Package 'epos'

Title: Epilepsy Ontologies' Similarities
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>.
Authors: Bernd Mueller
Maintainer: Bernd Mueller <[email protected]>
License: LGPL (>= 3)
Version: 1.1
Built: 2025-02-12 04:57:12 UTC
Source: https://github.com/bernd-mueller/epos

Help Index

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 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 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 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 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 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 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)
print(xtable::xtable(sortedNeuroTable, type = "latex"),
  file = "sortedNeuroTable.tex",
  include.rownames=FALSE)