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Further streamlining
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zsteinmetz committed Sep 19, 2023
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -28,7 +28,7 @@ The following functions are available:

* Calculating limits of detection `lod()` and limits of quantification `loq()`
as part of the `'calibration'` class to produce linear calibration curves
in accordance with German DIN 32645 (2008); ; see
in accordance with German DIN 32645 (2008); see
[vignette](https://zsteinmetz.de/envalysis/articles/calibration.html) for
details
* Calculating concentrations from calibration curves using `inv_predict()`
Expand Down
39 changes: 21 additions & 18 deletions vignettes/calibration.Rmd
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Expand Up @@ -41,8 +41,8 @@ tables: a sequence table and a sample table.

The sequence table contains gas-chromatography/mass spectrometry measurement
data of two phenolic compounds, these are tyrosol and vanillin. Besides the
samples, standard mixtures and extraction blanks were acquired in three separate
analysis batches. Each measurement resulted in an integrated peak area.
samples, standard mixtures and extraction blanks (type) were acquired in three
separate analysis batches. Each measurement resulted in an integrated peak area.

```{r, echo=F}
knitr::kable(phenolics$seq[c(1:4,73:74,76,78,80,83:84),], "simple",
Expand All @@ -51,24 +51,22 @@ knitr::kable(phenolics$seq[c(1:4,73:74,76,78,80,83:84),], "simple",

The sample table describes the samples' origin from a 29-day degradation
experiment, in which the phenolic compounds were either degraded in the dark by
the native soil microbial community or photooxidized under UV irradiation. The
samples were processed in threefold replication. Their weight [g], the volume
[mL] of extract solution, and the dilution factor were recorded.
the native soil microbial community or photooxidized under UV irradiation after
sterilizing the soil. The samples were processed in threefold replication. Their
weight [g], the volume [mL] of extract solution, and the dilution factor were
recorded.

```{r, echo=F}
knitr::kable(phenolics$samples[c(1:2,4,41:42),], "simple",
row.names = F)
```

In **envalysis**, the sample data is stored in a two-item list called `phenolics`.
The list items are named `seq` and `samples`.
In **envalysis**, the sample data is stored in a two-item list called
`phenolics`. The list items are named `seq` and `samples`.

```{r data}
data("phenolics")
str(phenolics)
phenolics$seq |> head()
phenolics$samples |> head()
```

## Simple calibation
Expand All @@ -79,10 +77,10 @@ understanding, the calibration workflow is first shown for a subset of data,
namely the first batch of tyrosol measurements. The subset is stored in
`tyrosol_1`.

Subsequently, the standards in this subset are used for calibration. The
All standards in the `tyrosol_1` subset are used for calibration. The
`'calibration'` object is stored as `cal_1`, which can be printed for additional
information including limits of detection and quantification, the adjusted R^2^,
blanks, and statistical checks of the underlying calibration model.
information including limits of detection and quantification, the adjusted
*R*^2^, blanks, and statistical checks of the underlying calibration model.

```{r simple_calibration, fig.align="center"}
tyrosol_1 <- subset(phenolics$seq, Compound == "Tyrosol" & Batch == 1)
Expand All @@ -106,15 +104,19 @@ head(tyrosol_1)
## Working with `data.table`s

To process all compounds and analysis batches together, the `phenolics` data is
converted to `data.tables`s.
converted to `data.table`s.

```{r data.table}
dt <- lapply(phenolics, as.data.table)
```

To replicate the following steps, try to organize your data in the same way as
shown before. If you want to read in your data directly as `data.table`, use
their `fread()` function, for instance.

## Batch calibration

Afterwards, the `calibration()` and `inv_predict()` are applied by compound and
Subsequently, `calibration()` and `inv_predict()` are applied by compound and
batch.

```{r calibration}
Expand All @@ -124,11 +126,12 @@ dt$seq[, `Calc Conc` := calibration(Area ~ `Spec Conc`, .SD[Type == "Standard"])
head(dt$seq)
```

Calibration parameters like LODs, LOQs, or adjusted R^2^ may be stored in a
Calibration parameters like LODs, LOQs, or adjusted *R*^2^ may be stored in a
separate list item for later use.

```{r parameters}
dt$cal <- dt$seq[Type == "Standard", as.list(calibration(Area ~ `Spec Conc`)),
dt$cal <- dt$seq[Type == "Standard", calibration(Area ~ `Spec Conc`) |>
as.list(c("coef", "adj.r.squared", "lod", "loq")),
by = .(Compound, Batch)]
print(dt$cal)
```
Expand Down Expand Up @@ -163,7 +166,7 @@ head(dt$res)
For plotting the data using **ggplot2**, the contents are summarized by mean and
confidence interval (CI).

```{r plotting, fig.align="center"}
```{r plotting, fig.align="center", fig.height=3.5}
dt$sum <- dt$res[, .(Content = mean(Content, na.rm = T),
CI = CI(Content, na.rm = T)),
by = .(Compound, Treatment, Day)]
Expand Down
152 changes: 58 additions & 94 deletions vignettes/calibration.md
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@@ -1,7 +1,7 @@
---
title: "Calibration workflow"
author: "Zacharias Steinmetz"
date: "2023-09-17"
date: "2023-09-19"
output:
html_document:
keep_md: yes
Expand Down Expand Up @@ -33,29 +33,30 @@ tables: a sequence table and a sample table.

The sequence table contains gas-chromatography/mass spectrometry measurement
data of two phenolic compounds, these are tyrosol and vanillin. Besides the
samples, standard mixtures and extraction blanks were acquired in three separate
analysis batches. Each measurement resulted in an integrated peak area.


Compound Type Name Area Batch Spec Conc
--------- ----------------- --------- ------------ ------ ----------
Tyrosol Extraction blank Blank 1 0.000000 1 NA
Tyrosol Extraction blank Blank 2 0.000000 1 NA
Tyrosol Sample ZS-001 328.343597 1 NA
Tyrosol Sample ZS-002 282.930939 1 NA
Tyrosol Standard 0 mg/L 0.000000 3 0.00000
Tyrosol Standard 1 mg/L 7.628456 3 0.97755
Tyrosol Standard 5 mg/L 35.566628 3 4.88775
Tyrosol Standard 20 mg/L 141.898056 3 19.55100
Tyrosol Standard 100 mg/L 715.496338 3 97.75500
Vanillin Sample ZS-001 1876.933716 1 NA
Vanillin Sample ZS-002 1578.626099 1 NA
samples, standard mixtures and extraction blanks (type) were acquired in three
separate analysis batches. Each measurement resulted in an integrated peak area.


Compound Type Batch Name Area Spec Conc
--------- ----------------- ------ --------- ------------ ----------
Tyrosol Extraction blank 1 Blank 1 0.000000 NA
Tyrosol Extraction blank 1 Blank 2 0.000000 NA
Tyrosol Sample 1 ZS-001 328.343597 NA
Tyrosol Sample 1 ZS-002 282.930939 NA
Tyrosol Standard 3 0 mg/L 0.000000 0.00000
Tyrosol Standard 3 1 mg/L 7.628456 0.97755
Tyrosol Standard 3 5 mg/L 35.566628 4.88775
Tyrosol Standard 3 20 mg/L 141.898056 19.55100
Tyrosol Standard 3 100 mg/L 715.496338 97.75500
Vanillin Sample 1 ZS-001 1876.933716 NA
Vanillin Sample 1 ZS-002 1578.626099 NA

The sample table describes the samples' origin from a 29-day degradation
experiment, in which the phenolic compounds were either degraded in the dark by
the native soil microbial community or photooxidized under UV irradiation. The
samples were processed in threefold replication. Their weight [g], the volume
[mL] of extract solution, and the dilution factor were recorded.
the native soil microbial community or photooxidized under UV irradiation after
sterilizing the soil. The samples were processed in threefold replication. Their
weight [g], the volume [mL] of extract solution, and the dilution factor were
recorded.


Name Day Lighting Sterilization Treatment Replicate Weight Extract Dilution
Expand All @@ -66,8 +67,8 @@ ZS-019 0 dark non-sterilized Biodegradation 1 2.5001
ZS-164 29 dark non-sterilized Biodegradation 2 2.4992 12.5 1
ZS-165 29 dark non-sterilized Biodegradation 3 2.5000 12.5 1

In **envalysis**, the sample data is stored in a two-item list called `phenolics`.
The list items are named `seq` and `samples`.
In **envalysis**, the sample data is stored in a two-item list called
`phenolics`. The list items are named `seq` and `samples`.


```r
Expand All @@ -80,9 +81,9 @@ str(phenolics)
# > $ seq :'data.frame': 160 obs. of 6 variables:
# > ..$ Compound : Factor w/ 2 levels "Tyrosol","Vanillin": 1 1 1 1 1 1 1 1 1 1 ...
# > ..$ Type : Factor w/ 3 levels "Extraction blank",..: 1 1 2 2 2 2 2 2 2 2 ...
# > ..$ Batch : int [1:160] 1 1 1 1 1 1 1 1 1 1 ...
# > ..$ Name : chr [1:160] "Blank 1" "Blank 2" "ZS-001" "ZS-002" ...
# > ..$ Area : num [1:160] 0 0 328 283 296 ...
# > ..$ Batch : int [1:160] 1 1 1 1 1 1 1 1 1 1 ...
# > ..$ Spec Conc: num [1:160] NA NA NA NA NA NA NA NA NA NA ...
# > $ samples:'data.frame': 42 obs. of 9 variables:
# > ..$ Name : chr [1:42] "ZS-001" "ZS-002" "ZS-003" "ZS-019" ...
Expand All @@ -96,41 +97,6 @@ str(phenolics)
# > ..$ Dilution : int [1:42] 5 5 5 5 5 5 2 2 2 2 ...
```

```r
phenolics$seq |> head()
```

```
# > Compound Type Name Area Batch Spec Conc
# > 1 Tyrosol Extraction blank Blank 1 0.0000 1 NA
# > 2 Tyrosol Extraction blank Blank 2 0.0000 1 NA
# > 3 Tyrosol Sample ZS-001 328.3436 1 NA
# > 4 Tyrosol Sample ZS-002 282.9309 1 NA
# > 5 Tyrosol Sample ZS-003 296.2863 1 NA
# > 6 Tyrosol Sample ZS-019 243.0258 1 NA
```

```r
phenolics$samples |> head()
```

```
# > Name Day Lighting Sterilization Treatment Replicate Weight Extract
# > 1 ZS-001 0 UV sterilized Photooxidation 1 2.5037 12.5
# > 2 ZS-002 0 UV sterilized Photooxidation 2 2.5018 12.5
# > 3 ZS-003 0 UV sterilized Photooxidation 3 2.5048 12.5
# > 4 ZS-019 0 dark non-sterilized Biodegradation 1 2.5001 12.5
# > 5 ZS-020 0 dark non-sterilized Biodegradation 2 2.4996 12.5
# > 6 ZS-021 0 dark non-sterilized Biodegradation 3 2.5026 12.5
# > Dilution
# > 1 5
# > 2 5
# > 3 5
# > 4 5
# > 5 5
# > 6 5
```

## Simple calibation

Since the two phenolic compounds were analyzed in three different batches, six
Expand All @@ -139,10 +105,10 @@ understanding, the calibration workflow is first shown for a subset of data,
namely the first batch of tyrosol measurements. The subset is stored in
`tyrosol_1`.

Subsequently, the standards in this subset are used for calibration. The
All standards in the `tyrosol_1` subset are used for calibration. The
`'calibration'` object is stored as `cal_1`, which can be printed for additional
information including limits of detection and quantification, the adjusted R^2^,
blanks, and statistical checks of the underlying calibration model.
information including limits of detection and quantification, the adjusted
*R*^2^, blanks, and statistical checks of the underlying calibration model.


```r
Expand Down Expand Up @@ -206,28 +172,32 @@ head(tyrosol_1)
```

```
# > Compound Type Name Area Batch Spec Conc Calc Conc
# > 1 Tyrosol Extraction blank Blank 1 0.0000 1 NA 0.00000
# > 2 Tyrosol Extraction blank Blank 2 0.0000 1 NA 0.00000
# > 3 Tyrosol Sample ZS-001 328.3436 1 NA 43.23037
# > 4 Tyrosol Sample ZS-002 282.9309 1 NA 37.18195
# > 5 Tyrosol Sample ZS-003 296.2863 1 NA 38.96072
# > 6 Tyrosol Sample ZS-019 243.0258 1 NA 31.86707
# > Compound Type Batch Name Area Spec Conc Calc Conc
# > 1 Tyrosol Extraction blank 1 Blank 1 0.0000 NA 0.00000
# > 2 Tyrosol Extraction blank 1 Blank 2 0.0000 NA 0.00000
# > 3 Tyrosol Sample 1 ZS-001 328.3436 NA 43.23037
# > 4 Tyrosol Sample 1 ZS-002 282.9309 NA 37.18195
# > 5 Tyrosol Sample 1 ZS-003 296.2863 NA 38.96072
# > 6 Tyrosol Sample 1 ZS-019 243.0258 NA 31.86707
```

## Working with `data.table`s

To process all compounds and analysis batches together, the `phenolics` data is
converted to `data.tables`s.
converted to `data.table`s.


```r
dt <- lapply(phenolics, as.data.table)
```

To replicate the following steps, try to organize your data in the same way as
shown before. If you want to read in your data directly as `data.table`, use
their `fread()` function, for instance.

## Batch calibration

Afterwards, the `calibration()` and `inv_predict()` are applied by compound and
Subsequently, `calibration()` and `inv_predict()` are applied by compound and
batch.


Expand All @@ -239,21 +209,22 @@ head(dt$seq)
```

```
# > Compound Type Name Area Batch Spec Conc Calc Conc
# > 1: Tyrosol Extraction blank Blank 1 0.0000 1 NA 0.00000
# > 2: Tyrosol Extraction blank Blank 2 0.0000 1 NA 0.00000
# > 3: Tyrosol Sample ZS-001 328.3436 1 NA 43.23037
# > 4: Tyrosol Sample ZS-002 282.9309 1 NA 37.18195
# > 5: Tyrosol Sample ZS-003 296.2863 1 NA 38.96072
# > 6: Tyrosol Sample ZS-019 243.0258 1 NA 31.86707
# > Compound Type Batch Name Area Spec Conc Calc Conc
# > 1: Tyrosol Extraction blank 1 Blank 1 0.0000 NA 0.00000
# > 2: Tyrosol Extraction blank 1 Blank 2 0.0000 NA 0.00000
# > 3: Tyrosol Sample 1 ZS-001 328.3436 NA 43.23037
# > 4: Tyrosol Sample 1 ZS-002 282.9309 NA 37.18195
# > 5: Tyrosol Sample 1 ZS-003 296.2863 NA 38.96072
# > 6: Tyrosol Sample 1 ZS-019 243.0258 NA 31.86707
```

Calibration parameters like LODs, LOQs, or adjusted R^2^ may be stored in a
Calibration parameters like LODs, LOQs, or adjusted *R*^2^ may be stored in a
separate list item for later use.


```r
dt$cal <- dt$seq[Type == "Standard", as.list(calibration(Area ~ `Spec Conc`)),
dt$cal <- dt$seq[Type == "Standard", calibration(Area ~ `Spec Conc`) |>
as.list(c("coef", "adj.r.squared", "lod", "loq")),
by = .(Compound, Batch)]
print(dt$cal)
```
Expand All @@ -266,13 +237,6 @@ print(dt$cal)
# > 4: Vanillin 1 25.17794786 51.529670 0.9998175 0.0040133 5.623778
# > 5: Vanillin 2 24.44314369 51.774820 0.9992359 0.0003509 11.408191
# > 6: Vanillin 3 10.62899985 50.819997 0.9999641 0.0000000 2.886073
# > blank_mean blank_sd
# > 1: 0.05024649 0.033985647
# > 2: 0.07946879 0.022149819
# > 3: 0.00000000 0.000000000
# > 4: 0.07709483 0.065037107
# > 5: 0.08453879 0.005712943
# > 6: 0.00000000 0.000000000
```

## Blank subtraction
Expand Down Expand Up @@ -303,13 +267,13 @@ head(dt$res)
```

```
# > Name Compound Type Area Batch Spec Conc Calc Conc Clean Conc Day
# > 1: ZS-001 Tyrosol Sample 328.3436 1 NA 43.23037 43.23037 0
# > 2: ZS-001 Vanillin Sample 1876.9337 1 NA 35.93572 35.93572 0
# > 3: ZS-002 Tyrosol Sample 282.9309 1 NA 37.18195 37.18195 0
# > 4: ZS-002 Vanillin Sample 1578.6261 1 NA 30.14667 30.14667 0
# > 5: ZS-003 Tyrosol Sample 296.2863 1 NA 38.96072 38.96072 0
# > 6: ZS-003 Vanillin Sample 1593.6272 1 NA 30.43779 30.43779 0
# > Name Compound Type Batch Area Spec Conc Calc Conc Clean Conc Day
# > 1: ZS-001 Tyrosol Sample 1 328.3436 NA 43.23037 43.23037 0
# > 2: ZS-001 Vanillin Sample 1 1876.9337 NA 35.93572 35.93572 0
# > 3: ZS-002 Tyrosol Sample 1 282.9309 NA 37.18195 37.18195 0
# > 4: ZS-002 Vanillin Sample 1 1578.6261 NA 30.14667 30.14667 0
# > 5: ZS-003 Tyrosol Sample 1 296.2863 NA 38.96072 38.96072 0
# > 6: ZS-003 Vanillin Sample 1 1593.6272 NA 30.43779 30.43779 0
# > Lighting Sterilization Treatment Replicate Weight Extract Dilution
# > 1: UV sterilized Photooxidation 1 2.5037 12.5 5
# > 2: UV sterilized Photooxidation 1 2.5037 12.5 5
Expand Down
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