View source: R/InternalFrequency_SE.r
| internalFrequency_solo | R Documentation | 
Calculates the internal frequencies of SV in internal cohorts, for SE
internalFrequency_solo(
  mergedFiles,
  smappath,
  smap,
  buildSVInternalDB = FALSE,
  smapdata,
  input_fmt = c("Text", "dataFrame"),
  path,
  pattern,
  outpath,
  win_indel = 10000,
  win_inv_trans = 50000,
  perc_similarity = 0.5,
  indelconf = 0.5,
  invconf = 0.01,
  fname,
  limsize = 1000,
  win_indel_parents = 5000,
  win_inv_trans_parents = 40000,
  transconf = 0.1,
  dbOutput = c("dataframe", "text"),
  returnMethod = c("Text", "dataFrame"),
  input_fmt_SV = c("Text", "dataFrame"),
  indexfile,
  perc_similarity_parents = 0.9,
  EnzymeType = c("SVmerge", "SE"),
  labelType = c("SVMerge", "SE", "Both"),
  SVMerge_path,
  SVMerge_pattern,
  SE_path,
  SE_pattern,
  Samplecodes,
  mergeKey,
  mergedKeyoutpath,
  mergedKeyFname
)
| mergedFiles | character. Path to the merged SV files. | 
| smappath | character. path to the query smap file. | 
| smap | character. File name for the smap | 
| buildSVInternalDB | boolean. Checking whether the merged solo file database exist. | 
| smapdata | character. Dataframe if input type chosen as dataframe. | 
| input_fmt | Format in which data is provided as an input to the function. | 
| path | character. Path to the solo file database. | 
| pattern | character. pattern of the file names to merge. | 
| outpath | character. Path where the merged samples are kept. | 
| win_indel | N umeric. Insertion and deletion error window. Default 10000. | 
| win_inv_trans | Numeric. Inversion and translocation error window. Default 50000. | 
| perc_similarity | Numeric . ThresholdPercentage similarity of the query SV and reference SV. Default 0.5. | 
| indelconf | Numeric. Threshold for insertion and deletion confidence. Default 0.5 | 
| invconf | Numeric. Threshold for inversion confidence.Default 0.01. | 
| fname | character. Filename in case dbOutput = Text. | 
| limsize | Numeric. Minimum size of SV that can be determined acurately by the Bionano SV caller. Default 1000. | 
| win_indel_parents | Numeric. Insertion and deletion error window to determine zygosity in case of parents. Default 5000. | 
| win_inv_trans_parents | Numeric. Inversion and translocation error window to determine zygosity in case of parents. Default 40000. | 
| transconf | Numeric. Threshold for translocation confidence. Default 0.1. | 
| dbOutput | character. Output of merged bionano data. | 
| returnMethod | character. Choice between Text and DataFrame. Required if you want to calculate internal frequency. | 
| input_fmt_SV | character. Choice between Text and DataFrame. | 
| indexfile | File containing connection between sample and nanoIDs | 
| perc_similarity_parents | Numeric . ThresholdPercentage similarity for zygosity determination. Default 0.9. | 
| EnzymeType | Character. Type of enzyme. Options Dual and DLE. | 
| labelType | character. Type of labels used for mapping. Choices are Dual, DLE and Both. | 
| SVMerge_path | character. Path for the Dual labelled cmap | 
| SVMerge_pattern | character. pattern of the dual files. | 
| SE_path | character. Path for the Dual labelled cmap | 
| SE_pattern | character. pattern of the dual files. | 
| Samplecodes | character. File containing relations and IDs associated to them. | 
| mergeKey | character. File containing sample ID and relation. | 
| mergedKeyoutpath | character. File path storing sample name and nanoID key information. | 
| mergedKeyFname | character. File name storing sample name and nanoID key information. | 
Calculated internal frequency in dataframe or text.
smapName = "NA12878_DLE1_VAP_solo5.smap"
smap = system.file("extdata", smapName, package="nanotatoR")
indelconf = 0.5; invconf = 0.01;transconf = 0.1;input_fmt="Text";
datInf <- internalFrequency_solo( smap = smap, 
buildSVInternalDB = FALSE, win_indel=10000, 
win_inv_trans=50000, EnzymeType = "SE",
mergedFiles = system.file("extdata", "nanotatoRControl.txt", package="nanotatoR"), 
perc_similarity_parents =0.9,
indexfile = system.file("extdata", "Sample_index.csv", package="nanotatoR"),
perc_similarity=0.5, indelconf=0.5, invconf=0.01, 
transconf=0.1, limsize=1000, win_indel_parents=5000,input_fmt="Text",
win_inv_trans_parents=40000,
returnMethod="dataFrame", input_fmt_SV = "Text")
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