README.md
In alisonwu19/BCB420.2019.BioGRID:
BCB420.2019.BioGRID
(BioGRID data annotatation of human genes)
Installation
[Alison Wu] <alison.wu@mail.utoronto.ca>
1 About this package:
This package describe the workflow to download interaction datasets from BioGRID,how to map the Entrez Gene IDs to HGNC symbols, and how to annotate the example gene set.
In this project ...
--BCB420.2019.BioGrid/
|__.gitignore
|__.Rbuildignore
|__BCB420.2019.BioGrid.Rproj
|__DESCRIPTION
|__dev/
|__rptTwee.R
|__toBrowser.R
|__inst/
|__extdata/
|entrez2sym.RData # Entrez ID to HGNC symbol mapping tool
|__xSetInt.tsv # annotated example edges
|__img/
|__[...] # image sources for .md document
|__scripts/
|__recoverIDs.R # utility to use biomaRt for ID mapping
|__LICENSE
|__NAMESPACE
|__R/
|__zzz.R
|__README.md # this file
2 BioGRID Data
BioGRID is a curated biological database of interactions. BioGRID interactions include protein-protein interactions, genetic interactions, chemical interactions, and post-translational modifications.
3 Data download and cleanup
To download the source data from BioGRID :
- Navigate to the BioGRID database
- Click "downloads" in the left corner, click "Current Release" and choose "BIOGRID-3.5.169" or follow the link here
- Download the following data file: BIOGRID-ORGANISM-3.5.169.tab2.zip (68.09 MB)
- Unzip the file
- Choose the file
BIOGRID-ORGANISM-Homo_sapiens-3.5.169.tab2.txt (122 MB) and place it into the your local working directory which is called data.
4 Mapping Entrez IDs to HGNC symbols
BioGrid genes are Entrez IDs. These IDs can be mapped to HGNC symbols.
Preparations: packages, functions, files
Install a few required packages to begin:
readr allows the program to read large datasets.
if (! requireNamespace("readr")) {
install.packages("readr")
}
biomaRt is a Bioconductor package that implements the RESTful API of biomart,
the annotation framwork for model organism genomes at the EBI. It is a Bioconductor package, and as such it needs to be loaded via the BiocManager,
if (! requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
if (! requireNamespace("biomaRt", quietly = TRUE)) {
BiocManager::install("biomaRt")
}
igraph is to compute statistics graphs and plots.
if (! requireNamespace("igraph")) {
install.packages("igraph")
}
4.1 Step one: which IDs do we have to map?
# Read the interaction data
tmp <-readr::read_delim(file.path("../data","BIOGRID-ORGANISM-Homo_sapiens-3.5.169.tab2.txt"),
delim = "\t",
skip = 1, col_names = c("BioGRID Interaction ID", "Entrez_ID1", "Entrez_ID2")) #468,058 rows
head(tmp)
# A tibble: 6 x 3
# `BioGRID Interaction ID` Entrez_ID1 Entrez_ID2
# 1 <dbl> <dbl> <dbl>
# 2 103 6416 2318
# 3 117 84665 88
# 4 183 90 2339
# 5 278 2624 5371
# 6 418 6118 6774
# 7 586 375 23163
# The number of unique IDs we have to map:
uENTREZ <- unique(c(tmp$Entrez_ID1, tmp$Entrez_ID2)) #23108 IDs
```
#### 4.2 Step two: mapping via biomaRt
We first map Entrez IDs to HGNC symbols in bulk using biomaRt. And then we can look for the remaining IDs that we could not map via UniProt IDs (from HGNC reference data).
###### 4.2.1 Constructing an ID-mapping tool
```R
# Map ENSP to HGNC symbols: open a "Mart" object ..
myMart <- biomaRt::useMart("ensembl", dataset="hsapiens_gene_ensembl")
tmp <- biomaRt::getBM(filters = "entrezgene",
attributes = c("entrezgene",
"hgnc_symbol"),
values = uENTREZ,
mart = myMart)
head(tmp)
# entrezgene hgnc_symbol
# 1 10036 CHAF1A
# 2 10048 RANBP9
# 3 10059 DNM1L
# 4 10084 PQBP1
# 5 10128 LRPPRC
# 6 10138 YAF2
nrow(tmp) # 17058 HGNC symbols have been retrieved for the 23108 Entrez IDs
There are potential problems with the data that biomart returns
There might be duplicates (more than one value returned).
sum(duplicated(tmp$entrezgene)) #96 duplicates
Or nothing returned for one Entrez ID
sum(! (uENTREZ) %in% tmp$entrezgene) # 6146
To fix the duplicates:
dupEntrez <- tmp$entrezgene[duplicated(tmp$entrezgene)]
tmp[tmp$entrezgene %in% dupEntrez, ]
entrezgene hgnc_symbol
# 321 5888 RAD51
# 322 5888
# 368 6606 SMN2
# 369 6606 SMN1
# 627 25788 FSBP
# 628 25788 RAD54B
# 936 8878 SQSTM1
# 937 8878
# 1566 2222 FDFT1
# 1567 2222
# 1717 5430 POLR2A
# 1718 5430
# 2049 23370 ARHGEF18
# 2050 23370
# 2919 11068 CYB561D2
#Remove the symbols with space
tmp[tmp$hgnc_symbol %in% c(""), ]
tmp <- tmp[ ! (tmp$hgnc_symbol %in% c("")), ]
#Check the UniprotID for the duplicates one
#SMN2 and SMN1 has the same UniprotID Q16637 - so assign arbitarily to either one
tmp <- tmp[ ! (tmp$hgnc_symbol %in% c("SMN2","FSBP", "HLA-DQA2", "LINC02210-CRHR1", "DEFB4B", "DEFB103A", "CCL3L1", "CRYAA2", "IQCJ-SCHIP1", "TBC1D3L", "SERF1B", "DDT", "STAG3L3", "ZNF468", "KIR2DS5", "FNTB", "CHURC1-FNTB", "GTF2H2", "LGALS7B", "PLEKHG7", "FAM187A", "CBWD3", "BOLA2B", "RPP21", "TRIM39-RPP21", "LGALS7", "GUSBP1", "LINC00680","ARL17B", "USP17L2", "GATD3A", "RNA5S3", "PRR4", "GAGE12F", "GAGE12G")), ]
#Check duplicates
any(duplicated(tmp$entrezgene)) #FALSE
```
Define the mapping tool after this cleanup
```R
entrez2sym <- tmp$hgnc_symbol
names(entrez2sym) <- tmp$entrezgene
head(entrez2sym)
# 10036 10048 10059 10084 10128 10138
#"CHAF1A" "RANBP9" "DNM1L" "PQBP1" "LRPPRC" "YAF2"
```
###### 4.2.2 Cleanup and validation of `entrez2sym`
```R
sel <- ! (uENTREZ %in% names(entrez2sym))
> x <- rep(NA, sum( sel))
> names(x) <- uENTREZ[ sel ]
> any(duplicated(c(names(x), names(entrez2sym)))) #FALSE
entrez2sym <- c(entrez2sym, x)
> all(uENTREZ %in% names(entrez2sym)) #TRUE
-check if there are empty strings
sel <- which(entrez2sym == "")
> length(sel) #0
Add out dated symbols
sel <- ( ! (entrez2sym %in% HGNC$sym)) & ( ! (is.na(entrez2sym)))
length( entrez2sym[ sel ] ) #175
length( unique(entrez2sym[ sel ])) #175
unkSym <- data.frame(unk = entrez2sym[ sel ],
new = NA,
stringsAsFactors = FALSE)
View(unkSym)
#add the outdated symbols(which are usually stored in the prev column in HGNC to entrez2sym)
for (i in seq_len(nrow(unkSym))) {
iPrev <- grep(unkSym$unk[i], HGNC$prev)[1]
if (length(iPrev) == 1) {
unkSym$new[i] <- HGNC$sym[iPrev]
}
```
4.3 Final Validation
Final validation for our mapping tool
```R
all(uENTREZ %in% names(entrez2sym)) #TRUE, all the Entrez IDs are mapped
sum(! is.na(entrez2sym)) #16916 symbols are found
sum(! is.na(entrez2sym)) * 100 / length(entrez2sym) #73.20%
#Save map
save(entrez2sym, file = file.path("inst", "extdata", "entrez2sym.RData"))
#Load file from RStudio project
load(file = file.path("inst", "extdata", "entrez2sym.RData"))
```
# 5 Annotating gene sets with STRING Data
We can now annotate the gene sets with BioGrid dta using our mapping tool.
```R
tmp <-readr::read_delim(file.path("../data", "BIOGRID-ORGANISM-Homo_sapiens-3.5.169.tab2.txt"),
delim = "\t",
skip = 1, col_names = c("BioGRID Interaction ID", "Entrez_ID1", "Entrez_ID2", "ID1", "ID2", "sys_name1", "sys_name2", "sym1", "sym2", "alias1", "alias2", "exp_name", "interaction_type", "author", "pubID", "org_ID1", "org_ID2", "interaction_throughput", "quant_score", "mod", "pheno", "qual", "tags", "ext"))
#the number of interaction that is genetic
nrow(tmp[tmp$interaction_type == "genetic",]) #5299
#the number of interaction that is physical
nrow(tmp[tmp$interaction_type == "physical",]) #462759
#Finally we map Entrez IDs to HGNC sysmbols
tmp$Entrez_ID1 <- entrez2sym[tmp$Entrez_ID1]
tmp$Entrez_ID2 <- entrez2sym[tmp$Entrez_ID2]
VALIDATE:
sum(is.na(tmp$Entrez_ID1)) #168627
sum(is.na(tmp$Entrez_ID2)) #169208
# we remove edges in which either one or the other node is NA to
# create our final data:
BioGrid <- tmp[( ! is.na(tmp$Entrez_ID1)) & ( ! is.na(tmp$Entrez_ID2)), ]
BioGridInt <- tmp[( ! is.na(tmp$Entrez_ID1)) & ( ! is.na(tmp$Entrez_ID2)), ]
#DOne
save(BioGridInt, file = file.path(".", "data", "BioGridInt.RData"))
#Network Graph
t <- data.frame(Num_GeneticInteractions = num_genetic, Percentage=round(num_genetic / total * 100, 2), Num_PhysicalInteractions= num_phy,Percentage=round(num_phy / total * 100, 2),stringsAsFactors = FALSE)
head(t)
Num_GeneticInteractions Percentage Num_PhysicalInteractions
1 5299 1.13 462759
Percentage.1
1 98.87
#the distribution
deg <- table(c(BioGridInt$Entrez_ID1, BioGridInt$Entrez_ID2))
hist(deg, breaks=50,
xlim = c(0, 1400),
col = "#3fafb388",
main = "STRING nodes degree distribution",
xlab = "degree (undirected graph)",
ylab = "Counts")
```
6 Annotation of the example gene set
To conclude, we annotate the example gene set, validate the annotation, and store the data.
```R
The specification of the sample set is copy-paste from the
BCB420 resources project.
xSet <- c("AMBRA1", "ATG14", "ATP2A1", "ATP2A2", "ATP2A3", "BECN1", "BECN2",
"BIRC6", "BLOC1S1", "BLOC1S2", "BORCS5", "BORCS6", "BORCS7",
"BORCS8", "CACNA1A", "CALCOCO2", "CTTN", "DCTN1", "EPG5", "GABARAP",
"GABARAPL1", "GABARAPL2", "HDAC6", "HSPB8", "INPP5E", "IRGM",
"KXD1", "LAMP1", "LAMP2", "LAMP3", "LAMP5", "MAP1LC3A", "MAP1LC3B",
"MAP1LC3C", "MGRN1", "MYO1C", "MYO6", "NAPA", "NSF", "OPTN",
"OSBPL1A", "PI4K2A", "PIK3C3", "PLEKHM1", "PSEN1", "RAB20", "RAB21",
"RAB29", "RAB34", "RAB39A", "RAB7A", "RAB7B", "RPTOR", "RUBCN",
"RUBCNL", "SNAP29", "SNAP47", "SNAPIN", "SPG11", "STX17", "STX6",
"SYT7", "TARDBP", "TFEB", "TGM2", "TIFA", "TMEM175", "TOM1",
"TPCN1", "TPCN2", "TPPP", "TXNIP", "UVRAG", "VAMP3", "VAMP7",
"VAMP8", "VAPA", "VPS11", "VPS16", "VPS18", "VPS33A", "VPS39",
"VPS41", "VTI1B", "YKT6")
Example genes are not among the known nodes?
x <- which( ! (xSet %in% c(BioGridInt$Entrez_ID1, BioGridInt$Entrez_ID2)))
cat(sprintf("\t%s\t(%s)\n", HGNC[xSet[x], "sym"], HGNC[xSet[x], "name"]))
ATP2A1 (ATPase sarcoplasmic/endoplasmic reticulum Ca2+ transporting 1)
ATP2A2 (ATPase sarcoplasmic/endoplasmic reticulum Ca2+ transporting 2)
BECN2 (beclin 2)
BIRC6 (baculoviral IAP repeat containing 6)
BLOC1S2 (biogenesis of lysosomal organelles complex 1 subunit 2)
BORCS5 (BLOC-1 related complex subunit 5)
BORCS7 (BLOC-1 related complex subunit 7)
BORCS8 (BLOC-1 related complex subunit 8)
CTTN (cortactin)
EPG5 (ectopic P-granules autophagy protein 5 homolog)
GABARAPL2 (GABA type A receptor associated protein like 2)
INPP5E (inositol polyphosphate-5-phosphatase E)
IRGM (immunity related GTPase M)
LAMP5 (lysosomal associated membrane protein family member 5)
MAP1LC3B (microtubule associated protein 1 light chain 3 beta)
MGRN1 (mahogunin ring finger 1)
OPTN (optineurin)
OSBPL1A (oxysterol binding protein like 1A)
PI4K2A (phosphatidylinositol 4-kinase type 2 alpha)
PIK3C3 (phosphatidylinositol 3-kinase catalytic subunit type 3)
RAB20 (RAB20, member RAS oncogene family)
RAB29 (RAB29, member RAS oncogene family)
RAB34 (RAB34, member RAS oncogene family)
RAB7A (RAB7A, member RAS oncogene family)
RPTOR (regulatory associated protein of MTOR complex 1)
RUBCNL (rubicon like autophagy enhancer)
SNAP29 (synaptosome associated protein 29)
SNAP47 (synaptosome associated protein 47)
SPG11 (SPG11, spatacsin vesicle trafficking associated)
STX17 (syntaxin 17)
STX6 (syntaxin 6)
TFEB (transcription factor EB)
TGM2 (transglutaminase 2)
TOM1 (target of myb1 membrane trafficking protein)
TPCN1 (two pore segment channel 1)
TPCN2 (two pore segment channel 2)
TPPP (tubulin polymerization promoting protein)
VPS11 (VPS11, CORVET/HOPS core subunit)
YKT6 (YKT6 v-SNARE homolog)
select the gene that are in the example set:
sel <- (BioGridInt$Entrez_ID1 %in% xSet) & (BioGridInt$Entrez_ID2 %in% xSet)
xSetInt <- BioGridInt[sel, c("Entrez_ID1", "Entrez_ID2")]
Statitics:
nrow(xSetInt) #24
Save the annotated set
writeLines(c("Entrez_ID1\tb",
sprintf("%s\t%s", xSetInt$Entrez_ID1, xSetInt$Entrez_ID2)),
con = "xSetInt.tsv")
The data set can be read back
myXset <- read.delim(file.path("inst", "extdata", "xSetInt.tsv"),
stringsAsFactors = FALSE)
myXset <- read.delim(system.file("extdata",
"xSetInt.tsv",
package = "BCB420.2019.BioGrid"),
stringsAsFactors = FALSE)
```
alisonwu19/BCB420.2019.BioGRID documentation built on May 6, 2019, 8:08 p.m.
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BCB420.2019.BioGRID
(BioGRID data annotatation of human genes)
Installation
[Alison Wu] <alison.wu@mail.utoronto.ca>
1 About this package:
This package describe the workflow to download interaction datasets from BioGRID,how to map the Entrez Gene IDs to HGNC symbols, and how to annotate the example gene set.
In this project ...
--BCB420.2019.BioGrid/
|__.gitignore
|__.Rbuildignore
|__BCB420.2019.BioGrid.Rproj
|__DESCRIPTION
|__dev/
|__rptTwee.R
|__toBrowser.R
|__inst/
|__extdata/
|entrez2sym.RData # Entrez ID to HGNC symbol mapping tool
|__xSetInt.tsv # annotated example edges
|__img/
|__[...] # image sources for .md document
|__scripts/
|__recoverIDs.R # utility to use biomaRt for ID mapping
|__LICENSE
|__NAMESPACE
|__R/
|__zzz.R
|__README.md # this file
2 BioGRID Data
BioGRID is a curated biological database of interactions. BioGRID interactions include protein-protein interactions, genetic interactions, chemical interactions, and post-translational modifications.
3 Data download and cleanup
To download the source data from BioGRID :
- Navigate to the BioGRID database
- Click "downloads" in the left corner, click "Current Release" and choose "BIOGRID-3.5.169" or follow the link here
- Download the following data file: BIOGRID-ORGANISM-3.5.169.tab2.zip (68.09 MB)
- Unzip the file
- Choose the file
BIOGRID-ORGANISM-Homo_sapiens-3.5.169.tab2.txt(122 MB) and place it into the your local working directory which is calleddata.
4 Mapping Entrez IDs to HGNC symbols
BioGrid genes are Entrez IDs. These IDs can be mapped to HGNC symbols.
Preparations: packages, functions, files
Install a few required packages to begin:
readrallows the program to read large datasets.
if (! requireNamespace("readr")) {
install.packages("readr")
}
biomaRtis a Bioconductor package that implements the RESTful API of biomart, the annotation framwork for model organism genomes at the EBI. It is a Bioconductor package, and as such it needs to be loaded via theBiocManager,
if (! requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
if (! requireNamespace("biomaRt", quietly = TRUE)) {
BiocManager::install("biomaRt")
}
igraphis to compute statistics graphs and plots.
if (! requireNamespace("igraph")) {
install.packages("igraph")
}
4.1 Step one: which IDs do we have to map?
# Read the interaction data
tmp <-readr::read_delim(file.path("../data","BIOGRID-ORGANISM-Homo_sapiens-3.5.169.tab2.txt"),
delim = "\t",
skip = 1, col_names = c("BioGRID Interaction ID", "Entrez_ID1", "Entrez_ID2")) #468,058 rows
head(tmp)
# A tibble: 6 x 3
# `BioGRID Interaction ID` Entrez_ID1 Entrez_ID2
# 1 <dbl> <dbl> <dbl>
# 2 103 6416 2318
# 3 117 84665 88
# 4 183 90 2339
# 5 278 2624 5371
# 6 418 6118 6774
# 7 586 375 23163
# The number of unique IDs we have to map:
uENTREZ <- unique(c(tmp$Entrez_ID1, tmp$Entrez_ID2)) #23108 IDs
```
#### 4.2 Step two: mapping via biomaRt
We first map Entrez IDs to HGNC symbols in bulk using biomaRt. And then we can look for the remaining IDs that we could not map via UniProt IDs (from HGNC reference data).
###### 4.2.1 Constructing an ID-mapping tool
```R
# Map ENSP to HGNC symbols: open a "Mart" object ..
myMart <- biomaRt::useMart("ensembl", dataset="hsapiens_gene_ensembl")
tmp <- biomaRt::getBM(filters = "entrezgene",
attributes = c("entrezgene",
"hgnc_symbol"),
values = uENTREZ,
mart = myMart)
head(tmp)
# entrezgene hgnc_symbol
# 1 10036 CHAF1A
# 2 10048 RANBP9
# 3 10059 DNM1L
# 4 10084 PQBP1
# 5 10128 LRPPRC
# 6 10138 YAF2
nrow(tmp) # 17058 HGNC symbols have been retrieved for the 23108 Entrez IDs
There are potential problems with the data that biomart returns
There might be duplicates (more than one value returned).
sum(duplicated(tmp$entrezgene)) #96 duplicates
Or nothing returned for one Entrez ID
sum(! (uENTREZ) %in% tmp$entrezgene) # 6146
To fix the duplicates:
dupEntrez <- tmp$entrezgene[duplicated(tmp$entrezgene)]
tmp[tmp$entrezgene %in% dupEntrez, ]
entrezgene hgnc_symbol
# 321 5888 RAD51
# 322 5888
# 368 6606 SMN2
# 369 6606 SMN1
# 627 25788 FSBP
# 628 25788 RAD54B
# 936 8878 SQSTM1
# 937 8878
# 1566 2222 FDFT1
# 1567 2222
# 1717 5430 POLR2A
# 1718 5430
# 2049 23370 ARHGEF18
# 2050 23370
# 2919 11068 CYB561D2
#Remove the symbols with space
tmp[tmp$hgnc_symbol %in% c(""), ]
tmp <- tmp[ ! (tmp$hgnc_symbol %in% c("")), ]
#Check the UniprotID for the duplicates one
#SMN2 and SMN1 has the same UniprotID Q16637 - so assign arbitarily to either one
tmp <- tmp[ ! (tmp$hgnc_symbol %in% c("SMN2","FSBP", "HLA-DQA2", "LINC02210-CRHR1", "DEFB4B", "DEFB103A", "CCL3L1", "CRYAA2", "IQCJ-SCHIP1", "TBC1D3L", "SERF1B", "DDT", "STAG3L3", "ZNF468", "KIR2DS5", "FNTB", "CHURC1-FNTB", "GTF2H2", "LGALS7B", "PLEKHG7", "FAM187A", "CBWD3", "BOLA2B", "RPP21", "TRIM39-RPP21", "LGALS7", "GUSBP1", "LINC00680","ARL17B", "USP17L2", "GATD3A", "RNA5S3", "PRR4", "GAGE12F", "GAGE12G")), ]
#Check duplicates
any(duplicated(tmp$entrezgene)) #FALSE
```
Define the mapping tool after this cleanup
```R
entrez2sym <- tmp$hgnc_symbol
names(entrez2sym) <- tmp$entrezgene
head(entrez2sym)
# 10036 10048 10059 10084 10128 10138
#"CHAF1A" "RANBP9" "DNM1L" "PQBP1" "LRPPRC" "YAF2"
```
###### 4.2.2 Cleanup and validation of `entrez2sym`
```R
sel <- ! (uENTREZ %in% names(entrez2sym))
> x <- rep(NA, sum( sel))
> names(x) <- uENTREZ[ sel ]
> any(duplicated(c(names(x), names(entrez2sym)))) #FALSE
entrez2sym <- c(entrez2sym, x)
> all(uENTREZ %in% names(entrez2sym)) #TRUE
-check if there are empty strings
sel <- which(entrez2sym == "")
> length(sel) #0
Add out dated symbols
sel <- ( ! (entrez2sym %in% HGNC$sym)) & ( ! (is.na(entrez2sym)))
length( entrez2sym[ sel ] ) #175
length( unique(entrez2sym[ sel ])) #175
unkSym <- data.frame(unk = entrez2sym[ sel ],
new = NA,
stringsAsFactors = FALSE)
View(unkSym)
#add the outdated symbols(which are usually stored in the prev column in HGNC to entrez2sym)
for (i in seq_len(nrow(unkSym))) {
iPrev <- grep(unkSym$unk[i], HGNC$prev)[1]
if (length(iPrev) == 1) {
unkSym$new[i] <- HGNC$sym[iPrev]
}
```
4.3 Final Validation
Final validation for our mapping tool
```R
all(uENTREZ %in% names(entrez2sym)) #TRUE, all the Entrez IDs are mapped
sum(! is.na(entrez2sym)) #16916 symbols are found
sum(! is.na(entrez2sym)) * 100 / length(entrez2sym) #73.20%
#Save map
save(entrez2sym, file = file.path("inst", "extdata", "entrez2sym.RData"))
#Load file from RStudio project
load(file = file.path("inst", "extdata", "entrez2sym.RData"))
```
# 5 Annotating gene sets with STRING Data
We can now annotate the gene sets with BioGrid dta using our mapping tool.
```R
tmp <-readr::read_delim(file.path("../data", "BIOGRID-ORGANISM-Homo_sapiens-3.5.169.tab2.txt"),
delim = "\t",
skip = 1, col_names = c("BioGRID Interaction ID", "Entrez_ID1", "Entrez_ID2", "ID1", "ID2", "sys_name1", "sys_name2", "sym1", "sym2", "alias1", "alias2", "exp_name", "interaction_type", "author", "pubID", "org_ID1", "org_ID2", "interaction_throughput", "quant_score", "mod", "pheno", "qual", "tags", "ext"))
#the number of interaction that is genetic
nrow(tmp[tmp$interaction_type == "genetic",]) #5299
#the number of interaction that is physical
nrow(tmp[tmp$interaction_type == "physical",]) #462759
#Finally we map Entrez IDs to HGNC sysmbols
tmp$Entrez_ID1 <- entrez2sym[tmp$Entrez_ID1]
tmp$Entrez_ID2 <- entrez2sym[tmp$Entrez_ID2]
VALIDATE:
sum(is.na(tmp$Entrez_ID1)) #168627
sum(is.na(tmp$Entrez_ID2)) #169208
# we remove edges in which either one or the other node is NA to
# create our final data:
BioGrid <- tmp[( ! is.na(tmp$Entrez_ID1)) & ( ! is.na(tmp$Entrez_ID2)), ]
BioGridInt <- tmp[( ! is.na(tmp$Entrez_ID1)) & ( ! is.na(tmp$Entrez_ID2)), ]
#DOne
save(BioGridInt, file = file.path(".", "data", "BioGridInt.RData"))
#Network Graph
t <- data.frame(Num_GeneticInteractions = num_genetic, Percentage=round(num_genetic / total * 100, 2), Num_PhysicalInteractions= num_phy,Percentage=round(num_phy / total * 100, 2),stringsAsFactors = FALSE)
head(t)
Num_GeneticInteractions Percentage Num_PhysicalInteractions
1 5299 1.13 462759
Percentage.1
1 98.87
#the distribution
deg <- table(c(BioGridInt$Entrez_ID1, BioGridInt$Entrez_ID2))
hist(deg, breaks=50,
xlim = c(0, 1400),
col = "#3fafb388",
main = "STRING nodes degree distribution",
xlab = "degree (undirected graph)",
ylab = "Counts")
```
6 Annotation of the example gene set
To conclude, we annotate the example gene set, validate the annotation, and store the data.
```R
The specification of the sample set is copy-paste from the
BCB420 resources project.
xSet <- c("AMBRA1", "ATG14", "ATP2A1", "ATP2A2", "ATP2A3", "BECN1", "BECN2", "BIRC6", "BLOC1S1", "BLOC1S2", "BORCS5", "BORCS6", "BORCS7", "BORCS8", "CACNA1A", "CALCOCO2", "CTTN", "DCTN1", "EPG5", "GABARAP", "GABARAPL1", "GABARAPL2", "HDAC6", "HSPB8", "INPP5E", "IRGM", "KXD1", "LAMP1", "LAMP2", "LAMP3", "LAMP5", "MAP1LC3A", "MAP1LC3B", "MAP1LC3C", "MGRN1", "MYO1C", "MYO6", "NAPA", "NSF", "OPTN", "OSBPL1A", "PI4K2A", "PIK3C3", "PLEKHM1", "PSEN1", "RAB20", "RAB21", "RAB29", "RAB34", "RAB39A", "RAB7A", "RAB7B", "RPTOR", "RUBCN", "RUBCNL", "SNAP29", "SNAP47", "SNAPIN", "SPG11", "STX17", "STX6", "SYT7", "TARDBP", "TFEB", "TGM2", "TIFA", "TMEM175", "TOM1", "TPCN1", "TPCN2", "TPPP", "TXNIP", "UVRAG", "VAMP3", "VAMP7", "VAMP8", "VAPA", "VPS11", "VPS16", "VPS18", "VPS33A", "VPS39", "VPS41", "VTI1B", "YKT6")
Example genes are not among the known nodes?
x <- which( ! (xSet %in% c(BioGridInt$Entrez_ID1, BioGridInt$Entrez_ID2)))
cat(sprintf("\t%s\t(%s)\n", HGNC[xSet[x], "sym"], HGNC[xSet[x], "name"]))
ATP2A1 (ATPase sarcoplasmic/endoplasmic reticulum Ca2+ transporting 1)
ATP2A2 (ATPase sarcoplasmic/endoplasmic reticulum Ca2+ transporting 2)
BECN2 (beclin 2)
BIRC6 (baculoviral IAP repeat containing 6)
BLOC1S2 (biogenesis of lysosomal organelles complex 1 subunit 2)
BORCS5 (BLOC-1 related complex subunit 5)
BORCS7 (BLOC-1 related complex subunit 7)
BORCS8 (BLOC-1 related complex subunit 8)
CTTN (cortactin)
EPG5 (ectopic P-granules autophagy protein 5 homolog)
GABARAPL2 (GABA type A receptor associated protein like 2)
INPP5E (inositol polyphosphate-5-phosphatase E)
IRGM (immunity related GTPase M)
LAMP5 (lysosomal associated membrane protein family member 5)
MAP1LC3B (microtubule associated protein 1 light chain 3 beta)
MGRN1 (mahogunin ring finger 1)
OPTN (optineurin)
OSBPL1A (oxysterol binding protein like 1A)
PI4K2A (phosphatidylinositol 4-kinase type 2 alpha)
PIK3C3 (phosphatidylinositol 3-kinase catalytic subunit type 3)
RAB20 (RAB20, member RAS oncogene family)
RAB29 (RAB29, member RAS oncogene family)
RAB34 (RAB34, member RAS oncogene family)
RAB7A (RAB7A, member RAS oncogene family)
RPTOR (regulatory associated protein of MTOR complex 1)
RUBCNL (rubicon like autophagy enhancer)
SNAP29 (synaptosome associated protein 29)
SNAP47 (synaptosome associated protein 47)
SPG11 (SPG11, spatacsin vesicle trafficking associated)
STX17 (syntaxin 17)
STX6 (syntaxin 6)
TFEB (transcription factor EB)
TGM2 (transglutaminase 2)
TOM1 (target of myb1 membrane trafficking protein)
TPCN1 (two pore segment channel 1)
TPCN2 (two pore segment channel 2)
TPPP (tubulin polymerization promoting protein)
VPS11 (VPS11, CORVET/HOPS core subunit)
YKT6 (YKT6 v-SNARE homolog)
select the gene that are in the example set:
sel <- (BioGridInt$Entrez_ID1 %in% xSet) & (BioGridInt$Entrez_ID2 %in% xSet) xSetInt <- BioGridInt[sel, c("Entrez_ID1", "Entrez_ID2")]
Statitics:
nrow(xSetInt) #24
Save the annotated set
writeLines(c("Entrez_ID1\tb", sprintf("%s\t%s", xSetInt$Entrez_ID1, xSetInt$Entrez_ID2)), con = "xSetInt.tsv")
The data set can be read back
myXset <- read.delim(file.path("inst", "extdata", "xSetInt.tsv"), stringsAsFactors = FALSE)
myXset <- read.delim(system.file("extdata", "xSetInt.tsv", package = "BCB420.2019.BioGrid"), stringsAsFactors = FALSE) ```
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