The POD package: A general introduction

The POD package is a re-implementation of a part of the functionality of the commercial tool “PROLab”, provided by QuoData (http://quodata.de/). The current version re-implements the evaluation of qualitative PCR experiments within a single laboratory, as described in (Uhlig et al. 2015).

An online tool has been setup to perform this kind of analysis (http://quodata.de/content/validation-qualitative-pcr-methods-single-laboratory/). The output of this online tool (as of February 2019) was used as a guideline for the R implementation. It should be noted, however, that some results of the POD package and the original implementation in PROLab might disagree marginally.

For instance, the graph provided by the online tool provides bars for each observation, the R implementation does not.

The Excel macro

The POD package performs analyses in its natural environment, the statistical programming languange R. However, an Excel macro can be used to access the R package and run basic analyses without R experience.

This tutorial briefly guides you through the macro.

Analysis

First of all, load the package:

library(POD)

## ##  PPP    OO   DDD
## ##  P  P  O  O  D  D
## ##  PPP  O    O D   D
## ##  P     O  O  D  D
## ##  P      OO   DDD
## ##
## ##  Loading package 'POD' version 1.2.0.
## ##  Type help(package=POD) for help.
## ##  Type citation(package='POD') for citation.
## ##  For your suggestions and bug-reports please contact: <[email protected]>
## ##  Latest patches can also be found at: <https://github.com/markusboenn/POD>
## ##

Get the excel macro:

# where to store a copy of the macro
dest <- "~"
exportExcelMacro(dest)

The message confirms the destination, where now you will find a file called “pod.xlsm”. Note that a logical is returned, which is FALSE if the a file with name “pod.xlsm” already exists in the destination directory.

Settings

Open the macro. There are two sheets. In sheet ‘Settings’ you have to specify essential information about your system and details of your experiment.

First, specify the path to your R executable.
Define a directory for temporary files.
Define the quantile of the limit of detection (LOD). As opposed to the R code, in the Excel macro this has to be an integer. Defaults to 95.

Input your data

Next, go ahead to sheet ‘Input’ to enter the data. Do not change the column headers, the position of the table or the order of columns!

The columns are as follows

nominal DNA concentration
number of successful PCR outcomes
total number of PCR experiments

Cells contain exemplary values by default. Default values can be used to test the macro.

Delete default values and enter your own values into columns. Click the button ‘compute POD’.

Diagram sheet

A new sheet appears. The graph shows the observed rate of success for each nominal DNA concentration and a fitted POD curve as well as its 95% confidence interval. In addition, the limit of detection (LOD) at 95% and its 95% confidence interval are provided.

Numerical values of the LOD₉₅ and its confidence intervals also appear on sheet ‘Input’.

If you choose another LOD quantile, 75 for instance, the output in the graph and the sheet ‘Input’ is adapted accordingly.

Appendix

Output of `sessionInfo()`

## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   pathto/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: pathto/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] POD_1.2.0
## 
## loaded via a namespace (and not attached):
##  [1] digest_0.6.37     R6_2.5.1          fastmap_1.2.0     xfun_0.50        
##  [5] maketools_1.3.1   cachem_1.1.0      knitr_1.49        htmltools_0.5.8.1
##  [9] rmarkdown_2.29    buildtools_1.0.0  lifecycle_1.0.4   cli_3.6.3        
## [13] sass_0.4.9        jquerylib_0.1.4   compiler_4.4.2    sys_3.4.3        
## [17] tools_4.4.2       evaluate_1.0.3    bslib_0.8.0       yaml_2.3.10      
## [21] jsonlite_1.8.9    rlang_1.1.4

References

Uhlig, Steffen, Kirstin Frost, Bertrand Colson, Kirsten Simon, Dietrich Mäde, Ralf Reiting, Petra Gowik, and Lutz Grohmann. 2015. “Validation of qualitative PCR methods on the basis of mathematical–statistical modelling of the probability of detection.” Accreditation and Quality Assurance 20 (2): 75–83. https://doi.org/10.1007/s00769-015-1112-9.

POD: How to use the Excel macro