| Type: | Package |
| Title: | Epilepsy Ontologies' Similarities |
| Version: | 1.1 |
| Author: | Bernd Mueller |
| Maintainer: | Bernd Mueller <bernd.mueller@zbmed.de> |
| Description: | Analysis and visualization of similarities between epilepsy ontologies based on text mining results by comparing ranked lists of co-occurring drug terms in the BioASQ corpus. The ranked result lists of neurological drug terms co-occurring with terms from the epilepsy ontologies EpSO, ESSO, EPILONT, EPISEM and FENICS undergo further analysis. The source data to create the ranked lists of drug names is produced using the text mining workflows described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558>, Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>, Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) <doi:10.1007/978-3-030-43887-6_52>, and Mueller, Bernd et al. (2022) <doi:10.1186/s13326-021-00258-w>. |
| Depends: | R (≥ 3.6.0) |
| License: | LGPL (≥ 3) |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.1 |
| URL: | https://github.com/bernd-mueller/epos |
| BugReports: | https://github.com/bernd-mueller/epos/issues |
| Imports: | hash, ggplot2, testthat, gridExtra, TopKLists, stringr, xtable, mongolite, stats, VennDiagram, cowplot |
| Suggests: | knitr, rmarkdown |
| NeedsCompilation: | no |
| Packaged: | 2024-03-15 09:42:31 UTC; belun |
| Repository: | CRAN |
| Date/Publication: | 2024-03-15 10:10:02 UTC |
Calculate the cosine similarity metric for two lists a and b
Description
Calculate the cosine similarity metric for two lists a and b
Usage
calcCosine(a, b)
Arguments
a |
list with elements that should be of same type as in list b |
b |
list with elements |
Value
co list with length of set b containing the cosine similarity coefficient at each position
Examples
calcCosine(c(1,2), c(2,3))
Calculate dsea scores of one list in comparison to reference list
Description
Calculate dsea scores of one list in comparison to reference list
Usage
calcDSEA(alist, N)
Arguments
alist |
list of drug names to be used for calculating dsea |
N |
numeric value with maximum length of lists for dsea calculation |
Value
list with dsea scores
Examples
calcDSEA(c("Valproic acid", "Lamotrigine", "Ketamin"), 3)
Calculate the dice similarity metric for two lists a and b
Description
Calculate the dice similarity metric for two lists a and b
Usage
calcDice(a, b)
Arguments
a |
list with elements that should be of same type as in list b |
b |
list with elements |
Value
di list with length of set b containing the dice similarity coefficient at each list element
Examples
calcDice(c(1,2), c(2,3))
Calculate enrichment of one list in comparison to reference list
Description
Calculate enrichment of one list in comparison to reference list
Usage
calcEnrichment(alist)
Arguments
alist |
the list to compare |
Value
list with calculated enrichment used for plotting
Examples
a <- calcEnrichment(c("Clobazam","Oxcarbazepine"))
Calculate the jaccard coefficient for two lists a and b
Description
Calculate the jaccard coefficient for two lists a and b
Usage
calcJaccard(a, b)
Arguments
a |
list with elements that should be of same type as in list b |
b |
list with elements |
Value
ja list with length of set b containing the jaccard similarity coefficient for each list element
Examples
calcJaccard(c(1,2), c(2,3))
Calculate cosine similarity metric
Description
Calculate cosine similarity metric
Usage
cosine(ainterb, lengtha, lengthb)
Arguments
ainterb |
integer value with number of intersecting elements between set a and b |
lengtha |
integer value with the number of items in set a |
lengthb |
integer value with the number of items in set b |
Value
cosine double vlaue with the cosine similarity coefficient
Examples
cosine(1,3,4)
Main function to call everything and produce the results
Description
Main function to call everything and produce the results
Usage
createBaseTable(coocepso, coocesso, coocepi, coocepisem, coocfenics)
Arguments
coocepso |
list of drug names sorted by frequency co-occuring with EpSO |
coocesso |
list of drug names sorted by frequency co-occuring with ESSO |
coocepi |
list of drug names sorted by frequency co-occuring with EPILONT |
coocepisem |
list of drug names sorted by frequency co-occuring with EPISEM |
coocfenics |
list of drug names sorted by frequency co-occuring with FENICS |
Value
result table containin the aggregated list of drug terms and their associations
Examples
utils::data(rawDrugNamesCoOcEpSO, package="epos")
utils::data(rawDrugNamesCoOcESSO, package="epos")
utils::data(rawDrugNamesCoOcEPILONT, package="epos")
utils::data(rawDrugNamesCoOcEPISEM, package="epos")
utils::data(rawDrugNamesCoOcFENICS, package="epos")
createBaseTable(coocepso = rawDrugNamesCoOcEpSO[1:150],
coocesso=rawDrugNamesCoOcESSO[1:150],
coocepi=rawDrugNamesCoOcEPILONT[1:150],
coocepisem=rawDrugNamesCoOcEPISEM[1:150],
coocfenics=rawDrugNamesCoOcFENICS[1:150])
Creates a vector with an X at each position where a drug from the druglist matches the ATC class list slatc
Description
Creates a vector with an X at each position where a drug from the druglist matches the ATC class list slatc
Usage
createDashVectorForATC(druglist, atchashda, atchashsec, slatc)
Arguments
druglist |
list of drug names |
atchashda |
hash retrieved from readAtcMapIntoHashMapDrugNamesAtcCodes |
atchashsec |
hash retrieved from readSecondLevelATC |
slatc |
list of ATC classes |
Value
list with crosses if the drug in druglist matches at the position of the ATC class in slatc
Examples
## Not run:
createDashVectorForATC(druglist, atchashda, atchashsec, slatc)
## End(Not run)
Creates the plot for all jaccard coefficients amongst the three epilepsy ontologies
Description
Creates the plot for all jaccard coefficients amongst the three epilepsy ontologies
Usage
createJaccardPlotDBMeSH(jmeshepso, jmeshesso, jmeshepi)
Arguments
jmeshepso |
list containing jaccard coefficients between mesh and epso for increasing k |
jmeshesso |
list containing jaccard coefficients between mesh and esso for increasing k |
jmeshepi |
list containing jaccard coefficients between mesh and epi for increasing k |
Value
jaccardepilepsyplot the ggplot object
Examples
## Not run:
jaccardepilepsyplot <- createJaccardPlotAll(jaccardepso, jaccardesso)
## End(Not run)
Creates the plot for all jaccard coefficients amongst the three epilepsy ontologies
Description
Creates the plot for all jaccard coefficients amongst the three epilepsy ontologies
Usage
createJaccardPlotMeSHFive(
jmeshepso,
jmeshesso,
jmeshepi,
jmeshepilepsyand,
jmeshepilepsyor
)
Arguments
jmeshepso |
list of jaccard coefficients between mesh and epso for increasing k |
jmeshesso |
list of jaccard coefficients between mesh and esso for increasing k |
jmeshepi |
list of jaccard coefficients between mesh and epi for increasing k |
jmeshepilepsyand |
list of jaccard coefficients between mesh and the intersection of epso, esso, and epi for increasing k |
jmeshepilepsyor |
list of jaccard coefficients between mesh and the union of epso, esso, and epi for increasing k |
Value
jaccardepilepsyplot the ggplot object
Examples
## Not run:
jaccardepilepsyplot <- createJaccardPlotAll(jaccardepso, jaccardesso)
## End(Not run)
Create the final resulting data frame
Description
Create the final resulting data frame
Usage
createNeuroTable(atchashda, atchashsec, dneuromaxk)
Arguments
atchashda |
hashmap retrieved from readAtcMapIntoHashMapDrugNamesAtcCodes |
atchashsec |
hashmap retrieved from readSecondLevelATC |
dneuromaxk |
data frame containing columns for each intersection, ATC class, and reference list |
Value
data frame containing drug names with additional columns listing association to ATC classes
Examples
utils::data(rawDrugNamesCoOcEpSO, package="epos")
utils::data(rawDrugNamesCoOcESSO, package="epos")
utils::data(rawDrugNamesCoOcEPILONT, package="epos")
utils::data(rawDrugNamesCoOcEPISEM, package="epos")
utils::data(rawDrugNamesCoOcFENICS, package="epos")
atchashda <-
readAtcMapIntoHashMapDrugNamesAtcCodes(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
atchashaa <-
readAtcMapIntoHashMapAtcCodesAtcNames(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
atchashsec <-
readSecondLevelATC(
system.file("extdata", "atc-secondlevel.map", package = "epos"), "\t")
epso <- rawDrugNamesCoOcEpSO
neuroepso <- filterNeuroDrugs(epso, atchashda)
esso <- rawDrugNamesCoOcESSO
neuroesso <- filterNeuroDrugs(esso, atchashda)
epi <- rawDrugNamesCoOcEPILONT
neuroepi <- filterNeuroDrugs(epi, atchashda)
episem <- rawDrugNamesCoOcEPISEM
neuroepisem <- filterNeuroDrugs(episem, atchashda)
fenics <- rawDrugNamesCoOcFENICS
neurofenics <- filterNeuroDrugs(fenics, atchashda)
mx <- max(
c(length(neuroepso), length(neuroesso), length(neuroepi),
length(neuroepisem), length(neurofenics)))
dneuro <-
data.frame(EpSO = c(neuroepso, rep(1, (mx-length(neuroepso)))),
ESSO = c(neuroesso, rep(1, (mx-length(neuroesso)))),
EPILONT = c(neuroepi, rep(1, (mx-length(neuroepi)))),
EPISEM = c(neuroepisem, rep(1, (mx-length(neuroepisem)))),
FENICS = c(neurofenics, rep(1, (mx-length(neurofenics)))))
dneuromaxk <- TopKLists::calculate.maxK(dneuro, L=5, d=5, v=10)
neurotable <- createNeuroTable(atchashda, atchashsec, dneuromaxk)
Creates the plot for all jaccard coefficients amongst the three epilepsy ontologies
Description
Creates the plot for all jaccard coefficients amongst the three epilepsy ontologies
Usage
createTanimotoBaseline(neuroepso, neuroesso, neuroepi, dneuromaxk)
Arguments
neuroepso |
list of neuro drug names co-occurring with epso |
neuroesso |
list of neuro drug names co-occurring with esso |
neuroepi |
list of neuro drug names co-occurring with epi |
dneuromaxk |
object returned from TopKLists::calculate.maxk |
Value
jaccardepilepsyplot the ggplot object
Examples
utils::data(rawDrugNamesCoOcEpSO, package="epos")
utils::data(rawDrugNamesCoOcESSO, package="epos")
utils::data(rawDrugNamesCoOcEPILONT, package="epos")
utils::data(rawDrugNamesCoOcEPISEM, package="epos")
atchashda <-
readAtcMapIntoHashMapDrugNamesAtcCodes(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
atchashaa <-
readAtcMapIntoHashMapAtcCodesAtcNames(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
atchashsec <-
readSecondLevelATC(
system.file("extdata", "atc-secondlevel.map", package = "epos"), "\t")
tepso <- rawDrugNamesCoOcEpSO
tesso <- rawDrugNamesCoOcESSO
tepi <- rawDrugNamesCoOcEPILONT
tepisem <- rawDrugNamesCoOcEPISEM
tfenics <- rawDrugNamesCoOcFENICS
neuroepso <- filterNeuroDrugs(tepso, atchashda)
neuroesso <- filterNeuroDrugs(tesso, atchashda)
neuroepi <- filterNeuroDrugs(tepi, atchashda)
neuroepisem <- filterNeuroDrugs(tepisem, atchashda)
neurofenics <- filterNeuroDrugs(tfenics, atchashda)
dneuro <-
data.frame(EpSO = neuroepso[1:210],
ESSO = neuroesso[1:210],
EPILONT = neuroepi[1:210],
EPISEM = neuroepisem[1:210],
FENICS = neurofenics[1:210])
dneuromaxk <- TopKLists::calculate.maxK(dneuro, 5, 5, 5)
tanimotobaseline <- createTanimotoBaseline(neuroepso, neuroesso, neuroepi, dneuromaxk)
Calculate dice similarity metric
Description
Calculate dice similarity metric
Usage
dice(ainterb, lengtha, lengthb)
Arguments
ainterb |
integer value with number of intersecting elements between set a and b |
lengtha |
integer value with the number of items in set a |
lengthb |
integer value with the number of items in set b |
Value
dice double vlaue with the dice similarity coefficient
Examples
dice(1, 3, 4)
Does the full plot on one page
Description
Does the full plot on one page
Usage
doFullPlot(
cosinemeshplot,
cosinedrugbankplot,
cosineepilepsyplot,
dicemeshplot,
dicedrugbankplot,
diceepilepsyplot,
jaccardmeshplot,
jaccarddrugbankplot,
jaccardepilepsyplot
)
Arguments
cosinemeshplot |
plot with cosine coefficients against MeSH |
cosinedrugbankplot |
plot with cosine coefficients against DrugBank |
cosineepilepsyplot |
plot with cosine coefficients of Epilepsy Ontologies |
dicemeshplot |
plot with dice coefficients against MeSH |
dicedrugbankplot |
plot with dice coefficients against DrugBank |
diceepilepsyplot |
plot with dice coefficients of Epilepsy Ontologies |
jaccardmeshplot |
plot with jaccard coefficients against MeSH |
jaccarddrugbankplot |
plot with jaccard coefficients against DrugBank |
jaccardepilepsyplot |
plot with jaccard coefficients of Epilepsy Ontologies |
Value
full
Examples
## Not run:
full <- doFullPlot (cosinemeshplot,
cosinedrugbankplot,
cosineepilepsyplot,
dicemeshplot,
dicedrugbankplot,
diceepilepsyplot,
jaccardmeshplot,
jaccarddrugbankplot,
jaccardepilepsyplot)
## End(Not run)
Create quad Venn Diagramm for overlapping concepts between EpSO, ESSO, EPILONT and EPISEM
Description
Create quad Venn Diagramm for overlapping concepts between EpSO, ESSO, EPILONT and EPISEM
Usage
drawVenn4()
Value
plot object
Examples
## Not run:
ggplot2::ggsave("venn4.png", plot = drawVenn4(), width=240, height=160,
units = "mm", dpi = 300)
## End(Not run)
Create quintuple Venn Diagramm for shared documents with co-occurrences of drug names between EpSO, ESSO, EPILONT and EPISEM
Description
Create quintuple Venn Diagramm for shared documents with co-occurrences of drug names between EpSO, ESSO, EPILONT and EPISEM
Usage
drawVenn4Doc()
Value
plot object
Examples
## Not run:
ggplot2::ggsave("venn4doc.png", plot = drawVenn4Doc(), width=240, height=160,
units = "mm", dpi = 300)
## End(Not run)
Create quad Venn Diagramm for shared documents with co-occurrences of drug names between EpSO, ESSO, EPILONT and EPISEM
Description
Create quad Venn Diagramm for shared documents with co-occurrences of drug names between EpSO, ESSO, EPILONT and EPISEM
Usage
drawVenn4DrugDoc()
Value
plot object
Examples
## Not run:
ggplot2::ggsave("venn4drugdoc.png", plot = drawVenn4DrugDoc(), width=240,
height=160, units = "mm", dpi = 300)
## End(Not run)
Create quad Venn Diagramm for shared synonyms between EpSO, ESSO, EPILONT and EPISEM
Description
Create quad Venn Diagramm for shared synonyms between EpSO, ESSO, EPILONT and EPISEM
Usage
drawVenn4Syn()
Value
plot object
Examples
## Not run:
ggplot2::ggsave("venn4syn.png", plot = drawVenn4Syn(), width=240,
height=160, units = "mm", dpi = 300)
## End(Not run)
Create quintuple Venn Diagramm for overlapping concepts between EpSO, ESSO, EPILONT, EPISEM and FENICS
Description
Create quintuple Venn Diagramm for overlapping concepts between EpSO, ESSO, EPILONT, EPISEM and FENICS
Usage
drawVenn5()
Value
plot object
Examples
## Not run:
ggplot2::ggsave("venn5.png", plot = drawVenn5(), width=240, height=160,
units = "mm", dpi = 300)
## End(Not run)
Create quintuple Venn Diagramm for shared documents between EpSO, ESSO, EPILONT, EPISEM and FENICS
Description
Create quintuple Venn Diagramm for shared documents between EpSO, ESSO, EPILONT, EPISEM and FENICS
Usage
drawVenn5Doc()
Value
plot object
Examples
## Not run:
ggplot2::ggsave("venn5doc.png", plot = drawVenn5Doc(), width=240, height=160,
units = "mm", dpi = 300)
## End(Not run)
Create quintuple Venn Diagramm for shared documents with co-occurrences of drug names between EpSO, ESSO, EPILONT, EPISEM and FENICS
Description
Create quintuple Venn Diagramm for shared documents with co-occurrences of drug names between EpSO, ESSO, EPILONT, EPISEM and FENICS
Usage
drawVenn5DrugDoc()
Value
plot object
Examples
## Not run:
ggplot2::ggsave("venn5drugdoc.png", plot = drawVenn5DrugDoc(), width=240,
height=160, units = "mm", dpi = 300)
## End(Not run)
Create quintuple Venn Diagramm for shared synonyms between EpSO, ESSO, EPILONT, EPISEM and FENICS
Description
Create quintuple Venn Diagramm for shared synonyms between EpSO, ESSO, EPILONT, EPISEM and FENICS
Usage
drawVenn5Syn()
Value
plot object
Examples
## Not run:
ggplot2::ggsave("venn5syn.png", plot = drawVenn5Syn(), width=240,
height=160, units = "mm", dpi = 300)
## End(Not run)
Create plot_grid from multiple plots
Description
Create plot_grid from multiple plots
Usage
drawVennGrid()
Value
plot object
Examples
## Not run:
cowplot::plot_grid(drawVenn4 (), drawVenn4Syn(), drawVenn5Doc (),
drawVenn5DrugDoc ())
ggplot2::ggsave("vennAB.png", plot = cowplot::plot_grid(drawVenn4 (),
drawVenn4Syn(), labels = c('A', 'B'), ncol = 1), width=240, height=320,
units = "mm", dpi = 300)
ggplot2::ggsave("vennAB.png", plot = cowplot::plot_grid(drawVenn4 (),
drawVenn4Syn(), labels = c('Concepts:', 'Synonyms:'), ncol = 1), width=240,
height=320, units = "mm", dpi = 300)
ggplot2::ggsave("vennCD.png", plot = cowplot::plot_grid(drawVenn5Doc (),
drawVenn5DrugDoc(), labels = c('Documents with B-Terms:',
'Documents with B- and C-Terms:'), ncol = 1), width=240, height=320,
units = "mm", dpi = 300)
ggplot2::ggsave("vennCD.png", plot = cowplot::plot_grid(drawVenn5Doc (),
drawVenn5DrugDoc(), labels = c('Documents with B-Terms:',
'Documents with B- and C-Terms:'), ncol = 1), width=240, height=320, units = "mm",
dpi = 300)
ggplot2::ggsave("vennCD.png", plot = cowplot::plot_grid(drawVenn4Doc (),
drawVenn4DrugDoc(), labels = c('Documents with B-Terms:',
'Documents with B- and C-Terms:'), ncol = 1), width=240, height=320,
units = "mm", dpi = 300)
ggplot2::ggsave("vennCD.png", plot = cowplot::plot_grid(drawVenn4Doc (),
drawVenn4DrugDoc(), labels = c('Documents\nwith B-Terms: ',
'Documents\nwith B- and C-Terms:'), ncol = 1), width=240, height=320,
units = "mm", dpi = 300)
ggplot2::ggsave("vennAB.png", plot = cowplot::plot_grid(drawVenn4 (),
drawVenn4Syn(), labels = c('i) Concepts:', 'ii) Synonyms:'), ncol = 1),
width=240, height=320, units = "mm", dpi = 300)
ggplot2::ggsave("vennCD.png", plot = cowplot::plot_grid(NULL,
drawVenn4Doc (), drawVenn4DrugDoc(),
labels = c('iii) Documents with B-Terms:',
'iv) Documents with B- and C-Terms:'), ncol = 1,
label_x = c(-0.105, -0.14), label_fontfamily = "Arial Nova Light",
label_fontface = "bold"), width=240, height=320, units = "mm", dpi = 300)
## End(Not run)
Filter a given list of drug names for having an ATC code, if not they are dropped
Description
Filter a given list of drug names for having an ATC code, if not they are dropped
Usage
filterApprovedDrugs(druglist, atchashda)
Arguments
druglist |
a list of drug names |
atchashda |
a hash containing the drug names as keys |
Value
approveddrugs a hash filtered for having an ATC code
Examples
utils::data(rawDrugNamesCoOcEpSO, package="epos")
atchashda <-
readAtcMapIntoHashMapDrugNamesAtcCodes(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
tepso <- genDictListFromRawFreq(rawDrugNamesCoOcEpSO)
filterApprovedDrugs(tepso, atchashda)
Filter a given list of drug names for having an ATC code starting with N indicating to be a drug for the Nervous System
Description
Filter a given list of drug names for having an ATC code starting with N indicating to be a drug for the Nervous System
Usage
filterNeuroDrugs(druglist, atchashda)
Arguments
druglist |
a list of drug names |
atchashda |
a hash containing the drug names as keys |
Value
neurodrugs a hash filtered for having an ATC code starting with N
Examples
utils::data(rawDrugNamesCoOcEpSO, package="epos")
atchashda <-
readAtcMapIntoHashMapDrugNamesAtcCodes(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
tepso <- genDictListFromRawFreq(rawDrugNamesCoOcEpSO)
nepso <- filterNeuroDrugs(tepso, atchashda)
Clears object that was loaded from harddrive into a list of terms sorted by frequency
Description
Clears object that was loaded from harddrive into a list of terms sorted by frequency
Clears object that was loaded from harddrive into a list of terms sorted by frequency
Usage
genDictListFromRawFreq(topfreqdictraw)
genDictListFromRawFreq(topfreqdictraw)
Arguments
topfreqdictraw |
list with terms from a dictionary sorted by frequency |
Value
a sorted list of terms
a sorted list of terms
Examples
## Not run:
genDictListFromRawFreq(epi)
## End(Not run)
utils::data(rawDrugNamesCoOcEpSO, package="epos")
genDictListFromRawFreq(rawDrugNamesCoOcEpSO)
Retrieve the list of drugs from the union of all reference lists
Description
Retrieve the list of drugs from the union of all reference lists
Usage
getRefAll()
Value
list of drugs from all reference lists
Examples
d <- getRefAll()
Receives a sorted hashmap with found entities from a dictionary
Description
Receives a sorted hashmap with found entities from a dictionary
Usage
getTermMatrix(dictionary, database, collection)
Arguments
dictionary |
Character vector that is the name of a dicitonary having pre-calculated stats. This can be MeSH, DrugBank, Agrovoc, EpSO, ESSO, or EPILONT |
database |
the name of the MongoDB database to be used |
collection |
the name of the MongoDB collection to be used |
Value
a sorted hashmap containing all found entities from the respective dictionaries with frequencies
Examples
## Not run:
mesh <- getTermMatrix("MeSH")
## End(Not run)
Calculate jaccard similarity metric for two sets a and b
Description
Calculate jaccard similarity metric for two sets a and b
Usage
jaccard(ainterb, aunionb, lengtha, lengthb)
Arguments
ainterb |
integer value with number of intersecting elements between set a and b |
aunionb |
integer value with number of union elements between set a and b |
lengtha |
length of set a |
lengthb |
length of set b |
Value
jac double value with the jaccard similarity coefficient
Examples
jaccard(1,3, 2, 3)
Plotting functions for DSEA lists
Description
Plotting functions for DSEA lists
Usage
plotDSEA(dsepso, dsesso, dsepi, dsepisem, dsfenics, dsspace, k)
Arguments
dsepso |
list with enrichment for EpSO |
dsesso |
list with enrichment for ESSO |
dsepi |
list with enrichment for EPILONT |
dsepisem |
list with enrichment for EPISEM |
dsfenics |
list with enrichment for FENICS |
dsspace |
list with enrichment for the combined ranked list |
k |
numeric value for the length to be plotted |
Value
the plot object
Examples
utils::data(rawDrugNamesCoOcEpSO, package="epos")
utils::data(rawDrugNamesCoOcESSO, package="epos")
utils::data(rawDrugNamesCoOcEPILONT, package="epos")
utils::data(rawDrugNamesCoOcEPISEM, package="epos")
utils::data(rawDrugNamesCoOcFENICS, package="epos")
atchashda <-
readAtcMapIntoHashMapDrugNamesAtcCodes(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
epso <- rawDrugNamesCoOcEpSO
neuroepso <- filterNeuroDrugs(epso, atchashda)
esso <- rawDrugNamesCoOcESSO
neuroesso <- filterNeuroDrugs(esso, atchashda)
epi <- rawDrugNamesCoOcEPILONT
neuroepi <- filterNeuroDrugs(epi, atchashda)
episem <- rawDrugNamesCoOcEPISEM
neuroepisem <- filterNeuroDrugs(episem, atchashda)
fenics <- rawDrugNamesCoOcFENICS
neurofenics <- filterNeuroDrugs(fenics, atchashda)
mx <- max(
c(length(neuroepso), length(neuroesso), length(neuroepi),
length(neuroepisem), length(neurofenics)))
dneuro <-
data.frame(EpSO = c(neuroepso, rep("", (mx-length(neuroepso)))),
ESSO = c(neuroesso, rep("", (mx-length(neuroesso)))),
EPILONT = c(neuroepi, rep("", (mx-length(neuroepi)))),
EPISEM = c(neuroepisem, rep("", (mx-length(neuroepisem)))),
FENICS = c(neurofenics, rep("", (mx-length(neurofenics)))))
dneuromaxk <- TopKLists::calculate.maxK(dneuro, L=5, d=5, v=5)
neurospace <- as.character(dneuromaxk$topkspace)
dsepso <- calcDSEA(neuroepso, mx)
dsesso <- calcDSEA(neuroesso, mx)
dsepi <- calcDSEA(neuroepi, mx)
dsepisem <- calcDSEA(neuroepisem, mx)
dsfenics <- calcDSEA(neurofenics, mx)
dsspace <- calcDSEA (neurospace, mx)
p <- plotDSEA(dsepso, dsesso, dsepi, dsepisem, dsfenics, dsspace, dneuromaxk$maxK)
## Not run:
ggplot2::ggsave("dsea.png",
p <- plotDSEA(dsepso, dsesso, dsepi, dsepisem, dsfenics, dsspace,
dneuromaxk$maxK), width=480, height=320, units = "mm", dpi = 300)
## End(Not run)
Plotting functions for enrichment lists
Description
Plotting functions for enrichment lists
Usage
plotEnrichment(enepso, enesso, enepi, enepisem, enfenics, enspace, k)
Arguments
enepso |
list with enrichment for EpSO |
enesso |
list with enrichment for ESSO |
enepi |
list with enrichment for EPILONT |
enepisem |
list with enrichment for EPISEM |
enfenics |
list with enrichment for FENICS |
enspace |
list with enrichment for the combined ranked list |
k |
numeric value for the length to be plotted |
Value
the plot object
Examples
utils::data(rawDrugNamesCoOcEpSO, package="epos")
utils::data(rawDrugNamesCoOcESSO, package="epos")
utils::data(rawDrugNamesCoOcEPILONT, package="epos")
utils::data(rawDrugNamesCoOcEPISEM, package="epos")
utils::data(rawDrugNamesCoOcFENICS, package="epos")
atchashda <-
readAtcMapIntoHashMapDrugNamesAtcCodes(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
epso <- rawDrugNamesCoOcEpSO
neuroepso <- filterNeuroDrugs(epso, atchashda)
esso <- rawDrugNamesCoOcESSO
neuroesso <- filterNeuroDrugs(esso, atchashda)
epi <- rawDrugNamesCoOcEPILONT
neuroepi <- filterNeuroDrugs(epi, atchashda)
episem <- rawDrugNamesCoOcEPISEM
neuroepisem <- filterNeuroDrugs(episem, atchashda)
fenics <- rawDrugNamesCoOcFENICS
neurofenics <- filterNeuroDrugs(fenics, atchashda)
mx <- max(
c(length(neuroepso), length(neuroesso), length(neuroepi),
length(neuroepisem), length(neurofenics)))
dneuro <-
data.frame(EpSO = c(neuroepso, rep("", (mx-length(neuroepso)))),
ESSO = c(neuroesso, rep("", (mx-length(neuroesso)))),
EPILONT = c(neuroepi, rep("", (mx-length(neuroepi)))),
EPISEM = c(neuroepisem, rep("", (mx-length(neuroepisem)))),
FENICS = c(neurofenics, rep("", (mx-length(neurofenics)))))
dneuromaxk <- TopKLists::calculate.maxK(dneuro, L=5, d=5, v=5)
neurospace <- as.character(dneuromaxk$topkspace)
enepso <- calcEnrichment(neuroepso)
enesso <- calcEnrichment(neuroesso)
enepi <- calcEnrichment(neuroepi)
enepisem <- calcEnrichment(neuroepisem)
enfenics <- calcEnrichment(neurofenics)
enspace <- calcEnrichment (neurospace)
p <- plotEnrichment(enepso, enesso, enepi, enepisem, enfenics, enspace, dneuromaxk$maxK)
Print Top 10 Drugs
Description
Print Top 10 Drugs
Usage
printTop10Drugs(neuroepso, neuroesso, neuroepi, neuroepisem, neurofenics)
Arguments
neuroepso |
Ranked list of drug names co-occurring with EpSO |
neuroesso |
Ranked list of drug names co-occurring with ESSO |
neuroepi |
Ranked list of drug names co-occurring with EPILONT |
neuroepisem |
Ranked list of drug names co-occurring with EPISEM |
neurofenics |
Ranked list of drug names co-occurring with FENICS |
Value
data frame with top 10 drugs for each ontology
Examples
utils::data(rawDrugNamesCoOcEpSO, package="epos")
utils::data(rawDrugNamesCoOcESSO, package="epos")
utils::data(rawDrugNamesCoOcEPILONT, package="epos")
utils::data(rawDrugNamesCoOcEPISEM, package="epos")
utils::data(rawDrugNamesCoOcFENICS, package="epos")
atchashda <-
readAtcMapIntoHashMapDrugNamesAtcCodes(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
atchashaa <-
readAtcMapIntoHashMapAtcCodesAtcNames(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
atchashsec <-
readSecondLevelATC(
system.file("extdata", "atc-secondlevel.map", package = "epos"), "\t")
epso <- rawDrugNamesCoOcEpSO
neuroepso <- filterNeuroDrugs(epso, atchashda)
esso <- rawDrugNamesCoOcESSO
neuroesso <- filterNeuroDrugs(esso, atchashda)
epi <- rawDrugNamesCoOcEPILONT
neuroepi <- filterNeuroDrugs(epi, atchashda)
episem <- rawDrugNamesCoOcEPISEM
neuroepisem <- filterNeuroDrugs(episem, atchashda)
fenics <- rawDrugNamesCoOcFENICS
neurofenics <- filterNeuroDrugs(fenics, atchashda)
top10table <- printTop10Drugs(neuroepso, neuroesso, neuroepi, neuroepisem, neurofenics)
## Not run:
print(xtable::xtable(top10table, type = "latex"),
file = "top10table.tex")
## End(Not run)
List drug terms with their frequency co-occurring with terms from the EPILONT ontology in publications since 2015 from the BioASQ 2020 corpus.
Description
List drug terms with their frequency co-occurring with terms from the EPILONT ontology in publications since 2015 from the BioASQ 2020 corpus.
Usage
rawDrugNamesCoOcEPILONT
Format
A named list of drug term frequencies
Source
The text mining workflows for data generation are described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558>, Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>, and Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) <doi:10.1007/978-3-030-43887-6_52>. The source data set for generating the data co-occurrence lists is the BioASQ 2020 corpus. The source ontology for the creation of the dictionary is the Epilepsy Ontology (EPILONT) from https://bioportal.bioontology.org/ontologies/EPILONT
Examples
utils::data(rawDrugNamesCoOcEPILONT, package="epos")
List drug terms with their frequency co-occurring with terms from the EPISEM ontology in publications since 2015 from the BioASQ 2020 corpus.
Description
List drug terms with their frequency co-occurring with terms from the EPISEM ontology in publications since 2015 from the BioASQ 2020 corpus.
Usage
rawDrugNamesCoOcEPISEM
Format
A named list of drug term frequencies
Source
The text mining workflows for data generation are described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558>, Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>, and Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) <doi:10.1007/978-3-030-43887-6_52>. The source data set for generating the data co-occurrence lists is the BioASQ 2020 corpus. The source ontology for the creation of the dictionary is the Epilepsy Semiology Ontology (EPISEM) from https://bioportal.bioontology.org/ontologies/EPISEM
Examples
utils::data(rawDrugNamesCoOcEPISEM, package="epos")
List drug terms with their frequency co-occurring with terms from the ESSO ontology in publications since 2015 from the BioASQ 2020 corpus.
Description
List drug terms with their frequency co-occurring with terms from the ESSO ontology in publications since 2015 from the BioASQ 2020 corpus.
Usage
rawDrugNamesCoOcESSO
Format
An object of class character of length 8620.
Source
The text mining workflows for data generation are described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558>, Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>, and Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) <doi:10.1007/978-3-030-43887-6_52>. The source data set for generating the data co-occurrence lists is the BioASQ 2020 corpus. The source ontology for the creation of the dictionary is Epilepsy Syndrome Seizure Ontology (ESSO) from https://bioportal.bioontology.org/ontologies/ESSO
Examples
utils::data(rawDrugNamesCoOcESSO, package="epos")
List drug terms with their frequency co-occurring with terms from the EpSO ontology in publications since 2015 from the BioASQ 2020 corpus.
Description
List drug terms with their frequency co-occurring with terms from the EpSO ontology in publications since 2015 from the BioASQ 2020 corpus.
Usage
rawDrugNamesCoOcEpSO
Format
A named list of drug term frequencies
Source
The text mining workflows for data generation are described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558>, Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>, and Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) <doi:10.1007/978-3-030-43887-6_52>. The source data set for generating the data co-occurrence lists is the BioASQ 2020 corpus. The source ontology for the creation of the dictionary is the Epilepsy and Seizure Ontology (EpSO) from https://bioportal.bioontology.org/ontologies/EPSO
Examples
utils::data(rawDrugNamesCoOcEpSO, package="epos")
List drug terms with their frequency co-occurring with terms from the FENICS ontology in publications from the BioASQ 2020 corpus.
Description
List drug terms with their frequency co-occurring with terms from the FENICS ontology in publications from the BioASQ 2020 corpus.
Usage
rawDrugNamesCoOcFENICS
Format
A named list of drug term frequencies
Source
The text mining workflows for data generation are described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558>, Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>, and Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) <doi:10.1007/978-3-030-43887-6_52>. The source data set for generating the data co-occurrence lists is the BioASQ 2020 corpus. The source ontology for the creation of the dictionary is the Functional Epilepsy Nomenclature for Ion Channels (FENICS) from https://bioportal.bioontology.org/ontologies/FENICS
Examples
utils::data(rawDrugNamesCoOcFENICS, package="epos")
Processes the input file db-atc.map to form a HashMap containing the drug names with ATC codes
Description
Processes the input file db-atc.map to form a HashMap containing the drug names with ATC codes
Usage
readAtcMapIntoHashMapAtcCodesAtcNames(filename, seperator)
Arguments
filename |
character vector with the file name of the file db-atc.map |
seperator |
character vector with the seperator used within the map-file |
Value
atchashaa hash with atc codes as keys and atc names as values
Examples
atchashaa <-
readAtcMapIntoHashMapAtcCodesAtcNames(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
Processes the input file db-atc.map to form a HashMap containing the drug names with ATC codes
Description
Processes the input file db-atc.map to form a HashMap containing the drug names with ATC codes
Usage
readAtcMapIntoHashMapDrugNamesAtcCodes(filename, seperator)
Arguments
filename |
character vector with the file name of the file db-atc.map |
seperator |
character vector with the seperator used within the map-file |
Value
atchashda hash with drug names as keys and atc codes as values
Examples
atchashda <- readAtcMapIntoHashMapDrugNamesAtcCodes(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
Read the second level ATC classes from the file atc-secondlevel.map
Description
Read the second level ATC classes from the file atc-secondlevel.map
Usage
readSecondLevelATC(filename, seperator)
Arguments
filename |
the file name that is supposed to be atc-secondlevel.map |
seperator |
the csv file delimiter |
Value
atchashsec a hash with second level ATC classes as keys and their names as values
Examples
atchashsec <-
readSecondLevelATC(
system.file("extdata", "atc-secondlevel.map", package = "epos"), "\t")
Sort table by scoring for each row
Description
Sort table by scoring for each row
Usage
sortTableByRefMatches(dntk)
Arguments
dntk |
the table returned from writeNeuroTable |
Value
the sorted table
Examples
utils::data(rawDrugNamesCoOcEpSO, package="epos")
utils::data(rawDrugNamesCoOcESSO, package="epos")
utils::data(rawDrugNamesCoOcEPILONT, package="epos")
utils::data(rawDrugNamesCoOcEPISEM, package="epos")
utils::data(rawDrugNamesCoOcFENICS, package="epos")
atchashda <-
readAtcMapIntoHashMapDrugNamesAtcCodes(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
atchashaa <-
readAtcMapIntoHashMapAtcCodesAtcNames(
system.file("extdata", "db-atc.map", package = "epos"), "\t")
atchashsec <-
readSecondLevelATC(
system.file("extdata", "atc-secondlevel.map", package = "epos"), "\t")
epso <- rawDrugNamesCoOcEpSO
neuroepso <- filterNeuroDrugs(epso, atchashda)
esso <- rawDrugNamesCoOcESSO
neuroesso <- filterNeuroDrugs(esso, atchashda)
epi <- rawDrugNamesCoOcEPILONT
neuroepi <- filterNeuroDrugs(epi, atchashda)
episem <- rawDrugNamesCoOcEPISEM
neuroepisem <- filterNeuroDrugs(episem, atchashda)
fenics <- rawDrugNamesCoOcFENICS
neurofenics <- filterNeuroDrugs(fenics, atchashda)
mx <- max(
c(length(neuroepso), length(neuroesso), length(neuroepi),
length(neuroepisem), length(neurofenics)))
dneuro <-
data.frame(EpSO = c(neuroepso, rep("", (mx-length(neuroepso)))),
ESSO = c(neuroesso, rep("", (mx-length(neuroesso)))),
EPILONT = c(neuroepi, rep("", (mx-length(neuroepi)))),
EPISEM = c(neuroepisem, rep("", (mx-length(neuroepisem)))),
FENICS = c(neurofenics, rep("", (mx-length(neurofenics)))))
suppressWarnings(dneuromaxk <- TopKLists::calculate.maxK(dneuro, L=5, d=5, v=5))
neurotable <- createNeuroTable(atchashda, atchashsec, dneuromaxk)
sortedNeuroTable <- sortTableByRefMatches(neurotable)
## Not run:
print(xtable::xtable(sortedNeuroTable, type = "latex"),
file = "sortedNeuroTable.tex",
include.rownames=FALSE)
## End(Not run)