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Updating script for reactome analysis #8

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548fd44
Updating script for reactome analysis
DeniseSl22 Apr 11, 2024
f226b32
Updating WP usecase
DeniseSl22 Sep 19, 2024
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70 changes: 60 additions & 10 deletions HGNC_pathways/secondaryHgncSymbolsInDatabases.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@ secondaryHGNCSymbols <- setdiff(HGNCMappings$secondarySymbol, HGNCMappings$prima
## 2. Data Download
We download the data for each database, based on their API, RDF, or other data download option (depending on availability).

### 1. KEGG
### 2.1. KEGG
The information in KEGG was obtained using an API call.
```{r, include=FALSE}
##Install and load required packages:
Expand All @@ -62,8 +62,7 @@ library(httr)
library(jsonlite)
library(data.table)

## pathway map IDs listed under "1.3 Lipid metabolism"
#lipid_kegg_maps <- c("00061", "00062", "00071", "00073", "00100", "00120", "00121", "00140", "00561", "00564", "00565", "00600", "00590", "00591", "00592", "01040")
## Get all pathway map IDs first:
kegg_maps_APIcall <- "https://rest.kegg.jp/list/pathway/hsa"
res_maps = GET(kegg_maps_APIcall)
Kegg_data_maps <- rawToChar(res_maps$content)
Expand All @@ -74,6 +73,7 @@ if(length(res_maps$content)>1){
Kegg_maps_total <- rbind(Kegg_maps_total,df_maps)
}

##Convert KEGG maps results to new API call to retrieve all genes within each map:
kegg_maps <- Kegg_maps_total$V1
##Add URL for query item:
kegg_genes_APIcall <- paste("https://rest.kegg.jp/link/hsa/", kegg_maps, sep="")
Expand All @@ -100,7 +100,7 @@ rm(list=setdiff(ls(), retain))
```
Unfortunately, the KEGG database does not allow to retrieve the labels within the pathway images, so we cannot use it further in our comparison.

### 2. Reactome
### 2.2. Reactome
For the Reactome content, we download the Physical Entity (PE) Identifier mapping files. Since the HGNC symbols are not available as a direct mapping, we retrieve all mappings from UniProt, Ensembl, and NCBI, to integrate their content on HGNC symbols.
```{r, include=FALSE}
##Only download the data, if not available already locally.
Expand Down Expand Up @@ -150,13 +150,25 @@ retain <- append(retain, "mergedReactome")
rm(list=setdiff(ls(), retain))
```

Compare the Reactome data against primary symbols first, then against secondary symbols, last count how many were not matching at all.
Compare the Reactome data against primary symbols first, then against secondary symbols, last count how many were not matching at all.
```{r}

##Check overlap between prim and sec:
uniqueSec <- setdiff(secondaryHGNCSymbols, primaryHGNCSymbols)

##Convert reactome data to vector format:
mergedReactomeVector <- mergedReactome[['HGNC']]
##Check primary symbols:
primaryMatchesReactome <- intersect(primaryHGNCSymbols, mergedReactomeVector)
##Amount:
amountPrimaryMatchesReactome <- length(primaryMatchesReactome)
##Check secondary symbols:
secondaryMatchesReactome <- intersect(uniqueSec, mergedReactomeVector)
##Amount:
amountSecondaryMatchesReactome <- length(secondaryMatchesReactome)
```


### 3. WikiPathways
### 2.3. WikiPathways
The WikiPathways data was queried in the RDF format.
```{r, include=FALSE}
##WikiPathways (original pathways)
Expand Down Expand Up @@ -230,11 +242,51 @@ showresultsWikiPathways <- resultsWikiPathways$results
remove(queryWikiPathways, resultsWikiPathways)
```

Compare the WikiPathways data against primary symbols first, then against secondary symbols, last count how many were not matching at all.
```{r}
##Check overlap between prim and sec:
uniqueSec <- setdiff(secondaryHGNCSymbols, primaryHGNCSymbols)

##Convert reactome data to vector format:
mergedWikiPathwaysVector <- showresultsWikiPathways[['HGNC']]
##Check primary symbols:
primaryMatchesWikiPathways <- intersect(primaryHGNCSymbols, mergedWikiPathwaysVector)
##Amount:
amountPrimaryMatchesWikiPathways <- length(primaryMatchesWikiPathways)
##Check secondary symbols:
secondaryMatchesWikiPathways <- intersect(uniqueSec, mergedWikiPathwaysVector)
##Amount:
amountSecondaryMatchesWikiPathways <- length(secondaryMatchesWikiPathways)
```



### 2.4. BiGG data Models
```{r, include=FALSE}
##Install and load required packages:
api_packages <- c("httr", "jsonlite", "data.table")
for (i in 1:length(api_packages)) {
if(!api_packages[i] %in% installed.packages()){install.packages(api_packages[i])}
}
#install.packages(c("httr", "jsonlite", "data.table"))
library(httr)
library(jsonlite)
library(data.table)

biggModelsAPIcall <- 'http://bigg.ucsd.edu/api/v2/models'
biggModels_maps = GET(biggModelsAPIcall)
biggModels_data_maps <- rawToChar(biggModels_maps$content)
biggModels_maps_total = data.frame()
if(length(biggModels_maps$content)>1){
cleaned_biggModels_data_maps <- fread(text = biggModels_data_maps, header=FALSE, fill = TRUE, sep = '\t')
df_maps <- data.frame(cleaned_biggModels_data_maps)
biggModels_maps_total <- rbind(biggModels_maps_total,df_maps)
}

```

### 5. Gene Ontology

### 2.5. Gene Ontology
From the Quick-GO website (https://www.ebi.ac.uk/QuickGO/), we downloaded data based on the following criteria: --> Use API instead!
I. Taxon: 9606 (Homo sapiens, human).

Expand All @@ -253,14 +305,12 @@ work_DIR <- getwd()

## 3. Data Comparison
We compare the data for each database to the secondary HGNC symbols.

```{r}
plot(cars)
```

## 4. Data Visualization
We visualize the data for all databases combined.

```{r}
plot(cars)
```
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