filter_intercell_network | R Documentation |
The intercell database of OmniPath covers a very broad range of possible
ways of cell to cell communication, and the pieces of information, such as
localization, topology, function and interaction, are combined from many,
often independent sources. This unavoidably result some weird and
unexpected combinations which are false positives in the context of
intercellular communication. intercell_network
provides a shortcut (high_confidence
) to do basic quality filtering.
For custom filtering or experimentation with the parameters we offer this
function.
filter_intercell_network(
network,
transmitter_topology = c("secreted", "plasma_membrane_transmembrane",
"plasma_membrane_peripheral"),
receiver_topology = "plasma_membrane_transmembrane",
min_curation_effort = 2,
min_resources = 1,
min_references = 0,
min_provenances = 1,
consensus_percentile = 50,
loc_consensus_percentile = 30,
ligand_receptor = FALSE,
simplify = FALSE,
unique_pairs = FALSE,
omnipath = TRUE,
ligrecextra = TRUE,
kinaseextra = FALSE,
pathwayextra = FALSE,
...
)
network |
An intercell network data frame, as provided by
|
transmitter_topology |
Character vector: topologies allowed for the entities in transmitter role. Abbreviations allowed: "sec", "pmtm" and "pmp". |
receiver_topology |
Same as |
min_curation_effort |
Numeric: a minimum value of curation effort (resource-reference pairs) for network interactions. Use zero to disable filtering. |
min_resources |
Numeric: minimum number of resources for interactions. The value 1 means no filtering. |
min_references |
Numeric: minimum number of references for interactions. Use zero to disable filtering. |
min_provenances |
Numeric: minimum number of provenances (either resources or references) for interactions. Use zero or one to disable filtering. |
consensus_percentile |
Numeric: percentile threshold for the consensus
score of generic categories in intercell annotations. The consensus
score is the number of resources supporting the classification of an
entity into a category based on combined information of many resources.
Here you can apply a cut-off, keeping only the annotations supported
by a higher number of resources than a certain percentile of each
category. If |
loc_consensus_percentile |
Numeric: similar to
|
ligand_receptor |
Logical. If |
simplify |
Logical: keep only the most often used columns. This function combines a network data frame with two copies of the intercell annotation data frames, all of them already having quite some columns. With this option we keep only the names of the interacting pair, their intercellular communication roles, and the minimal information of the origin of both the interaction and the annotations. |
unique_pairs |
Logical: instead of having separate rows for each
pair of annotations, drop the annotations and reduce the data frame to
unique interacting pairs. See |
omnipath |
Logical: shortcut to include the omnipath dataset in the interactions query. |
ligrecextra |
Logical: shortcut to include the ligrecextra dataset in the interactions query. |
kinaseextra |
Logical: shortcut to include the kinaseextra dataset in the interactions query. |
pathwayextra |
Logical: shortcut to include the pathwayextra dataset in the interactions query. |
... |
If |
An intercell network data frame filtered.
intercell_network
unique_intercell_network
simplify_intercell_network
intercell
intercell_categories
intercell_generic_categories
intercell_summary
icn <- intercell_network()
icn_f <- filter_intercell_network(
icn,
consensus_percentile = 75,
min_provenances = 3,
simplify = TRUE
)
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