Minor fixes

joss/paper.bib

@@ -376,7 +376,7 @@ @article{fda.usc
 @manual{shiny,
 	author = {Chang, Winston and Cheng, Joe and Allaire, JJ and Sievert, Carson and Schloerke, Barret and Xie, Yihui and Allen, Jeff and McPherson, Jonathan and Dipert, Alan and Borges, Barbara},
 	note = {R package version 1.7.1},
-	title = {shiny: Web Application Framework for R},
+	title = {shiny: Web Application Framework for {R}},
 	url = {https://CRAN.R-project.org/package=shiny},
 	year = {2021}
 }
@@ -401,7 +401,7 @@ @article{ruijter_evaluation_2013
 
 @article{ruijter_removal_2015,
 	abstract = {Quantitative PCR (qPCR) is the method of choice in gene expression analysis. However, the number of groups or treatments, target genes and technical replicates quickly exceeds the capacity of a single run on a qPCR machine and the measurements have to be spread over more than 1 plate. Such multi-plate measurements often show similar proportional differences between experimental conditions, but different absolute values, even though the measurements were technically carried out with identical procedures. Removal of this between-plate variation will enhance the power of the statistical analysis on the resulting data. Inclusion and application of calibrator samples, with replicate measurements distributed over the plates, assumes a multiplicative difference between plates. However, random and technical errors in these calibrators will propagate to all samples on the plate. To avoid this effect, the systematic bias between plates can be removed with a correction factor based on all overlapping technical and biological replicates between plates. This approach removes the requirement for all calibrator samples to be measured successfully on every plate. This paper extends an already published factor correction method to the use in multi-plate qPCR experiments. The between-run correction factor is derived from the target quantities which are calculated from the quantification threshold, PCR efficiency and observed Cq value. To enable further statistical analysis in existing qPCR software packages, an efficiency-corrected Cq value is reported, based on the corrected target quantity and a PCR efficiency per target. The latter is calculated as the mean of the PCR efficiencies taking the number of reactions per amplicon per plate into account. Export to the RDML format completes an RDML-supported analysis pipeline of qPCR data ranging from raw fluorescence data, amplification curve analysis and application of reference genes to statistical analysis.},
-	author = {Ruijter, Jan M. and {Ruiz Villalba}, Adrián and Hellemans, Jan and Untergasser, Andreas and van den Hoff, Maurice J. B.},
+	author = {Ruijter, Jan M. and {Ruiz Villalba}, Adrián and Hellemans, Jan and Untergasser, Andreas and {van den Hoff}, Maurice J. B.},
 	doi = {10.1016/j.bdq.2015.07.001},
 	issn = {2214-7535},
 	journal = {Biomolecular Detection and Quantification},
@@ -472,7 +472,7 @@ @amc.uva.nl;
 
 @article{ruijter_efficiency_2021,
 	abstract = {Quantitative PCR (qPCR) aims to measure the DNA or RNA concentration in diagnostic and biological samples based on the quantification cycle (Cq) value observed in the amplification curves. Results of qPCR experiments are regularly calculated as if all assays are 100\% efficient or reported as just Cq, \ensuremath{\Delta}Cq, or \ensuremath{\Delta}\ensuremath{\Delta}Cq values.When the reaction shows specific amplification, it should be deemed to be positive, regardless of the observed Cq. Because the Cq is highly dependent on amplification efficiency that can vary among targets and samples, accurate calculation of the target quantity and relative gene expression requires that the actual amplification efficiency be taken into account in the analysis and reports. PCR efficiency is frequently derived from standard curves, but this approach is affected by dilution errors and hampered by properties of the standard and the diluent. These factors affect accurate quantification of clinical and biological samples used in diagnostic applications and collected in challenging conditions. PCR efficiencies determined from individual amplification curves avoid these confounders. To obtain unbiased efficiency-corrected results, we recommend absolute quantification with a single undiluted calibrator with a known target concentration and efficiency values derived from the amplification curves of the calibrator and the unknown samples.For meaningful diagnostics or biological interpretation, the reported results of qPCR experiments should be efficiency corrected. To avoid ambiguity, the Minimal Information for Publications on Quantitative Real-Time PCR Experiments (MIQE) guidelines checklist should be extended to require the methods that were used (1) to determine the PCR efficiency and (2) to calculate the reported target quantity and relative gene expression value.},
-	author = {Ruijter, Jan M. and Barnewall, Rebecca J and Marsh, Ian B and Szentirmay, Andrew N and Quinn, Jane C and van Houdt, Robin and Gunst, Quinn D and van den Hoff, Maurice J B},
+	author = {Ruijter, Jan M. and Barnewall, Rebecca J and Marsh, Ian B and Szentirmay, Andrew N and Quinn, Jane C and {van Houdt}, Robin and Gunst, Quinn D and {van den Hoff}, Maurice J B},
 	doi = {10.1093/clinchem/hvab052},
 	issn = {0009-9147},
 	journal = {Clinical Chemistry},

joss/paper.md

@@ -73,7 +73,7 @@ by the user according to rather subjective criteria (_e.g._,
 sigmoidal shape, slope, noise, presence of a "hook effect")
 [@burdukiewicz_algorithms_2018; @spiess_system-specific_2016;
 @spiess_impact_2015; @hanschmann_looptag_2021_2]. While positive qPCR reactions 
-usually exhibit a sigmoidal shape, negative ACs display a rather flat and linear trajectory (Figure \autoref{fig:fig_1}).
+usually exhibit a sigmoidal shape, negative ACs display a rather flat and linear trajectory (\autoref{fig:fig_1}).
 
 ![Analysis of ACs using the `PCRedux` package. A) ACs exhibit a high diversity in their appearance. The left plot (positive)
 shows ACs of which almost all are sigmoidal. The
@@ -90,7 +90,7 @@ calculated and plotted for the three classes. Data
 from htPCR dataset [@ritz_qpcr:_2008].\label{fig:fig_1}](fig_1.png)
 
 So how can ACs be objectively and
-reproducible assessed and automatically interpreted (_e.g._, as
+reproducibly assessed and automatically interpreted (_e.g._, as
 positive/negative/ambiguous or low/high quality)? For high-throughput
 experiments, manual evaluation is not feasible because of mental exhaustion errors or non-reproducibility from arbitrary thresholds or subjective assessments. 
 While internal laboratory guidelines seem to partially remedy this, they are usually not standardized with other labs.
@@ -108,7 +108,7 @@ numerically or analytically derived, quantifiable, informative properties of sca
 
 # Software engineering
 
-`PCRedux` (v.\~1.1-2, [MIT license](https://mit-license.org/)) is an `R` package
+`PCRedux` (v.1.1-2, [MIT license](https://mit-license.org/)) is an `R` package
 (S3 class system). `R` was chosen because it provides comprehensive tools for reproducible
 statistical and bioinformatics analyses [@gentleman_bioconductor:_2004;
 @gentleman_statistical_2007; @rodiger_r_2015; @liu_r_2014;
@@ -158,13 +158,13 @@ Application examples in the context of machine learning can be found in the @PCR
 
 ## Graphical User Interface:
 
-`run_PCRedux()` invokes a graphical user interface (figure \autoref{fig:fig_2}) based
+`run_PCRedux()` invokes a graphical user interface (\autoref{fig:fig_2}) based
 on the `Shiny` technology [@shiny], providing features as a downstream accessible table.
 
 ![Graphical user interface for the analyses of qPCR data. A) The `run_PCRedux()`
 GUI for analysis and tabular display can use browsers or R environments that
 support `ECMA Script` and `HTML`. In this example, the GUI was used in `RKWard`
-(v.~0.7.2, Linux, Kubuntu 21.10, [@rodiger_rkward:_2012]). B) Optionally, information about the current state of errors can be obtained via the R console.\label{fig:fig_2}](fig_2.png)
+(v.0.7.2, Linux, Kubuntu 21.10, [@rodiger_rkward:_2012]). B) Optionally, information about the current state of errors can be obtained via the R console.\label{fig:fig_2}](fig_2.png)
 
 ## Datasets and Data Labeling