Nothing
R/mainAnnotator.r
In nanotatoR: nanotatoR: next generation structural variant annotation and classification
Defines functions
nanotatoR_main
Documented in
nanotatoR_main
#' Annotation of Bionano SV.
#'
#' @param smap character. Path to SMAP file.
#' @param inputfmtBed character. Choice between Text and DataFrame
#' as input for bed file.
#' @param bed Text Choice between UCSC bed or Bionano bed.
#' @param n numeric Number of genes to report which are nearest to
#' the breakpoint.
#' Default is 3.
#' @param mergedFiles_BN character. Path to the merged BN SV files.
#' @param mergedFiles_INF character. Path to the merged BN SV files.
#' @param buildSVInternalDB boolean. Checking whether the merged solo
#' file database exist or you need to build it. Default= TRUE.
#' @param buildBNInternalDB boolean. Checking whether the merged Bionano
#' file database exist or you need to build it. Default= TRUE.
#' @param soloPath character. Path to the solo file database.
#' @param solopattern character. pattern of the file names to merge.
#' @param InternalDBpath character. Path to the BNFile file database.
#' @param InternalDBpattern character. pattern of the BNFile names to merge.
#' @param smapName character. Name of the smap file.
#' @param win_indel_INF Numeric. Insertion and deletion error window.
#' @param win_inv_trans_INF Numeric. Inversion and translocation error window.
#' @param perc_similarity_INF Numeric . ThresholdPercentage similarity
#' of the query SV and reference SV.
#' @param input_fmt_DGV character. Choice between Text and DataFrame
#' for input to DGV.
#' @param input_fmt_INF character. Choice between Text and DataFrame
#' for input to INF.
#' @param dbOutput_BN character. Output of merged bionano data.
#' @param fname_BN character. Filename in case dbOutput_BN = Text.
#' @param dbOutput_Int character. Output of solo bionano data.
#' @param fname_Int character. Filename in case dbOutput_Int = Text.
#' @param returnMethod_GeneList character. Return Methods from the
#' gene_list_generation
#' function, choice between Text and Dataframe.
#' @param returnMethod_bedcomp character. Return Methods from the compSmapbed
#' function, choice between Text and Dataframe.
#' @param returnMethod_DGV character. Return Methods from
#' the DGV_extraction function, choice between
#' Text and Dataframe.
#' @param returnMethod_Internal character. Return Methods
#' from the internalFrequency function, choice between
#' Text and Dataframe.
#' @param returnMethod_BNCohort character. Return
#' Methods from the Bionano function, choice between
#' Text and Dataframe.
#' @param returnMethod_decipher character.
#' Return Methods from the decipher Frequency function, choice between
#' Text and Dataframe.
#' @param limsize Numeric. Minimum size of SV that can be determined
#' accurately by the Bionano SV caller. Default 1000.
#' @param win_indel_parents Numeric. Insertion and deletion error window to
#' determine zygosity in case of parents. Default 5000.
#' @param win_inv_trans_parents Numeric. Inversion and translocation error
#' window to determine zygosity in case of parents. Default 40000.
#' @param indelconf Numeric. Threshold for insertion and deletion confidence.
#' @param invconf Numeric. Threshold for inversion confidence.
#' @param transconf Numeric. Threshold for translocation confidence.
#' @param hgpath character. Path to Database of Genomic Variants (DGV)
#' Text file.
#' @param decipherpath character. Path to DECIPHER. Text file.
#' @param win_indel_DGV Numeric. Insertion and deletion error window.
#' @param win_inv_trans_DGV Numeric. Inversion and translocation error window.
#' @param perc_similarity_DGV Numeric . ThresholdPercentage similarity
#' of the query SV and reference SV.
#' @param method_entrez character. Input Method for terms for entrez. Choices
#' are "Single","Multiple" and "Text".
#' @param termPath character. Path and file name for textfile for terms.
#' @param term character. Single or Multiple Terms as vectord.
#' @param thresh integer. Threshold for the number of terms sent to entrez.
#' Note if large lists are sent to ncbi, it might fail to get
#' processed. Default is 5.
#' @param input_fmt_geneList character. Choice of gene list input
#' Text or Dataframe.
#' @param input_fmt_BN character. Choice of Bionano dataset input
#' Text or Dataframe.
#' @param input_fmt_decipher character. Choice of gene list input
#' Text or Dataframe.
#' @param input_fmt_svMap character. Choice of SVmap input for final step
#' Text or Dataframe.
#' @param dat_geneList Dataframe Input data containing geneList data.
#' @param outpath Character Directory to the output file.
#' @param outputFilename Character Output filename for the annotated smap.
#' @param RZIPpath Character. Path for the Rtools zip.exe
#' @return Excel file containing the annotated SV map, tabs divided based on type of SVs.
#' @return Text files containg gene list and terms associated with them are stored as text files.
#' \dontrun{
#' @examples
#' terms <- "Muscle Weakness"
#' gene <- gene_list_generation(method = "Single", term = terms, returnMethod = "dataFrame")
#' mergedFiles <- system.file ("extdata", "BNSOLO2_merged.txt",
#' package = "nanotatoR")
#' RzipFile = "zip.exe"
#' RZIPpath <- system.file("extdata", RzipFile, package = "nanotatoR")
#' smapName <- "F1.1_TestSample1_solo_hg19.smap"
#' smappath <- system.file("extdata", smapName, package = "nanotatoR")
#' path <- system.file("extdata", "SoloFile/", package = "nanotatoR")
#' hgpath <- system.file ("extdata",
#' "GRCh37_hg19_variants_2016-05-15.txt", package = "nanotatoR")
#' decipherpath <- system.file("extdata", "population_cnv.txt", package =
#' "nanotatoR")
#' bedFile <- system.file("extdata", "Homo_sapiens.Hg19.bed",
#' package="nanotatoR")
#' pattern <- "_hg19.smap"
#' nM <- nanotatoR_main(smap = smappath, bed = bedFile,
#' inputfmtBed = c("BNBED"),
#' n = 3, buildSVInternalDB = TRUE, soloPath = path, solopattern = pattern,
#' input_fmt_INF = c("dataFrame"), buildBNInternalDB = FALSE,
#' returnMethod_bedcomp = c("dataFrame"), returnMethod_DGV = c("dataFrame"),
#' returnMethod_Internal = c("dataFrame"), input_fmt_DGV = c("dataFrame"),
#' hgpath = hgpath, smapName = smapName, limsize=1000, win_indel_parents=5000,
#' decipherpath = decipherpath, dbOutput_Int = "dataframe",
#' win_inv_trans_parents=40000, win_indel_DGV = 10000,
#' input_fmt_geneList = c("dataFrame"), input_fmt_svMap = c("dataFrame"),
#' input_fmt_decipher = "dataFrame",input_fmt_BN = "dataFrame",
#' returnMethod_GeneList = c("dataFrame"),returnMethod_BNCohort =
#' c("dataFrame"),
#' returnMethod_decipher = c("dataFrame"), mergedFiles_BN = mergedFiles,
#' dat_geneList = gene , method_entrez = "",
#' outpath = smappath, outputFilename = "test",
#' RZIPpath = RZIPpath)
#' }
#' @importFrom stats na.omit
#' @export
nanotatoR_main<-function(
smap, bed, inputfmtBed = c("BED", "BNBED"),
n=3, InternalDBpath, InternalDBpattern, dbOutput_Int,
fname_Int, dbOutput_BN, fname_BN,
buildSVInternalDB=FALSE, soloPath, solopattern,
returnMethod_bedcomp = c("Text","dataFrame"), mergedFiles_BN,
win_indel_INF = 10000, win_inv_trans_INF = 50000,
perc_similarity_INF = 0.5, indelconf = 0.5, invconf = 0.01,
transconf = 0.1,returnMethod_DGV = c("Text","dataFrame"),
hgpath, win_indel_DGV = 10000, win_inv_trans_DGV = 50000,
perc_similarity_DGV = 0.5,returnMethod_Internal = c("Text","dataFrame"),
input_fmt_DGV = c("Text","dataFrame"), input_fmt_BN = c("Text","dataFrame"),
input_fmt_INF = c("Text","dataFrame"),
input_fmt_decipher = c("Text","dataFrame"),
input_fmt_svMap = c("Text","dataFrame"), dat_geneList,
decipherpath, input_fmt_geneList = c("Text","dataFrame"),
returnMethod_GeneList = c("Text","dataFrame"), buildBNInternalDB = FALSE,
returnMethod_BNCohort = c("Text","dataFrame"), mergedFiles_INF,
returnMethod_decipher = c("Text","dataFrame"),
method_entrez = c("Single","Multiple","Text"), smapName, termPath,
term, thresh = 5, limsize = 1000, win_indel_parents = 5000,
win_inv_trans_parents = 40000,
outpath, outputFilename = "" , RZIPpath)
{
if (method_entrez =="Text"){
dat_geneList <- gene_list_generation(method_entrez = "Text",
termPath=termPath,
returnMethod = returnMethod_GeneList,thresh = 20)
}
else if (method_entrez == "Multiple"){
dat_geneList <- gene_list_generation(method_entrez = "Multiple",
term = term, returnMethod = returnMethod_GeneList,thresh = 20)
}
else if (method_entrez == "Single"){
dat_geneList <- gene_list_generation(method_entrez = "Single",
term = term, returnMethod = returnMethod_GeneList)
}
else if (method_entrez == "" && length(dat_geneList)> 0){
dat_geneList <- dat_geneList
}
else{
stop("GeneList or term required for analysis!!!")
}
datcompSmap<-compSmapbed(smap = smap,bed = bed,
inputfmtBed = inputfmtBed, n = n,
returnMethod_bedcomp = returnMethod_bedcomp)
datDGV<- DGV_extraction(hgpath = hgpath, smap_data
= datcompSmap,input_fmt_DGV = input_fmt_DGV,
win_indel_DGV = win_indel_DGV,
win_inv_trans_DGV = win_inv_trans_DGV,
perc_similarity_DGV = perc_similarity_DGV,returnMethod
= returnMethod_DGV)
if(buildSVInternalDB==FALSE){
datInf<-internalFrequency(mergedFiles
= mergedFiles_INF , buildSVInternalDB=FALSE,
smapdata=datDGV, input_fmt=input_fmt_INF, win_indel
= win_indel_INF ,
win_inv_trans = win_inv_trans_INF, smap = smapName,
perc_similarity =perc_similarity_INF, indelconf, invconf,
transconf,
limsize = limsize,returnMethod = returnMethod_Internal)
}
else{
datInf<-internalFrequency(buildSVInternalDB = TRUE,
path = soloPath, pattern
= solopattern, outpath, dbOutput = dbOutput_Int,
smapdata = datDGV, smap = smapName,input_fmt = input_fmt_INF,
limsize = limsize, win_indel = win_indel_INF,
win_inv_trans = win_inv_trans_INF,
perc_similarity
= perc_similarity_INF, indelconf , invconf, transconf,
returnMethod= returnMethod_Internal)
}
if(buildBNInternalDB==FALSE){
datchort<-cohortFrequency(internalBNDB
= mergedFiles_BN, buildBNInternalDB = FALSE,
smapdata = datInf, input_fmt
= input_fmt_BN, win_indel = win_indel_INF,
win_inv_trans = win_inv_trans_INF,
perc_similarity = perc_similarity_INF,
indelconf = indelconf, invconf = invconf, limsize=limsize,
transconf = transconf,returnMethod = returnMethod_BNCohort)
}
else{
datchort<-cohortFrequency(buildBNInternalDB = TRUE,
smapdata = datInf, input_fmt = input_fmt_BN,
BNDBPath = InternalDBpath,
BNDBPattern = InternalDBpattern, win_indel =
win_indel_INF, dbOutput = dbOutput_BN,
win_inv_trans = win_inv_trans_INF,
perc_similarity = perc_similarity_INF, indelconf = indelconf,
invconf = invconf, limsize=limsize,
transconf = transconf,returnMethod = returnMethod_BNCohort)
}
datdecipher<-Decipher_extraction(decipherpath =
decipherpath, smap_data = datchort,
input_fmt = input_fmt_decipher,win_indel = win_indel_INF,
perc_similarity = perc_similarity_INF,
returnMethod = returnMethod_decipher)
run_bionano_filter(input_fmt_geneList = "dataFrame",
input_fmt_svMap = input_fmt_svMap,
SVFile = NULL,svData = datdecipher,dat_geneList = dat_geneList,
outpath = outpath,outputFilename = outputFilename,RZIPpath = RZIPpath)
}
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nanotatoR documentation built on Nov. 8, 2020, 6:54 p.m.
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Defines functions nanotatoR_main
Documented in nanotatoR_main
#' Annotation of Bionano SV.
#'
#' @param smap character. Path to SMAP file.
#' @param inputfmtBed character. Choice between Text and DataFrame
#' as input for bed file.
#' @param bed Text Choice between UCSC bed or Bionano bed.
#' @param n numeric Number of genes to report which are nearest to
#' the breakpoint.
#' Default is 3.
#' @param mergedFiles_BN character. Path to the merged BN SV files.
#' @param mergedFiles_INF character. Path to the merged BN SV files.
#' @param buildSVInternalDB boolean. Checking whether the merged solo
#' file database exist or you need to build it. Default= TRUE.
#' @param buildBNInternalDB boolean. Checking whether the merged Bionano
#' file database exist or you need to build it. Default= TRUE.
#' @param soloPath character. Path to the solo file database.
#' @param solopattern character. pattern of the file names to merge.
#' @param InternalDBpath character. Path to the BNFile file database.
#' @param InternalDBpattern character. pattern of the BNFile names to merge.
#' @param smapName character. Name of the smap file.
#' @param win_indel_INF Numeric. Insertion and deletion error window.
#' @param win_inv_trans_INF Numeric. Inversion and translocation error window.
#' @param perc_similarity_INF Numeric . ThresholdPercentage similarity
#' of the query SV and reference SV.
#' @param input_fmt_DGV character. Choice between Text and DataFrame
#' for input to DGV.
#' @param input_fmt_INF character. Choice between Text and DataFrame
#' for input to INF.
#' @param dbOutput_BN character. Output of merged bionano data.
#' @param fname_BN character. Filename in case dbOutput_BN = Text.
#' @param dbOutput_Int character. Output of solo bionano data.
#' @param fname_Int character. Filename in case dbOutput_Int = Text.
#' @param returnMethod_GeneList character. Return Methods from the
#' gene_list_generation
#' function, choice between Text and Dataframe.
#' @param returnMethod_bedcomp character. Return Methods from the compSmapbed
#' function, choice between Text and Dataframe.
#' @param returnMethod_DGV character. Return Methods from
#' the DGV_extraction function, choice between
#' Text and Dataframe.
#' @param returnMethod_Internal character. Return Methods
#' from the internalFrequency function, choice between
#' Text and Dataframe.
#' @param returnMethod_BNCohort character. Return
#' Methods from the Bionano function, choice between
#' Text and Dataframe.
#' @param returnMethod_decipher character.
#' Return Methods from the decipher Frequency function, choice between
#' Text and Dataframe.
#' @param limsize Numeric. Minimum size of SV that can be determined
#' accurately by the Bionano SV caller. Default 1000.
#' @param win_indel_parents Numeric. Insertion and deletion error window to
#' determine zygosity in case of parents. Default 5000.
#' @param win_inv_trans_parents Numeric. Inversion and translocation error
#' window to determine zygosity in case of parents. Default 40000.
#' @param indelconf Numeric. Threshold for insertion and deletion confidence.
#' @param invconf Numeric. Threshold for inversion confidence.
#' @param transconf Numeric. Threshold for translocation confidence.
#' @param hgpath character. Path to Database of Genomic Variants (DGV)
#' Text file.
#' @param decipherpath character. Path to DECIPHER. Text file.
#' @param win_indel_DGV Numeric. Insertion and deletion error window.
#' @param win_inv_trans_DGV Numeric. Inversion and translocation error window.
#' @param perc_similarity_DGV Numeric . ThresholdPercentage similarity
#' of the query SV and reference SV.
#' @param method_entrez character. Input Method for terms for entrez. Choices
#' are "Single","Multiple" and "Text".
#' @param termPath character. Path and file name for textfile for terms.
#' @param term character. Single or Multiple Terms as vectord.
#' @param thresh integer. Threshold for the number of terms sent to entrez.
#' Note if large lists are sent to ncbi, it might fail to get
#' processed. Default is 5.
#' @param input_fmt_geneList character. Choice of gene list input
#' Text or Dataframe.
#' @param input_fmt_BN character. Choice of Bionano dataset input
#' Text or Dataframe.
#' @param input_fmt_decipher character. Choice of gene list input
#' Text or Dataframe.
#' @param input_fmt_svMap character. Choice of SVmap input for final step
#' Text or Dataframe.
#' @param dat_geneList Dataframe Input data containing geneList data.
#' @param outpath Character Directory to the output file.
#' @param outputFilename Character Output filename for the annotated smap.
#' @param RZIPpath Character. Path for the Rtools zip.exe
#' @return Excel file containing the annotated SV map, tabs divided based on type of SVs.
#' @return Text files containg gene list and terms associated with them are stored as text files.
#' \dontrun{
#' @examples
#' terms <- "Muscle Weakness"
#' gene <- gene_list_generation(method = "Single", term = terms, returnMethod = "dataFrame")
#' mergedFiles <- system.file ("extdata", "BNSOLO2_merged.txt",
#' package = "nanotatoR")
#' RzipFile = "zip.exe"
#' RZIPpath <- system.file("extdata", RzipFile, package = "nanotatoR")
#' smapName <- "F1.1_TestSample1_solo_hg19.smap"
#' smappath <- system.file("extdata", smapName, package = "nanotatoR")
#' path <- system.file("extdata", "SoloFile/", package = "nanotatoR")
#' hgpath <- system.file ("extdata",
#' "GRCh37_hg19_variants_2016-05-15.txt", package = "nanotatoR")
#' decipherpath <- system.file("extdata", "population_cnv.txt", package =
#' "nanotatoR")
#' bedFile <- system.file("extdata", "Homo_sapiens.Hg19.bed",
#' package="nanotatoR")
#' pattern <- "_hg19.smap"
#' nM <- nanotatoR_main(smap = smappath, bed = bedFile,
#' inputfmtBed = c("BNBED"),
#' n = 3, buildSVInternalDB = TRUE, soloPath = path, solopattern = pattern,
#' input_fmt_INF = c("dataFrame"), buildBNInternalDB = FALSE,
#' returnMethod_bedcomp = c("dataFrame"), returnMethod_DGV = c("dataFrame"),
#' returnMethod_Internal = c("dataFrame"), input_fmt_DGV = c("dataFrame"),
#' hgpath = hgpath, smapName = smapName, limsize=1000, win_indel_parents=5000,
#' decipherpath = decipherpath, dbOutput_Int = "dataframe",
#' win_inv_trans_parents=40000, win_indel_DGV = 10000,
#' input_fmt_geneList = c("dataFrame"), input_fmt_svMap = c("dataFrame"),
#' input_fmt_decipher = "dataFrame",input_fmt_BN = "dataFrame",
#' returnMethod_GeneList = c("dataFrame"),returnMethod_BNCohort =
#' c("dataFrame"),
#' returnMethod_decipher = c("dataFrame"), mergedFiles_BN = mergedFiles,
#' dat_geneList = gene , method_entrez = "",
#' outpath = smappath, outputFilename = "test",
#' RZIPpath = RZIPpath)
#' }
#' @importFrom stats na.omit
#' @export
nanotatoR_main<-function(
smap, bed, inputfmtBed = c("BED", "BNBED"),
n=3, InternalDBpath, InternalDBpattern, dbOutput_Int,
fname_Int, dbOutput_BN, fname_BN,
buildSVInternalDB=FALSE, soloPath, solopattern,
returnMethod_bedcomp = c("Text","dataFrame"), mergedFiles_BN,
win_indel_INF = 10000, win_inv_trans_INF = 50000,
perc_similarity_INF = 0.5, indelconf = 0.5, invconf = 0.01,
transconf = 0.1,returnMethod_DGV = c("Text","dataFrame"),
hgpath, win_indel_DGV = 10000, win_inv_trans_DGV = 50000,
perc_similarity_DGV = 0.5,returnMethod_Internal = c("Text","dataFrame"),
input_fmt_DGV = c("Text","dataFrame"), input_fmt_BN = c("Text","dataFrame"),
input_fmt_INF = c("Text","dataFrame"),
input_fmt_decipher = c("Text","dataFrame"),
input_fmt_svMap = c("Text","dataFrame"), dat_geneList,
decipherpath, input_fmt_geneList = c("Text","dataFrame"),
returnMethod_GeneList = c("Text","dataFrame"), buildBNInternalDB = FALSE,
returnMethod_BNCohort = c("Text","dataFrame"), mergedFiles_INF,
returnMethod_decipher = c("Text","dataFrame"),
method_entrez = c("Single","Multiple","Text"), smapName, termPath,
term, thresh = 5, limsize = 1000, win_indel_parents = 5000,
win_inv_trans_parents = 40000,
outpath, outputFilename = "" , RZIPpath)
{
if (method_entrez =="Text"){
dat_geneList <- gene_list_generation(method_entrez = "Text",
termPath=termPath,
returnMethod = returnMethod_GeneList,thresh = 20)
}
else if (method_entrez == "Multiple"){
dat_geneList <- gene_list_generation(method_entrez = "Multiple",
term = term, returnMethod = returnMethod_GeneList,thresh = 20)
}
else if (method_entrez == "Single"){
dat_geneList <- gene_list_generation(method_entrez = "Single",
term = term, returnMethod = returnMethod_GeneList)
}
else if (method_entrez == "" && length(dat_geneList)> 0){
dat_geneList <- dat_geneList
}
else{
stop("GeneList or term required for analysis!!!")
}
datcompSmap<-compSmapbed(smap = smap,bed = bed,
inputfmtBed = inputfmtBed, n = n,
returnMethod_bedcomp = returnMethod_bedcomp)
datDGV<- DGV_extraction(hgpath = hgpath, smap_data
= datcompSmap,input_fmt_DGV = input_fmt_DGV,
win_indel_DGV = win_indel_DGV,
win_inv_trans_DGV = win_inv_trans_DGV,
perc_similarity_DGV = perc_similarity_DGV,returnMethod
= returnMethod_DGV)
if(buildSVInternalDB==FALSE){
datInf<-internalFrequency(mergedFiles
= mergedFiles_INF , buildSVInternalDB=FALSE,
smapdata=datDGV, input_fmt=input_fmt_INF, win_indel
= win_indel_INF ,
win_inv_trans = win_inv_trans_INF, smap = smapName,
perc_similarity =perc_similarity_INF, indelconf, invconf,
transconf,
limsize = limsize,returnMethod = returnMethod_Internal)
}
else{
datInf<-internalFrequency(buildSVInternalDB = TRUE,
path = soloPath, pattern
= solopattern, outpath, dbOutput = dbOutput_Int,
smapdata = datDGV, smap = smapName,input_fmt = input_fmt_INF,
limsize = limsize, win_indel = win_indel_INF,
win_inv_trans = win_inv_trans_INF,
perc_similarity
= perc_similarity_INF, indelconf , invconf, transconf,
returnMethod= returnMethod_Internal)
}
if(buildBNInternalDB==FALSE){
datchort<-cohortFrequency(internalBNDB
= mergedFiles_BN, buildBNInternalDB = FALSE,
smapdata = datInf, input_fmt
= input_fmt_BN, win_indel = win_indel_INF,
win_inv_trans = win_inv_trans_INF,
perc_similarity = perc_similarity_INF,
indelconf = indelconf, invconf = invconf, limsize=limsize,
transconf = transconf,returnMethod = returnMethod_BNCohort)
}
else{
datchort<-cohortFrequency(buildBNInternalDB = TRUE,
smapdata = datInf, input_fmt = input_fmt_BN,
BNDBPath = InternalDBpath,
BNDBPattern = InternalDBpattern, win_indel =
win_indel_INF, dbOutput = dbOutput_BN,
win_inv_trans = win_inv_trans_INF,
perc_similarity = perc_similarity_INF, indelconf = indelconf,
invconf = invconf, limsize=limsize,
transconf = transconf,returnMethod = returnMethod_BNCohort)
}
datdecipher<-Decipher_extraction(decipherpath =
decipherpath, smap_data = datchort,
input_fmt = input_fmt_decipher,win_indel = win_indel_INF,
perc_similarity = perc_similarity_INF,
returnMethod = returnMethod_decipher)
run_bionano_filter(input_fmt_geneList = "dataFrame",
input_fmt_svMap = input_fmt_svMap,
SVFile = NULL,svData = datdecipher,dat_geneList = dat_geneList,
outpath = outpath,outputFilename = outputFilename,RZIPpath = RZIPpath)
}
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