It is strongly recommended to create a virtual environment that encapsulates your INDRA installation. Running the following commands will create a new environment and install INDRA into it. Replace /path/to/virtual/env with your desired location. This can be, e.g., ~/.virtualenvs/indra.
virtualenv -p python3 /path/to/virtual/env
source /path/to/virtual/env/bin/activate
pip install indra
pip uninstall -y enum34
deactivateThis package can be installed directly from GitHub by running the following commands in R:
if( !require(devtools) ) install.packages("devtools")
devtools::install_github("ArtemSokolov/indRa")The INDRA module is exposed through indra() function. It allows for direct access to any part of the INDRA API.
library( indRa )
## If using virtualenv
reticulate::use_virtualenv( "/path/to/virtual/env", required=TRUE )
## Access to INDRA is available through indra() function
indra()
# Module(indra)
## Reading a sentence with TRIPS
trips <- indra()$sources$trips
sentence <- 'MAP2K1 phosphorylates MAPK3 at Thr-202 and Tyr-204'
trips_processor <- trips$process_text( sentence )
trips_processor$statements
# [[1]]
# Phosphorylation(MAP2K1(), MAPK3(), T, 202)
#
# [[2]]
# Phosphorylation(MAP2K1(), MAPK3(), Y, 204)Extraneous output can be toggled through the Python logging mechanism
pyLogging <- reticulate::import( "logging" )
indra()$logger$setLevel( pyLogging$WARNING )The package provides additional functionality for constructing and manipulating interaction networks, based on statements retrieved from INDRA DB REST queries. A simple list of edges can be constructed through IDBquery(), which creates a data frame encapsulating the output of indra.sources.indra_db_rest.api.get_statements for all entities with HGNC mappings.
IDBquery( "SIK3" )
# # A tibble: 32 x 5
# Hash Activity EvCnt Src Trgt
# <chr> <chr> <int> <chr> <chr>
# 1 7430532589474606 Phosphorylation 9 SIK3 PER2
# 2 -21145377478765295 Phosphorylation 8 SIK3 HDAC4
# 3 -260602555584320 Inhibition 4 SIK3 CRTC2
# 4 12365252834703035 Activation 3 SIK3 STK11
# 5 22779893853211724 Activation 3 SIK3 SIK3
# # … with 27 more rows
IDBquery( object="KLF4" )
# # A tibble: 621 x 5
# Hash Activity EvCnt Src Trgt
# <chr> <chr> <int> <chr> <chr>
# 1 34237034834082983 Activation 19 MIR145 KLF4
# 2 -1893896626253041 Acetylation 17 EP300 KLF4
# 3 -4171272971594532 IncreaseAmount 15 KLF4 KLF4
# 4 -2531422713809198 Activation 15 KLF4 KLF4
# 5 1490195882280611 Activation 15 STAT3 KLF4
# # … with 616 more rowsThe package includes implementation of the Dijkstra's graph search algorithm for discovering paths between a source (e.g., a kinase) and downstream targets (e.g., transcription factors). The algorithm accepts an arbitrary path scoring function; by default, it uses lpgm (length-penalized geometric mean) provided with the package.
## Find paths from JAK2 to downstream Interferon TFs
PW <- dijkstra( "JAK2", trgts=c("NFKB1", "STAT1", "STAT2", "STAT3", "IRF1", "IRF3") )
# # A tibble: 7 x 3
# Gene Path Score
# <chr> <list> <dbl>
# 1 STAT3 <tibble [1 × 4]> 5.56
# 2 STAT1 <tibble [1 × 4]> 4.56
# 3 STAT2 <tibble [1 × 4]> 2.20
# 4 NFKB1 <tibble [3 × 4]> 3.52
# 5 IRF1 <tibble [3 × 4]> 3.15
# 6 IRF3 <tibble [3 × 4]> 3.05
## Paths to individual targets can be retrieved from the Path column
P <- with(PW, setNames(Path, Gene))
P[["NFKB1"]]
# # A tibble: 3 x 4
# Activity EvCnt Src Trgt
# <chr> <int> <chr> <chr>
# 1 Phosphorylation 261 JAK2 STAT3
# 2 Activation 329 STAT3 IL6
# 3 Activation 12 IL6 NFKB1The search can be guided through the blacklist argument, which specifies which set of nodes should NOT be expanded. This guarantees that the final paths will not go through the blacklisted nodes. Note that blacklisting one or more targets does not prevent the algorithm from finding a path to them:
## STAT3 is both a target and blacklisted
PW2 <- dijkstra( "JAK2", c("NFKB1","IRF3","STAT3"), blacklist="STAT3" )
# # A tibble: 3 x 3
# Gene Path Score
# <chr> <list> <dbl>
# 1 STAT3 <tibble [1 × 4]> 5.56
# 2 IRF3 <tibble [3 × 4]> 2.65
# 3 NFKB1 <tibble [3 × 4]> 2.65
## The path to NFKB1 no longer goes through STAT3
PW2$Path[[3]]
# # A tibble: 3 x 4
# Activity EvCnt Src Trgt
# <chr> <int> <chr> <chr>
# 1 Phosphorylation 96 JAK2 STAT1
# 2 Activation 134 STAT1 IFNG
# 3 Activation 6 IFNG NFKB1The paths discovered through the graph search algorithms above can be visualized using plotPaths(). The function accepts one or more Path data frames discovered by, e.g., dijkstra() and returns a ggplot object that can be further modified using the standard grammar of graphics. Following the JAK2 example above, we may want to visualize the paths using our custom color scheme for the edges:
plotPaths( PW$Path ) + ggthemes::scale_color_few()
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent