Version 1
: Received: 31 March 2022 / Approved: 12 April 2022 / Online: 12 April 2022 (08:46:49 CEST)
How to cite:
Uhlig, S.; Weidner, C.; Colson, B. Statistical Modelling and Experimental Design for the Validation of Droplet Digital PCR Methods. Preprints.org2022, 2022040104. https://doi.org/10.20944/preprints202204.0104.v1.
Uhlig, S.; Weidner, C.; Colson, B. Statistical Modelling and Experimental Design for the Validation of Droplet Digital PCR Methods. Preprints.org 2022, 2022040104. https://doi.org/10.20944/preprints202204.0104.v1.
Cite as:
Uhlig, S.; Weidner, C.; Colson, B. Statistical Modelling and Experimental Design for the Validation of Droplet Digital PCR Methods. Preprints.org2022, 2022040104. https://doi.org/10.20944/preprints202204.0104.v1.
Uhlig, S.; Weidner, C.; Colson, B. Statistical Modelling and Experimental Design for the Validation of Droplet Digital PCR Methods. Preprints.org 2022, 2022040104. https://doi.org/10.20944/preprints202204.0104.v1.
Abstract
For the in-house validation of a droplet digital PCR method, a factorial experimental design was implemented. This design serves different purposes. On the one hand, it is an efficient design in relation to the workload involved in achieving a desirable level of reliability of variance estimates. On the other hand, it allows a partitioning of total variance into different components, thus providing information regarding the dominant sources of random variation. The statistical modelling reflects the actual measurement mechanism, establishing relationships between nominal target DNA copies per well, the range of variation of copy numbers per droplet, probability of detection values, and estimated numbers of copies.
Keywords
Method validation; droplet digital PCR; orthogonal factorial design; variance components; Poisson assumption; cloglog model; target DNA copies per droplet; Monte Carlo; prediction interval
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
