paper.md

title	tags	authors	affiliations	date	bibliography	header-includes
tidyqpcr: Quantitative PCR analysis in the tidyverse.	quantitative PCR qPCR tidyverse R MIQE	name orcid affiliation Edward W. J. Wallace 0000-0001-8025-6361 1 name orcid affiliation Samuel J. Haynes 0000-0002-3366-1812 1	name index Institute for Cell Biology, School of Biological Sciences, The University of Edinburgh, 1	25 July 2021	paper.bib	\usepackage{draftwatermark}

Summary

Quantitative polymerase chain reaction (qPCR) is a fundamental technique in molecular biology to detect and quantify DNA and RNA. Here we present the tidyqpcr software package for user-friendly qPCR analysis using the tidyverse suite of R packages. tidyqpcr offers a consistent user interface and structure for qPCR analysis, within the tidyverse paradigm of spreadsheet-like rectangular data frames and generic functions that build up complex analyses in a series of simple steps. tidyqpcr focuses on experimental design in microwell plates, and relative quantification using changes in quantification cycle ($\Delta Cq$). Overall, tidyqpcr empowers scientists to conduct reproducible, flexible, and best-practice compliant quantitative PCR analysis.

Statement of need

Quantitative PCR is among the most common techniques in biological and biomedical research, used for the quantification of DNA and RNA. There is a critical need for rigorous analysis and reporting of qPCR experiments, codified in the minimum information for publication of quantitative real-time PCR experiments (MIQE) guidelines [@Bustin:2009]. Yet it is common for qPCR to be analysed either by closed-source software supplied by the manufacturers of PCR machines, or by highly variable, in-house analysis scripts that have not been peer-reviewed. Some open-source libraries for qPCR analysis are available, notably qpcR [@Spiess:2018] and pcr [@Ahmed:2018]. qpcR is a feature rich qPCR analysis package relying on an object-oriented approach using S4 classes. pcr is a less extensive qPCR analysis package based on the tidyverse suite of generic data-science tools using the paradigm of tidy data (spreadsheet-like rectangular data frames). However, available packages either assume extensive prior R knowledge, overlook best-practices in qPCR experiments, or lack extensive documentation. There remains a need for a qPCR analysis package that integrates with the user-friendly tidyverse, encourages the use of MIQE best-practice compliant experimental design, and provides detailed example analysis pipelines as R vignettes.

Our package, tidyqpcr, addresses the need for best-practice, novice-friendly qPCR analysis in the tidyverse paradigm. tidyqpcr aims to be:

• Empowering: tidyqpcr combines a free, open-source qPCR analysis R package with online teaching materials.
• Reproducible: tidyqpcr scripts produce paper-ready figures straight from raw data with identical results across computers.
• Flexible: tidyqpcr follows the 'tidy' data paradigm to ensure scalability and adaptability.
• Best-practice compliant: tidyqpcr encourages standardised, reliable experimental design by prioritising MIQE-compliant best practices.

tidyqpcr can be used to analyse qPCR data from any nucleic acid source - DNA for qPCR or ChIP-qPCR, RNA for RT-qPCR. Currently tidyqpcr has functions that support relative quantification, but not yet absolute quantification.

tidyqpcr's current features allow users to:

• use a single data type for analysis as every object is a tibble / data frame.
• lay out and display 96/384-well plates for easy experimental setup (label_plate_rowcol, create_blank_plate, ...).
• flexibly assign metadata to samples for visualisation with ggplot2 (see vignettes).
• read in quantification cycle (Cq) and raw data from Roche LightCycler machines with single-channel fluorescence (read_lightcycler_1colour_cq, read_lightcycler_1colour_raw).
• calibrate primer sets including estimating efficiencies and visualization of curves (calculate_efficiency).
• visualize amplification and melt curves (calculate_drdt_plate)
• perform normalisation and relative quantification to one or more reference targets by the $\Delta Cq$ method (calculate_normcq, calculate_deltacq_bysampleid).
• estimate differential expression across multiple samples by the $\Delta \Delta Cq$ method (calculate_deltadeltacq_bytargetid).
• accelerate further downstream analysis and visualization by writing tidy data frames that are fully compatible with the tidyverse suite.

We have conducted a series of user interviews to improve tidyqpcr's capabilities and documentation. The ease-of-use and documentation of tidyqpcr will enable efficient best-practice analysis of qPCR data by both novice and experienced programmers.

Acknowledgements

We thank everyone in the eLife Innovation Leaders 2020 program for all their help developing tidyqpcr, in particular program leader Emmy Tsang and our mentor Aidan Budd. We thank Sander Granneman, Stefanie Butland and Sean Hughes for feedback and encouragement. We thank all those who have participated in interviews, including; Flic Anderson, Jamie Auxillos, David Barrass, Rosey Bayne, Elliott Chapman, Magnus Gwynne, Liz Hughes, Chris Katanski and Stuart McKellar. Edward Wallace is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (Grant Number 208779/Z/17/Z). Samuel Haynes is funded by the EASTBIO UKRI-BBSRC DTP.

References