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Tiny rewording
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vignettes/xcms-preprocessing.Rmd

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@@ -689,9 +689,9 @@ spectra(data) <- sps_cent
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#' Plot the centroided data for Serine
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data |>
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filterRt(rt = c(175, 189)) |>
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filterMz(mz = c(106.02, 106.07)) |>
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plot()
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filterRt(rt = c(175, 189)) |>
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filterMz(mz = c(106.02, 106.07)) |>
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plot()
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```
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The impact of the centroiding is clearly visible: each signal for an ion in a
@@ -1059,7 +1059,7 @@ for all possible refinement options).
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To fuse the wrongly split peaks in the second row, we use the
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`MergeNeighboringPeaksParam` algorithm that merges chromatographic peaks that
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are overlapping on the *m/z* and retention time dimension for which the signal
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between them is lower than a certain value. We specify `expandRt = 4` to expand
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between them is higher than a certain value. We specify `expandRt = 4` to expand
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the retention time width of each peak by 4 seconds on each side and set `minProp
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= 0.75`. All chromatographic peaks with a distance tail to head in retention
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time dimension that is less `2 * expandRt` and for which the intensity between
@@ -1105,8 +1105,8 @@ bpc_raw <- chromatogram(data, aggregationFun = "max", chromPeaks = "none")
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plot(bpc_raw, peakType = "none")
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```
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While both samples were measured with the same setup in the same measurement
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run, slight drifts of the signal are visible. These were also already visible in
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Both samples were measured with the same setup in the same measurement run, but
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still small drifts of the signal are visible. These were also already visible in
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the EIC for serine, that we plot again below.
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```{r}
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This step now grouped chromatographic peaks across samples and defined so called
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LC-MS features (or simply features). We can thus now run the alignment using the
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*peakGroups* algorithm. The main parameter to define the hook peaks is (again)
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*peakGroups* algorithm. The main parameter to define the hook peaks is
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`minFraction`. Similar to the definition above, `minFraction` refers to the
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proportion of samples in which a chromatographic peak needs to be present. By
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setting `minFraction = 1` we base the alignment on features with peaks

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