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## ----message=FALSE, include=FALSE, results="hide", setup, echo=FALSE----------
knitr::opts_chunk$set(
echo = TRUE,
eval = TRUE,
message = FALSE,
warning = FALSE,
collapse = TRUE,
tidy = FALSE,
cache = FALSE,
dev = "png",
comment = "#>"
)
library(rbioapi)
rba_options(timeout = 30, skip_error = TRUE)
## ----rba_reactome_analysis----------------------------------------------------
## 1 We create a simple vector with our genes
genes <- c(
"p53", "BRCA1", "cdk2", "Q99835", "CDC42", "CDK1", "KIF23", "PLK1", "RAC2",
"RACGAP1", "RHOA", "RHOB", "MSL1", "PHF21A", "INSR", "JADE2", "P2RX7",
"CCDC101", "PPM1B", "ANAPC16", "CDH8", "HSPA1L", "CUL2", "ZNF302", "CUX1",
"CYTH2", "SEC22C", "EIF4E3", "ROBO2", "CXXC1", "LINC01314", "ATP5F1"
)
## 2 We call reactome analysis with the default parameters
analyzed <- rba_reactome_analysis(
input = genes,
projection = TRUE,
p_value = 0.01
)
## 3 As always, we use str() to inspect the resutls
str(analyzed, 1)
## 4 Note that in the summary element: (analyzed$summary)
### 4.a because we supplied a simple vector, the analysis type was: over-representation
### 4.b You need the token for other rba_reactome_analysis_* functions
## 5 Analsis results are in the pathways data frame:
## ----analysis_results, echo=FALSE---------------------------------------------
if (utils::hasName(analyzed, "pathways") && is.data.frame(analyzed$pathways)) {
DT::datatable(
data = jsonlite::flatten(analyzed$pathways),
options = list(
scrollX = TRUE,
paging = TRUE,
fixedHeader = TRUE,
keys = TRUE,
pageLength = 5
)
)
} else {
print("Vignette building failed. It is probably because the web service was down during the building.")
}
## ----rba_reactome_analysis_pdf/download, eval=FALSE---------------------------
# # download a full pdf report
# rba_reactome_analysis_pdf(
# token = analyzed$summary$token,
# species = 9606
# )
#
# # download the result in compressed json.gz format
# rba_reactome_analysis_download(
# token = analyzed$summary$token,
# request = "results",
# save_to = "reactome_results.json"
# )
## ----rba_reactome_analysis_import, eval=FALSE---------------------------------
# re_uploaded <- rba_reactome_analysis_import(input = "reactome_results.json")
## ----rba_reactome_query_ex1---------------------------------------------------
## 1 query a pathway Entry
pathway <- rba_reactome_query(
ids = "R-HSA-109581",
enhanced = TRUE
)
## 2 As always we use str() to inspect the output's structure
str(pathway, 2)
## 3 You can compare it with the webpage of R-HSA-202939 entry:
# https://reactome.org/content/detail/R-HSA-202939
## ----rba_reactome_query_ex2---------------------------------------------------
## 1 query a protein Entry
protein <- rba_reactome_query(
ids = 66247,
enhanced = TRUE
)
## 2 As always we use str() to inspect the output's structure
str(protein, 1)
## 3 You can compare it with the webpage of R-HSA-202939 entry:
# https://reactome.org/content/detail/R-HSA-202939
## ----rba_reactome_xref--------------------------------------------------------
## 1 We Supply HGNC ID to find what is the corresponding database ID in Reactome
xref_protein <- rba_reactome_xref("CD40")
## 2 As always use str() to inspect the output's structure
str(xref_protein, 1)
## ----xref_mapping-------------------------------------------------------------
## 1 Again, consider CD40 protein:
xref_mapping <- rba_reactome_mapping(
id = "CD40",
resource = "hgnc",
map_to = "pathways"
)
## ----xref_mapping_df, echo=FALSE----------------------------------------------
if (is.data.frame(xref_mapping)) {
DT::datatable(
data = xref_mapping,
options = list(
scrollX = TRUE,
paging = TRUE,
fixedHeader = TRUE,
keys = TRUE,
pageLength = 10
)
)
} else {
print("Vignette building failed. It is probably because the web service was down during the building.")
}
## ----sessionInfo, echo=FALSE--------------------------------------------------
sessionInfo()
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