Analyze Single Lab Qualitative PCR Outcomes

Description

Compute the POD curve and the LOD value to validate a qualitative PCR method of a single laboratory.

Usage

analyzeSingleLab(x = NULL, X = NULL, S = NULL, N = NULL,
  qLOD = 95, b = 1)

Arguments

Details

According to the suggestion of Uhlig et al. (2015), the corrective parameter b is set to 1 if it is close to 1 (simplified fit). However, if sensitivity is better than achievable according to the theoretical POD curve or average amplification probability is higher at higher dilution levels than at lower dilution levels, the b is estimated from the data (full fit). The value of b can be changed by the user. However, it is not recommended to do so. In particular unexperienced users struggle with decimal commas and decimal dots, transforming digits from strings into numeric values etc. To lower the burden, beginning with package version 1.2.0 this function automatically and only where necessary

• adds column names (with warning)

• transforms values in all columns from factor or character into numeric values

• thereby substituting decimal commas by decimal dots

• transforms columns 'S' and 'N' to integer (link{as.integer})

Value

A list with following items

x

Input data plus extra columns

b

The parameter b, as provided by the user

fit.glm.simple

Results for the simplified GLM

fit.glm.full

Results for the full GLM

where "fit.glm.simple" and "fit.glm.full" are lists with the following parameters

b

The parameter b (estimated from the model)

lambda

The parameter \lambda (estimated from the model)

model

The generalized linear model (GLM) fit to the data

lod

A named vector of LOD values

lodci

The 95% confidence interval of the LOD

warn

A character vector containing warnings that appeared during GLM fit

References

Uhlig et al. Accred Qual Assur (2015) 20: 75. https://doi.org/10.1007/s00769-015-1112-9

Examples

x <- cbind(
 X=c(0.1,1,2,5,10,20),
 S=c( 0,5,6,6,6,6 ),
 N=c( 6,6,6,6,6,6 )
)
obj <- analyzeSingleLab(x=x)

Compute the Probability Of Detection (POD)

Description

Compute the Probability Of Detection (POD) in qualitative PCR experiments carried out by a single laboratory.

Usage

computePOD(x, lambda = 1, b = 1)

Arguments

Value

The POD function as described in Uhlig et al., 2015

References

Uhlig et al. Accred Qual Assur (2015) 20: 75. https://doi.org/10.1007/s00769-015-1112-9

Examples

# the optimal POD
computePOD(exp(seq(1, 10, 1)), 1, 1)
# some other POD
computePOD(exp(seq(1, 10, 1)), 0.5, 1.29)

Support Other Platforms

Description

Export formatted data or code for use by other platforms

Usage

exportQuodata(obj)

exportSAS(obj)

exportExcelMacro(dest)

Arguments

Details

The output of exportQuodata can be used on the QuoData website (http://quodata.de/content/validation-qualitative-pcr-methods-single-laboratory). Function exportExcelMacro() creates an Excel macro in the specified directory. Existing files (older versions for instance) will not be overwritten! To create the macro in the current directory, set destination to "" (Windows) or "." (Linux), respectively.

Value

Nothing is returned by exportQuodata() and exportSAS(). Function exportExcelMacro() returns a boolean, FALSE if a file with name 'pod.xlsm' already exists, TRUE otherwise.

See Also

getwd, dir

Examples

x <- cbind(
 X=c( 0.1,1,2,5,10,20 ),
 S=c( 0,5,6,6,6,6 ),
 N=c( 6,6,6,6,6,6 )
)
obj <- analyzeSingleLab(x=x)
exportQuodata(obj)

Generate Plot to Analyze Single Lab PCR Outcomes

Description

Show POD curve and LOD value to validate qualitative PCR methods of a single laboratory.

Usage

plotPOD(obj, model = c("auto", "simple", "full"), qLOD = 95,
  show.ci = TRUE, show.warnings = FALSE, wmark = TRUE, unit = "",
  xlim = NULL, .title = list(main = "", xlab = "Number of DNA copies",
  ylab = "POD and ROD"))

Arguments

Details

The graph generated by this function gives the laboratory-specific rates of detection (RODs) as blue diamonds. The blue curve denotes the mean POD curve along with the corresponding 95 \% confidence range highlighted as the grey band. The POD curve under ideal conditions is displayed as the black dashed curve.

If model is set to "auto", a plausiblity test is applied to determine if the POD curve bases on the simplified or on full parameter estimation. If the corrective parameter determined from the full model significantly differs from 1, a message is shown in the plot. Testing for significant deviation is currently done by checking the condition 1-b>0.2. The threshould 0.2 has been determined empirically to agree with the original webtool and might be changed in future versions of the package.

Three cases can be distinguished. First, the value for the slope parameter b is significantly less than 1. This means the average amplification probability is higher at higher dilution levels than at lower dilution levels. Such a situation can be related to: inhibitory matrix effects, a large variability in the amplification process from the one test to another under repeatability conditions, or accidental problems causing false positives if the number of copies of the target DNA sequence is less than 1. Second, the calculated POD curve indicates sensitivity better than achievable according to the theoretical POD curve. Third, the number of positive test results is significantly higher than expected at nominal copies of nominal DNA concentrations in [0.5,1.5]. In this case check the correctness of the serial dilution.

Another warning appears if the LOD of interest exceeds the highest number of considered nominal copies.

The unit is add to the LOD value, in front of the confidence intervall.

Value

The passed list 'obj' is returned invisibly.

Examples

x <- cbind(
 X=c(0.1,1,2,5,10,20),
 S=c( 0,5,6,6,6,6 ),
 N=c( 6,6,6,6,6,6 )
)
obj <- analyzeSingleLab(x=x)
plotPOD(obj)

Summary of POD objects

Description

Generate nicely formatted output of the POD object

Usage

## S3 method for class 'pod'
print(x, ...)

Arguments

Value

Nothing is returned.

Examples

x <- cbind(
 X=c( 0.1,1,2,5,10,20 ),
 S=c( 0,5,6,6,6,6 ),
 N=c( 6,6,6,6,6,6 )
)
obj <- analyzeSingleLab(x=x)
print(obj)
# or just
obj

obj <- analyzeSingleLab(x=x, qLOD=c(50, 70, 95))
obj

Get Test Data

Description

Some data to test the functionality of the package

Usage

grohmann2015collaborative(lab = NULL)

sas.logistic()

Arguments

Value

If a lab is not NULL, a data.frame with three columns ('X', 'S', 'N') is returned. If lab is NULL, these three columns are supplemented by a fourth column indicating the laboratory.

Data grohmann2015collaborative was generated by Grohmann et al. (2015) and has been used as exemplary data by Uhlig et al. (2015) to assess performance of their statistical approach to validate PCR results. Data sas.logistic was taken from the part of the SAS manual dealing with logistic regression (https://support.sas.com/documentation/onlinedoc/stat/ex_code/132/logiex14.html).

References

Grohmann et al. Accred Qual Assur (2015) 20: 85. https://doi.org/10.1007/s00769-015-1108-5 Uhlig et al. Accred Qual Assur (2015) 20: 75. https://doi.org/10.1007/s00769-015-1112-9

Examples

x.all <- grohmann2015collaborative()
x.5 <- grohmann2015collaborative(5)
sas <- sas.logistic()