PTMs which provides enzyme-substrate reactions
in a very similar way to the aforementioned interactions. PTMs refer
generally to enzymatic modification of proteins after their synthesis in the
ribosomes. PTMs can be highly context-specific and they play a main role
in the activation/inhibition of biological pathways.
In the next code chunk, we download the PTMs for human. We first check
the different available source databases, even though we do not perform any
filter. Then, we select and print the reactions involving a specific
enzyme-substrate pair. Those reactions lack information about activation or
inhibition. To obtain that information, we match the data with
OmniPath interactions. Finally, we show that it is also possible to
build a graph using this information, and to retrieve PTMs from mouse or rat.
## We check the different PTMs databases
enzsub_resources()
## [1] "BEL-Large-Corpus_ProtMapper" "DEPOD" "HPRD"
## [4] "HPRD_MIMP" "KEA" "Li2012"
## [7] "MIMP" "NCI-PID_ProtMapper" "PhosphoNetworks"
## [10] "PhosphoSite" "PhosphoSite_MIMP" "PhosphoSite_ProtMapper"
## [13] "ProtMapper" "REACH_ProtMapper" "RLIMS-P_ProtMapper"
## [16] "Reactome_ProtMapper" "SIGNOR" "SIGNOR_ProtMapper"
## [19] "Sparser_ProtMapper" "dbPTM" "phosphoELM"
## [22] "phosphoELM_MIMP"
## We query and store the enzyme-PTM interactions into a dataframe.
## No filtering by databases in this case.
enzsub <- enzyme_substrate()
## We can select and print the reactions between a specific kinase and
## a specific substrate
print_interactions(dplyr::filter(
enzsub,
enzyme_genesymbol == "MAP2K1",
substrate_genesymbol == "MAPK3"
))
## Warning: Unknown or uninitialised column: `is_stimulation`.
## # A tibble: 6 × 5
## enzyme interaction substrate modification n_resources
## <chr> <chr> <chr> <chr> <int>
## 1 MAP2K1 (Q02750) ====> MAPK3_Y204 (P27361) phosphorylation 8
## 2 MAP2K1 (Q02750) ====> MAPK3_T202 (P27361) phosphorylation 8
## 3 MAP2K1 (Q02750) ====> MAPK3_T207 (P27361) phosphorylation 2
## 4 MAP2K1 (Q02750) ====> MAPK3_Y210 (P27361) phosphorylation 2
## 5 MAP2K1 (Q02750) ====> MAPK3_T80 (P27361) phosphorylation 1
## 6 MAP2K1 (Q02750) ====> MAPK3_Y222 (P27361) phosphorylation 1
## In the previous results, we can see that enzyme-PTM relationships do not
## contain sign (activation/inhibition). We can generate this information
## based on the protein-protein OmniPath interaction dataset.
interactions <- omnipath()
enzsub <- signed_ptms(enzsub, interactions)
## We select again the same kinase and substrate. Now we have information
## about inhibition or activation when we print the enzyme-PTM relationships
print_interactions(
dplyr::filter(
enzsub,
enzyme_genesymbol=="MAP2K1",
substrate_genesymbol=="MAPK3"
)
)
## enzyme interaction substrate modification n_resources
## 3 MAP2K1 (Q02750) ==( + )==> MAPK3_Y204 (P27361) phosphorylation 8
## 1 MAP2K1 (Q02750) ==( + )==> MAPK3_T202 (P27361) phosphorylation 8
## 4 MAP2K1 (Q02750) ==( + )==> MAPK3_Y210 (P27361) phosphorylation 2
## 6 MAP2K1 (Q02750) ==( + )==> MAPK3_T207 (P27361) phosphorylation 2
## 2 MAP2K1 (Q02750) ==( + )==> MAPK3_T80 (P27361) phosphorylation 1
## 5 MAP2K1 (Q02750) ==( + )==> MAPK3_Y222 (P27361) phosphorylation 1
## We can also transform the enzyme-PTM relationships into a graph.
enzsub_g <- enzsub_graph(enzsub = enzsub)
## We download PTMs for mouse
enzsub <- enzyme_substrate(
resources = c("PhosphoSite", "SIGNOR"),
organism = 10090
)
Complexes
Some studies indicate that around 80% of the human proteins operate in
complexes, and many proteins belong to several different complexes
(Berggård, Linse, and James 2007). These complexes play critical roles in a large variety of
biological processes. Some well-known examples are the proteasome and the
ribosome. Thus, the description of the full set of protein complexes
functioning in cells is essential to improve our understanding of biological
processes.
The complexes query provides access to more than 20000 protein
complexes. This comprehensive database has been created by integrating
different resources. We now download these molecular complexes filtering by
some of the source databases. We check the complexes where a couple of
specific genes participate. First, we look for the complexes where any of
these two genes participate. We then identify the complex where these two
genes are jointly involved. Finally, we perform an enrichment analysis with
the genes taking part in that complex. You should keep an eye on this complex
since it will be used again in the forthcoming sections.
## We check the different complexes databases
complex_resources()
## [1] "CFinder" "CORUM" "CellChatDB" "CellPhoneDB" "Cellinker" "Compleat"
## [7] "ComplexPortal" "Guide2Pharma" "Havugimana2012" "ICELLNET" "KEGG-MEDICUS" "NetworkBlast"
## [13] "PDB" "SIGNOR" "SPIKE" "hu.MAP" "hu.MAP2"
## We query and store complexes from some sources into a dataframe.
the_complexes <- complexes(resources = c("CORUM", "hu.MAP"))
## We check all the molecular complexes where a set of genes participate
query_genes <- c("WRN", "PARP1")
## Complexes where any of the input genes participate
wrn_parp1_complexes <- unique(
complex_genes(
the_complexes,
genes = query_genes,
all_genes = FALSE
)
)
## We print the components of the different selected components
head(wrn_parp1_complexes$components_genesymbols, 6)
## [1] "NCAPD2_NCAPG_NCAPH_PARP1_SMC2_SMC4_XRCC1"
## [2] "CCNA2_CDK2_LIG1_PARP1_POLA1_POLD1_POLE_RFC1_RFC2_RPA1_RPA2_RPA3_TOP1"
## [3] "CCNA2_CCNB1_CDK1_PARP1_POLA1_POLD1_POLE_RFC1_RFC2_RPA1_RPA2_RPA3_TOP1"
## [4] "MRE11_PARP1_RAD50_TERF2_TERF2IP_XRCC5_XRCC6"
## [5] "TERF2_WRN"
## [6] "CALR_DHX30_H2AX_H2BC26_HSPA5_NPM1_PARP1"
## Complexes where all the input genes participate jointly
complexes_query_genes_join <- unique(
complex_genes(
the_complexes,
genes = query_genes,
all_genes = TRUE
)
)
## We print the components of the different selected components
complexes_query_genes_join$components_genesymbols
## [1] "PARP1_WRN_XRCC5_XRCC6"
wrn_parp1_cplx_genes <- unlist(
strsplit(wrn_parp1_complexes$components_genesymbols, "_")
)
## We can perform an enrichment analyses with the genes in the complex
enrichment <- gprofiler2::gost(
wrn_parp1_cplx_genes,
significant = TRUE,
user_threshold = 0.001,
correction_method = "fdr",
sources = c("GO:BP", "GO:CC", "GO:MF")
)
## We show the most significant results
enrichment$result %>%
dplyr::select(term_id, source, term_name, p_value) %>%
dplyr::top_n(5, -p_value)
## term_id source term_name p_value
## 1 GO:0051276 GO:BP chromosome organization 1.488106e-31
## 2 GO:0006259 GO:BP DNA metabolic process 2.684684e-30
## 3 GO:0006281 GO:BP DNA repair 1.463364e-26
## 4 GO:0005694 GO:CC chromosome 1.078139e-29
## 5 GO:0005654 GO:CC nucleoplasm 4.198822e-27
Annotations
Biological annotations are statements, usually traceable and curated, about
the different features of a biological entity. At the genetic level,
annotations describe the biological function, the subcellular situation,
the DNA location and many other related properties of a particular gene or
its gene products.
The annotations query provides a large variety of data about proteins
and complexes. These data come from dozens of databases and each kind of
annotation record contains different fields. Because of this, here we have a
record_id field which is unique within the records of each database. Each row
contains one key value pair and you need to use the record_id to connect the
related key-value pairs (see examples below).
Now, we focus in the annotations of the complex studied in the previous
section. We first inspect the different available databases in the omnipath
webserver. Then, we download the annotations for our complex itself as a
biological entity. We find annotations related to the nucleus and
transcriptional control, which is in agreement with the enrichment analysis
results of its individual components.
## We check the different annotation databases
annotation_resources()
## [1] "Adhesome" "Almen2009" "Baccin2019" "CORUM_Funcat"
## [5] "CORUM_GO" "CSPA" "CSPA_celltype" "CancerDrugsDB"
## [9] "CancerGeneCensus" "CancerSEA" "CellCall" "CellCellInteractions"
## [13] "CellChatDB" "CellChatDB_complex" "CellPhoneDB" "CellPhoneDB_complex"
## [17] "CellTalkDB" "CellTypist" "Cellinker" "Cellinker_complex"
## [21] "ComPPI" "CytoSig" "DGIdb" "DisGeNet"
## [25] "EMBRACE" "Exocarta" "GO_Intercell" "GPCRdb"
## [29] "Guide2Pharma" "HGNC" "HPA_secretome" "HPA_subcellular"
## [33] "HPA_tissue" "HPMR" "HumanCellMap" "ICELLNET"
## [37] "ICELLNET_complex" "IntOGen" "Integrins" "InterPro"
## [41] "KEGG-PC" "Kirouac2010" "LOCATE" "LRdb"
## [45] "Lambert2018" "MCAM" "MSigDB" "Matrisome"
## [49] "MatrixDB" "Membranome" "NetPath" "OPM"
## [53] "PROGENy" "PanglaoDB" "Phobius" "Phosphatome"
## [57] "Ramilowski2015" "Ramilowski_location" "SIGNOR" "SignaLink_function"
## [61] "SignaLink_pathway" "Surfaceome" "TCDB" "TFcensus"
## [65] "TopDB" "UniProt_family" "UniProt_keyword" "UniProt_location"
## [69] "UniProt_tissue" "UniProt_topology" "Vesiclepedia" "Wang"
## [73] "Zhong2015" "connectomeDB2020" "iTALK" "kinase.com"
## [77] "scConnect" "scConnect_complex" "talklr"
## We can further investigate the features of the complex selected
## in the previous section.
## We first get the annotations of the complex itself:
cplx_annot <- annotations(
proteins = paste0("COMPLEX:", wrn_parp1_complexes$components_genesymbols)
)
head(dplyr::select(cplx_annot, source, label, value), 10)
## # A tibble: 10 × 3
## source label value
## <chr> <chr> <chr>
## 1 HumanCellMap localization nucleoplasm
## 2 HumanCellMap method SAFE
## 3 HumanCellMap localization chromatin
## 4 HumanCellMap method SAFE
## 5 ComPPI location nucleus
## 6 ComPPI location nucleus
## 7 ComPPI location nucleus
## 8 ComPPI location nucleus
## 9 ComPPI location cytosol
## 10 ComPPI location nucleus
Afterwards, we explore the annotations of the individual components of the
complex in some databases. We check the pathways where these proteins
are involved. Once again, we also find many nucleus related annotations when
checking their cellular location.
Then, we explore some annotations of its individual components. Pathways
where the proteins belong:
comp_annot <- annotations(
proteins = wrn_parp1_cplx_genes,
resources = "NetPath"
)
dplyr::select(comp_annot, genesymbol, value)
## # A tibble: 86 × 2
## genesymbol value
## <chr> <chr>
## 1 CDK2 Interleukin-2 (IL-2)
## 2 CDK2 Epidermal growth factor receptor (EGFR)
## 3 CDK2 Thyroid-stimulating hormone (TSH)
## 4 CDK2 Kit Receptor
## 5 CDK2 Notch
## 6 CDK2 B cell receptor (BCR)
## 7 CDK2 Oncostatin-M (OSM)
## 8 CDK2 Advanced glycation end-products (AGE/RAGE)
## 9 CDK2 Transforming growth factor beta (TGF-beta) receptor
## 10 CDK2 Inhibitor of differentiation (ID)
## # ℹ 76 more rows
Subcellular localization of our proteins:
subcell_loc <- annotations(
proteins = wrn_parp1_cplx_genes,
resources = "ComPPI"
)
Since we have same record_id for some results of our query, we spread these
records across columns:
tidyr::spread(subcell_loc, label, value) %>%
dplyr::arrange(desc(score)) %>%
dplyr::top_n(10, score)
## # A tibble: 10 × 7
## uniprot genesymbol entity_type source record_id location score
## <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 P35580 MYH10 protein ComPPI 10158 cytosol 0.9999999999638732
## 2 Q9NYB0 TERF2IP protein ComPPI 4117 nucleus 0.9999999999187149
## 3 P15927 RPA2 protein ComPPI 4343 nucleus 0.999999999827968
## 4 Q15554 TERF2 protein ComPPI 854 nucleus 0.9999999956992
## 5 P16104 H2AX protein ComPPI 17438 nucleus 0.9999999922553344
## 6 P09884 POLA1 protein ComPPI 20949 nucleus 0.999999990784
## 7 O95347 SMC2 protein ComPPI 14574 nucleus 0.9999999903232
## 8 P12956 XRCC6 protein ComPPI 2961 nucleus 0.99999997629184
## 9 P78527 PRKDC protein ComPPI 13352 nucleus 0.999999951616
## 10 Q15021 NCAPD2 protein ComPPI 14405 nucleus 0.9999999491968
The way above, we more or less reconstituted the data as it is in the
original resource. The same can be done much easier by passing the
wide = TRUE parameter to annotations. In this case,
if the data contains more than one resources, a list of data frames
will be returned.
signalink_pathways <- annotations(
resources = "SignaLink_pathway",
wide = TRUE
)
Intercell
Cells perceive cues from their microenvironment and neighboring cells, and
respond accordingly to ensure proper activities and coordination between
them. The ensemble of these communication process is called inter-cellular
signaling (intercell).
Intercell query provides information about the roles of proteins in
inter-cellular signaling (e.g. if a protein is a ligand, a receptor, an
extracellular matrix (ECM) component, etc.) This query type is very similar to
annotations. However, intercell data does not come from original
sources, but combined from several databases by us into categories (we also
refer to the original sources).
We first inspect the different categories available in the OmniPath webserver.
Then, we focus again in our previously selected complex and we check its
the location of its individual components in the inter-cellular context. We
can however see that the components of this particular complex are
intracellular.
## We check some of the different intercell categories
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"
## We import the intercell data into a dataframe
intercell_loc <- intercell(
scope = "generic",
aspect = "locational"
)
## We check the intercell annotations for the individual components of
## our previous complex. We filter our data to print it in a good format
dplyr::filter(intercell_loc, genesymbol %in% wrn_parp1_cplx_genes) %>%
dplyr::distinct(genesymbol, parent, .keep_all = TRUE) %>%
dplyr::select(category, genesymbol, parent) %>%
dplyr::arrange(genesymbol)
## # A tibble: 80 × 3
## category genesymbol parent
## <chr> <chr> <chr>
## 1 extracellular ACTB extracellular
## 2 intracellular ACTB intracellular
## 3 intracellular ATE1 intracellular
## 4 intracellular BANF1 intracellular
## 5 secreted CALR secreted
## 6 extracellular CALR extracellular
## 7 intracellular CALR intracellular
## 8 transmembrane CAMK2D transmembrane
## 9 plasma_membrane CAMK2D plasma_membrane
## 10 intracellular CAMK2D intracellular
## # ℹ 70 more rows
The intercell_network function creates the most complete network,
including many interactions which are false positives in the context of
interacellular communication. It is highly recommended to apply some
quality filtering on this network. The high_confidence parameter performs
a quiet stringent filtering:
icn <- intercell_network(high_confidence = TRUE)
Using the function filter_intercell_network instead, you have much more
flexibility to adjust the stringency of the filtering to the needs of your
analysis. See the full list of options in the docs of the function.
icn <-
intercell_network() %>%
filter_intercell_network(
min_curation_effort = 1,
consensus_percentile = 33
)
## We close graphical connections
while (!is.null(dev.list())) dev.off()
