diff --git a/Updated_RaMP_Vignette.Rmd b/Updated_RaMP_Vignette.Rmd index 1e5c487..125ddd2 100644 --- a/Updated_RaMP_Vignette.Rmd +++ b/Updated_RaMP_Vignette.Rmd @@ -26,7 +26,7 @@ editor_options: This vignette will provide basic steps for interacting with [RaMP-DB](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876005/) (Relational database of Metabolomic Pathways). -Details on RaMP-DB installation are also avaialble through GitHub (https://github.com/RAMP-project/RAMP). +Details on RaMP-DB installation are also avaialble through GitHub (https://github.com/ncats/RaMP-DB/). Questions can be asked through the Issues tab or by sending an email to [NCATSRaMP\@nih.gov](mailto:NCATSRaMP@nih.gov). RaMP-DB supports queries and enrichment analyses. @@ -239,7 +239,7 @@ This function takes in a vector of metabolites as an input and returns a vector ```{r} analytes.of.interest <- c("chebi:15422", "hmdb:HMDB0000064", "hmdb:HMDB0000148", "wikidata:Q426660") -new.ontologies <- getOntoFromMeta(analytes = analytes.of.interest, db=rampDB) +new.ontologies <- getOntoFromMeta(mets = analytes.of.interest, db=rampDB) datatable(new.ontologies) ``` @@ -333,11 +333,11 @@ Users can retrieve chemical classes and chemical property information from input ### Retrieve Chemical Classes from Input Metabolites RaMP incorporates Classyfire and lipidMAPS classes. -The function chemicalClassSurvey() function takes as input a vector of metabolites and outputs the classes associated with each metabolite input. +The function getChemClass() function takes as input a vector of metabolites and outputs the classes associated with each metabolite input. ```{r} metabolites.of.interest = c("pubchem:64969", "chebi:16958", "chemspider:20549", "kegg:C05598", "chemspider:388809", "pubchem:53861142", "hmdb:HMDB0001138", "hmdb:HMDB0029412") -chemical.classes <- chemicalClassSurvey(mets = metabolites.of.interest, db=rampDB) +chemical.classes <- getChemClass(mets = metabolites.of.interest, db=rampDB) metabolite.classes <- as.data.frame(chemical.classes$met_classes) datatable(metabolite.classes) @@ -369,7 +369,7 @@ classy_fire_classes <- chemical.enrichment$ClassyFire_class datatable(classy_fire_classes) ``` -*Note*: To explicitly view the results of mapping input IDs to RaMP, users can run the chemicalClassSurvey() function as noted in above in the section "Retrieve Chemical Class from Input Metabolites". +*Note*: To explicitly view the results of mapping input IDs to RaMP, users can run the getChemClass() function as noted in above in the section "Retrieve Chemical Class from Input Metabolites". ## Connect to Different Versions of RaMP Users are able to download previous versions of RaMP, and can input queries in these earlier versions. Some annotations have been added or changed since updated versions have been posted. diff --git a/Updated_RaMP_Vignette.html b/Updated_RaMP_Vignette.html index 9378c87..b1346c8 100644 --- a/Updated_RaMP_Vignette.html +++ b/Updated_RaMP_Vignette.html @@ -11,7 +11,7 @@ - + RaMP-DB 3.0 Vignette @@ -9586,7 +9586,7 @@

RaMP-DB 3.0 Vignette

Jaden Sauer, Ewy Mathé

-

2024-09-16

+

2024-09-26

@@ -9595,7 +9595,7 @@

2024-09-16

Introduction

This vignette will provide basic steps for interacting with RaMP-DB (Relational database of Metabolomic Pathways).

-

Details on RaMP-DB installation are also avaialble through GitHub (https://github.com/RAMP-project/RAMP). Questions can be +

Details on RaMP-DB installation are also avaialble through GitHub (https://github.com/ncats/RaMP-DB/). Questions can be asked through the Issues tab or by sending an email to NCATSRaMP@nih.gov.

RaMP-DB supports queries and enrichment analyses. Supported queries are:

@@ -9630,11 +9630,11 @@

Introduction

listAvailableRaMPDbVersions() 
## [1] "Locally available versions of RaMP SQLite DB, currently on your computer:"
-## [1] "2.6.3" "2.5.4" "2.4.2" "2.4.1" "2.3.1"
+## [1] "2.6.3" "2.6.2" "2.5.4" "2.5.0" "2.3.1"
 ## [1] "Available remote RaMP SQLite DB versions for download:"
 ## [1] "2.5.4" "2.5.0" "2.4.3" "2.4.2" "2.4.0" "2.3.2" "2.3.1"
 ## [1] "The following RaMP Database versions are available for download:"
-## [1] "2.5.0" "2.4.3" "2.4.0" "2.3.2"
+## [1] "2.4.3" "2.4.2" "2.4.0" "2.3.2"
 ## [1] "Use the command db <- RaMP(<new_version_number>) to download the specified version."
# load a local RaMP database or download the latest RaMP database version from the repository.
 # If the version is not specified, the latest local version will be used.
@@ -9652,8 +9652,8 @@ 
Preparing your input for RaMP

   geneprefixes <- getPrefixesFromAnalytes("gene", db=rampDB)
 
   datatable(rbind(metabprefixes, geneprefixes))
-
- +
+

Input External Data Set

@@ -9685,10 +9685,10 @@

Retrieve Analytes From Input Pathway(s)

## [1] "fired!"
 ## [1] "Timing .."
 ##    user  system elapsed 
-##   0.593   0.286   1.802
+## 0.247 0.036 0.381 
datatable(myanalytes)
-
- +
+

To retrieve information from multiple pathways, input a vector of pathway names:

myanalytes <- getAnalyteFromPathway(pathway=c("Wnt Signaling Pathway", 
@@ -9696,7 +9696,7 @@ 
Retrieve Analytes From Input Pathway(s)

 
## [1] "fired!"
 ## [1] "Timing .."
 ##    user  system elapsed 
-##   0.619   0.054   0.680

+##   0.247   0.028   0.277

Retrieve Pathways From Input Analyte(s)

@@ -9709,12 +9709,11 @@

Retrieve Pathways From Input Analyte(s)

genes MDM2 and TP53, and the two metabolites glutamate and creatine.

pathwaydfids <- getPathwayFromAnalyte(c("ensembl:ENSG00000135679", "hmdb:HMDB0000064","hmdb:HMDB0000148", "ensembl:ENSG00000141510"), db=rampDB)
## [1] "Starting getPathwayFromAnalyte()"
-## [1] "Working on ID List..."
 ## [1] "finished getPathwayFromAnalyte()"
 ## [1] "Found 328 associated pathways."
datatable(pathwaydfids)
-
- +
+

Note that each row returns a pathway attributed to one of the input analytes. To retrieve the number of unique pathways returned for all analytes or each analyte, try the following:

@@ -9725,7 +9724,7 @@

Retrieve Pathways From Input Analyte(s)

(paste("Number of Unique Pathways Returned for",x,":", length(unique(pathwaydfids[which(pathwaydfids$commonName==x),]$pathwayId))))})
## [[1]]
-## [1] "Number of Unique Pathways Returned for L-Glutamic acid,Glutamate : 126"
+## [1] "Number of Unique Pathways Returned for L-Glutamate : 126"
 ## 
 ## [[2]]
 ## [1] "Number of Unique Pathways Returned for TP53 : 144"
@@ -9801,8 +9800,8 @@ 
Perform Pathway Enrichment

 datatable(clusters$fishresults %>% mutate_if(is.numeric, ~ round(., 8)),
   rownames = FALSE
 )
-
- +
+

To view clustered pathway results:

pathwayResultsPlot(filtered.fisher.results, text_size = 8, perc_analyte_overlap = 0.2, 
     min_pathway_tocluster = 2, perc_pathway_overlap = 0.2, interactive = FALSE, db=rampDB)
@@ -9834,8 +9833,8 @@

Retrieve Metabolites from Ontologies

## [1] "Found 1580 metabolites associated with the input ontology terms." ## [1] "Finished getting metabolies from ontology terms." 
datatable(head(new.metabolites, n=10))
-
- +
+

Retrieve Ontologies from Input Metabolites

@@ -9845,10 +9844,10 @@

Retrieve Ontologies from Input Metabolites

from the user’s defined metabolites.

analytes.of.interest <- c("chebi:15422", "hmdb:HMDB0000064",
         "hmdb:HMDB0000148", "wikidata:Q426660")
-new.ontologies <- getOntoFromMeta(analytes = analytes.of.interest, db=rampDB)
+new.ontologies <- getOntoFromMeta(mets = analytes.of.interest, db=rampDB)
 datatable(new.ontologies)
-
- +
+
@@ -9884,8 +9883,8 @@

Retrieve Analytes Involved in the Same Reaction

## [1] "There are no ChEBI metabolite IDs in the input. Skipping metabolite to protein query step." 
#just show HMDB analyte associations
 datatable(new.transcripts)
-
- +
+

Reaction visualizations

@@ -9916,11 +9915,10 @@

Plot Reaction Classes

## The input list has 10 chebi IDs.
## The input list has 6 uniprot IDs.
## [1] "Passed the getReactionClassStats"
-## [1] "humanProtein TRUE"
 ## [1] "Completed reaction class query..."
plotReactionClasses(reaction.classes)
-
- +
+

Plot Gene-Metabolite Network

@@ -9929,8 +9927,8 @@

Plot Gene-Metabolite Network

function uses the dataframe created by rampFastCata() as an input. These plots are completely interactive.

plotCataNetwork(new.transcripts)
-
- +
+

Plot Analyte Overlap

@@ -9948,8 +9946,8 @@

Plot Analyte Overlap

## Finished getReactionsForAnalytes()
# just show the reactions with at least one metabolite and one protein in commmon.
 datatable(subset(reactionsLists$metProteinCommonReactions))
-
- +
+

Three reaction lists are returned, metabolites-to-reactions, proteins-to-reactions, and reactions that have at least one metaboite and one protein from the input analyte list.

@@ -9957,8 +9955,8 @@

Plot Analyte Overlap

plotAnalyteOverlapPerRxnLevel() will generate an interactive upset plot of overlapping input compounds at reaction class level 1.

plotAnalyteOverlapPerRxnLevel(reactionsLists)
-
- +
+
@@ -9970,10 +9968,10 @@

Chemical Descriptors

Retrieve Chemical Classes from Input Metabolites

RaMP incorporates Classyfire and lipidMAPS classes. The function -chemicalClassSurvey() function takes as input a vector of metabolites -and outputs the classes associated with each metabolite input.

+getChemClass() function takes as input a vector of metabolites and +outputs the classes associated with each metabolite input.

metabolites.of.interest = c("pubchem:64969", "chebi:16958", "chemspider:20549", "kegg:C05598", "chemspider:388809", "pubchem:53861142", "hmdb:HMDB0001138", "hmdb:HMDB0029412")
-chemical.classes <- chemicalClassSurvey(mets = metabolites.of.interest, db=rampDB)
+chemical.classes <- getChemClass(mets = metabolites.of.interest, db=rampDB)
## [1] "Starting Chemical Class Survey"
 ## [1] "...finished metabolite list query..."
 ## [1] "...finished DB population query..."
@@ -9982,8 +9980,8 @@ 
Retrieve Chemical Classes from Input Metabolites

 ## [1] "Finished Chemical Class Survey"
metabolite.classes <- as.data.frame(chemical.classes$met_classes)
 datatable(metabolite.classes)
-
- +
+

Retrieve Chemical Property Information from Input Metabolites

@@ -9997,8 +9995,8 @@

Retrieve Chemical Property Information from Input Metabolites

## Finished Chemical Property Query
chemical.data <- chemical.properties$chem_props
 datatable(chemical.data)
-
- +
+

Perform Chemical Enrichment

@@ -10029,11 +10027,11 @@

Perform Chemical Enrichment

# To retrieve results for the ClassyFire Class:
 classy_fire_classes <- chemical.enrichment$ClassyFire_class
 datatable(classy_fire_classes)
-
- +
+

Note: To explicitly view the results of mapping input IDs to -RaMP, users can run the chemicalClassSurvey() function as noted in above -in the section “Retrieve Chemical Class from Input Metabolites”.

+RaMP, users can run the getChemClass() function as noted in above in the +section “Retrieve Chemical Class from Input Metabolites”.

@@ -10051,13 +10049,13 @@

Connect to Different Versions of RaMP

Current.Ramp <- getAnalyteFromPathway(db = Current.db, pathway = c('Pentose Phosphate Pathway')) datatable(Current.Ramp)
sessionInfo()
-
## R version 4.3.2 (2023-10-31)
-## Platform: x86_64-apple-darwin20 (64-bit)
-## Running under: macOS Sonoma 14.6.1
+
## R version 4.4.1 (2024-06-14)
+## Platform: aarch64-apple-darwin20
+## Running under: macOS Sonoma 14.0
 ## 
 ## Matrix products: default
-## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
-## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
+## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
+## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
 ## 
 ## locale:
 ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
@@ -10074,25 +10072,25 @@ 
Connect to Different Versions of RaMP

 ## loaded via a namespace (and not attached):
 ##  [1] gtable_0.3.5         xfun_0.47            bslib_0.8.0         
 ##  [4] ggplot2_3.5.1        visNetwork_2.1.2     htmlwidgets_1.6.4   
-##  [7] lattice_0.21-9       vctrs_0.6.5          tools_4.3.2         
+##  [7] lattice_0.22-6       vctrs_0.6.5          tools_4.4.1         
 ## [10] crosstalk_1.2.1      generics_0.1.3       curl_5.2.2          
 ## [13] Polychrome_1.5.1     tibble_3.2.1         fansi_1.0.6         
 ## [16] RSQLite_2.3.7        highr_0.11           blob_1.2.4          
 ## [19] janeaustenr_1.0.0    pkgconfig_2.0.3      tokenizers_0.3.0    
-## [22] Matrix_1.6-4         data.table_1.16.0    dbplyr_2.4.0        
-## [25] scatterplot3d_0.3-44 lifecycle_1.0.4      compiler_4.3.2      
+## [22] Matrix_1.7-0         data.table_1.16.0    dbplyr_2.5.0        
+## [25] scatterplot3d_0.3-44 lifecycle_1.0.4      compiler_4.4.1      
 ## [28] farver_2.1.2         munsell_0.5.1        htmltools_0.5.8.1   
 ## [31] SnowballC_0.7.1      sass_0.4.9           yaml_2.3.10         
 ## [34] lazyeval_0.2.2       tidytext_0.4.2       plotly_4.10.4       
 ## [37] pillar_1.9.0         jquerylib_0.1.4      tidyr_1.3.1         
 ## [40] upsetjs_1.11.1       cachem_1.1.0         tidyselect_1.2.1    
 ## [43] digest_0.6.37        stringi_1.8.4        purrr_1.0.2         
-## [46] labeling_0.4.3       fastmap_1.2.0        grid_4.3.2          
+## [46] labeling_0.4.3       fastmap_1.2.0        grid_4.4.1          
 ## [49] colorspace_2.1-1     cli_3.6.3            utf8_1.2.4          
-## [52] withr_3.0.1          filelock_1.0.2       scales_1.3.0        
+## [52] withr_3.0.1          filelock_1.0.3       scales_1.3.0        
 ## [55] bit64_4.0.5          rmarkdown_2.28       httr_1.4.7          
 ## [58] bit_4.0.5            memoise_2.0.1        evaluate_0.24.0     
-## [61] knitr_1.48           viridisLite_0.4.2    BiocFileCache_2.10.1
+## [61] knitr_1.48           viridisLite_0.4.2    BiocFileCache_2.12.0
 ## [64] rlang_1.1.4          Rcpp_1.0.13          glue_1.7.0          
 ## [67] DBI_1.2.3            rstudioapi_0.16.0    jsonlite_1.8.8      
 ## [70] R6_2.5.1