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Merge pull request #10 from jorainer/jomain
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Tiny rewording
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jorainer authored Oct 6, 2023
2 parents 5468331 + 69d0c19 commit 5041a60
Showing 1 changed file with 7 additions and 7 deletions.
14 changes: 7 additions & 7 deletions vignettes/xcms-preprocessing.Rmd
Original file line number Diff line number Diff line change
Expand Up @@ -689,9 +689,9 @@ spectra(data) <- sps_cent
#' Plot the centroided data for Serine
data |>
filterRt(rt = c(175, 189)) |>
filterMz(mz = c(106.02, 106.07)) |>
plot()
filterRt(rt = c(175, 189)) |>
filterMz(mz = c(106.02, 106.07)) |>
plot()
```

The impact of the centroiding is clearly visible: each signal for an ion in a
Expand Down Expand Up @@ -1059,7 +1059,7 @@ for all possible refinement options).
To fuse the wrongly split peaks in the second row, we use the
`MergeNeighboringPeaksParam` algorithm that merges chromatographic peaks that
are overlapping on the *m/z* and retention time dimension for which the signal
between them is lower than a certain value. We specify `expandRt = 4` to expand
between them is higher than a certain value. We specify `expandRt = 4` to expand
the retention time width of each peak by 4 seconds on each side and set `minProp
= 0.75`. All chromatographic peaks with a distance tail to head in retention
time dimension that is less `2 * expandRt` and for which the intensity between
Expand Down Expand Up @@ -1105,8 +1105,8 @@ bpc_raw <- chromatogram(data, aggregationFun = "max", chromPeaks = "none")
plot(bpc_raw, peakType = "none")
```

While both samples were measured with the same setup in the same measurement
run, slight drifts of the signal are visible. These were also already visible in
Both samples were measured with the same setup in the same measurement run, but
still small drifts of the signal are visible. These were also already visible in
the EIC for serine, that we plot again below.

```{r}
Expand Down Expand Up @@ -1177,7 +1177,7 @@ data <- groupChromPeaks(data, pdp)

This step now grouped chromatographic peaks across samples and defined so called
LC-MS features (or simply features). We can thus now run the alignment using the
*peakGroups* algorithm. The main parameter to define the hook peaks is (again)
*peakGroups* algorithm. The main parameter to define the hook peaks is
`minFraction`. Similar to the definition above, `minFraction` refers to the
proportion of samples in which a chromatographic peak needs to be present. By
setting `minFraction = 1` we base the alignment on features with peaks
Expand Down

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