Topic 1-04: Get pathways from Reactome

reactome.db

There is a reactome.db on Bioconductor.

The version of the data:

library(reactome.db)

## Loading required package: AnnotationDbi

## Loading required package: stats4

## Loading required package: BiocGenerics

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## Loading required package: IRanges

## Loading required package: S4Vectors

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reactome.db

## ReactomeDb object:
## | DBSCHEMA: REACTOME_DB
## | DBSCHEMAVERSION: 89
## | SOURCENAME: Reactome
## | SOURCEURL: https://reactome.org/download/current/databases/gk_current.sql.gz
## | SOURCEDATE: 19997
## | Supporting package: AnnotationDbi
## | Db type: ReactomeDb

## 
## Please see: help('select') for usage information

In it, the important objects are

• reactomePATHID2EXTID contains mappings between reacotme pathway IDs and gene entrez IDs
• reactomePATHID2NAME contains pathway names

library(reactome.db)
tb = toTable(reactomePATHID2EXTID)
head(tb)

##           DB_ID gene_id
## 1  R-HSA-109582       1
## 2  R-HSA-114608       1
## 3  R-HSA-168249       1
## 4  R-HSA-168256       1
## 5 R-HSA-6798695       1
## 6   R-HSA-76002       1

p2n = toTable(reactomePATHID2NAME)
head(p2n)

##           DB_ID
## 1 R-BTA-1971475
## 2 R-BTA-1369062
## 3  R-BTA-382556
## 4 R-BTA-9033807
## 5  R-BTA-418592
## 6  R-BTA-392170
##                                                                     path_name
## 1 Bos taurus: A tetrasaccharide linker sequence is required for GAG synthesis
## 2                           Bos taurus: ABC transporters in lipid homeostasis
## 3                          Bos taurus: ABC-family proteins mediated transport
## 4                                    Bos taurus: ABO blood group biosynthesis
## 5                       Bos taurus: ADP signalling through P2Y purinoceptor 1
## 6                      Bos taurus: ADP signalling through P2Y purinoceptor 12

In the previous code, we use the function toTable() to retrieve the data as a data frame. You can also try as.list() on the two objects and compare the output.

Reactome contains pathway for multiple organisms. In the reactome ID, teh second section contains the organism, e.g. in previous output HSA.

sort(table( gsub("^R-(\\w+)-\\d+$", "\\1", p2n[, 1]) ))

## 
##  MTU  PFA  SCE  SPO  DDI  CEL  DRE  XTR  DME  CFA  SSC  BTA  RNO  GGA  MMU  HSA 
##   13  641  855  872 1035 1366 1374 1520 1528 1714 1734 1742 1750 1762 1762 2711

barplot(sort(table( gsub("^R-(\\w+)-\\d+$", "\\1", p2n[, 1]) )))

Practice

tb = toTable(reactomePATHID2EXTID)
tb = tb[grep("-HSA-", tb[, 1]), ]
n_gene = table(tb[, 1])
hist(n_gene)

hist(n_gene, nc = 100)

n_gene[n_gene > 2000]

## 
## R-HSA-1430728  R-HSA-162582 R-HSA-1643685  R-HSA-168256  R-HSA-392499 
##          2347          3231          2304          2394          2387

p2n = toTable(reactomePATHID2NAME)
p2n[p2n[, 1] %in% names(n_gene[n_gene > 2000]), ]

##               DB_ID                            path_name
## 11250 R-HSA-1643685                Homo sapiens: Disease
## 11665  R-HSA-168256          Homo sapiens: Immune System
## 11905 R-HSA-1430728             Homo sapiens: Metabolism
## 11923  R-HSA-392499 Homo sapiens: Metabolism of proteins
## 12694  R-HSA-162582    Homo sapiens: Signal Transduction