Title: Permutation Randomization
Version: 1.0.0
Description: Provides randomization using permutation for applications. To provide a Quality Control (QC) check, QC samples can be randomized within strata. A second function allows for the ability to ”switch” samples to meet set requirements and perform a certain amount of minimization on these switches. The functions are flexible for users by specifying strata size and number of QC samples per strata. The randomization meets the following requirements • QC sample requirements: QC samples not adjacent, QC samples from same mother must follow certain patterns. • Matched sample sets must be within a single strata, and next to each other.
URL: https://github.com/m-mellers/permRand
BugReports: https://github.com/m-mellers/permRand/issues
Encoding: UTF-8
RoxygenNote: 7.3.2
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Imports: magrittr, dplyr, tidyr, stringr
License: GPL (≥ 3)
Depends: R (≥ 3.5)
LazyData: true
NeedsCompilation: no
Packaged: 2025-08-28 16:18:36 UTC; michellemellers
Author: Michelle Mellers [aut, cre, cph], Thaddeus Haight [aut]
Maintainer: Michelle Mellers <michelle.mellers.ctr@usuhs.edu>
Repository: CRAN
Date/Publication: 2025-09-03 08:00:02 UTC

permRand: Permutation Randomization

Description

Provides randomization using permutation for applications. To provide a Quality Control (QC) check, QC samples can be randomized within strata. A second function allows for the ability to ”switch” samples to meet set requirements and perform a certain amount of minimization on these switches. The functions are flexible for users by specifying strata size and number of QC samples per strata. The randomization meets the following requirements • QC sample requirements: QC samples not adjacent, QC samples from same mother must follow certain patterns. • Matched sample sets must be within a single strata, and next to each other.

Author(s)

Maintainer: Michelle Mellers michelle.mellers.ctr@usuhs.edu [copyright holder]

Authors:

See Also

Useful links:

A dataset containing QC sample IDs and packing location.

Description

A dataset containing QC sample IDs and packing location.

Format

QC sample IDs and Packing Location

serumID

Serum ID

rack

Rack

row

Row

col

Column

event

Sample Time

motherSerumID

Mother Serum ID

motherRack

Mother Rack

motherRow

Mother Row

motherCol

Mother Column

studyID

Study ID

Author(s)

Michelle Mellers

Data Randomization

Description

Randomizes aliquots to batches.

Usage

allRand(dataR, batchTot, numQC, withinN, numMatch, chkRep)

Arguments

dataR

Data for randomization.

batchTot

c(batchTot1, batchTot2) sizes of plates, just use one plate per batch, batch size inclusive of QC samples.

numQC

Number of QC samples per batch.

withinN

Number of samples away that the QC samples must be from each other.

numMatch

Number of QC samples from a single mother within a batch.

chkRep

Check if there is a repeat of the groups within the batches.

Value

A dataset with serum order randomized.

Examples

serumRand <- allRand(dataR=serumMaster3,batchTot=c(40,44), numQC=2,withinN=2,numMatch=2,chkRep=1)

Tests the number in each batch.

Description

Counts the number of samples that are in each of the batches.

Usage

batchCount(dataS, batchSizeT)

Arguments

dataS

Test dataset.

batchSizeT

Batch size to test for.

Value

The ID of any batch that does not contain the specified number of samples.

Examples

test <- batchCount(dataS=serumRand,batchSizeT=84)

Number of QC in each batch.

Description

Count how many QC samples are in each of the batches, and if it doesn't match the number specified.

Usage

countQC(dataS, QCN)

Arguments

dataS

Test dataset.

QCN

Number of QC samples per batch.

Value

The output includes any batches that does not contain the number of QC samples specified.

Examples

test <- countQC(dataS=serumRand,QCN=4)

A dataset containing locations of empty QC samples.

Description

A dataset containing locations of empty QC samples.

Format

Empty QC samples

serumID

Serum ID

rack

Rack

row

Row

col

Column

Author(s)

Michelle Mellers

Formats data

Description

The function formatRand formats the dataset for the randomization function. This function inputs serum data for both the study subjects and QC.

Usage

formatRand(QCdata, serumIDR, serumPack)

Arguments

QCdata

QC data.

serumIDR

Serum data with serumIDs.

serumPack

Serum data with packing lists.

Value

A dataset that is formatted and ready for the randomization file.

Examples

serumMaster <- formatRand(QCdata=QCMaster,serumIDR=serumIDs,serumPack=serumLoc)

A dataset containing locations of the mother QC samples.

Description

A dataset containing locations of the mother QC samples.

Format

Mother QC samples

serumID

Serum ID

rack

Rack

row

Row

col

Column

Author(s)

Michelle Mellers

Number of single group in sequence.

Description

Tests if a large number of cases or controls are next to each other.

Usage

orderCases(dataI, betW)

Arguments

dataI

Dataset to be tested.

betW

Number of cases or controls to check if they are next to each other.

Value

The output stores if there are any cases or controls together beyond a certain specified value.

Examples

test <- orderCases(dataI=serumRand,betW=4)

Output labels.

Description

Creates output labels for the randomized groups.

Usage

outputLab(dataOut, blind, origP, maxRows, maxCols, newPack)

Arguments

dataOut

Dataset to be formatted for packing list.

blind

Indicator 0/1 select if a blinded (1) or unblinded(0) packing list is to be generated.

origP

Indicator, 0/1, inclusion of the original packing location (1) or deletion of the packing location (0).

maxRows

Maximum row for the output dataset.

maxCols

Maximum column for the output dataset.

newPack

0/1 indicator to generate new packing locations.

Value

A dataset to be used for packing lists.

Examples

blind <- outputLab(dataOut=serumRand,blind=1,origP=0,maxRows=9,maxCols=9,newPack=1)
unBlindSw <- outputLab(serumSwitchP,blind=0,origP=.,maxRows=.,maxCols=.,newPack=0)

QC Identifiers

Description

We first assign IDs linking mother/child and events using the function randTest.

Usage

randTest(dataMom, dataChild, maxAliq, nEvent)

Arguments

dataMom

The mother dataset.

dataChild

Child dataset.

maxAliq

Number of aliquots per mother aliquot.

nEvent

Number of aliquots per each of event or lab.

Value

The output of the function is a dataset with the ID links.

Examples

randTest(dataMom=motherQC,dataChild=emptyQC,maxAliq=4, nEvent=c(28,27,28,30))

A dataset containing serum sample IDs linked with studyIDs.

Description

A dataset containing serum sample IDs linked with studyIDs.

Format

Serum Sample IDs linked with studyIDs

studyID

Study Subject ID

event

Sample Time

serumID

Serum ID

Author(s)

Michelle Mellers

A dataset containing the a packing list with serum samples.

Description

A dataset containing the a packing list with serum samples.

Format

Packing list of serum samples

serumID

Serum ID

rack

Rack

row

Row

col

Column

Author(s)

Michelle Mellers

A dataset containing information for QC and serum samples.

Description

A dataset containing information for QC and serum samples.

Format

Serum and QC sample Information

serumID

Serum ID

studyID

Study ID

event

Sample Time

rack

Rack

row

Row

col

Column

ccID

Group ID

caseControl

Case or Control Indicator

QCsamp

Indicates if QC sample

Author(s)

Michelle Mellers

A dataset containing randomized QC and serum samples.

Description

A dataset containing randomized QC and serum samples.

Format

Randomized Serum and QC sample Information

serumID

Serum ID

studyID

Study ID

event

Sample Time

rack

Rack

row

Row

col

Column

ccID

Group ID

caseControl

Case or Control Indicator

QCsamp

Indicates if QC sample

batchN

Batch Number

loc

Location

Author(s)

Michelle Mellers

A dataset containing switched and randomized QC and serum samples.

Description

A dataset containing switched and randomized QC and serum samples.

Format

Switched and Randomized Serum and QC sample Information

serumID

Serum ID

studyID

Study ID

event

Sample Time

ccID

Group ID

caseControl

Case or Control Indicator

QCsamp

Indicates if QC sample

batchN

Batch Number

loc

Location

outoforder

Marker if Switched

Author(s)

Michelle Mellers

A dataset containing switched and randomized QC and serum samples with packing locations.

Description

A dataset containing switched and randomized QC and serum samples with packing locations.

Format

Switched and Randomized Serum and QC sample Information

serumID

Serum ID

rack

Rack

row

Row

col

Column

studyID

Study ID

event

Sample Time

ccID

Group ID

caseControl

Case or Control Indicator

QCsamp

Indicates if QC sample

batchN

Batch Number

loc

Location

outoforder

Marker if Switched

Author(s)

Michelle Mellers

Switching Generating Function

Description

Minimizes switches without completely re-randomizing the locations.

Usage

switchR(dataIn, numqc, numqcM, batchS)

Arguments

dataIn

Randomized dataset.

numqc

Number of QC samples per set.

numqcM

Numberof QC matching samples.

batchS

New batch size.

Value

A dataset with switches indicated.

Examples

serumSwitch <- switchR(dataIn=serumRand,numqc=2,numqcM=2,batchS=43)

Sample groups within batches

Description

Tests and finds sample groups that are across batches.

Usage

testCCAcross(dataS)

Arguments

dataS

The test dataset.

Value

The output lists all batches with not enough QC sample sets or the QC samples do not come from the same mother.

Examples

test <- testCCAcross(dataS=serumRand)

Ensures complete sets.

Description

Tests if sets are next to each other. Any sets that are not next to each other are flagged.

Usage

testPair(dataS)

Arguments

dataS

Test dataset.

Value

The output reports any sets that are separated in the "loc".

Examples

test <- testPair(dataS=serumRand)

Tests QC matches

Description

Measures that every batch has at least the specified number of matching QC sample sets in a batch.

Usage

testQCmatch(dataS, numQCs, numMatch)

Arguments

dataS

Randomized data.

numQCs

Number of QCs specified per dataset.

numMatch

Number of QC samples form a single mother within a batch.

Value

The output lists all batches with not enough QC sample sets or the QC samples do not come from the same mother.

Examples

test <- testQCmatch(dataS=serumRand,numQCs=4,numMatch=2)

A dataset containing the example non-randomized serum sample file.

Description

A dataset containing the example non-randomized serum sample file.

Format

A data frame with four datasets:

serumIDs

Serum Sample IDs Linked with StudyIDs

serumLoc

Serum ID packing list

emptyQC

Empty QC containers packing list

motherQC

QC samples to be aliquoted packing list

Author(s)

Michelle Mellers

Test Dataset

Description

Generates test data for the randomization functions.

Usage

testRand(rowSize, colSize, studySize, expNS, numCC, QCpct, child)

Arguments

rowSize

Max row size.

colSize

Max column size.

studySize

Number of cases.

expNS

Number of aliquots per case/control.

numCC

Number of controls per case.

QCpct

Percent of QCs for number of samples.

child

Number of children per "mother' aliquot.

Value

A practice dataset.

Examples

testR <- testRand(rowSize=20,colSize=15,studySize=1000,expNS=7000,numCC=2,QCpct=0.05,child=4)

Unique IDs

Description

Test for unique IDs.

Usage

uniqueID(testD, IDN)

Arguments

testD

Test dataset.

IDN

ID to test.

Value

Any IDs that are not unique.

Examples

test <- uniqueID(serumRand,"serumID")