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importing-custom-data-formats.Rmd
introduction-to-gcims.Rmd
+one_chrom <- getChromatogram(ket1afterfilter, dt_range = 10.4)
-one_chrom_smoothed <- smooth(one_chrom, rt_length_s = 3, rt_order = 2) @@ -305,7 +305,7 @@
Smoothinggeom_line(aes(x = retention_time_s, y = intensity, colour = Status)) + labs(x = "Retention time (s)", y = "Intensity (a.u.)"))
You can also apply it to a single sample:
+You can also apply it to a single sample:
+#> • Maximum RIP intensity at: (dt: 7.813 ms, rt: 51.87 s)ket1_smoothed <- smooth( ket1afterfilter, @@ -441,13 +441,13 @@
Peaks #> ℹ Drift time scales: 1, 11, 17, 25, 30 #> ℹ RIP was detected #> • At drift time: [7.533 - 7.933] ms -#> • Maximum RIP intensity at: (dt: 7.813 ms, rt: 63.96 s)
peak_list_ket1 <- peaks(ket1)
plot_interactive(plot(ket1) +
overlay_peaklist(peak_list_ket1, color_by = "PeakID"))
Then do it on the whole dataset:
+Then do it on the whole dataset:
findPeaks(
dataset,
@@ -469,19 +469,19 @@ Peaks
#> ℹ Drift time scales: 1, 11, 17, 25, 30
#> ℹ RIP was detected
#> • At drift time: [7.533 - 7.933] ms
-#> • Maximum RIP intensity at: (dt: 7.813 ms, rt: 62.4 s)
+#> • Maximum RIP intensity at: (dt: 7.813 ms, rt: 49.53 s)
#> Using the following scales
#> ℹ Retention time scales: 1, 26, 39, 59, 64
#> ℹ Drift time scales: 1, 11, 17, 25, 30
#> ℹ RIP was detected
#> • At drift time: [7.533 - 7.933] ms
-#> • Maximum RIP intensity at: (dt: 7.813 ms, rt: 63.96 s)
+#> • Maximum RIP intensity at: (dt: 7.813 ms, rt: 51.87 s)
#> Using the following scales
#> ℹ Retention time scales: 1, 26, 39, 59, 64
#> ℹ Drift time scales: 1, 11, 17, 25, 30
#> ℹ RIP was detected
-#> • At drift time: [7.493 - 7.933] ms
-#> • Maximum RIP intensity at: (dt: 7.813 ms, rt: 62.4 s)
You can get any other sample if you like, plot it and plot its peaks on top:
@@ -490,14 +490,14 @@Peaks
plot_interactive(plot(ket2) + overlay_peaklist(peaks(ket2)) )
Or plot all the peaks from all the dataset together, overlayed on a +
Or plot all the peaks from all the dataset together, overlayed on a single sample:
plt <- plot(ket2) +
overlay_peaklist(peaks(dataset), color_by = "SampleID")
plot_interactive(plt)
+-plt <- plot(ket2) + overlay_peaklist(dplyr::filter(peak_list_clustered, !is.na(cluster)), color_by = "cluster") plot_interactive(plt)
The resulting cluster sizes (median position of individual clusters) +
The resulting cluster sizes (median position of individual clusters) is not a good reference for integration. We are working on this.
- +plt <- plot(ket2) + overlay_peaklist(peak_clustering$cluster_stats, color_by = "cluster") plot_interactive(plt)
+one_chrom <- getChromatogram(ket1, dt_range = 10.4)
one_chrom <- estimateBaseline(one_chrom, rt_length_s = 200) @@ -620,9 +620,9 @@
Baseline correction#> Adding another scale for x, which will replace the existing scale. #> Scale for y is already present. #> Adding another scale for y, which will replace the existing scale. -#> Warning: Removed 27 rows containing missing values or values outside the scale range +#> Warning: Removed 23 rows containing missing values or values outside the scale range #> (`geom_rect()`). -#> Warning: Removed 35 rows containing missing values or values outside the scale range +#> Warning: Removed 31 rows containing missing values or values outside the scale range #> (`geom_point()`).
peak_table <- peakTable(peak_list, aggregate_conflicting_peaks = max)
t(tail(t(peak_table$peak_table_matrix)))
-#> Cluster22 Cluster19 Cluster25 Cluster18 Cluster06 Cluster07
-#> Ketones1 66.81995 5394.202 123.4277 4712.837 NA NA
-#> Ketones2 114.77568 5532.655 NA 5225.228 113.5576 101.8653
-#> Ketones3 109.31888 5909.382 174.9371 5482.594 207.6015 111.9301
+#> Cluster10 Cluster22 Cluster17 Cluster18 Cluster03 Cluster01
+#> Ketones1 4721.764 70.1966 5378.463 5222.479 NA NA
+#> Ketones2 4739.447 115.7510 5593.831 5336.872 117.1233 100.6062
+#> Ketones3 5031.766 113.9425 5784.134 6197.367 213.2812 106.6575
We showed the last 6 clusters where we have “NA” Values
As we can see the “NA” values were imputed
# Implemented until here
end_time <- Sys.time()
message("The vignette ran in ", format(end_time - start_time))
-#> The vignette ran in 1.785006 mins
The object, modified
-The object, modified
- - -The object, modified
- - -The object, modified
- -The object, with a baseline estimated
@@ -176,10 +161,7 @@filterDt()
: Filter in Drift time
decimate()
: Decimate an object
align()
: Align an object
alignDt()
: Align an object in drift time
alignRt_ptw()
: Align an object in retention time
alignRt_pow()
: Align an object in retention time
alignRt_ip()
: Align an object in retention time
prealign()
: Align an object in drift time
estimateBaseline()
: Estimate the baseline in an object
baseline()
: Get the baseline of an object
baseline(object) <- value
: Set the baseline of an object
Method for alignment, should be "ptw" or "pow" -if pow is selected the package "pow must be installed, to do so visit: +if pow is selected the package "pow" must be installed, to do so visit: https://github.com/sipss/pow
if TRUE a multiplicative correction will be done in retention time before applying the other algorithm
if TRUE
a multiplicative correction will be done in retention time before applying the other algorithm
One number, the index of the sample to use as reference for the alignment in retention time, if NULL the reference will be calculated automatically depending on the method
One number, by default 2, the maximum order of the polynomial for the parametric time warping alignment.
additional parameters for POW alignment
align-GCIMSSample-method.Rd
Align a GCIMSSample object, in drift and retention time
+Align a GCIMSSample object, in retention time
A GCIMSSample object
The reference position of the Reactant Ion Peak in the dataset (in ms)
Method for alignment, should be "ptw" or "pow"
The retention times corresponding to ric_ref
minimun injection point, to calculate where to begin the spectrums and cut as few points as posible, to be used in injection point alignment
retention time reference for alignment to injection point
Method for alignment, should be "ptw" or "pow"
if TRUE
, align the drift time axis using a multiplicative correction
if TRUE a multiplicative correction will be done in retention time before applying the other algorithm
One number, by default 2, the maximum order of the polynomial for the parametric time warping alignment.
Additional arguments passed on to the alignment method.
alignDt-GCIMSSample-method.Rd
alignDt.Rd
# S4 method for GCIMSSample
-alignDt(object, rip_ref_ms)
alignDt(object, rip_ref_ms)
alignRt_ip-GCIMSSample-method.Rd
alignRt_ip.Rd
# S4 method for GCIMSSample
-alignRt_ip(object, min_start, rt_ref)
alignRt_ip(object, min_start, rt_ref)
alignRt_pow-GCIMSSample-method.Rd
alignRt_pow.Rd
# S4 method for GCIMSSample
-alignRt_pow(
+ alignRt_pow(
object,
ric_ref,
ric_ref_rt,
diff --git a/reference/alignRt_ptw-GCIMSSample-method.html b/reference/alignRt_ptw-GCIMSSample-method.html
deleted file mode 100644
index 3780b4c..0000000
--- a/reference/alignRt_ptw-GCIMSSample-method.html
+++ /dev/null
@@ -1,120 +0,0 @@
-
-Align a GCIMSSample in retention time using parametric time warping — alignRt_ptw,GCIMSSample-method • GCIMS
-
-
-
-
-
-
-
-
-
-
- Align a GCIMSSample in retention time using parametric time warping
-
- alignRt_ptw-GCIMSSample-method.Rd
-
-
-
- Align a GCIMSSample in retention time using parametric time warping
-
-
-
- # S4 method for GCIMSSample
-alignRt_ptw(object, ric_ref, ric_ref_rt, ploynomial_order_ptw)
-
-
-
- Arguments
- - object
-A GCIMSSample object
-
-
-- ric_ref
-The reference Reverse Ion Chromatogram
-
-
-- ric_ref_rt
-The retention times corresponding to ric_ref
-
-
-- ploynomial_order_ptw
-maximum order of the polynomial to be used
-
-
-
- Value
-
-
-The modified GCIMSSample
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/reference/alignRt_ptw.html b/reference/alignRt_ptw.html
new file mode 100644
index 0000000..807444c
--- /dev/null
+++ b/reference/alignRt_ptw.html
@@ -0,0 +1,119 @@
+
+Align a GCIMSSample in retention time using parametric time warping — alignRt_ptw • GCIMS
+
+
+
+
+
+
+
+
+
+
+ Align a GCIMSSample in retention time using parametric time warping
+
+ alignRt_ptw.Rd
+
+
+
+ Align a GCIMSSample in retention time using parametric time warping
+
+
+
+ alignRt_ptw(object, ric_ref, ric_ref_rt, ploynomial_order = 5)
+
+
+
+ Arguments
+ - object
+A GCIMSSample object
+
+
+- ric_ref
+The reference Reverse Ion Chromatogram
+
+
+- ric_ref_rt
+The retention times corresponding to ric_ref
+
+
+- ploynomial_order
+maximum order of the polynomial to be used by default 5
+
+
+
+ Value
+
+
+The modified GCIMSSample
+
+
+
+
+
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+
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diff --git a/reference/index.html b/reference/index.html
index e67be59..112f0ac 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -69,7 +69,7 @@ All functions
Create a DelayedOperation object
- dtime()
getTIS()
getRIC()
plotTIS()
plotRIC()
filterDt()
decimate()
align()
alignDt()
alignRt_ptw()
alignRt_pow()
alignRt_ip()
estimateBaseline()
baseline()
`baseline<-`()
integratePeaks()
+ dtime()
getTIS()
getRIC()
plotTIS()
plotRIC()
filterDt()
decimate()
align()
prealign()
estimateBaseline()
baseline()
`baseline<-`()
integratePeaks()
GCIMS Generics
@@ -119,9 +119,9 @@ All functions
- Align a GCIMSSample object, in drift and retention time
+ Align a GCIMSSample object, in retention time
-
+
Align a GCIMSSample in drift time with a multiplicative correction
@@ -129,15 +129,15 @@ All functions
Plots to interpret alignment results
-
+
Align a GCIMSSample in retention time with a multiplicative correction
-
+
Align a GCIMSSample in retention time with parametric optimized warping
-
+
Align a GCIMSSample in retention time using parametric time warping
@@ -304,6 +304,10 @@ All functions plot_interactive()
Make a plot interactive
+
+
+
+ Align a GCIMSSample object, in drift time and to the injection point in retention time
diff --git a/reference/plot_interactive.html b/reference/plot_interactive.html
index 6909a6d..afe9346 100644
--- a/reference/plot_interactive.html
+++ b/reference/plot_interactive.html
@@ -88,7 +88,7 @@ Examples
ggplot2::geom_point(ggplot2::aes(x = x, y = y))
plot_interactive(plt)
-
+