Process Control and Internal Validation of Forensic STR Kits

Description

STR-validator is a free and open source R-package intended for process control and internal validation of forensic STR DNA typing kit. Its graphical user interface simplifies the analysis of data exported from e.g. GeneMapper software, without extensive knowledge about R. It provides functions to import, view, edit, and export data. After analysis the results, generated plots, heat-maps, and data can be saved in a project for easy access. Currently, analysis modules for stutter, balance, dropout, mixture, concordance, typing result, precision, pull-up, and analytical thresholds are available. In addition there are functions to analyze the GeneMapper bins- and panels files. EPG like plots can be generate from data. STR-validator can greatly increase the speed of validation by reducing the time and effort needed to analyze the validation data. It allows exploration of the characteristics of DNA typing kits according to ENFSI and SWGDAM recommendations. This facilitates the implementation of probabilistic interpretation of DNA results.

STR-validator was written and is maintained by Oskar Hansson, senior forensic scientist at Oslo University Hospital (OUS), Section for Forensic Biology. The work initially received external funding from the European Union seventh Framework Programme (FP7/2007-2013) under grant agreement no 285487 (EUROFORGEN-NoE) but development and maintenance is now performed as a part of my position at OUS, and on personal spare time.

Effort has been made to assure correct results. Refer to the main website for a list of functions specifically tested at build time.

Click Index at the bottom of the page to see a complete list of functions.

Created and maintained by:
Oskar Hansson, Section for Forensic Biology (OUS, Norway)

More information can be found at:
https://sites.google.com/site/forensicapps/strvalidator

Info and user community at Facebook:
https://www.facebook.com/pages/STR-validator/240891279451450?ref=tn_tnmn

https://www.facebook.com/groups/strvalidator/

The source code is hosted at GitHub:
https://github.com/OskarHansson/strvalidator

Please report bugs to:
https://github.com/OskarHansson/strvalidator/issues

Author(s)

Oskar Hansson oskhan@ous-hf.no

References

Recommended Minimum Criteria for the Validation of Various Aspects of the DNA Profiling Process http://enfsi.eu/wp-content/uploads/2016/09/minimum_validation_guidelines_in_dna_profiling_-_v2010_0.pdf Validation Guidelines for Forensic DNA Analysis Methods (2012) http://media.wix.com/ugd/4344b0_cbc27d16dcb64fd88cb36ab2a2a25e4c.pdf

Add Color Information.

Description

Add color information 'Color', 'Dye' or 'R Color'.

Usage

addColor(
  data,
  kit = NA,
  have = NA,
  need = NA,
  overwrite = FALSE,
  ignore.case = FALSE,
  debug = FALSE
)

Arguments

Details

Primers in forensic STR typing kits are labeled with a fluorescent dye. The dyes are represented with single letters (Dye) in exported result files or with strings (Color) in 'panels' files. For visualization in R the R color names are used (R.Color). The function can add new color schemes matched to the existing, or it can convert a vector containing one scheme to another.

Value

data.frame with additional columns for added colors, or vector with converted values.

Examples

# Get marker and colors for SGM Plus.
df <- getKit("SGMPlus", what = "Color")
# Add dye color.
dfDye <- addColor(data = df, need = "Dye")
# Add all color alternatives.
dfAll <- addColor(data = df)
# Convert a dye vector to R colors
addColor(data = c("R", "G", "Y", "B"), have = "dye", need = "r.color")

Adds New Data Columns to a Data Frame

Description

Adds values from columns in 'new.data' to 'data' by keys.

Usage

addData(
  data,
  new.data,
  by.col,
  then.by.col = NULL,
  exact = TRUE,
  ignore.case = TRUE,
  what = NULL,
  debug = FALSE
)

Arguments

Details

Information in columns in data frame 'new.data' is added to data frame 'data' based on primary key value in column 'by.col', and optionally on secondary key values in column 'then.by.col'.

Value

data.frame the original data frame containing additional columns.

Examples

# Get marker names and alleles for Promega PowerPlex ESX 17.
x <- getKit("ESX17", what = "Allele")
# Get marker names and colors for Promega PowerPlex ESX 17.
y <- getKit("ESX17", what = "Color")
# Add color information to allele information.
z <- addData(data = x, new.data = y, by.col = "Marker")
print(x)
print(y)
print(z)

Add Data

Description

GUI wrapper for addData.

Usage

addData_gui(env = parent.frame(), savegui = NULL, debug = FALSE, parent = NULL)

Arguments

Details

Simplifies the use of the addData function by providing a graphical user interface to it.

Value

TRUE

See Also

addData

Add Dye Information

Description

GUI wrapper to the addColor function.

Usage

addDye_gui(env = parent.frame(), savegui = NULL, debug = FALSE, parent = NULL)

Arguments

Details

Convenience GUI for the use of addColor and addOrder to add 'Dye', 'Color', 'R.Color', and marker 'Order' to a dataset. 'Dye' is the one letter abbreviations for the fluorophores commonly used to label primers in forensic STR typing kits (e.g. R and Y), 'Color' is the corresponding color name (e.g. red and yellow), 'R.Color' is the plot color used in R (e.g. red and black). 'Order' is the marker order in the selected kit. NB! Existing columns will be overwritten.

Value

TRUE

See Also

addColor

Add Missing Markers.

Description

Add missing markers to a dataset given a set of markers.

Usage

addMarker(data, marker, ignore.case = FALSE, debug = FALSE)

Arguments

Details

Given a dataset or a vector with sample names the function loops through each sample and add any missing markers. Returns a dataframe where each sample have at least one row per marker in the specified marker vector. Use sortMarker to sort the markers according to a specified kit. Required columns are: 'Sample.Name'.

Value

data.frame.

Add Missing Markers

Description

GUI wrapper for the addMarker function.

Usage

addMarker_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the addMarker function by providing a graphical user interface to it.

Value

TRUE

See Also

addMarker

Add Marker Order.

Description

Add marker order to data frame containing a column 'Marker'.

Usage

addOrder(
  data,
  kit = NULL,
  overwrite = FALSE,
  ignore.case = FALSE,
  debug = FALSE
)

Arguments

Details

Markers in a kit appear in a certain order. Not all STR-validator functions keep the original marker order in the result. A column indicating the marker order is added to the dataset. This is especially useful when exporting the data to an external spread-sheet software and allow to quickly sort the data in the correct order.

Value

data.frame with additional numeric column 'Order'.

Examples

# Load a dataset containing two samples.
data("set2")
# Add marker order when kit is known.
addOrder(data = set2, kit = "SGMPlus")

Add Size Information.

Description

Add size information to alleles.

Usage

addSize(data, kit = NA, bins = TRUE, ignore.case = FALSE, debug = FALSE)

Arguments

Details

Adds a column 'Size' with the fragment size in base pair (bp) for each allele as estimated from kit bins OR calculated from offset and repeat. The bins option return NA for alleles not in bin. The calculate option handles all named alleles including micro variants (e.g. '9.3'). Handles 'X' and 'Y' by replacing them with '1' and '2'.

Value

data.frame with additional columns for added size.

Add Size Information

Description

GUI wrapper for the addSize function.

Usage

addSize_gui(env = parent.frame(), savegui = NULL, debug = FALSE, parent = NULL)

Arguments

Details

Simplifies the use of the addSize function by providing a graphical user interface to it.

Value

TRUE

See Also

addSize

Log Audit Trail.

Description

Adds an audit trail to a dataset.

Usage

auditTrail(
  obj,
  f.call = NULL,
  key = NULL,
  value = NULL,
  label = NULL,
  arguments = TRUE,
  exact = TRUE,
  remove = FALSE,
  package = NULL,
  rversion = TRUE,
  timestamp = TRUE
)

Arguments

Details

Automatically add or updates an attribute 'audit trail' with arguments and parameters extracted from the function call. To list the arguments with the default set but not overridden arguments=TRUE must be set (default). Additional custom key-value pairs can be added. The label is extracted from the function name from f.call. Specify package to include the version number of a package.

Value

object with added or updated attribute 'audit trail'.

Examples

# A simple function with audit trail logging.
myFunction <- function(x, a, b = 5) {
  x <- x + a + b
  x <- auditTrail(obj = x, f.call = match.call(), package = "strvalidator")
  return(x)
}
# Run the function.
myData <- myFunction(x = 10, a = 2)
# Check the audit trail.
cat(attr(myData, "audit trail"))

# Remove the audit trail.
myData <- auditTrail(myData, remove = TRUE)
# Confirm that the audit trail is removed.
cat(attr(myData, "audit trail"))

Calculate Analytical Threshold

Description

Calculate analytical thresholds estimates.

Usage

calculateAT(
  data,
  ref = NULL,
  mask.height = TRUE,
  height = 500,
  mask.sample = TRUE,
  per.dye = TRUE,
  range.sample = 20,
  mask.ils = TRUE,
  range.ils = 10,
  k = 3,
  rank.t = 0.99,
  alpha = 0.01,
  ignore.case = TRUE,
  word = FALSE,
  debug = FALSE
)

Arguments

Details

Calculate the analytical threshold (AT) according to method 1, 2, and 4 as recommended in the reference by analyzing the background signal (noise). In addition method 7, a log-normal version of method 1 has been implemented. Method 1: The average signal + 'k' * the standard deviation. Method 2: The percentile rank method. The percentage of noise peaks below 'rank.t'. Method 4: Utilize the mean and standard deviation and the critical value obtained from the t-distribution for confidence interval 'alpha' (one-sided) and observed peaks analyzed (i.e. not masked) minus one as degrees of freedom, and the number of samples. Method 7: The average natural logarithm of the signal + k * the standard deviation.

If samples containing DNA are used, a range around the allelic peaks can be masked from the analysis to discard peaks higher than the noise. Masking can be within each dye or across all dye channels. Similarly a range around the peaks of the internal lane standard (ILS) can be masked across all dye channels. Which can bleed-through in week samples (i.e. negative controls) The mean, standard deviation, and number of peaks are calculated per dye per sample, per sample, globally across all samples, and globally across all samples per dye, for each method to estimate AT. Also the complete percentile rank list is calculated.

Value

list of three data frames. The first with result per dye per sample, per sample, globally across all samples, and globally across all samples per dye, for each method. The second is the complete percentile rank list. The third is the masked raw data used for calculation to enable manual check of the result.

References

J. Bregu et.al., Analytical thresholds and sensitivity: establishing RFU thresholds for forensic DNA analysis, J. Forensic Sci. 58 (1) (2013) 120-129, ISSN 1556-4029, DOI: 10.1111/1556-4029.12008. doi:10.1111/1556-4029.12008

See Also

maskAT, checkSubset

Calculate Analytical Threshold

Description

Calculate analytical thresholds estimate using linear regression.

Usage

calculateAT6(
  data,
  ref,
  amount = NULL,
  weighted = TRUE,
  alpha = 0.05,
  ignore.case = TRUE,
  debug = FALSE
)

Arguments

Details

Calculate the analytical threshold (AT) according to method 6 as outlined in the reference. In short serial dilutions are analyzed and the average peak height is calculated. Linear regression or Weighted linear regression with amount of DNA as the predictor for the peak height is performed. Method 6: A simplified version of the upper limit approach. AT6 = y-intercept + t-statistic * standard error of the regression. Assumes the y-intercept is not different from the mean blank signal. The mean blank signal should be included in the confidence range ('Lower' to 'AT6' in the resulting data frame). NB! This is an indirect method to estimate AT and should be verified by other methods. From the reference: A way to determine the validity of this approach is based on whether the y-intercept +- (1-a)100 contains the mean blank signal. If the mean blank signal is included in the y-intercept band, the following relationship [i.e. AT6] can be used to determine the AT. However, it should be noted that the ATs derived in this manner need to be calculated for each color and for all preparations (i.e., different injections, sample preparation volumes, post-PCR cleanup, etc.). NB! Quality sensors must be removed prior to analysis.

Value

data.frame with columns 'Amount', 'Height', 'Sd', 'Weight', 'N', 'Alpha', 'Lower', 'Intercept', and 'AT6'.

References

J. Bregu et.al., Analytical thresholds and sensitivity: establishing RFU thresholds for forensic DNA analysis, J. Forensic Sci. 58 (1) (2013) 120-129, ISSN 1556-4029, DOI: 10.1111/1556-4029.12008. doi:10.1111/1556-4029.12008

See Also

calculateAT6_gui, calculateAT, calculateAT_gui, lm

Calculate Analytical Threshold

Description

GUI wrapper for the calculateAT6 function.

Usage

calculateAT6_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Scores dropouts for a dataset.

Value

TRUE

See Also

calculateAT6, calculateAT, calculateAT_gui, checkSubset

Calculate Analytical Threshold

Description

GUI wrapper for the maskAT and calculateAT function.

Usage

calculateAT_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateAT and calculateAT function by providing a graphical user interface. In addition there are integrated control functions.

Value

TRUE

See Also

calculateAT, maskAT, checkSubset

Calculate Stochastic Thresholds

Description

Calculates point estimates for the stochastic threshold using multiple models.

Usage

calculateAllT(
  data,
  kit,
  p.dropout = 0.01,
  p.conservative = 0.05,
  rm.sex = TRUE,
  debug = FALSE
)

Arguments

Details

Expects output from calculateDropout as input. The function calls calculateT repeatedly to estimate the stochastic threshold using different models. The output is a data.frame summarizing the result. Use the modelDropout_gui to plot individual models.

Explanation of the result: Explanatory_variable - Drop-out is the dependent variable. An allele in heterozygous markers in the reference profile is chosen and drop-out is scored if the other allele is not observed in the sample, i.e. below the AT. The 'Random' method chose a random allele, while the 'LMW' and 'HMW' method chose the low and high molecular weight allele, respectively. The 'Locus' method score drop-out if any of the two alleles has dropped out. As explanatory variable the peak height of the surviving allele '(Ph)', average profile peak height '(H)', the logarithm of the surviving allele 'log(Ph)', and the logarithm of the average profile peak height 'log(H)' is used. P(dropout)=x.xx@T - is the point estimate for corresponding to the specified accepted risk of drop-out. P(dropout>x.xx)<0.05@T - is the conservative point estimate corresponding to a stochastic threshold with a risk <0.05 that the actual drop-out probability is >x.xx Hosmer-Lemeshow_p - p-value from the Hosmer-Lemeshow test. A value <0.05 indicates poor fit between the model and the observations.

Value

TRUE

See Also

calculateDropout, calculateT, modelDropout_gui, plotDropout_gui

Calculate Stochastic Thresholds

Description

GUI wrapper to the calculateAllT function.

Usage

calculateAllT_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Convenience GUI for the use of calculateAllT to calculate point estimates for the stochastic threshold using multiple models.

Value

TRUE

See Also

calculateAllT

Calculate Allele

Description

Calculates summary statistics for alleles per marker over the entire dataset.

Usage

calculateAllele(
  data,
  threshold = NULL,
  sex.rm = FALSE,
  kit = NULL,
  debug = FALSE
)

Arguments

Details

Creates a table of the alleles in the dataset sorted by number of observations.For each allele the proportion of total observations is calculated. Using a threshold this can be used to separate likely artefacts from likely drop-in peaks. In addition the observed allele frequency is calculated. If columns 'Height' and/or 'Size' are available summary statistics is calculated. NB! The function removes NA's and OL's prior to analysis.

Value

data.frame with columns 'Marker', 'Allele', 'Peaks', 'Size.Min', 'Size.Mean', 'Size.Max', 'Height.Min', 'Height.Mean', 'Height.Max', 'Total.Peaks', 'Allele.Proportion', 'Sum.Peaks', and 'Allele.Frequency'.

See Also

data.table

Calculate Allele

Description

GUI wrapper for the calculateAllele function.

Usage

calculateAllele_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateAllele function by providing a graphical user interface to it.

Value

TRUE

Calculate Capillary Balance

Description

Calculates the ILS inter capillary balance.

Usage

calculateCapillary(samples.table, plot.table, sq = 0, run = "", debug = FALSE)

Arguments

Details

Calculates the inter capillary balance for the internal lane standard (ILS). Require information from both the 'samples.table' and the 'plot.table'.

Value

data.frame with with columns 'Instrument', 'Instrument.ID', 'Run', 'Mean.Height', 'SQ', 'Injection', 'Capillary', 'Well', 'Comment'.

Calculate Capillary Balance

Description

GUI wrapper for the calculateCapillary function.

Usage

calculateCapillary_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateCapillary function by providing a graphical user interface.

Value

TRUE

See Also

calculateCapillary

Calculate Concordance.

Description

Calculates concordance and discordance for profiles in multiple datasets.

Usage

calculateConcordance(
  data,
  kit.name = NA,
  no.marker = "NO MARKER",
  no.sample = "NO SAMPLE",
  delimeter = ",",
  list.all = FALSE,
  debug = FALSE
)

Arguments

Details

Takes a list of datasets as input. It is assumed that each unique sample name represent a result originating from the same source DNA and thus is expected to give identical DNA profiles. The function first compare the profiles for each sample across datasets and lists discordant results. Then it performs a pair-wise comparison and compiles a concordance table. The tables are returned as two data frames in a list. NB! Typing and PCR artefacts (spikes, off-ladder peaks, stutters etc.) must be removed before analysis. NB! It is expected that the unique set of marker names across a dataset is present in each sample for that dataset (a missing marker is a discordance).

Value

list of data.frames (discordance table, and pair-wise comparison).

Calculate Concordance

Description

GUI wrapper for the calculateConcordance function.

Usage

calculateConcordance_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateConcordance function by providing a graphical user interface.

Value

TRUE

See Also

calculateConcordance

Calculate Allele Copies

Description

Calculates the number of alleles in each marker.

Usage

calculateCopies(
  data,
  observed = FALSE,
  copies = TRUE,
  heterozygous = FALSE,
  debug = FALSE
)

Arguments

Details

Calculates the number of unique values in the 'Allele*' columns for each marker, the number of allele copies, or indicate heterozygous loci. Observed - number of unique alleles. Copies - number of allele copies, '1' for heterozygotes and '2' for homozygotes. Heterozygous - '1' for heterozygous and '0' for homozygous loci. NB! The 'copies' and 'heterozygous' option are intended for known complete profiles, while 'observed' can be used for any samples to count the number of peaks. Sample names must be unique. The result is per marker but repeated for each row of that marker. Data in 'fat' format is auto slimmed.

Value

data.frame the original data frame with optional columns 'Observed', 'Copies', and 'Heterozygous'.

Calculate Allele Copies

Description

GUI wrapper for the link{calculateCopies} function.

Usage

calculateCopies_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateCopies function by providing a graphical user interface to it.

Value

TRUE

See Also

calculateCopies

Calculate Drop-out Events

Description

Calculate drop-out events (allele and locus) and records the surviving peak height.

Usage

calculateDropout(
  data,
  ref,
  threshold = NULL,
  method = c("1", "2", "X", "L"),
  ignore.case = TRUE,
  sex.rm = FALSE,
  qs.rm = TRUE,
  kit = NULL,
  debug = FALSE
)

Arguments

Details

Calculates drop-out events. In case of allele dropout the peak height of the surviving allele is given. Homozygous alleles in the reference set can be either single or double notation (X or X X). Markers present in the reference set but not in the data set will be added to the result. NB! 'Sample.Name' in 'ref' must be unique core name of replicate sample names in 'data'. Use checkSubset to make sure subsetting works as intended. There are options to remove sex markers and quality sensors from analysis.

NB! There are several methods of scoring drop-out events for regression. Currently the 'MethodX', 'Method1', and 'Method2' are endorsed by the DNA commission (see Appendix B in ref 1). However, an alternative method is to consider the whole locus and score drop-out if any allele is missing.

Explanation of the methods: Dropout - all alleles are scored according to AT. This is pure observations and is not used for modeling. MethodX - a random reference allele is selected and drop-out is scored in relation to the the partner allele. Method1 - the low molecular weight allele is selected and drop-out is scored in relation to the partner allele. Method2 - the high molecular weight allele is selected and drop-out is scored in relation to the partner allele. MethodL - drop-out is scored per locus i.e. drop-out if any allele has dropped out.

Method X/1/2 records the peak height of the partner allele to be used as the explanatory variable in the logistic regression. The locus method L also do this when there has been a drop-out, if not the the mean peak height for the locus is used. Peak heights for the locus method are stored in a separate column.

Value

data.frame with columns 'Sample.Name', 'Marker', 'Allele', 'Height', 'Dropout', 'Rfu', 'Heterozygous', and 'Model'. Dropout: 0 indicate no dropout, 1 indicate allele dropout, and 2 indicate locus dropout. Rfu: height of surviving allele. Heterozygous: 1 for heterozygous and 0 for homozygous. And any of the following containing the response (or explanatory) variable used for modeling by logistic regression in function modelDropout: 'MethodX', 'Method1', 'Method2', 'MethodL' and 'MethodL.Ph'.

References

Peter Gill et.al., DNA commission of the International Society of Forensic Genetics: Recommendations on the evaluation of STR typing results that may include drop-out and/or drop-in using probabilistic methods, Forensic Science International: Genetics, Volume 6, Issue 6, December 2012, Pages 679-688, ISSN 1872-4973, 10.1016/j.fsigen.2012.06.002. doi:10.1016/j.fsigen.2012.06.002

Peter Gill, Roberto Puch-Solis, James Curran, The low-template-DNA (stochastic) threshold-Its determination relative to risk analysis for national DNA databases, Forensic Science International: Genetics, Volume 3, Issue 2, March 2009, Pages 104-111, ISSN 1872-4973, 10.1016/j.fsigen.2008.11.009. doi:10.1016/j.fsigen.2008.11.009

Examples

data(set4)
data(ref4)
drop <- calculateDropout(data = set4, ref = ref4, kit = "ESX17", ignore.case = TRUE)

Calculate Dropout Events

Description

GUI wrapper for the calculateDropout function.

Usage

calculateDropout_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Scores dropouts for a dataset.

Value

TRUE

See Also

calculateDropout, checkSubset

Calculate Heterozygote Balance

Description

Calculates the heterozygote (intra-locus) peak balance.

Usage

calculateHb(
  data,
  ref,
  hb = 1,
  kit = NULL,
  sex.rm = FALSE,
  qs.rm = FALSE,
  ignore.case = TRUE,
  exact = FALSE,
  word = FALSE,
  debug = FALSE
)

Arguments

Details

Calculates the heterozygote (intra-locus) peak balance for a dataset. Known allele peaks will be extracted using the reference prior to analysis. Calculates the heterozygote balance (Hb), size difference between heterozygous alleles (Delta), and mean peak height (MPH). NB! 'X' and 'Y' will be handled as '1' and '2' respectively.

Value

data.frame with with columns 'Sample.Name', 'Marker', 'Delta', 'Hb', 'MPH'.

Examples

data(ref2)
data(set2)
# Calculate average balances.
calculateHb(data = set2, ref = ref2)

Calculate Heterozygote Balance

Description

GUI wrapper for the calculateHb function.

Usage

calculateHb_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateHb function by providing a graphical user interface.

Value

TRUE

See Also

link{calculateHb}, link{checkSubset}

Calculate Peak Height.

Description

Calculate peak height metrics for samples.

Usage

calculateHeight(
  data,
  ref = NULL,
  na.replace = NULL,
  add = TRUE,
  exclude = NULL,
  sex.rm = FALSE,
  qs.rm = FALSE,
  kit = NULL,
  ignore.case = TRUE,
  exact = FALSE,
  word = FALSE,
  debug = FALSE
)

Arguments

Details

Calculates the total peak height (TPH), and number of observed peaks (Peaks), for each sample by default. If a reference dataset is provided average peak height (H), and profile proportion (Proportion) are calculated.

H is calculated according to the formula (references [1][2]): H = sum(peak heights)/(n[het] + 2n[hom] Where: n[het] = number of observed heterozygous alleles n[hom] = number of observed homozygous alleles

Important: The above formula has a drawback that when many alleles have dropped out, i.e. when only few alleles are detected, H can be overestimated. For example, if there are only 1 (homozygote) peak observed in the profile, with a height of 100 RFU, then H=100 RFU. This means that the value of H will always be between half the analytical threshold (AT/2) and the peak height of the observed allele (if only one). For this reason Tvedebrink et al. actually modified the estimate to take the number of expected alleles into account when estimating the expected peak height (reference [3]). Basically, they adjust the estimated peak height for the fact that they know how many alleles that fall below the AT, such that the expected peak height could be estimated lower than AT. In addition, they account for degradation using a log-linear relationship on peak heights and fragment length.

Tip: If it is known that all expected peaks are observed and no unexpected peaks are present, the dataset can be used as a reference for itself.

Note: If a reference dataset is provided the known alleles will be extracted from the dataset.

Value

data.frame with with at least columns 'Sample.Name', 'TPH', and 'Peaks'.

References

[1] Torben Tvedebrink, Poul Svante Eriksen, Helle Smidt Mogensen, Niels Morling, Evaluating the weight of evidence by using quantitative short tandem repeat data in DNA mixtures Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 59, Issue 5, 2010, Pages 855-874, 10.1111/j.1467-9876.2010.00722.x. doi:10.1111/j.1467-9876.2010.00722.x

[2] Torben Tvedebrink, Helle Smidt Mogensen, Maria Charlotte Stene, Niels Morling, Performance of two 17 locus forensic identification STR kits-Applied Biosystems's AmpFlSTR NGMSElect and Promega's PowerPlex ESI17 kits Forensic Science International: Genetics, Volume 6, Issue 5, 2012, Pages 523-531, 10.1016/j.fsigen.2011.12.006. doi:10.1016/j.fsigen.2011.12.006

[3] Torben Tvedebrink, Maria Asplund, Poul Svante Eriksen, Helle Smidt Mogensen, Niels Morling, Estimating drop-out probabilities of STR alleles accounting for stutters, detection threshold truncation and degradation Forensic Science International: Genetics Supplement Series, Volume 4, Issue 1, 2013, Pages e51-e52, 10.1016/j.fsigss.2013.10.026. doi:10.1016/j.fsigss.2013.10.026

Calculate Peak Height

Description

GUI wrapper for the calculateHeight function.

Usage

calculateHeight_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateHeight function by providing a graphical user interface to it.

Value

TRUE

References

Torben Tvedebrink, Poul Svante Eriksen, Helle Smidt Mogensen, Niels Morling, Evaluating the weight of evidence by using quantitative short tandem repeat data in DNA mixtures Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 59, Issue 5, 2010, Pages 855-874, 10.1111/j.1467-9876.2010.00722.x. doi:10.1111/j.1467-9876.2010.00722.x

See Also

calculateHeight

Calculate Inter-locus Balance

Description

Calculates the inter-locus balance.

Usage

calculateLb(
  data,
  ref = NULL,
  option = "prop",
  by.dye = FALSE,
  ol.rm = TRUE,
  sex.rm = FALSE,
  qs.rm = FALSE,
  na = NULL,
  kit = NULL,
  ignore.case = TRUE,
  word = FALSE,
  exact = FALSE,
  debug = FALSE
)

Arguments

Details

The inter-locus balance (Lb), or profile balance, can be calculated as a proportion of the whole, normalized, or as centred quantities (as in the reference but using the mean total marker peak height instead of H). Lb can be calculated globally across the complete profile or within each dye channel. All markers must be present in each sample. Data can be unfiltered or filtered since the sum of peak heights by marker is used. A reference dataset is required to filter the dataset, which also adds any missing markers. A kit should be provided for filtering of known profile, sex markers, or quality sensors. If not automatic detection will be attempted. If missing, dye will be added according to kit. Off-ladder alleles and quality sensors are by default removed from the dataset. Sex markers are optionally removed. Some columns in the result may vary: TPH: Total (marker) Peak Height. TPPH: Total Profile Peak Height. MTPH: Maximum (sample) Total Peak Height. MPH: Mean (marker) Peak Height.

Value

data.frame with at least columns 'Sample.Name', 'Marker', 'TPH', 'Peaks', and 'Lb'. See description for additional columns.

References

Torben Tvedebrink et.al., Performance of two 17 locus forensic identification STR kits-Applied Biosystems's AmpFlSTR NGMSElect and Promega's PowerPlex ESI17 kits, Forensic Science International: Genetics, Volume 6, Issue 5, September 2012, Pages 523-531, ISSN 1872-4973, 10.1016/j.fsigen.2011.12.006. doi:10.1016/j.fsigen.2011.12.006

Examples

# Load data.
data(set2)

# Calculate inter-locus balance.
res <- calculateLb(data = set2)
print(res)

Calculate Locus Balance

Description

GUI wrapper for the calculateLb function.

Usage

calculateLb_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateLb function by providing a graphical user interface.

Value

TRUE

See Also

link{calculateLb}, link{checkSubset}

Calculate Mixture.

Description

Calculate Mx, drop-in, and

Usage

calculateMixture(
  data,
  ref1,
  ref2,
  ol.rm = TRUE,
  ignore.dropout = TRUE,
  debug = FALSE
)

Arguments

Details

Given a set of mixture results, reference profiles for the major component, and reference profile for the minor component the function calculates the mixture proportion (Mx), the average Mx, the absolute difference D=|Mx-AvgMx| for each marker, the percentage profile for the minor component, number of drop-ins. The observed and expected number of free alleles for the minor component (used to calculate the profile percentage) is also given.

NB! All sample names must be unique within and between each reference dataset. NB! Samples in ref1 and ref2 must be in 'sync'. The first sample in ref1 is combined with the first sample in ref2 to make a mixture sample. For example: ref1 "A" and ref2 "B" match mixture samples "A_B_1", "A_B_2" and so on. NB! If reference datasets have unequal number of unique samples the smaller dataset will limit the calculation.

Mixture proportion is calculated in accordance with:
Locus style (minor:MAJOR) | Mx
AA:AB | (A-B)/(A+B)
AB:AA | (2*B)/(A+B)
AB:AC | B/(B+C)
AA:BB | A/(A+B)
AB:CC | (A+B)/(A+B+C)
AB:CD | (A+B)/(A+B+C+D)
AB:AB | NA - cannot be calculated
AA:AA | NA - cannot be calculated

Value

data.frame with columns 'Sample.Name', 'Marker', 'Style', 'Mx', 'Average', 'Difference', 'Observed', 'Expected', 'Profile', and 'Dropin'.

References

Bright, Jo-Anne, Jnana Turkington, and John Buckleton. "Examination of the Variability in Mixed DNA Profile Parameters for the Identifiler Multiplex." Forensic Science International: Genetics 4, no. 2 (February 2010): 111-14. doi:10.1016/j.fsigen.2009.07.002. doi:10.1016/j.fsigen.2009.07.002

Calculate Mixture

Description

GUI wrapper for the calculateMixture function.

Usage

calculateMixture_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateMixture function by providing a graphical user interface.

Value

TRUE

See Also

calculateMixture, checkSubset

Analyze Off-ladder Alleles

Description

Analyze the risk for off-ladder alleles.

Usage

calculateOL(kit, db, virtual = TRUE, limit = TRUE, debug = FALSE)

Arguments

Details

By analyzing the allelic ladders the risk for getting off-ladder (OL) alleles are calculated. The frequencies from a provided population database is used to calculate the risk per marker and in total for the given kit(s). Virtual alleles can be excluded from the calculation. Small frequencies can be limited to the estimate 5/2N.

Value

data.frame with columns 'Kit', 'Marker', 'Database', 'Risk', and 'Total'.

Analyze Off-ladder Alleles

Description

GUI wrapper for the calculateOL function.

Usage

calculateOL_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = TRUE,
  parent = NULL
)

Arguments

Details

By analysis of the allelic ladder the risk for getting off-ladder (OL) alleles are calculated. The frequencies from a provided population database is used to calculate the risk per marker and in total for the given kit(s). Virtual alleles can be excluded from the calculation. Small frequencies can be limited to the estimate 5/2N.

Value

TRUE

See Also

calculateOL

Calculate Bins Overlap

Description

Analyses the bins overlap between colors.

Usage

calculateOverlap(
  data,
  db = NULL,
  penalty = NULL,
  virtual = TRUE,
  debug = FALSE
)

Arguments

Details

By analyzing the bins overlap between dye channels a measure of the risk for spectral pull-up artefacts can be obtain. The default result is a matrix with the total bins overlap in number of base pairs. If an allele frequency database is provided the overlap at each bin is multiplied with the frequency of the corresponding allele. If no frequence exist for that allele a frequency of 5/2N will be used. X and Y alleles is given the frequency 1. A penalty matrix can be supplied to reduce the effect by spectral distance, meaning that overlap with the neighboring dye can be counted in full (100 while a non neighbor dye get its overlap reduced (to e.g. 10

Value

data.frame with columns 'Kit', 'Color', [dyes], 'Sum', and 'Score'.

Calculate Bins Overlap

Description

GUI wrapper for the calculateOverlap function.

Usage

calculateOverlap_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = TRUE,
  parent = NULL
)

Arguments

Details

By analysis of the bins overlap between dye channels a measure of the risk for spectral pull-up artefacts can be obtain. The default result is a matrix with the total bins overlap in number of base pairs. If an allele frequency database is provided the overlap at each bin is multiplied with the frequency of the corresponding allele. If no frequence exist for that allele a frequency of 5/2N will be used. X and Y alleles is given the frequency 1. A scoring matrix can be supplied to reduce the effect by spectral distance, meaning that overlap with the neighboring dye can be counted in full (100 while a non neighbor dye get its overlap reduced (to e.g. 10

Value

TRUE

See Also

calculateOverlap

Calculate Peaks

Description

Calculates the number of peaks in samples.

Usage

calculatePeaks(
  data,
  bins = c(0, 2, 3),
  labels = NULL,
  ol.rm = FALSE,
  by.marker = FALSE,
  debug = FALSE
)

Arguments

Details

Count the number of peaks in a sample profile based on values in the 'Height' column. Each sample is labeled according to custom labels defined by the number of peaks. Peaks can be counted by sample or by marker within a sample. There is an option to discard off-ladder peaks ('OL'). The default purpose for this function is to categorize contamination in negative controls, but it can be used to simply calculating the number of peaks in any sample. NB! A column 'Peaks' for the number of peaks will be created. If present it will be overwritten. NB! A column 'Group' for the sample group will be created. If present it will be overwritten. NB! A column 'Id' will be created by combining the content in the 'Sample.Name' and 'File' column (if available). The unique entries in the 'Id' column will be the definition of a unique sample. If 'File' is present this allows for identical sample names in different batches (files) to be identified as unique samples. If 'Id' is present it will be overwritten.

Value

data.frame with with additional columns 'Peaks', 'Group', and 'Id'.

Calculate Peaks

Description

GUI wrapper for the calculatePeaks function.

Usage

calculatePeaks_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Counts the number of peaks in samples and markers with option to discard off-ladder peaks and to label groups according to maximum number of peaks.

Value

TRUE

See Also

calculatePeaks

Calculate Spectral Pull-up

Description

Calculates possible pull-up peaks.

Usage

calculatePullup(
  data,
  ref,
  pullup.range = 6,
  block.range = 12,
  ol.rm = FALSE,
  ignore.case = TRUE,
  word = FALSE,
  discard = FALSE,
  limit = 1,
  debug = FALSE
)

Arguments

Details

Calculates possible pull-up (aka. bleed-through) peaks in a dataset. Known alleles are identified and the analysis window range is marked. If the blocking range of known alleles overlap, they are excluded from the analysis. Pull-up peaks within the data point analysis window, around known alleles, are identified, the data point difference, and the ratio is calculated. Off-ladder ('OL') alleles are included by default but can be excluded. All known peaks included in the analysis are by default written to the result even if they did not cause any pull-up. These rows can be discarded from the result.

Value

data.frame with with columns 'Sample.Name', 'Marker', 'Dye', 'Allele', 'Height', 'Size', 'Data.Point', 'P.Marker', 'P.Dye', 'P.Allele', 'P.Height', 'P.Size', 'P.Data.Point', 'Delta', 'Ratio'.

Calculate Spectral Pull-up

Description

GUI wrapper for the calculatePullup function.

Usage

calculatePullup_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculatePullup function by providing a graphical user interface.

Value

TRUE

See Also

calculatePullup, checkSubset

Calculate Ratio

Description

Calculates the peak height ratio between specified loci.

Usage

calculateRatio(
  data,
  ref = NULL,
  numerator = NULL,
  denominator = NULL,
  group = NULL,
  ol.rm = TRUE,
  ignore.case = TRUE,
  word = FALSE,
  exact = FALSE,
  debug = FALSE
)

Arguments

Details

Default is to calculate the ratio between all unique pairwise combinations of markers/loci. If equal number of markers are provided in the numerator and the denominator the provided pairwise ratios will be calculated. If markers are provided in only the numerator or only the denominator the ratio of all possible combinations of the provided markers and the markers not provided will be calculated. If the number of markers provided are different in the numerator and in the denominator the shorter vector will be repeated to equal the longer vector in length. Data can be unfiltered or filtered since the sum of peak heights per marker is used. Off-ladder alleles is by default removed from the dataset before calculations.

Value

data.frame with with columns 'Sample.Name', 'Marker', 'Delta', 'Hb', 'Lb', 'MPH', 'TPH'.

Examples

data(set2)
# Calculate ratio between the shortest and longest marker in each dye.
numerator <- c("D3S1358", "AMEL", "D19S433")
denominator <- c("D2S1338", "D18S51", "FGA")
calculateRatio(data = set2, numerator = numerator, denominator = denominator)
calculateRatio(data = set2, numerator = NULL, denominator = "AMEL")
calculateRatio(data = set2, numerator = c("AMEL", "TH01"), denominator = NULL)
calculateRatio(data = set2, numerator = NULL, denominator = NULL)

Calculate Ratio

Description

GUI wrapper for the calculateRatio function.

Usage

calculateRatio_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateRatio function by providing a graphical user interface.

Value

TRUE

See Also

link{calculateRatio}, link{checkSubset}

Calculate Result Type

Description

Calculate the result type for samples.

Usage

calculateResultType(
  data,
  kit = NULL,
  add.missing.marker = TRUE,
  threshold = NULL,
  mixture.limits = NULL,
  partial.limits = NULL,
  subset.name = NA,
  marker.subset = NULL,
  debug = FALSE
)

Arguments

Details

Calculates result types for samples in 'data'. Defined types are: 'No result', 'Mixture', 'Partial', and 'Complete'. Subtypes can be defined by parameters. An integer passed to 'threshold' defines a subtype of 'Complete' "Complete profile all peaks >threshold". An integer or vector passed to 'mixture.limits' define subtypes of 'Mixture' "> [mixture.limits] markers". An integer or vector passed to 'partial.limits' define subtypes of 'Partial' "> [partial.limits] peaks". A string with marker names separated by pipe (|) passed to 'marker.subset' and a string 'subset.name' defines a subtype of 'Partial' "Complete [subset.name]".

Value

data.frame with columns 'Sample.Name','Type', and 'Subtype'.

Calculate Result Type

Description

GUI wrapper for the calculateResultType function.

Usage

calculateResultType_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of calculateResultType by providing a graphical user interface.

Value

TRUE

See Also

calculateResultType

Calculate Profile Slope.

Description

Calculate profile slope for samples.

Usage

calculateSlope(data, ref, conf = 0.975, kit = NULL, debug = FALSE, ...)

Arguments

Details

Calculates the profile slope for each sample. The slope is calculated as a linear model specified by the response (natural logarithm of peak height) by the term size (in base pair). If 'Size' is not present in the dataset, one or multiple kit names can be given as argument 'kit'. The specified kits will be used to estimate the size of each allele. If 'kit' is NULL the kit(s) will be automatically detected, and the 'Size' will be calculated.

The column 'Group' can be used to separate datasets to be compared, and if so 'kit' must be a vector of equal length as the number of groups, and in the same order. If not the first 'kit' will be recycled for all groups.

Data will be filtered using the reference profiles.

Value

data.frame with with columns 'Sample.Name', 'Kit', 'Group', 'Slope', 'Error', 'Peaks', 'Lower', and 'Upper'.

Calculate Profile Slope

Description

GUI wrapper for the calculateSlope function.

Usage

calculateSlope_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateSlope function by providing a graphical user interface.

Value

TRUE

See Also

calculateSlope

Detect Spike

Description

Detect samples with possible spikes in the DNA profile.

Usage

calculateSpike(
  data,
  threshold = NULL,
  tolerance = 2,
  kit = NULL,
  quick = FALSE,
  debug = FALSE
)

Arguments

Details

Creates a list of possible spikes by searching for peaks aligned vertically (i.e. nearly identical size). There are two methods to search. The default method (quick=FALSE) method that calculates the distance between each peak in a sample, and the quick and dirty method (quick=TRUE) that rounds the size and then group peaks with identical size. The rounding method is faster because it uses the data.table package. The accurate method is slower because it uses nested loops - the first through each sample to calculate the distance between all peaks, and the second loops through the distance matrix to identify which peaks lies within the tolerance. NB! The quick method may not catch all spikes since two peaks can be separated by rounding e.g. 200.5 and 200.6 becomes 200 and 201 respectively.

Value

data.frame

See Also

data.table

Detect Spike

Description

GUI wrapper for the calculateSpike function.

Usage

calculateSpike_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateSpike function by providing a graphical user interface.

Value

TRUE

See Also

calculateSpike

Summary Statistics

Description

Calculate summary statistics for the selected target and scope.

Usage

calculateStatistics(
  data,
  target,
  quant = 0.95,
  group = NULL,
  count = NULL,
  decimals = -1,
  debug = FALSE
)

Arguments

Details

Calculate summary statistics for the given target column ('X') across the entire dataset or grouped by one or multiple columns, and counts the number of unique values in the given count column ('Y'). Returns a data.frame with the grouped columns, number of unique values 'Y.n', number of observations 'X.n', the minimum value 'X.Min', the mean value 'X.Mean', standard deviation 'X.Stdv', and the provided percentile 'X.Perc.##'. For more details see unique, min, mean, sd, quantile.

Value

data.frame with summary statistics.

Calculate Statistics

Description

GUI wrapper for the calculateStatistics function.

Usage

calculateStatistics_gui(
  data = NULL,
  target = NULL,
  quant = 0.95,
  group = NULL,
  count = NULL,
  decimals = 4,
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateStatistics function by providing a graphical user interface. Preselected values can be provided as arguments.

Value

TRUE

See Also

link{quantile}, link{min}, link{max}, link{mean}, link{sd}

Calculate Stutter

Description

Calculate statistics for stutters.

Usage

calculateStutter(
  data,
  ref,
  back = 2,
  forward = 1,
  interference = 0,
  replace.val = NULL,
  by.val = NULL,
  debug = FALSE
)

Arguments

Details

Calculates stutter ratios based on the 'reference' data set and a defined analysis range around the true allele.

NB! Off-ladder alleles ('OL') is NOT included in the analysis. NB! Labeled pull-ups or artefacts within stutter range IS included in the analysis.

There are three levels of allowed overlap (interference). 0 = no interference (default): calculate the ratio for a stutter only if there are no overlap between the stutter or its allele with the analysis range of another allele. 1 = stutter-stutter interference: calculate the ratio for a stutter even if the stutter or its allele overlap with a stutter within the analysis range of another allele. 2 = stutter-allele interference: calculate the ratio for a stutter even if the stutter and its allele overlap with the analysis range of another allele.

Value

data.frame with extracted result.

Calculate Stutter

Description

GUI wrapper for the calculateStutter function.

Usage

calculateStutter_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the calculateStutter function by providing a graphical user interface to it.

Value

TRUE

See Also

calculateStutter, checkSubset

Calculate Stochastic Threshold

Description

Calculates point estimates for the stochastic threshold.

Usage

calculateT(
  data,
  log.model = FALSE,
  p.dropout = 0.01,
  pred.int = 0.95,
  debug = FALSE
)

Arguments

Details

Given a data.frame with observed values for the dependent variable (column 'Dep') and explanatory values (column 'Exp') point estimates corresponding to a risk level of p.dropout are calculated using logistic regression: glm(Dep~Exp, family=binomial("logit"). A conservative estimate is calculated from the pred.int. In addition the model parameters B0 (intercept) and B1 (slope), Hosmer-Lemeshow test statistic (p-value), and the number of observed and dropped out alleles is returned.

Value

vector with named parameters

See Also

calculateDropout, calculateAllT, modelDropout_gui, plotDropout_gui

Check Dataset

Description

Check a data.frame before analysis.

Usage

checkDataset(
  name,
  reqcol = NULL,
  slim = FALSE,
  slimcol = NULL,
  string = NULL,
  stringcol = NULL,
  env = parent.frame(),
  parent = NULL,
  debug = FALSE
)

Arguments

Details

Check that the object exist, there are rows, the required columns exist, if data.frame is 'fat', and if invalid strings exist. Show error message if not.

Check Subset

Description

Check the result of subsetting

Usage

checkSubset(
  data,
  ref,
  console = TRUE,
  ignore.case = TRUE,
  word = FALSE,
  exact = FALSE,
  debug = FALSE
)

Arguments

Details

Check if ref and sample names are unique for subsetting. Prints the result to the R-prompt.

See Also

grep

Check Subset

Description

GUI wrapper for the checkSubset function.

Usage

checkSubset_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the checkSubset function by providing a graphical user interface to it.

Value

TRUE

See Also

checkSubset

Convert Columns

Description

Internal helper function.

Usage

colConvert(
  data,
  columns = "Height|Size|Data.Point",
  ignore.case = TRUE,
  fixed = FALSE,
  debug = FALSE
)

Arguments

Details

Takes a data frame as input and return it after converting known numeric columns to numeric.

Value

data.frame.

Column Names

Description

Internal helper function.

Usage

colNames(data, slim = TRUE, concatenate = NULL, numbered = TRUE, debug = FALSE)

Arguments

Details

Takes a data frame as input and return either column names occurring once or multiple times. Matching is done by the 'base name' (the substring to the left of the last period, if any). The return type is a string vector by default, or a single string of column names separated by a string 'concatenate' (see 'collapse' in paste for details). There is an option to limit multiple names to those with a number suffix.

Value

character, vector or string.

Column Actions

Description

Perform actions on columns.

Usage

columns(
  data,
  col1 = NA,
  col2 = NA,
  operator = "&",
  fixed = NA,
  target = NA,
  start = 1,
  stop = 1,
  debug = FALSE
)

Arguments

Details

Perform actions on columns in a data frame. There are five actions: concatenate, add, multiply, subtract, divide. The selected action can be performed on two columns, or one column and a fixed value, or a new column can be added. A target column for the result is specified. NB! if the target column already exist it will be overwritten, else it will be created. A common use is to create a unique Sample.Name from the existing Sample.Name column and e.g. the File.Name or File.Time columns. It can also be used to calculate the Amount from the Concentration.

Value

data frame.

See Also

substr

Examples

# Get a sample dataset.
data(set2)
# Add concatenate Sample.Name and Dye.
set2 <- columns(data = set2, col1 = "Sample.Name", col2 = "Dye")
# Multiply Height by 4.
set2 <- columns(data = set2, col1 = "Height", operator = "*", fixed = 4)
# Add a new column.
set2 <- columns(data = set2, operator = "&", fixed = "1234", target = "Batch")

Column Actions

Description

GUI wrapper for the columns function.

Usage

columns_gui(env = parent.frame(), savegui = NULL, debug = FALSE, parent = NULL)

Arguments

Details

Simplifies the use of the columns function by providing a graphical user interface to it.

Value

TRUE

Combine Bins And Panels Files.

Description

Combines useful information into one dataset.

Usage

combineBinsAndPanels(bin, panel)

Arguments

Details

Combines information from two sources ('Bins' and 'Panels' file) to create a dataset containing information about panel name, marker name, alleles in the allelic ladder, their size and size range, a flag indicating virtual alleles, fluorophore color, repeat size, marker range. The short name, full name, and sex marker flag is populated through the makeKit_gui user interface. In addition the function calculates an estimated offset for each marker, which can be used for creating EPG like plots. Note: offset is estimated by taking the smallest physical ladder fragment e.g. 98.28 for D3 in ESX17. Round this to an integer (98) and finally subtract the number of base pair for that repeat i.e. 4*9=36, which gives an offset of 98-36 = 62 bp. Microvariants are handled by taking the decimal part multiplied with 10 and adding this to the number of base pair e.g. 9.3 = 4*9 + 0.3*10 = 39 bp.

Value

data frame containing columns 'Panel', 'Marker', 'Allele', 'Size', 'Size.Min', 'Size.Max', 'Virtual', 'Color', 'Repeat', 'Marker.Min', 'Marker.Max', 'Offset', 'Short.Name', 'Full.Name'

Combine Datasets

Description

GUI for combining two datasets.

Usage

combine_gui(env = parent.frame(), debug = FALSE, parent = NULL)

Arguments

Details

Simple GUI to combine two datasets using the rbind.fill function. NB! Datasets must have identical column names but not necessarily in the same order.

Value

TRUE

Crop Or Replace

Description

GUI simplifying cropping and replacing values in data frames.

Usage

cropData_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Select a data frame from the drop-down and a target column. To remove rows with 'NA' check the appropriate box. Select to discard or replace values and additional options. Click button to 'Apply' changes. Multiple actions can be performed on one dataset before saving as a new dataframe. NB! Check that data type is correct before click apply to avoid strange behavior. If data type is numeric any string will become a numeric 'NA'.

Value

TRUE

See Also

trim_gui, editData_gui, combine_gui

Detect Kit

Description

Finds the most likely STR kit for a dataset.

Usage

detectKit(data, index = FALSE, debug = FALSE)

Arguments

Details

The function first check if there is a 'kit' attribute for the dataset. If there was a 'kit' attribute, and a match is found in getKit the corresponding kit or index is returned. If an attribute does not exist the function looks at the markers in the dataset and returns the most likely kit(s).

Value

integer or string indicating the detected kit.

Edit or View Data Frames

Description

GUI to edit and view data frames.

Usage

editData_gui(
  env = parent.frame(),
  savegui = NULL,
  data = NULL,
  name = NULL,
  edit = TRUE,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Select a data frame from the drop-down to view or edit a dataset. It is possible to save as a new dataframe. To enable sorting by clicking the column headers the view mode must be used (i.e. edit = FALSE). There is an option to limit the number of rows shown that can be used to preview large datasets that may otherwise cause performance problems. Attributes of the dataset can be views in a separate window.

Value

TRUE

See Also

trim_gui, cropData_gui, combine_gui

Export

Description

Exports or saves various objects.

Usage

export(
  object,
  name = NA,
  use.object.name = is.na(name),
  env = parent.frame(),
  path = NA,
  ext = "auto",
  delim = "\t",
  width = 3000,
  height = 2000,
  res = 250,
  overwrite = FALSE,
  debug = FALSE
)

Arguments

Details

Export objects to a directory on the file system. Currently only objects of class data.frames or ggplot are supported. data.frame objects will be exported as '.txt' and ggplot objects as '.png'. .RData applies to all supported object types.

Value

NA if all objects were exported OR, data.frame with columns 'Object', 'Name', and 'New.Name' with objects that were not exported.

Export

Description

GUI wrapper for the export function.

Usage

export_gui(
  obj = listObjects(env = env, obj.class = c("data.frame", "ggplot")),
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the export function by providing a graphical user interface to it. Currently all available objects provided are selected by default.

Value

TRUE

See Also

export

Filter Profile

Description

Filter peaks from profiles.

Usage

filterProfile(
  data,
  ref = NULL,
  add.missing.loci = FALSE,
  keep.na = FALSE,
  ignore.case = TRUE,
  exact = FALSE,
  word = FALSE,
  invert = FALSE,
  sex.rm = FALSE,
  qs.rm = FALSE,
  kit = NULL,
  filter.allele = TRUE,
  debug = FALSE
)

Arguments

Details

Filters out the peaks matching (or not matching) specified known profiles from typing data containing 'noise' such as stutters. If 'ref' does not contain a 'Sample.Name' column it will be used as reference for all samples in 'data'. The 'invert' option filters out peaks NOT matching the reference (e.g. drop-in peaks). Sex markers and quality sensors can be removed. NB! add.missing.loci overrides keep.na. Returns data where allele names match/not match 'ref' allele names. Required columns are: 'Sample.Name', 'Marker', and 'Allele'.

Value

data.frame with extracted result.

Filter Profile

Description

GUI wrapper for the filterProfile function.

Usage

filterProfile_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the filterProfile function by providing a graphical user interface to it. All data not matching/matching the reference will be discarded. Useful for filtering stutters and artifacts from raw typing data or to identify drop-ins.

Value

TRUE

See Also

filterProfile, checkSubset

Generate EPG

Description

Visualizes an EPG from DNA profiling data.

Usage

generateEPG(
  data,
  kit,
  title = NULL,
  wrap = TRUE,
  boxplot = FALSE,
  peaks = TRUE,
  collapse = TRUE,
  silent = FALSE,
  ignore.case = TRUE,
  at = 0,
  scale = "free",
  limit.x = TRUE,
  label.size = 3,
  label.angle = 0,
  label.vjust = 1,
  label.hjust = 0.5,
  expand = 0.1,
  debug = FALSE
)

Arguments

Details

Generates a electropherogram like plot from 'data' and 'kit'. If 'Size' is not present it is estimated from kit information and allele values. If 'Height' is not present a default of 1000 RFU is used. Off-ladder alleles can be plotted if 'Size' is provided. There are various options to customize the plot scale and labels. It is also possible to plot 'distributions' of peak heights as boxplots.

Value

ggplot object.

Generate EPG

Description

GUI wrapper for the generateEPG function.

Usage

generateEPG_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the generateEPG function by providing a graphical user interface to it.

Value

TRUE

See Also

generateEPG

Get Allele Frequency Database

Description

Gives access to allele frequency databases.

Usage

getDb(db.name.or.index = NULL, debug = FALSE)

Arguments

Details

The function provides access to allele frequency databases stored in the file database.txt in the package directory. It returns the specified allele frequency database.

Value

data.frame with allele frequency database information. If no matching database or database index is found NA is returned. If the database was not found NULL is returned.

Examples

# Show available allele frequency databases.
getDb()

Get Kit

Description

Provides information about STR kits.

Usage

getKit(
  kit = NULL,
  what = NA,
  show.messages = FALSE,
  .kit.info = NULL,
  debug = FALSE
)

Arguments

Details

The function returns the following information for a kit specified in kits.txt: Panel name, short kit name (unique, user defined), full kit name (user defined), marker names, allele names, allele sizes (bp), minimum allele size, maximum allele size (bp), flag for virtual alleles, marker color, marker repeat unit size (bp), minimum marker size, maximum marker, marker offset (bp), flag for sex markers (TRUE/FALSE).

If no matching kit or kit index is found NA is returned. If kit='NULL' or '0' a vector of available kits is printed and NA returned.

Value

data.frame with kit information.

Examples

# Show all information stored for kit with short name 'ESX17'.
getKit("ESX17")

Get Settings.

Description

Accepts a key string and returns the corresponding value.

Usage

getSetting(key)

Arguments

Details

Accepts a key string and returns the corresponding value from the settings.txt file located within the package folders exdata sub folder.

Value

character the retrieved value or NA if not found.

Get Language Strings

Description

Accepts a language code and gui. Returns the corresponding language strings.

Usage

getStrings(language = NA, gui = NA, key = NA, encoding = NA, about = FALSE)

Arguments

Details

Accepts a language code, gui, and key. Returns the corresponding language strings for the specified gui function or key from a text file named as the language code. Replaces backslash + n with new line character (only if 'gui' is specified).

Value

character vector or data.table with the retrieved values. NULL if file or gui was not found.

Save Image

Description

A simple GUI wrapper for ggsave.

Usage

ggsave_gui(
  ggplot = NULL,
  name = "",
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simple GUI wrapper for ggsave.

Value

TRUE

See Also

ggsave

Guess Profile

Description

Guesses the correct profile based on peak height.

Usage

guessProfile(
  data,
  ratio = 0.6,
  height = 50,
  na.rm = FALSE,
  ol.rm = TRUE,
  debug = FALSE
)

Arguments

Details

Takes typing data from single source samples and filters out the presumed profile based on peak height and a ratio. Keeps the two highest peaks if their ratio is above the threshold, or the single highest peak if below the threshold.

Value

data.frame 'data' with genotype rows only.

Examples

# Load an example dataset.
data(set2)
# Filter out probable profile with criteria at least 70% Hb.
guessProfile(data = set2, ratio = 0.7)

Guess Profile

Description

GUI wrapper for the guessProfile function.

Usage

guessProfile_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the guessProfile function by providing a graphical user interface to it.

Value

TRUE

See Also

guessProfile, checkSubset

Height To Peak.

Description

Helper function to convert a peak into a plotable polygon.

Usage

heightToPeak(data, width = 1, keep.na = TRUE, debug = FALSE)

Arguments

Details

Converts a single height and size value to a plotable 0-height-0 triangle/peak value. Makes 3 data points from each peak size for plotting a polygon representing a peak. Factors in other columns might get converted to factor level.

Value

data.frame with new values.

Import Data

Description

Import text files and apply post processing.

Usage

import(
  folder = TRUE,
  extension = "txt",
  suffix = NA,
  prefix = NA,
  import.file = NA,
  folder.name = NA,
  file.name = TRUE,
  time.stamp = TRUE,
  separator = "\t",
  ignore.case = TRUE,
  auto.trim = FALSE,
  trim.samples = NULL,
  trim.invert = FALSE,
  auto.slim = FALSE,
  slim.na = TRUE,
  na.strings = c("NA", ""),
  debug = FALSE
)

Arguments

Details

Imports text files (e.g. GeneMapper results exported as text files) as data frames. Options to import one or multiple files. For multiple files it is possible to specify prefix, suffix, and file extension to create a file name filter. The file name and/or file time stamp can be imported. NB! Empty strings ("") and NA strings ("NA") are converted to NA. See list.files and read.table for additional details.

Value

data.frame with imported result.

See Also

trim, slim, list.files, read.table

Import Data

Description

GUI wrapper for the import function.

Usage

import_gui(env = parent.frame(), savegui = NULL, debug = FALSE, parent = NULL)

Arguments

Details

Simplifies the use of the import function by providing a graphical user interface to it.

Value

TRUE

See Also

import

List Objects

Description

Internal helper function to list objects in an environment.

Usage

listObjects(
  env = parent.frame(),
  obj.class = NULL,
  sort = NULL,
  decreasing = TRUE,
  debug = FALSE
)

Arguments

Details

Internal helper function to retrieve a list of objects from a workspace. Take an environment as argument and optionally an object class. Returns a list of objects of the specified class in the environment.

Value

character vector with the object names or NULL.

Examples

## Not run: 
# List data frames in the workspace.
listObjects(obj.class = "data.frame")
# List functions in the workspace.
listObjects(obj.class = "function")

## End(Not run)

Make Kit

Description

Add new kits or edit the kit file.

Usage

makeKit_gui(env = parent.frame(), savegui = NULL, debug = FALSE, parent = NULL)

Arguments

Details

A graphical user interface for reading information from 'bins' and 'panels' file for the creation of additional kits. It is also possible to edit the short and full name of existing kits or removing kits. The gender marker of each kits is auto detected but can be changed manually. #' NB! Short name must be unique.

Value

TRUE

See Also

readBinsFile, readPanelsFile, combineBinsAndPanels

Mask And Prepare Data To Analyze Analytical Threshold

Description

Break-out function to prepare data for the function calculateAT.

Usage

maskAT(
  data,
  ref = NULL,
  mask.height = TRUE,
  height = 500,
  mask.sample = TRUE,
  per.dye = TRUE,
  range.sample = 20,
  mask.ils = TRUE,
  range.ils = 10,
  ignore.case = TRUE,
  word = FALSE,
  debug = FALSE
)

Arguments

Details

Prepares the 'SamplePlotSizingTable' for analysis of analytical threshold. It is needed by the plot functions for control of masking. The preparation consist of converting the 'Height' and 'Data.Point' column to numeric (if needed), then dye channel information is extracted from the 'Dye.Sample.Peak' column and added to its own 'Dye' column, known fragments of the internal lane standard (marked with an asterisk '*') is flagged as 'TRUE' in a new column 'ILS'.

Value

data.frame with added columns 'Dye' and 'ILS'.

See Also

calculateAT

Model And Plot Drop-out Events

Description

Model the probability of drop-out and plot graphs.

Usage

modelDropout_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

calculateDropout score drop-out events relative to a user defined LDT in four different ways: (1) by reference to the low molecular weight allele (Method1), (2) by reference to the high molecular weight allele (Method2), (3) by reference to a random allele (MethodX), and (4) by reference to the locus (MethodL). Options 1-3 are recommended by the DNA commission (see reference), while option 4 is included for experimental purposes. Options 1-3 may discard many dropout events while option 4 catches all drop-out events. On the other hand options 1-3 can score events below the LDT, while option 4 cannot, making accurate predictions possible below the LDT. This is also why the number of observed drop-out events may differ between model plots and heatmap, scatterplot, and ecdf.

Method X/1/2 records the peak height of the partner allele to be used as the explanatory variable in the logistic regression. The locus method L also do this when there has been a drop-out, if not the the mean peak height for the locus is used. Peak heights for the locus method are stored in a separate column.

Using the scored drop-out events and the peak heights of the surviving alleles the probability of drop-out can be modeled by logistic regression as described in Appendix B in reference [1]. P(dropout|H) = B0 + B1*H, where 'H' is the peak height or log(peak height). This produces a plot with the predicted probabilities for a range of peak heights. There are options to print the model parameters, mark the stochastic threshold at a specified probability of drop-out, include the underlying observations, and to calculate a specified prediction interval. A conservative estimate of the stochastic threshold can be calculated from the prediction interval: the risk of observing a drop-out probability greater than the specified threshold limit, at the conservative peak height, is less than a specified value (e.g. 1-0.95=0.05). By default the gender marker is excluded from the dataset used for modeling, and the peak height is used as explanatory variable. The logarithm of the average peak height 'H' can be used instead of the allele/locus peak height [3] (The implementation of 'H' has limitations when dropout is present. See calculateHeight). To evaluate the goodness of fit for the logistic regression the Hosmer-Lemeshow test is used [4]. A value below 0.05 indicates a poor fit. Alternatives to the logistic regression method are discussed in reference [5] and [6].

Explanation of the result: Dropout - all alleles are scored according to the limit of detection threshold (LDT). This is the observations and is not used for modeling. Rfu - peak height of the surviving allele. MethodX - a random reference allele is selected and drop-out is scored in relation to the the partner allele. Method1 - the low molecular weight allele is selected and drop-out is scored if the high molecular weight allele is missing. Method2 - the high molecular weight allele is selected and drop-out is scored if the low molecular weight allele is missing. MethodL - drop-out is scored per locus i.e. drop-out if any allele is missing. MethodL.Ph - peak height of the surviving allele if one allele has dropped out, or the average peak height if no drop-out.

Value

TRUE

References

[1] Peter Gill et.al., DNA commission of the International Society of Forensic Genetics: Recommendations on the evaluation of STR typing results that may include drop-out and/or drop-in using probabilistic methods, Forensic Science International: Genetics, Volume 6, Issue 6, December 2012, Pages 679-688, ISSN 1872-4973, 10.1016/j.fsigen.2012.06.002. doi:10.1016/j.fsigen.2012.06.002

[2] Peter Gill, Roberto Puch-Solis, James Curran, The low-template-DNA (stochastic) threshold-Its determination relative to risk analysis for national DNA databases, Forensic Science International: Genetics, Volume 3, Issue 2, March 2009, Pages 104-111, ISSN 1872-4973, 10.1016/j.fsigen.2008.11.009. doi:10.1016/j.fsigen.2008.11.009

[3] Torben Tvedebrink, Poul Svante Eriksen, Helle Smidt Mogensen, Niels Morling, Estimating the probability of allelic drop-out of STR alleles in forensic genetics, Forensic Science International: Genetics, Volume 3, Issue 4, September 2009, Pages 222-226, ISSN 1872-4973, 10.1016/j.fsigen.2009.02.002. doi:10.1016/j.fsigen.2009.02.002

[4] H. DW Jr., S. Lemeshow, Applied Logistic Regression, John Wiley & Sons, 2004.

[5] A.A. Westen, L.J.W. Grol, J. Harteveld, A.S. Matai, P. de Knijff, T. Sijen, Assessment of the stochastic threshold, back- and forward stutter filters and low template techniques for NGM, Forensic Science International: Genetetics, Volume 6, Issue 6 December 2012, Pages 708-715, ISSN 1872-4973, 10.1016/j.fsigen.2012.05.001. doi:10.1016/j.fsigen.2012.05.001

[6] R. Puch-Solis, A.J. Kirkham, P. Gill, J. Read, S. Watson, D. Drew, Practical determination of the low template DNA threshold, Forensic Science International: Genetetics, Volume 5, Issue 5, November 2011, Pages 422-427, ISSN 1872-4973, 10.1016/j.fsigen.2010.09.001. doi:10.1016/j.fsigen.2010.09.001

See Also

calculateDropout, plotDropout_gui, hoslem.test

Plot Analytical Threshold

Description

GUI simplifying the creation of plots from analytical threshold data.

Usage

plotAT_gui(env = parent.frame(), savegui = NULL, debug = FALSE, parent = NULL)

Arguments

Details

Select data to plot in the drop-down menu. Plot regression data Automatic plot titles can be replaced by custom titles. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

See Also

https://ggplot2.tidyverse.org/ for details on plot settings.

Plot Balance

Description

GUI simplifying the creation of plots from balance data.

Usage

plotBalance_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Select a dataset to plot and the typing kit used (if not automatically detected). Plot heterozygote peak balance versus the average locus peak height, the average profile peak height 'H', or by the difference in repeat units (delta). Plot inter-locus balance versus the average locus peak height, or the average profile peak height 'H'. Automatic plot titles can be replaced by custom titles. Sex markers can be excluded. It is possible to plot logarithmic ratios. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

See Also

https://ggplot2.tidyverse.org/ for details on plot settings.

Plot Capillary Balance

Description

GUI simplifying the creation of plots from capillary balance data.

Usage

plotCapillary_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Select a dataset to plot from the drop-down menu. Plot capillary balance as a dotplot, boxplot or as a distribution. Automatic plot titles can be replaced by custom titles. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

See Also

https://ggplot2.tidyverse.org/ for details on plot settings.

Plot Contamination

Description

GUI simplifying the creation of plots from negative control data.

Usage

plotContamination_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Select data to plot in the drop-down menu. Automatic plot titles can be replaced by custom titles. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

References

Duncan Taylor et.al., Validating multiplexes for use in conjunction with modern interpretation strategies, Forensic Science International: Genetics, Volume 20, January 2016, Pages 6-19, ISSN 1872-4973, 10.1016/j.fsigen.2015.09.011. doi:10.1016/j.fsigen.2015.09.011

Plot Distribution

Description

GUI simplifying the creation of distribution plots.

Usage

plotDistribution_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Plot the distribution of data as cumulative distribution function, probability density function, or count. First select a dataset, then select a group (in column 'Group' if any), finally select a column to plot the distribution of. It is possible to overlay a boxplot and to plot logarithms. Various smoothing kernels and bandwidths can be specified. The bandwidth or the number of bins can be specified for the histogram. Automatic plot titles can be replaced by custom titles. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

See Also

log, geom_density

Plot Drop-out Events

Description

GUI simplifying the creation of plots from dropout data.

Usage

plotDropout_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Plot dropout data as heatmap arranged by, average peak height, amount, concentration, or sample name. It is also possible to plot the empirical cumulative distribution (ecdp) of the peak heights of surviving heterozygote alleles (with dropout of the partner allele), or a dotplot of all dropout events. The peak height of homozygote alleles can be included in the ecdp. Automatic plot titles can be replaced by custom titles. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

References

Antoinette A. Westen, Laurens J.W. Grol, Joyce Harteveld, Anuska S.Matai, Peter de Knijff, Titia Sijen, Assessment of the stochastic threshold, back- and forward stutter filters and low template techniques for NGM, Forensic Science International: Genetetics, Volume 6, Issue 6, December 2012, Pages 708-715, ISSN 1872-4973, 10.1016/j.fsigen.2012.05.001. doi:10.1016/j.fsigen.2012.05.001

See Also

https://ggplot2.tidyverse.org/ for details on plot settings.

plotEPG2

Description

EPG data visualizer (interactive)

Usage

plotEPG2(
  mixData,
  kit,
  refData = NULL,
  AT = NULL,
  ST = NULL,
  dyeYmax = TRUE,
  plotRepsOnly = TRUE,
  options = NULL
)

Arguments

Details

Plots peak height with corresponding allele for sample(s) for a given kit.

Value

sub A plotly widget

Author(s)

Oyvind Bleka

Plot EPG

Description

GUI wrapper for the plotEPG2 function.

Usage

plotEPG2_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the plotEPG2 function by providing a graphical user interface.

Value

TRUE

See Also

plotEPG2

Plot Empirical Cumulative Distributions

Description

GUI simplifying the creation of empirical cumulative distribution plots.

Usage

plotGroups_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Plot the distribution of data as cumulative distribution function for multiple groups. First select a dataset, then select columns to flat, group, and plot by. For example, if a genotype dataset is selected and data is flattened by Sample.Name the 'group by' and 'plot by' values must be identical for all rows for a given sample. Automatic plot titles can be replaced by custom titles. Group names can be changed. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

See Also

stat_ecdf

Plot Kit Marker Ranges

Description

GUI for plotting marker ranges for kits.

Usage

plotKit_gui(env = parent.frame(), savegui = NULL, debug = FALSE, parent = NULL)

Arguments

Details

Create an overview of the size range for markers in different kits. It is possible to select multiple kits, specify titles, font size, distance between two kits, distance between dye channels, and the transparency of dyes.

Value

TRUE

Plot Peaks

Description

GUI simplifying the creation of plots from result type data.

Usage

plotPeaks_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Plot result type data. It is possible to customize titles and font size. Data can be plotted as as frequency or proportion. The values can be printed on the plot with custom number of decimals. There are several color palettes to chose from. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

Plot Precision

Description

GUI simplifying the creation of plots from precision data.

Usage

plotPrecision_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Plot precision data for size, height, or data point as dotplot or boxplot. Plot per marker or all in one. Use the mean value or the allele designation as x-axis labels. Automatic plot titles can be replaced by custom titles. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

See Also

https://ggplot2.tidyverse.org/ for details on plot settings.

Plot Pull-up

Description

GUI simplifying the creation of plots from pull-up data.

Usage

plotPullup_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Select a dataset to plot and the typing kit used (if not automatically detected). Plot pull-up peak ratio versus the peak height of the known allele Automatic plot titles can be replaced by custom titles. Sex markers can be excluded. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

See Also

https://ggplot2.tidyverse.org/ for details on plot settings.

Plot Ratio

Description

GUI simplifying the creation of plots from marker ratio data.

Usage

plotRatio_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Select data to plot in the drop-down menu. Automatic plot titles can be replaced by custom titles. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

See Also

https://ggplot2.tidyverse.org/ for details on plot settings.

Plot Result Type

Description

GUI simplifying the creation of plots from result type data.

Usage

plotResultType_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Plot result type data. It is possible to customize titles and font size. Data can be plotted as as frequency or proportion. The values can be printed on the plot with custom number of decimals. There are several color palettes to chose from. Automatic plot titles can be replaced by custom titles. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

Plot Profile Slope

Description

GUI simplifying the creation of plots from slope data.

Usage

plotSlope_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Select a dataset to plot. Plot slope by sample. Automatic plot titles can be replaced by custom titles. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

See Also

https://ggplot2.tidyverse.org/ for details on plot settings.

Plot Stutter

Description

GUI simplifying the creation of plots from stutter data.

Usage

plotStutter_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Select data to plot in the drop-down menu. Check that the correct kit has been detected. Plot stutter data by parent allele or by peak height. Automatic plot titles can be replaced by custom titles. A name for the result is automatically suggested. The resulting plot can be saved as either a plot object or as an image.

Value

TRUE

See Also

https://ggplot2.tidyverse.org/ for details on plot settings.

Read Bins file

Description

Reads GeneMapper 'Bins' files.

Usage

readBinsFile(bin.files, debug = FALSE)

Arguments

Details

Reads useful information from 'Bins' files and save it as a data.frame.

Value

data.frame containing the columns 'Panel', 'Marker', 'Allele', 'Size', 'Size.Min', 'Size.Max', 'Virtual'.

Read Panels File

Description

Reads GeneMapper Panels files.

Usage

readPanelsFile(panel.files, debug = FALSE)

Arguments

Details

Reads useful information from Panels files and save it as a data.frame.

Value

data.frame containing the columns 'Panel', 'Marker', 'Color', 'Marker.Min', 'Marker.Max', 'Repeat'.

ESX17 Positive Control Profile

Description

A dataset in 'GeneMapper' format containing the DNA profile of the ESX17 positive control sample with homozygotes as one entry.

Usage

data(ref1)

Format

A data frame with 17 rows and 4 variables

ESX17 Positive Control Profile

Description

A dataset in 'GeneMapper' format containing the DNA profile of the ESX17 positive control sample with homozygotes as two entries.

Usage

data(ref11)

Format

A data frame with 17 rows and 4 variables

SGMPlus example data

Description

A slimmed reference dataset containing an arbitrary SGMPlus DNA profile.

Usage

data(ref2)

Format

A data frame with 16 rows and 3 variables

ESX17 example data for dropout analysis.

Description

Reference profiles for source samples. Text file in GeneMapper format.

Format

ASCII text file

ESX17 example data for dropout analysis.

Description

A slimmed dataset containing reference profiles for source samples in set4. Reference 'A2' has double entries for homozygotes. Reference 'F2' has single entries for homozygotes. Reference 'bc' has double entries for homozygotes, and lower case sample name.

Usage

data(ref4)

Format

A data frame with 98 rows and 3 variables

ESX17 example data for mixture analysis.

Description

A slimmed dataset containing the reference profile for the major component in set5.

Usage

data(ref51)

Format

A data frame with 34 rows and 3 variables

ESX17 example data for mixture analysis.

Description

A slimmed dataset containing the reference profile for the minor component in set5.

Usage

data(ref52)

Format

A data frame with 34 rows and 3 variables

Fusion example data for dropout analysis.

Description

A slimmed dataset containing the reference profile for the samples in set6. NB! Marker order is different from set6. NB! Reference R has a Y marker with NA.

Usage

data(ref61)

Format

A data frame with 89 rows and 3 variables

Fusion example data for dropout analysis.

Description

A slimmed dataset containing the reference profile for the samples in set6. NB! Marker order is same as set6. NB! Reference R has a Y marker with NA.

Usage

data(ref62)

Format

A data frame with 89 rows and 3 variables

ESSplex SE QS example data for inhibition analysis.

Description

A slimmed dataset containing the reference profile for the samples in set7.

Usage

data(ref7)

Format

A data frame with 35 rows and 4 variables

Remove Artefacts

Description

Remove artefact peaks from data.

Usage

removeArtefact(
  data,
  artefact = NULL,
  marker = NULL,
  allele = NULL,
  threshold = NULL,
  na.rm = FALSE,
  debug = FALSE
)

Arguments

Details

Removes identified artefacts from the dataset. Likely artefacts can be identified using the function calculateAllele. The output should then be provided to the 'artefact'. Alternatively known artefacts can be provided using the 'marker' and 'allele' arguments.

Value

data.frame with spikes removed.

Remove Artefact

Description

GUI wrapper for the removeArtefact function.

Usage

removeArtefact_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the removeArtefact function by providing a graphical user interface to it.

Value

TRUE

Remove Spikes

Description

Remove spikes from data.

Usage

removeSpike(data, spike, invert = FALSE, debug = FALSE)

Arguments

Details

Removes identified spikes from the dataset. Spikes are identified using the function calculateSpike and provided as a separate dataset. NB! Samples must have unique identifiers. Some laboratories use non-unique names for e.g. negative controls. To allow identification of specific samples when multiple batches are imported into one dataset an id is automatically created by combining the sample name and the file name. This work well as long as there is at most 1 identically named sample in each file (batch). To enable multiple identically named samples in one file, the sample names can be prefixed with the lane or well number before importing them to STR-validator.

Value

data.frame with spikes removed.

Remove Spike

Description

GUI wrapper for the removeSpike function.

Usage

removeSpike_gui(
  env = parent.frame(),
  savegui = NULL,
  debug = FALSE,
  parent = NULL
)

Arguments

Details

Simplifies the use of the removeSpike function by providing a graphical user interface to it.

Value

TRUE

sample_tableToList

Description

Converting table to list format (helpfunction)

Usage

sample_tableToList(table)

Arguments

Value

outL A data list

Author(s)

Oyvind Bleka

Save Object

Description

Save an object in the specified environment.

Usage

saveObject(
  name = NULL,
  object,
  parent = NULL,
  suggest = "",
  env = parent.frame(),
  remove = NULL,
  debug = FALSE
)

Arguments

Details

Saves an object with the given name in the specified environment if it does not exist. If the object exist a message box ask if the object should be overwritten. The function can also be used to re-name if 'name' is not provided (NULL). The 'suggest' provides a suggested name for the object to re-name.

Value

logical TRUE if object was saved FALSE if not.

Scramble Alleles

Description

Scrambles alleles in a dataset to anonymize the profile.

Usage

scrambleAlleles(data, db = "ESX 17 Hill")

Arguments

Details

Internal helper function to create example data. Assumes data with unique alleles per marker i.e. no duplications. This allow for sampling without replacement see sample. Sex markers are currently not scrambled i.e. they are kept intact. Alleles in the dataset is replaced with random alleles sampled from the allele database. If 'Size' is in the dataset it will be replaced by an estimated size. If 'Data.Point' is present it will be removed.

Value

data.frame with changes in 'Allele' column.

Typing data in 'GeneMapper' format

Description

A dataset containing ESX17 genotyping result for 8 replicates of the positive control sample, a negative control and ladder.

Usage

data(set1)

Format

A data frame with 170 rows and 13 variables

SGMPlus example data

Description

A slimmed dataset containing SGM Plus genotyping result for 2 replicates of 'sampleA'.

Usage

data(set2)

Format

A data frame with 32 rows and 5 variables

ESX17 example data for dropout analysis.

Description

Data from dilution experiment for dropout analysis. Text file with exported GeneMapper genotypes table.

Format

ASCII text file

ESX17 example data for dropout analysis.

Description

A slimmed dataset containing data from dilution experiment for dropout analysis (from set3). One sample replicate has lower case sample name (bc9).

Usage

data(set4)

Format

A data frame with 1609 rows and 5 variables

ESX17 example data for mixture analysis.

Description

A slimmed dataset containing data from mixture experiment for Mx analysis.

Usage

data(set5)

Format

A data frame with 1663 rows and 7 variables

Fusion example data for dropout analysis.

Description

A slimmed dataset containing data from sensitivity experiment for dropout analysis.

Usage

data(set6)

Format

A data frame with 1848 rows and 7 variables

ESSplex SE QS example data for inhibition analysis.

Description

A slimmed dataset containing data from inhibition experiment.

Usage

data(set7)

Format

A data frame with 883 rows and 7 variables

Slim Data Frames

Description

Slim data frames with repeated columns.

Usage

slim(data, fix = NULL, stack = NULL, keep.na = TRUE, debug = FALSE)

Arguments

Details

Stack repeated columns into single columns. For example, the following data frame: Sample.Name|Marker|Allele.1|Allele.2|Size.1|Size.2|Data.Point.. using this command: slim(data, fix=c("Sample.Name","Marker"), stack=c("Allele","Size")) would result in this data frame (NB! 'Data.Point' is dropped): Sample.Name|Marker|Allele|Size

Value

data.frame

Slim Data Frames

Description

GUI wrapper for the slim function.

Usage

slim_gui(env = parent.frame(), savegui = NULL, debug = FALSE, parent = NULL)

Arguments

Details

Simplifies the use of the slim function by providing a graphical user interface to it.

Value

TRUE

See Also

slim

Sort Markers

Description

Sort markers and dye as they appear in the EPG.

Usage

sortMarker(data, kit, add.missing.levels = FALSE, debug = FALSE)

Arguments

Details

Change the order of factor levels for 'Marker' and 'Dye' according to 'kit'. Levels in data must be identical with kit information.

Value

data.frame with factor levels sorted according to 'kit'.

Graphical User Interface For The STR-validator Package

Description

GUI simplifying the use of the strvalidator package.

Usage

strvalidator(debug = FALSE)

Arguments

Details

The graphical user interface give easy access to all graphical versions of the functions available in the strvalidator package. It connects functions 'under the hood' to allow a degree of automation not available using the command based functions. In addition it provides a project based workflow.

Click Index at the bottom of the help page to see a complete list of functions.

Value

TRUE

Examples

# To start the graphical user interface.
## Not run: 
strvalidator()

## End(Not run)

Trim Data

Description

Extract data from a dataset.

Usage

trim(
  data,
  samples = NULL,
  columns = NULL,
  word = FALSE,
  ignore.case = TRUE,
  invert.s = FALSE,
  invert.c = FALSE,
  rm.na.col = TRUE,
  rm.empty.col = TRUE,
  missing = NA,
  debug = FALSE
)

Arguments

Details

Simplifies extraction of specific data from a larger dataset. Look for samples in column named 'Sample.Name', 'Sample.File.Name', or the first column containing the string 'Sample' in mentioned order (not case sensitive). Remove unwanted columns.

Value

data.frame with extracted result.

Trim Data

Description

GUI wrapper for the trim function.

Usage

trim_gui(env = parent.frame(), savegui = NULL, debug = FALSE, parent = NULL)

Arguments

Details

Simplifies the use of the trim function by providing a graphical user interface to it.

Value

TRUE

See Also

trim