# Introduction to CalibraCurve

Karin Schork1*

1Medizinisches Proteom-Center, Ruhr-Universität Bochum

*karin.schork@rub.de

#### 18 May 2026

#### Package

CalibraCurve 1.3.0

# 1 Introduction

Targeted mass spectrometry based experiments (e.g. proteomics, metabolomics or lipidomics) are used for validation of biomarkers or for quantitative assays. During the development of these assays, calibration curves are a valuable tool to assess the upper and lower limit of quantification and the linearity of the obtained measurements. The resulting model may also allow to predict concentrations from measured intensities (absolute quantification).

CalibraCurve is a tool to generate and visualize calibration curves. The tool is already established as a KNIME workflow based on an R script (Kohl, Stepath, Bracht, Megger, Sitek, Marcus, and Eisenacher, 2020) and a service in de.NBI and ELIXIR Germany. The code base was re-worked into an R package without changing the underlying algorithm. Several improvements were made:

- allowing xlsx files and SummarizedExperiment objects as input
- generate plots with `ggplot2` (Wickham, 2016) for nicer, publication-ready visualization
- more customization possibilities for the plots (colours etc.)
- added possibility to generate multiple curves in one graph
- re-worked and improved error messages
- added a function to predict concentrations based on the model
- additional nextflow workflow, which can be integrated into other workflows or platforms like MAcWorP

# 2 Installation of `CalibraCurve`

To install the latest version of `CalibraCurve` from Bioconductor use the following code snippet:

    if (!requireNamespace("BiocManager", quietly = TRUE)) {
            install.packages("BiocManager")
        }

    BiocManager::install("CalibraCurve")

# 3 Asking for help

If you need help with `CalibraCurve`, encountered a bug or have suggestions for improvements leave a post in the Bioconductor support site with the `CalibraCurve` tag or start an issue on our github page.

# 4 Citing `CalibraCurve`

We hope that CalibraCurve will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!

    ## Citation info
    citation("CalibraCurve")
    #> To cite package 'CalibraCurve' in publications use:
    #>
    #>   Kohl M, Stepath M, Bracht T, Megger D, Sitek B, Marcus K, Eisenacher
    #>   M (2020). "CalibraCurve: A Tool for Calibration of Targeted MS-Based
    #>   Measurements." _Proteomics_, 1900143. doi:10.1002/pmic.201900143
    #>   <https://doi.org/10.1002/pmic.201900143>.
    #>   <https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.201900143>.
    #>
    #> A BibTeX entry for LaTeX users is
    #>
    #>   @Article{,
    #>     title = {CalibraCurve: A Tool for Calibration of Targeted MS-Based Measurements},
    #>     author = {Michael Kohl and Markus Stepath and Thilo Bracht and Dominik A Megger and Barbara Sitek and Katrin Marcus and Martin Eisenacher},
    #>     year = {2020},
    #>     journal = {Proteomics},
    #>     volumne = {20},
    #>     pages = {1900143},
    #>     doi = {https://doi.org/10.1002/pmic.201900143},
    #>     url = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.201900143},
    #>   }

# 5 Quick start to using `CalibraCurve`

CalibraCurve’s main task is to calculate calibration curves for targeted mass spectrometry based data like proteomics, metabolomics or lipidomics. The experimental setup is the following: for the analyte of interest, samples with different known concentrations levels of this analyte are prepared. Ideally, these samples are measured in replicates, leading to intensities or areas as a measurement.

To calculate the calibration curve, two pieces of information are needed: The known concentration levels and the measured intensities for each sample.

There are two options to provide this information for CalibraCurve. First, a set of Excel files may be provided that contain at least a Concentration and a Measurement column each. Second, a SummarizedExperiment object may be provided. This object may contain measurements for different analytes, one analyte per row. Information about the concentration levels have to be provided in the colData part.

## 5.1 Example data set

CalibraCurve contains test data from the `msqc1` R package (Kockmann, Trachsel, Panse, Wahlander, Selevsek, Grossmann, Wolski, and Schlapbach, 2016). More precisely, parts of the dilution series from the `msqc1_dil` object.

This data was filtered in the following way: - using only QTRAP, TSQVantage and QExactive instruments (PRM or SRM method) - use only data on the level of y-ions, no precursors - use only the heavy isotope version of the peptide

`SummarizedExperiment` objects with the corresponding data for each peptide sequence is given as .rds files.

Additionally, for the peptide sequence “GGPFSDSYR” the data is stored as .xlsx tables, separated by instrument and ion type.

## 5.2 Import one or more xlsx, csv or txt files

A single `.xlsx`, `.csv` or `.txt` file, containing at least a “Concentration” and a “Measurement” column, can be imported using the `readDataTable` function.

The number of the column belonging to the concentration levels has to be defined as `concCol`. The number of the column belonging to the substance or analyte has to be defined as `measCol`. The resulting object can then be directly used in the main function `CalibraCurve` to generate and visualize the Calibration curve.

Here, an example is given using a `.xlsx` file:

    library("CalibraCurve")

    file <- system.file("extdata", "MSQC1_xlsx", "GGPFSDSYR_QExactive_y5.xlsx",
                        package = "CalibraCurve")

    D <- readDataTable(dataPath = file,
                        fileType = "xlsx",
                        concCol = 16, # column "amount" containing concentrations
                        measCol = 12, # column "Area" containing measurements
                        naStrings = c("NA", "NaN", "Filtered", "#NV"),
                        sheet = 1)
    print(head(D))
    #>    Concentration Measurement
    #> 16            25    63532736
    #> 17            25    72636312
    #> 18            25    63185244
    #> 13            50   142634912
    #> 14            50    94896768
    #> 15            50    67706488

The resulting object is a dataframe with two columns, “Concentration” and “Measurement”. The rows are sorted from lowest to highest concentration levels. This dataframe can then be directly used in the main function `CalibraCurve` to generate and visualize the Calibration curve.

Alternatively, a whole folder of `.xlsx`, `.csv` or `.txt` files can be imported using the `readMultipleTables` function. All files in the folder have to follow the same structure, i.e. having the “Concentration” and “Measurement” column in the same place.

Here is an example using a folder containing 9 `.xlsx` files:

    library("CalibraCurve")

    folder <- system.file("extdata", "MSQC1_xlsx", package = "CalibraCurve")

    D <- readMultipleTables(dataFolder = folder,
                            fileType = "xlsx",
                            concCol = 16,
                            measCol = 12)
    print(D)
    #> $GGPFSDSYR_QExactive_y5
    #>    Concentration Measurement
    #> 16            25    63532736
    #> 17            25    72636312
    #> 18            25    63185244
    #> 13            50   142634912
    #> 14            50    94896768
    #> 15            50    67706488
    #> 10           200   584484352
    #> 11           200   408839680
    #> 12           200   337659296
    #> 7           1000  2688073216
    #> 8           1000  2024798720
    #> 9           1000  1936675456
    #> 4           2000  2988372480
    #> 5           2000  2982280448
    #> 6           2000  4745236480
    #> 1           5000 10947928064
    #> 2           5000 16187528192
    #> 3           5000 13521389568
    #>
    #> $GGPFSDSYR_QExactive_y6
    #>    Concentration Measurement
    #> 16            25    42505600
    #> 17            25    49378136
    #> 18            25    42739496
    #> 13            50    94398144
    #> 14            50    63172188
    #> 15            50    44371872
    #> 10           200   390347488
    #> 11           200   270616320
    #> 12           200   224925344
    #> 7           1000  1801370496
    #> 8           1000  1353777920
    #> 9           1000  1304889088
    #> 4           2000  1991971712
    #> 5           2000  1988677376
    #> 6           2000  3203979008
    #> 1           5000  7434415616
    #> 2           5000 10999006208
    #> 3           5000  9158412288
    #>
    #> $GGPFSDSYR_QExactive_y7
    #>    Concentration Measurement
    #> 16            25     4357065
    #> 17            25     5220379
    #> 18            25     4293157
    #> 13            50     9211622
    #> 14            50     6375533
    #> 15            50     4412843
    #> 10           200    40054380
    #> 11           200    27407376
    #> 12           200    12618111
    #> 7           1000   178959088
    #> 8           1000   137778240
    #> 9           1000   129679664
    #> 4           2000   190292000
    #> 5           2000   196466864
    #> 6           2000   331479904
    #> 1           5000   723865344
    #> 2           5000  1073278592
    #> 3           5000   907921792
    #>
    #> $GGPFSDSYR_QTRAP_y5
    #>    Concentration Measurement
    #> 16            25      636182
    #> 17            25      624453
    #> 18            25      656312
    #> 13            50     1344918
    #> 14            50     1391938
    #> 15            50     1333177
    #> 10           200     4956311
    #> 11           200     5285020
    #> 12           200     5365300
    #> 7           1000    22445056
    #> 8           1000    22948416
    #> 9           1000    22717552
    #> 4           2000    28129572
    #> 5           2000    28504010
    #> 6           2000    27675910
    #> 1           5000    65790580
    #> 2           5000    68766688
    #> 3           5000    68546360
    #>
    #> $GGPFSDSYR_QTRAP_y6
    #>    Concentration Measurement
    #> 16            25      158025
    #> 17            25      156321
    #> 18            25      167139
    #> 13            50      304044
    #> 14            50      318554
    #> 15            50      317089
    #> 10           200     1247797
    #> 11           200     1283142
    #> 12           200     1303153
    #> 7           1000     5370834
    #> 8           1000     5439073
    #> 9           1000     5355637
    #> 4           2000     6830844
    #> 5           2000     6802424
    #> 6           2000     6527015
    #> 1           5000    16103898
    #> 2           5000    17477076
    #> 3           5000    17297898
    #>
    #> $GGPFSDSYR_QTRAP_y7
    #>    Concentration Measurement
    #> 16            25       19337
    #> 17            25       21675
    #> 18            25       20447
    #> 13            50       45252
    #> 14            50       40547
    #> 15            50       43635
    #> 10           200      160839
    #> 11           200      166228
    #> 12           200      166188
    #> 7           1000      708102
    #> 8           1000      715868
    #> 9           1000      701413
    #> 4           2000      928816
    #> 5           2000      914337
    #> 6           2000      924336
    #> 1           5000     2362928
    #> 2           5000     2484053
    #> 3           5000     2497078
    #>
    #> $GGPFSDSYR_TSQVantage_y5
    #>    Concentration Measurement
    #> 16            25      726363
    #> 17            25      776707
    #> 18            25      638435
    #> 13            50      938616
    #> 14            50     1499013
    #> 15            50     1646262
    #> 10           200     5734246
    #> 11           200     5328412
    #> 12           200     5906098
    #> 7           1000    31725796
    #> 8           1000    34535764
    #> 9           1000    32574730
    #> 4           2000    59979412
    #> 5           2000    59192548
    #> 6           2000    55664044
    #> 1           5000   156006784
    #> 2           5000   158682160
    #> 3           5000   157924304
    #>
    #> $GGPFSDSYR_TSQVantage_y6
    #>    Concentration Measurement
    #> 16            25      374307
    #> 17            25      398118
    #> 18            25      349628
    #> 13            50      475989
    #> 14            50      830615
    #> 15            50      894860
    #> 10           200     3049708
    #> 11           200     2837996
    #> 12           200     3143589
    #> 7           1000    16948022
    #> 8           1000    18544714
    #> 9           1000    17462780
    #> 4           2000    32045960
    #> 5           2000    31821972
    #> 6           2000    29836298
    #> 1           5000    82836872
    #> 2           5000    83833328
    #> 3           5000    82925984
    #>
    #> $GGPFSDSYR_TSQVantage_y7
    #>    Concentration Measurement
    #> 16            25       37309
    #> 17            25       51757
    #> 18            25       38181
    #> 13            50       56026
    #> 14            50       89100
    #> 15            50       98100
    #> 10           200      334119
    #> 11           200      318106
    #> 12           200      343906
    #> 7           1000     1833054
    #> 8           1000     1976417
    #> 9           1000     1892401
    #> 4           2000     3511667
    #> 5           2000     3492670
    #> 6           2000     3200100
    #> 1           5000     8909107
    #> 2           5000     8990250
    #> 3           5000     8934693

    str(D)
    #> List of 9
    #>  $ GGPFSDSYR_QExactive_y5 :'data.frame': 18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 6.35e+07 7.26e+07 6.32e+07 1.43e+08 9.49e+07 ...
    #>  $ GGPFSDSYR_QExactive_y6 :'data.frame': 18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 42505600 49378136 42739496 94398144 63172188 ...
    #>  $ GGPFSDSYR_QExactive_y7 :'data.frame': 18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 4357065 5220379 4293157 9211622 6375533 ...
    #>  $ GGPFSDSYR_QTRAP_y5     :'data.frame': 18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 636182 624453 656312 1344918 1391938 ...
    #>  $ GGPFSDSYR_QTRAP_y6     :'data.frame': 18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 158025 156321 167139 304044 318554 ...
    #>  $ GGPFSDSYR_QTRAP_y7     :'data.frame': 18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 19337 21675 20447 45252 40547 ...
    #>  $ GGPFSDSYR_TSQVantage_y5:'data.frame': 18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 726363 776707 638435 938616 1499013 ...
    #>  $ GGPFSDSYR_TSQVantage_y6:'data.frame': 18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 374307 398118 349628 475989 830615 ...
    #>  $ GGPFSDSYR_TSQVantage_y7:'data.frame': 18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 37309 51757 38181 56026 89100 ...

Each file in the folder is imported and the resulting dataframes are combined as a list. Each list entry contains the data belonging to one of the input files, which here present different measurements of the same peptide that differ in their used fragment ion and machine.

## 5.3 Import SummarizedExperiments object

A SummarizedExperiment object stored as an .rds file can be imported using the function `readDataSE` function. The column name of the colData part belonging to the concentration levels have to be defined as `concColName`. The column name of the rowData part belonging to the substance or analyte have to be defined as `substColName`.

Here, we will use the default options.

    library("CalibraCurve")

    file <- system.file("extdata", "MSQC1", "msqc1_dil_GGPFSDSYR.rds",
                        package = "CalibraCurve")

    D <- readDataSE(file, concColName = "amount_fmol", substColName = "Substance")

    print(D)
    #> $QExactive_y5
    #>     Concentration Measurement
    #> 106            25    63532736
    #> 107            25    72636312
    #> 108            25    63185244
    #> 103            50   142634912
    #> 104            50    94896768
    #> 105            50    67706488
    #> 100           200   584484352
    #> 101           200   408839680
    #> 102           200   337659296
    #> 97           1000  2688073216
    #> 98           1000  2024798720
    #> 99           1000  1936675456
    #> 94           2000  2988372480
    #> 95           2000  2982280448
    #> 96           2000  4745236480
    #> 91           5000 10947928064
    #> 92           5000 16187528192
    #> 93           5000 13521389568
    #>
    #> $QExactive_y6
    #>    Concentration Measurement
    #> 88            25    42505600
    #> 89            25    49378136
    #> 90            25    42739496
    #> 85            50    94398144
    #> 86            50    63172188
    #> 87            50    44371872
    #> 82           200   390347488
    #> 83           200   270616320
    #> 84           200   224925344
    #> 79          1000  1801370496
    #> 80          1000  1353777920
    #> 81          1000  1304889088
    #> 76          2000  1991971712
    #> 77          2000  1988677376
    #> 78          2000  3203979008
    #> 73          5000  7434415616
    #> 74          5000 10999006208
    #> 75          5000  9158412288
    #>
    #> $QExactive_y7
    #>    Concentration Measurement
    #> 70            25     4357065
    #> 71            25     5220379
    #> 72            25     4293157
    #> 67            50     9211622
    #> 68            50     6375533
    #> 69            50     4412843
    #> 64           200    40054380
    #> 65           200    27407376
    #> 66           200    12618111
    #> 61          1000   178959088
    #> 62          1000   137778240
    #> 63          1000   129679664
    #> 58          2000   190292000
    #> 59          2000   196466864
    #> 60          2000   331479904
    #> 55          5000   723865344
    #> 56          5000  1073278592
    #> 57          5000   907921792
    #>
    #> $QTRAP_y5
    #>    Concentration Measurement
    #> 52            25      636182
    #> 53            25      624453
    #> 54            25      656312
    #> 49            50     1344918
    #> 50            50     1391938
    #> 51            50     1333177
    #> 46           200     4956311
    #> 47           200     5285020
    #> 48           200     5365300
    #> 43          1000    22445056
    #> 44          1000    22948416
    #> 45          1000    22717552
    #> 40          2000    28129572
    #> 41          2000    28504010
    #> 42          2000    27675910
    #> 37          5000    65790580
    #> 38          5000    68766688
    #> 39          5000    68546360
    #>
    #> $QTRAP_y6
    #>    Concentration Measurement
    #> 34            25      158025
    #> 35            25      156321
    #> 36            25      167139
    #> 31            50      304044
    #> 32            50      318554
    #> 33            50      317089
    #> 28           200     1247797
    #> 29           200     1283142
    #> 30           200     1303153
    #> 25          1000     5370834
    #> 26          1000     5439073
    #> 27          1000     5355637
    #> 22          2000     6830844
    #> 23          2000     6802424
    #> 24          2000     6527015
    #> 19          5000    16103898
    #> 20          5000    17477076
    #> 21          5000    17297898
    #>
    #> $QTRAP_y7
    #>    Concentration Measurement
    #> 16            25       19337
    #> 17            25       21675
    #> 18            25       20447
    #> 13            50       45252
    #> 14            50       40547
    #> 15            50       43635
    #> 10           200      160839
    #> 11           200      166228
    #> 12           200      166188
    #> 7           1000      708102
    #> 8           1000      715868
    #> 9           1000      701413
    #> 4           2000      928816
    #> 5           2000      914337
    #> 6           2000      924336
    #> 1           5000     2362928
    #> 2           5000     2484053
    #> 3           5000     2497078
    #>
    #> $TSQVantage_y5
    #>     Concentration Measurement
    #> 160            25      726363
    #> 161            25      776707
    #> 162            25      638435
    #> 157            50      938616
    #> 158            50     1499013
    #> 159            50     1646262
    #> 154           200     5734246
    #> 155           200     5328412
    #> 156           200     5906098
    #> 151          1000    31725796
    #> 152          1000    34535764
    #> 153          1000    32574730
    #> 148          2000    59979412
    #> 149          2000    59192548
    #> 150          2000    55664044
    #> 145          5000   156006784
    #> 146          5000   158682160
    #> 147          5000   157924304
    #>
    #> $TSQVantage_y6
    #>     Concentration Measurement
    #> 142            25      374307
    #> 143            25      398118
    #> 144            25      349628
    #> 139            50      475989
    #> 140            50      830615
    #> 141            50      894860
    #> 136           200     3049708
    #> 137           200     2837996
    #> 138           200     3143589
    #> 133          1000    16948022
    #> 134          1000    18544714
    #> 135          1000    17462780
    #> 130          2000    32045960
    #> 131          2000    31821972
    #> 132          2000    29836298
    #> 127          5000    82836872
    #> 128          5000    83833328
    #> 129          5000    82925984
    #>
    #> $TSQVantage_y7
    #>     Concentration Measurement
    #> 124            25       37309
    #> 125            25       51757
    #> 126            25       38181
    #> 121            50       56026
    #> 122            50       89100
    #> 123            50       98100
    #> 118           200      334119
    #> 119           200      318106
    #> 120           200      343906
    #> 115          1000     1833054
    #> 116          1000     1976417
    #> 117          1000     1892401
    #> 112          2000     3511667
    #> 113          2000     3492670
    #> 114          2000     3200100
    #> 109          5000     8909107
    #> 110          5000     8990250
    #> 111          5000     8934693

    str(D)
    #> List of 9
    #>  $ QExactive_y5 :'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 6.35e+07 7.26e+07 6.32e+07 1.43e+08 9.49e+07 ...
    #>  $ QExactive_y6 :'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 42505600 49378136 42739496 94398144 63172188 ...
    #>  $ QExactive_y7 :'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 4357065 5220379 4293157 9211622 6375533 ...
    #>  $ QTRAP_y5     :'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 636182 624453 656312 1344918 1391938 ...
    #>  $ QTRAP_y6     :'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 158025 156321 167139 304044 318554 ...
    #>  $ QTRAP_y7     :'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 19337 21675 20447 45252 40547 ...
    #>  $ TSQVantage_y5:'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 726363 776707 638435 938616 1499013 ...
    #>  $ TSQVantage_y6:'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 374307 398118 349628 475989 830615 ...
    #>  $ TSQVantage_y7:'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 37309 51757 38181 56026 89100 ...

The resulting object is a list of dataframes, each with two columns, “Concentration” and “Measurement”. The rows are sorted from lowest to highest concentration levels.

This list of dataframes can then be directly used in the main function `CalibraCurve` to generate and visualize the Calibration curve.

Alternatively, also a SummarizedExperiment object, that was already imported in R, can be read in directly by `readDataSE`:

    library("CalibraCurve")

    file <- system.file("extdata", "MSQC1", "msqc1_dil_GGPFSDSYR.rds",
                        package = "CalibraCurve")

    SE_data <- readRDS(file)

    D <- readDataSE(rawDataSE = SE_data,
                    concColName = "amount_fmol",
                    substColName = "Substance")

    print(D)
    #> $QExactive_y5
    #>     Concentration Measurement
    #> 106            25    63532736
    #> 107            25    72636312
    #> 108            25    63185244
    #> 103            50   142634912
    #> 104            50    94896768
    #> 105            50    67706488
    #> 100           200   584484352
    #> 101           200   408839680
    #> 102           200   337659296
    #> 97           1000  2688073216
    #> 98           1000  2024798720
    #> 99           1000  1936675456
    #> 94           2000  2988372480
    #> 95           2000  2982280448
    #> 96           2000  4745236480
    #> 91           5000 10947928064
    #> 92           5000 16187528192
    #> 93           5000 13521389568
    #>
    #> $QExactive_y6
    #>    Concentration Measurement
    #> 88            25    42505600
    #> 89            25    49378136
    #> 90            25    42739496
    #> 85            50    94398144
    #> 86            50    63172188
    #> 87            50    44371872
    #> 82           200   390347488
    #> 83           200   270616320
    #> 84           200   224925344
    #> 79          1000  1801370496
    #> 80          1000  1353777920
    #> 81          1000  1304889088
    #> 76          2000  1991971712
    #> 77          2000  1988677376
    #> 78          2000  3203979008
    #> 73          5000  7434415616
    #> 74          5000 10999006208
    #> 75          5000  9158412288
    #>
    #> $QExactive_y7
    #>    Concentration Measurement
    #> 70            25     4357065
    #> 71            25     5220379
    #> 72            25     4293157
    #> 67            50     9211622
    #> 68            50     6375533
    #> 69            50     4412843
    #> 64           200    40054380
    #> 65           200    27407376
    #> 66           200    12618111
    #> 61          1000   178959088
    #> 62          1000   137778240
    #> 63          1000   129679664
    #> 58          2000   190292000
    #> 59          2000   196466864
    #> 60          2000   331479904
    #> 55          5000   723865344
    #> 56          5000  1073278592
    #> 57          5000   907921792
    #>
    #> $QTRAP_y5
    #>    Concentration Measurement
    #> 52            25      636182
    #> 53            25      624453
    #> 54            25      656312
    #> 49            50     1344918
    #> 50            50     1391938
    #> 51            50     1333177
    #> 46           200     4956311
    #> 47           200     5285020
    #> 48           200     5365300
    #> 43          1000    22445056
    #> 44          1000    22948416
    #> 45          1000    22717552
    #> 40          2000    28129572
    #> 41          2000    28504010
    #> 42          2000    27675910
    #> 37          5000    65790580
    #> 38          5000    68766688
    #> 39          5000    68546360
    #>
    #> $QTRAP_y6
    #>    Concentration Measurement
    #> 34            25      158025
    #> 35            25      156321
    #> 36            25      167139
    #> 31            50      304044
    #> 32            50      318554
    #> 33            50      317089
    #> 28           200     1247797
    #> 29           200     1283142
    #> 30           200     1303153
    #> 25          1000     5370834
    #> 26          1000     5439073
    #> 27          1000     5355637
    #> 22          2000     6830844
    #> 23          2000     6802424
    #> 24          2000     6527015
    #> 19          5000    16103898
    #> 20          5000    17477076
    #> 21          5000    17297898
    #>
    #> $QTRAP_y7
    #>    Concentration Measurement
    #> 16            25       19337
    #> 17            25       21675
    #> 18            25       20447
    #> 13            50       45252
    #> 14            50       40547
    #> 15            50       43635
    #> 10           200      160839
    #> 11           200      166228
    #> 12           200      166188
    #> 7           1000      708102
    #> 8           1000      715868
    #> 9           1000      701413
    #> 4           2000      928816
    #> 5           2000      914337
    #> 6           2000      924336
    #> 1           5000     2362928
    #> 2           5000     2484053
    #> 3           5000     2497078
    #>
    #> $TSQVantage_y5
    #>     Concentration Measurement
    #> 160            25      726363
    #> 161            25      776707
    #> 162            25      638435
    #> 157            50      938616
    #> 158            50     1499013
    #> 159            50     1646262
    #> 154           200     5734246
    #> 155           200     5328412
    #> 156           200     5906098
    #> 151          1000    31725796
    #> 152          1000    34535764
    #> 153          1000    32574730
    #> 148          2000    59979412
    #> 149          2000    59192548
    #> 150          2000    55664044
    #> 145          5000   156006784
    #> 146          5000   158682160
    #> 147          5000   157924304
    #>
    #> $TSQVantage_y6
    #>     Concentration Measurement
    #> 142            25      374307
    #> 143            25      398118
    #> 144            25      349628
    #> 139            50      475989
    #> 140            50      830615
    #> 141            50      894860
    #> 136           200     3049708
    #> 137           200     2837996
    #> 138           200     3143589
    #> 133          1000    16948022
    #> 134          1000    18544714
    #> 135          1000    17462780
    #> 130          2000    32045960
    #> 131          2000    31821972
    #> 132          2000    29836298
    #> 127          5000    82836872
    #> 128          5000    83833328
    #> 129          5000    82925984
    #>
    #> $TSQVantage_y7
    #>     Concentration Measurement
    #> 124            25       37309
    #> 125            25       51757
    #> 126            25       38181
    #> 121            50       56026
    #> 122            50       89100
    #> 123            50       98100
    #> 118           200      334119
    #> 119           200      318106
    #> 120           200      343906
    #> 115          1000     1833054
    #> 116          1000     1976417
    #> 117          1000     1892401
    #> 112          2000     3511667
    #> 113          2000     3492670
    #> 114          2000     3200100
    #> 109          5000     8909107
    #> 110          5000     8990250
    #> 111          5000     8934693

    str(D)
    #> List of 9
    #>  $ QExactive_y5 :'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 6.35e+07 7.26e+07 6.32e+07 1.43e+08 9.49e+07 ...
    #>  $ QExactive_y6 :'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 42505600 49378136 42739496 94398144 63172188 ...
    #>  $ QExactive_y7 :'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 4357065 5220379 4293157 9211622 6375533 ...
    #>  $ QTRAP_y5     :'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 636182 624453 656312 1344918 1391938 ...
    #>  $ QTRAP_y6     :'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 158025 156321 167139 304044 318554 ...
    #>  $ QTRAP_y7     :'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 19337 21675 20447 45252 40547 ...
    #>  $ TSQVantage_y5:'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 726363 776707 638435 938616 1499013 ...
    #>  $ TSQVantage_y6:'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 374307 398118 349628 475989 830615 ...
    #>  $ TSQVantage_y7:'data.frame':   18 obs. of  2 variables:
    #>   ..$ Concentration: num [1:18] 25 25 25 50 50 50 200 200 200 1000 ...
    #>   ..$ Measurement  : num [1:18] 37309 51757 38181 56026 89100 ...

## 5.4 Apply CalibraCurve

As soon as the data have been imported, they can directly be used inside the `CalibraCurve` function. CalibraCurve has a lot of options, e.g. thresholds or customizing options for the graphics. All of these options have sensible default values, so you can just try out CalibraCurve without thinking about these. However for optimal results or publication-ready visualizations some adjustments may be necessary. Details and examples about customizing the visualization are given in the vignette “Customizing the visualizations of CalibraCurve”.

    RES <- CalibraCurve(D)

CalibraCurve outputs several tables and two kinds of plots: the calibration curve and the response factor plots. The calibration curves are stort in the list entry `plot_CC_list` of the result object. By default, each calibration curve is plotted separately. Please be aware that by default the plots are not exported (you have to specify an `output_path` for that).

As an example, we plot here the calibration curve for the fourth analyte:

    print(RES$plot_CC_list[[4]])
    #> Warning in ggplot2::scale_y_continuous(trans = "log10"): log-10 transformation
    #> introduced infinite values.

The calibration curve is plotted as a red line, both axes are shown on logarithmic scale. The grey background shows the linear range (here between XY and 1000 fmol), the curve was only fitted to data points inside this range. The grey data point outside of the linear range visibly do not fit to the curve.

Additionally, we can now look at the corresponding response factor plot:

    print(RES$plot_RF_list[[4]])

For each data point the so called response factor is calculated. For the data point \(i\) it is defined as (intensity - y intercept)/concentration.

The nominator describes the signal produced by the analyte (intensity minus the intersect) and the denominator the concentration or amount of the analyte. Ideally the mean response factors for each concentration level (shown as the larger dots) would lie in a straight line and not vary much for the different concentration levels. In orange, thresholds based on 80% and 120% of the overall mean response factor (within the linear range) are plotted. Values within these borders are coloured in green, the ones outside in pink. For all concentration levels within the final linear range the response factors lie within these borders, serving as a further quality check of the results and linearity of the response.

For each analyte, two result tables are produced to give inside into the steps of the algorithm. The first one gives an overview of the results on the level of concentration levels:

    print(RES$RES[[4]]$result_table_conc_levels)
    #>   substance concentration mean_measurement estimated_measurement
    #> 1  QTRAP_y5            25         638982.3                661747
    #> 2  QTRAP_y5            50        1356677.7               1275920
    #> 3  QTRAP_y5           200        5202210.3               4960959
    #> 4  QTRAP_y5          1000       22703674.7              24614497
    #> 5  QTRAP_y5          2000       28103164.0              49181421
    #> 6  QTRAP_y5          5000       67701209.3             122882190
    #>   removed_while_cleaning       CV CV_within_thres preliminary_linear_range
    #> 1                  FALSE 2.521674            TRUE                     TRUE
    #> 2                  FALSE 2.292034            TRUE                     TRUE
    #> 3                  FALSE 4.165634            TRUE                     TRUE
    #> 4                  FALSE 1.109806            TRUE                     TRUE
    #> 5                  FALSE 1.475567            TRUE                     TRUE
    #> 6                  FALSE 2.449464            TRUE                     TRUE
    #>   mean_percentage_bias SD_percentage_bias CV_percentage_bias
    #> 1             3.706559           2.623536           70.78090
    #> 2             6.574496           2.531495           38.50477
    #> 3             4.973153           4.303218           86.52898
    #> 4             7.778030           1.025634           13.18630
    #> 5                   NA                 NA                 NA
    #> 6                   NA                 NA                 NA
    #>   mean_response_factor RF_within_thres final_linear_range
    #> 1             23656.34            TRUE               TRUE
    #> 2             26182.07            TRUE               TRUE
    #> 3             25773.18            TRUE               TRUE
    #> 4             22656.10            TRUE               TRUE
    #> 5             14027.80           FALSE              FALSE
    #> 6             13530.73           FALSE              FALSE

There is one row for each concentration level. It contains the mean measurement, the estimated measurement by the linear model and further information on the different steps of the algorithm (e.g. if the concentration level was removed during the cleaning step or if it was resent in the preliminary linear range). The last column shows which concentration levels ended up in the final linear range.

For this specific analyte, no concentration level was removed during the cleaning step and all levels fulfilled the CV criterion and where included in the preliminary linear range. The two highest concentration levels were subsequently removed during the calculation of the final linear range.

The second table offers a more detailed view for each individual replicate:

    print(RES$RES[[4]]$result_table_obs)
    #>    substance concentration measurement removed_while_cleaning percentage_bias
    #> 1   QTRAP_y5            25      636182                  FALSE      4.16251048
    #> 2   QTRAP_y5            25      624453                  FALSE      6.07223273
    #> 3   QTRAP_y5            25      656312                  FALSE      0.88493278
    #> 4   QTRAP_y5            50     1344918                  FALSE      5.61713795
    #> 5   QTRAP_y5            50     1391938                  FALSE      9.44504912
    #> 6   QTRAP_y5            50     1333177                  FALSE      4.66129990
    #> 7   QTRAP_y5           200     4956311                  FALSE      0.09459052
    #> 8   QTRAP_y5           200     5285020                  FALSE      6.59548206
    #> 9   QTRAP_y5           200     5365300                  FALSE      8.22938630
    #> 10  QTRAP_y5          1000    22445056                  FALSE      8.83074046
    #> 11  QTRAP_y5          1000    22948416                  FALSE      6.78180664
    #> 12  QTRAP_y5          1000    22717552                  FALSE      7.72154173
    #> 13  QTRAP_y5          2000    28129572                  FALSE              NA
    #> 14  QTRAP_y5          2000    28504010                  FALSE              NA
    #> 15  QTRAP_y5          2000    27675910                  FALSE              NA
    #> 16  QTRAP_y5          5000    65790580                  FALSE              NA
    #> 17  QTRAP_y5          5000    68766688                  FALSE              NA
    #> 18  QTRAP_y5          5000    68546360                  FALSE              NA
    #>    response_factor RF_within_thres final_linear_range
    #> 1         23544.32            TRUE               TRUE
    #> 2         23075.16            TRUE               TRUE
    #> 3         24349.52            TRUE               TRUE
    #> 4         25946.88            TRUE               TRUE
    #> 5         26887.28            TRUE               TRUE
    #> 6         25712.06            TRUE               TRUE
    #> 7         24543.69            TRUE               TRUE
    #> 8         26187.23            TRUE               TRUE
    #> 9         26588.63            TRUE               TRUE
    #> 10        22397.48            TRUE               TRUE
    #> 11        22900.84            TRUE               TRUE
    #> 12        22669.98            TRUE               TRUE
    #> 13        14041.00           FALSE              FALSE
    #> 14        14228.22           FALSE              FALSE
    #> 15        13814.17           FALSE              FALSE
    #> 16        13148.60           FALSE              FALSE
    #> 17        13743.82           FALSE              FALSE
    #> 18        13699.76           FALSE              FALSE

This table offers a more detailed view, e.g. it can be seen that every single replicate within the final linear range also fulfills the response factore criterion.

If multiple analytes are analyzed, a further summary table is generated:

    print(RES$summary_tab)
    #>                   substance    intercept        coeff        r2 LLOQ ULOQ
    #> QExactive_y5   QExactive_y5 10373596.693 2110785.5772 0.9009888   25 1000
    #> QExactive_y6   QExactive_y6  7332829.885 1405113.0256 0.8976782   25 1000
    #> QExactive_y7   QExactive_y7  1036751.715  133030.2906 0.8404977   25 1000
    #> QTRAP_y5           QTRAP_y5    47573.936   24566.9233 0.9912075   25 1000
    #> QTRAP_y6           QTRAP_y6    16843.745    5837.8402 0.9918128   25 1000
    #> QTRAP_y7           QTRAP_y7     1965.934     770.7869 0.9885624   25 1000
    #> TSQVantage_y5 TSQVantage_y5   -72408.091   30413.7556 0.9824233   25 5000
    #> TSQVantage_y6 TSQVantage_y6   -43283.577   16247.5431 0.9808731   25 5000
    #> TSQVantage_y7 TSQVantage_y7    -2770.665    1758.9092 0.9789053   25 5000
    #>                                                    eq
    #> QExactive_y5     y = 1e+07 + 2.1e+06 * x (R2 = 0.901)
    #> QExactive_y6   y = 7.3e+06 + 1.4e+06 * x (R2 = 0.898)
    #> QExactive_y7      y = 1e+06 + 1.3e+05 * x (R2 = 0.84)
    #> QTRAP_y5       y = 4.8e+04 + 2.5e+04 * x (R2 = 0.991)
    #> QTRAP_y6       y = 1.7e+04 + 5.8e+03 * x (R2 = 0.992)
    #> QTRAP_y7         y = 2e+03 + 7.7e+02 * x (R2 = 0.989)
    #> TSQVantage_y5   y = -7.2e+04 + 3e+04 * x (R2 = 0.982)
    #> TSQVantage_y6 y = -4.3e+04 + 1.6e+04 * x (R2 = 0.981)
    #> TSQVantage_y7 y = -2.8e+03 + 1.8e+03 * x (R2 = 0.979)

This table contains one row per analyte and contains more detailed information on the linear model (coefficients and R squared) and the lower and upper limit of quantification. In this case it can be seen that the QExactive leads to the worst R squared value overall, while the TSQCVantage lead to a wider linear range.

## 5.5 Use calibration curve for prediction

The resulting calibration curves can be used to predict the analytes’ concentration based on measured intensities. You need to define a vector with measured intensities and feed it into the `predictConcentration` function together with the Calibracurve result object for this particular analyte. Imagine we have measured three new samples, with an intensity of 1e6, 1e7 and 1e8, respectively.

    newdata <- c(1000000, #1e6
                10000000, #1e7
                100000000) # 1e8

    CC_res = RES$RES[[4]]

    predictConcentration(CC_res = RES$RES[[4]], newdata = newdata)
    #> Warning in predictConcentration(CC_res = RES$RES[[4]], newdata = newdata): At least one predicted concentration is outside the final
    #>                 linear range. Results may be unreliable.
    #>   intensity   predConc linear_range
    #> 1     1e+06   38.76863         TRUE
    #> 2     1e+07  405.11488         TRUE
    #> 3     1e+08 4068.57728        FALSE

In the table, the predicted concentrations based on the linear model are given. Please note the warning message: The predicted concentration for the intensity 1e8 falls outside of the linear range for this analyte. Therefore this value may be unreliable, like is shown by the warning message.

# 6 Further information

- information on customizing the graphics can be found in vignette “Customizing the visualizations of CalibraCurve”
- more detailed information on the parameters and settings can be found in the publication (Kohl Stepath et al., 2020)

# 7 Comparison with other related packages

There are not many R packages that have functionality for the analysis of targeted mass spectrometry based data, in particular for calculating and visualizing calibration curves. Therefore, `CalibraCurve` complements other existing packages in this research area and is a valuable addition for the field of targeted proteomics, metabolomics and lipidomics. There are some packages with functionalities for targeted proteomics data that cover different parts of the whole workflow. These may be used in combination with `CalibraCurve`, e.g. to handle the raw data, process them or to do further statistical analysis on the predicted concentrations (e.g. group comparisons).

- `MSstatsLOBD` estimates the limit of detection and limit of blank (LoB) for targeted experiments. In contrast to `CalibraCurve` it does not estimate the lower and upper limit of quantification (linear range) and does only provide minimal functionality for visualizing calibration curves. Response factor plots or the ability to predict concentrations are also not provided.
- The package `msqc1` is a data package containing targeted proteomics data from the MSQC1 standard samples for benchmarking of different machines and methods. We will use parts of the dilution series data in the following as an example (see also section “Example data set”).
- The package `specL` produces spectra libraries that can be used to identify peptides in targeted proteomics data and therefore covers the very beginning of the targeted data analysis workflow.
- The package `MSstats` contains a lot of functionality for the statistical analysis of proteomics data (targeted and untargeted). For example, estimate the required sample size for an experiment, summarizing data to protein level, visualizations and statistical analysis (group comparisons).
- `mzR` provides parsing for differnt standard file formats used for mass spectrometry data.
- The packages `xcms` and `MSnbase`provide functionality for processing and visualizing mass spectrometry based (proteomics) data.

# 8 R session information

    #> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
    #>  setting  value
    #>  version  R version 4.6.0 Patched (2026-05-01 r89994)
    #>  os       macOS Tahoe 26.3.1
    #>  system   aarch64, darwin23
    #>  ui       X11
    #>  language (EN)
    #>  collate  C
    #>  ctype    en_US.UTF-8
    #>  tz       America/New_York
    #>  date     2026-05-18
    #>  pandoc   3.9.0.2 @ /usr/local/bin/ (via rmarkdown)
    #>  quarto   1.7.32 @ /usr/local/bin/quarto
    #>
    #> ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
    #>  package              * version   date (UTC) lib source
    #>  abind                  1.4-8     2024-09-12 [2] CRAN (R 4.6.0)
    #>  backports              1.5.1     2026-04-03 [2] CRAN (R 4.6.0)
    #>  bibtex                 0.5.2     2026-02-03 [2] CRAN (R 4.6.0)
    #>  Biobase                2.73.1    2026-05-18 [2] Bioconductor 3.24 (R 4.6.0)
    #>  BiocGenerics           0.59.2    2026-05-18 [2] Bioconductor 3.24 (R 4.6.0)
    #>  BiocManager            1.30.27   2025-11-14 [2] CRAN (R 4.6.0)
    #>  BiocStyle            * 2.41.0    2026-05-18 [2] Bioconductor 3.24 (R 4.6.0)
    #>  bookdown               0.46      2025-12-05 [2] CRAN (R 4.6.0)
    #>  bslib                  0.11.0    2026-05-16 [2] CRAN (R 4.6.0)
    #>  cachem                 1.1.0     2024-05-16 [2] CRAN (R 4.6.0)
    #>  CalibraCurve         * 1.3.0     2026-05-18 [1] Bioconductor 3.24 (R 4.6.0)
    #>  checkmate              2.3.4     2026-02-03 [2] CRAN (R 4.6.0)
    #>  cli                    3.6.6     2026-04-09 [2] CRAN (R 4.6.0)
    #>  DelayedArray           0.39.2    2026-05-18 [2] Bioconductor 3.24 (R 4.6.0)
    #>  dichromat              2.0-0.1   2022-05-02 [2] CRAN (R 4.6.0)
    #>  digest                 0.6.39    2025-11-19 [2] CRAN (R 4.6.0)
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# 9 Bibliography

This vignette was generated using BiocStyle (Oleś, 2026) with knitr (Xie, 2025) and rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2026) running behind the scenes.

Citations made with RefManageR (McLean, 2017).

Allaire, J., Y. Xie, et al. (2026). rmarkdown: Dynamic Documents for R. R package version 2.31. URL: https://github.com/rstudio/rmarkdown.

Kockmann, T., C. Trachsel, et al. (2016). “Targeted proteomics coming of age - SRM, PRM, and DIA performance evaluated from a core facility perspective”. In: PROTEOMICS. DOI: 10.1002/pmic.201500502. URL: http://onlinelibrary.wiley.com/doi/10.1002/pmic.201500502/full.

Kohl, M., M. Stepath, et al. (2020). “CalibraCurve: A Tool for Calibration of Targeted MS-Based Measurements”. In: Proteomics, p. 1900143. DOI: https://doi.org/10.1002/pmic.201900143. URL: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.201900143.

McLean, M. W. (2017). “RefManageR: Import and Manage BibTeX and BibLaTeX References in R”. In: The Journal of Open Source Software. DOI: 10.21105/joss.00338.

Oleś, A. (2026). BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.41.0. DOI: 10.18129/B9.bioc.BiocStyle. URL: https://bioconductor.org/packages/BiocStyle.

Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN: 978-3-319-24277-4. URL: https://ggplot2.tidyverse.org.

Xie, Y. (2025). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.51. URL: https://yihui.org/knitr/.