# HG changeset patch
# User galaxyp
# Date 1550244405 18000
# Node ID 36d38d2cf88ca55cb0473984e1317d79f5495f60
# Parent 17c54820f3be1342342001eb15bc43ffe3c3458a
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/MALDIquant commit f127be2141cf22e269c85282d226eb16fe14a9c1
diff -r 17c54820f3be -r 36d38d2cf88c maldi_macros.xml
--- a/maldi_macros.xml Thu Oct 25 07:32:17 2018 -0400
+++ b/maldi_macros.xml Fri Feb 15 10:26:45 2019 -0500
@@ -18,11 +18,12 @@
- bioconductor-cardinal
+ r-base
+ bioconductor-cardinal
r-maldiquantforeign
r-maldiquant
- r-ggplot2
- r-gridextra
+ r-ggplot2
+ r-gridextra
diff -r 17c54820f3be -r 36d38d2cf88c maldi_quant_peakdetection.xml
--- a/maldi_quant_peakdetection.xml Thu Oct 25 07:32:17 2018 -0400
+++ b/maldi_quant_peakdetection.xml Fri Feb 15 10:26:45 2019 -0500
@@ -1,4 +1,4 @@
-
+
Peak detection, binning and filtering for mass-spectrometry imaging data
@@ -37,6 +37,7 @@
## Import imzML file
coordinate_matrix = as.matrix(read.delim("$restriction_conditional.coordinates_file", header = $restriction_conditional.coordinates_header, stringsAsFactors = FALSE))[,1:2]
+ coordinate_matrix = coordinate_matrix[,c($restriction_conditional.column_x, $restriction_conditional.column_y)]
maldi_data <- importImzMl('infile.imzML',
coordinates = coordinate_matrix, centroided = $centroids)
@@ -99,15 +100,16 @@
cardinal_mzs = Cardinal::mz(msidata)
maldi_data = list()
for(number_spectra in 1:ncol(msidata)){
- maldi_data[[number_spectra]] = createMassSpectrum(mass = cardinal_mzs, intensity = iData(msidata)[,number_spectra])
- coordinates_info = cbind(cardinal_coordinates, c(1:length(maldi_data)))}
+ maldi_data[[number_spectra]] = createMassSpectrum(mass = cardinal_mzs, intensity = iData(msidata)[,number_spectra])}
+ coordinates_info = cbind(cardinal_coordinates, c(1:length(maldi_data)))
}else{
peaks = list()
for (spectra in 1:ncol(msidata))
{
single_peaks = createMassPeaks(Cardinal::mz(msidata), Cardinal::spectra(msidata)[,spectra], snr=as.numeric(rep("NA", nrow(msidata))))
peaks[[spectra]] = single_peaks
- }}
+ }
+ coordinates_info = cbind(cardinal_coordinates, c(1:length(peaks)))}
#end if
#end if
@@ -125,10 +127,20 @@
## plot input file spectrum:
#if $centroids:
- plot(peaks[[1]], main="First spectrum of input file")
+ ## Choose random spectra for QC plots
+ random_spectra = sample(1:length(peaks), 4, replace=FALSE)
+ par(mfrow = c(2, 2), oma=c(0,0,2,0))
+ for (random_sample in random_spectra){
+ plot(peaks[[random_sample]],sub="", main=paste0("spectrum ", random_sample))}
+ title("Input spectra", outer=TRUE, line=0)
+
#else
- avgSpectra <- averageMassSpectra(maldi_data,method="mean")
- plot(avgSpectra, main="Average spectrum of input file")
+ ## Choose random spectra for QC plots
+ random_spectra = sample(1:length(maldi_data), 4, replace=FALSE)
+ par(mfrow = c(2, 2), oma=c(0,0,2,0))
+ for (random_sample in random_spectra){
+ plot(maldi_data[[random_sample]],sub="", main=paste0("spectrum ", random_sample))}
+ title("Input spectra", outer=TRUE, line=0)
#end if
@@ -162,10 +174,11 @@
annotation_input = input_tabular[,c($tabular_annotation.column_x, $tabular_annotation.column_y, $tabular_annotation.column_names)]
colnames(annotation_input) = c("x", "y", "annotation") ## rename annotations header to default name "annotation"
- ## merge with coordinate information of MSI data
+ ## merge provided annotation with coordinate information of MSI data
colnames(coordinates_info)[3] = "pixel_index"
merged_annotation = merge(coordinates_info, annotation_input, by=c("x", "y"), all.x=TRUE)
merged_annotation[is.na(merged_annotation)] = "NA"
+ ## order coordinate information according to pixel index to make sure that the order stays the same
merged_annotation = merged_annotation[order(merged_annotation\$pixel_index),]
samples = as.factor(merged_annotation\$annotation)
@@ -199,20 +212,28 @@
maldi_data <- averageMassSpectra(maldi_data, labels=samples,method="mean") ## use average spectra for peak picking
pixelnames = levels(samples)
summarized_spectra = TRUE
-
+ random_spectra = sample(1:length(maldi_data), 4, replace=TRUE)
#end if
peaks <- detectPeaks(maldi_data, method="$method.methods_conditional.peak_method",
halfWindowSize=$method.methods_conditional.halfWindowSize,SNR=$method.methods_conditional.snr)
## QC plot and numbers
- ## plot old spectrum with baseline in blue and picked peaks in green
- noise = estimateNoise(maldi_data[[1]], method= "$method.methods_conditional.peak_method")
- plot(maldi_data[[1]], main="First spectrum with noise line (blue) and picked peaks (green)")
- lines(noise[,1], noise[,2]*$method.methods_conditional.snr, col="blue")
- points(peaks[[1]], col="green", pch=20)
+ ## plot old spectra with baseline in blue and picked peaks in green
+ par(mfrow = c(2, 2), oma=c(0,0,2,0))
+ for (random_sample in random_spectra){
+ noise = estimateNoise(maldi_data[[random_sample]], method= "$method.methods_conditional.peak_method")
+ plot(maldi_data[[random_sample]], sub="", main=paste0("spectrum ", random_sample))
+ lines(noise[,1], noise[,2]*$method.methods_conditional.snr, col="blue")
+ points(peaks[[random_sample]], col="green", pch=20)}
+ title("S/N in blue and picked peaks in green", outer=TRUE, line=0)
+
## plot new spectrum
- plot(peaks[[1]], main="First spectrum after peak detection")
+ par(mfrow = c(2, 2), oma=c(0,0,2,0))
+ for (random_sample in random_spectra){
+ plot(peaks[[random_sample]], sub="", main=paste0("spectrum ", random_sample))}
+ title("Picked peaks", outer=TRUE, line=0)
+
pixel_number = length(peaks)
minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
@@ -242,14 +263,27 @@
print('monoisotopic peaks')
##monoisotopic peaks
- peaks = monoisotopicPeaks(peaks, minCor=$method.methods_conditional.minCor, tolerance=$method.methods_conditional.tolerance, distance=$method.methods_conditional.distance, size=$method.methods_conditional.size)
+ ## keep peaks to plot them with monoisotopic peaks
+ picked_peaks = peaks
- ## QC plot and numbers
+ peaks = monoisotopicPeaks(peaks, minCor=$method.methods_conditional.minCor,
+ tolerance=$method.methods_conditional.tolerance,
+ distance=$method.methods_conditional.distance,
+ size=$method.methods_conditional.size)
+
## plot old spectrum with picked isotopes as green dots
- plot(peaks[[1]], main="First spectrum with picked monoisotopic peaks (green)")
- points(peaks[[1]], col="green", pch=20)
- ## plot new spectrum
- plot(peaks[[1]], main="First spectrum after monoisotopic peaks detection")
+ par(mfrow = c(2, 2), oma=c(0,0,2,0))
+ for (random_sample in random_spectra){
+ plot(picked_peaks[[random_sample]], sub="", main=paste0("spectrum ", random_sample))
+ points(peaks[[random_sample]], col="green", pch=20)}
+ title(paste0("Monoisotopic peaks in green"), outer=TRUE, line=0)
+
+
+ par(mfrow = c(2, 2), oma=c(0,0,2,0))
+ for (random_sample in random_spectra){
+ plot(peaks[[random_sample]], sub="", main=paste0("spectrum ", random_sample))}
+ title("Monoisotopic peaks", outer=TRUE, line=0)
+
minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
mean_features = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
@@ -273,6 +307,89 @@
write.table(featureMatrix2, file="$intensity_matrix", quote = FALSE, row.names = TRUE, col.names=FALSE, sep = "\t")
}else{print("There are no spectra with peaks left")}
+
+
+ #elif str( $method.methods_conditional.method ) == 'Align':
+
+ print('align')
+ ##align spectra with 2 separate functions
+
+
+ #if str($method.methods_conditional.reference_for_alignment.align_ref) == 'no_reference':
+ ## 1) calculate warping:
+ warping_function <- determineWarpingFunctions(peaks,
+ tolerance=$method.methods_conditional.tolerance, method="$method.methods_conditional.warping_method",
+ allowNoMatches=$method.methods_conditional.allow_nomatch, minFrequency = $method.methods_conditional.reference_for_alignment.min_frequency)
+ ## 2) warp spectra:
+ peaks = warpMassPeaks(peaks, warping_function, emptyNoMatches=$method.methods_conditional.empty_nomatch)
+
+
+ #elif str($method.methods_conditional.reference_for_alignment.align_ref) == 'yes_reference':
+
+ ## create reference mass_vector from tabular file
+ mass_vector = read.delim("$method.methods_conditional.reference_for_alignment.reference_file", header = $method.methods_conditional.reference_for_alignment.reference_header, stringsAsFactors = FALSE)[,$method.methods_conditional.reference_for_alignment.mz_column]
+ int_vector = rep(1,length(mass_vector))
+ mass_list = createMassPeaks(mass_vector, int_vector)
+
+ #if str($method.methods_conditional.reference_for_alignment.separate_alignment) == "FALSE"
+ print('default alignment')
+
+ ## 1) calculate warping:
+ warping_function <- determineWarpingFunctions(peaks,
+ tolerance=$method.methods_conditional.tolerance, method="$method.methods_conditional.warping_method",
+ allowNoMatches=$method.methods_conditional.allow_nomatch, reference = mass_list)
+ ## 2) warp spectra:
+ peaks = warpMassPeaks(peaks, warping_function, emptyNoMatches=$method.methods_conditional.empty_nomatch)
+
+ #elif str($method.methods_conditional.reference_for_alignment.separate_alignment) == "TRUE"
+ print('spectra wise alignment')
+
+ peaks_new_list =list()
+
+ for (pixelnb in 1:length(peaks))
+ {
+
+ ## 1) calculate warping:
+ warping_function <- determineWarpingFunctions(peaks[[pixelnb]],
+ tolerance=$method.methods_conditional.tolerance, method="$method.methods_conditional.warping_method",
+ allowNoMatches=$method.methods_conditional.allow_nomatch, reference = mass_list)
+ ## 2) warp spectra:
+ peaks_new_list = warpMassPeaks(list(peaks[[pixelnb]]), warping_function, emptyNoMatches=$method.methods_conditional.empty_nomatch)
+ }
+ peaks_new = peaks_new_list
+
+ #end if
+
+ #end if
+
+ ## QC plot and numbers
+ par(mfrow = c(2, 2), oma=c(0,0,2,0))
+ for (random_sample in random_spectra){
+ plot(peaks[[random_sample]], sub="", main=paste0("spectrum ", random_sample))}
+ title("Aligned spectra", outer=TRUE, line=0)
+ minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
+ maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
+ mean_features = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
+ medint = round(median(unlist(lapply(peaks,intensity))), digits=2)
+ number_features = length(unique(unlist(lapply(peaks,mass))))
+ aligned = c(minmz, maxmz,number_features,mean_features, medint)
+ QC_numbers= cbind(QC_numbers, aligned)
+ vectorofactions = append(vectorofactions, "aligned")
+
+ if (length(peaks[!sapply(peaks, isEmpty)])>0){
+ featureMatrix <- intensityMatrix(peaks)
+ ## only for profile imzML file: featurematrix is overwritten:
+ #if $infile.ext == 'imzml'
+ #if str($centroids) == "FALSE"
+ featureMatrix <- intensityMatrix(peaks, maldi_data)
+ #end if
+ #end if
+ featureMatrix2 =cbind(pixelnames, featureMatrix)
+ colnames(featureMatrix2)[1] = c("mz")
+ featureMatrix2 = t(featureMatrix2)
+ write.table(featureMatrix2, file="$intensity_matrix", quote = FALSE, row.names = TRUE, col.names=FALSE, sep = "\t")
+ }else{print("There are no spectra with peaks left")}
+
#elif str( $method.methods_conditional.method ) == 'Binning':
print('binning')
@@ -281,7 +398,10 @@
peaks <- binPeaks(peaks, tolerance=$method.methods_conditional.bin_tolerance, method="$method.methods_conditional.bin_method")
## QC plot and numbers
- plot(peaks[[1]], main="First spectrum after binning")
+ par(mfrow = c(2, 2), oma=c(0,0,2,0))
+ for (random_sample in random_spectra){
+ plot(peaks[[random_sample]], sub="", main=paste0("spectrum ", random_sample))}
+ title("Binned spectra", outer=TRUE, line=0)
minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
mean_features = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
@@ -329,7 +449,10 @@
#end if
##QC plot and numbers
- plot(peaks[[1]], main="First spectrum after m/z filtering")
+ par(mfrow = c(2, 2), oma=c(0,0,2,0))
+ for (random_sample in random_spectra){
+ plot(peaks[[random_sample]], sub="", main=paste0("spectrum ", random_sample))}
+ title("Filtered spectra", outer=TRUE, line=0)
minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
mean_features = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
@@ -368,7 +491,7 @@
}else{print("There are no spectra with peaks left")}
## print table with QC values
-rownames(QC_numbers) = c("min m/z", "max mz", "# features", "median \n# features", "median\nintensity")
+rownames(QC_numbers) = c("min m/z", "max mz", "# features", "median \n# peaks (int.>0)", "median\nintensity")
plot(0,type='n',axes=FALSE,ann=FALSE)
grid.table(t(QC_numbers))
@@ -376,14 +499,14 @@
if (summarized_spectra == FALSE){
#if $infile.ext == 'imzml'
- MALDIquantForeign::exportImzMl(peaks, file="out.imzMl", processed=$export_processed)
+ MALDIquantForeign::exportImzMl(peaks, file="out.imzMl", processed=TRUE)
#elif $infile.ext == 'tabular'
masspeaks_coordinates = matrix(unlist(strsplit(as.character(pixelnames), "\\_")), ncol=3, byrow=TRUE)
## extract x and y values and create the coordinate matrix in case tabular was input
peaklist_coordinates = unique(cbind(as.numeric(masspeaks_coordinates[,2]), as.numeric(masspeaks_coordinates[,3])))
- exportImzMl(peaks, file="out.imzMl", processed=$export_processed, coordinates=peaklist_coordinates)
+ exportImzMl(peaks, file="out.imzMl", processed=TRUE, coordinates=peaklist_coordinates)
#elif $infile.ext == 'rdata'
- MALDIquantForeign::exportImzMl(peaks, file="out.imzMl", processed=$export_processed, coordinates=cardinal_coordinates)
+ MALDIquantForeign::exportImzMl(peaks, file="out.imzMl", processed=TRUE, coordinates=cardinal_coordinates)
#end if
}
@@ -392,24 +515,26 @@
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@@ -596,7 +767,7 @@
...
-- Optional: Tabular file with pixel coordinates to restrict reading of imzML files to coordinates of interest. The file has to contain x values in the first column and y values in the second columns. Further columns are allowed. Tabular files with any header name or no header at all are supported.
+- Optional: Tabular file with pixel coordinates to restrict reading of imzML files to coordinates of interest. Tabular files with any header name or no header at all are supported.
::
@@ -629,16 +800,21 @@
- Detect peaks on average mass spectra: Spectra with the same annotation (taken from the annotation tabular input) are averaged and peak picking is performed on the average spectrum of each annotation group. The exported imzML is empty and cannot be used for further analysis steps. The peaklist and intensity matrix outputs contain the annotation group names with their averaged intensity values. Filtering steps have to be done in the same run as the peak picking.
-- Monoisotopic peaks: detection of monoisotopic peaks
+- Monoisotopic peaks: Keeps only the monoisotopic peaks
+- Spectra alignment (warping): alignment for (re)calibration of m/z values.
+
+ - without external reference m/z: internal reference is obtained by filtering (default 90%) and binning the peaks to find landmark peaks and their average m/z
+ - with external reference m/z: the m/z provided in a tabular file are used as a reference, at least 10 reference values are recommended
+ - non linear warping (parametric time warping plus binning) to match the reference peaks (internal or external) to the peaks with the given tolerance. At least two m/z per spectrum are needed for the alignment. This requirement can be skipped by setting "Don't throw an error when less than 2 reference m/z were found in a spectrum" to yes. If the not aligned spectra should be set to zero select yes in "If TRUE the intensity values of MassSpectrum or MassPeaks objects with missing (NA) warping functions are set to zero". In order to remove such empty spectra set "Should empty spectra be removed" to yes.
- Peak binning: After the alignment the peak positions (m/z) are very similar but not identical. The binning is needed to make similar peak m/z values identical. The algorithm is based on the following workflow:
- 1. Put all mass in a sorted vector.
+ 1. Put all m/z in a sorted vector.
2. Calculate differences between each neighbor.
- 3. Divide the mass vector at the largest gap (largest difference) and form a left and a right bin.
+ 3. Divide the m/z vector at the largest gap (largest difference) and form a left and a right bin.
4. Rerun step 3 for the left and/or the right bin if they don't fulfill the following criteria:
- All peaks in a bin are near to the mean (abs(mass-meanMass)/meanMass < tolerance).
@@ -655,8 +831,8 @@
**Output**
-- centroided imzML file (processed or continuous), imzML file is empty when 'Detect peaks on average mass spectra' is chosen.
-- pdf with mass spectra plots after each preprocessing step and a table with key values after each preprocessing step
+- centroided, processed imzML file, imzML file is empty when 'Detect peaks on average mass spectra' is chosen.
+- pdf with mass spectra plots of four random spectra and a table with key values after each preprocessing step
- peak list (tabular file) with the columns "snr", "mass", "intensity" and "spectrum"
- tabular file with intensity matrix (m/z in rows and spectra in columns). If the input file was imzML in profile mode the intensities before peak picking are also stored in the matrix . For all other inputs not picked values are set to NA. For peak picking on the average of multiple spectra, each spectra group is a column with mean intensities for each m/z.
diff -r 17c54820f3be -r 36d38d2cf88c test-data/Preprocessing1_QC.pdf
Binary file test-data/Preprocessing1_QC.pdf has changed
diff -r 17c54820f3be -r 36d38d2cf88c test-data/Preprocessing2_QC.pdf
Binary file test-data/Preprocessing2_QC.pdf has changed
diff -r 17c54820f3be -r 36d38d2cf88c test-data/Preprocessing3_QC.pdf
Binary file test-data/Preprocessing3_QC.pdf has changed
diff -r 17c54820f3be -r 36d38d2cf88c test-data/align_reference_test2.tabular
--- a/test-data/align_reference_test2.tabular Thu Oct 25 07:32:17 2018 -0400
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,2 +0,0 @@
-350
-340
diff -r 17c54820f3be -r 36d38d2cf88c test-data/inputpeptides.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/inputpeptides.tabular Fri Feb 15 10:26:45 2019 -0500
@@ -0,0 +1,3 @@
+152 mass1
+328.9 mass2
+185.2 mass3
diff -r 17c54820f3be -r 36d38d2cf88c test-data/outfile1.ibd
Binary file test-data/outfile1.ibd has changed
diff -r 17c54820f3be -r 36d38d2cf88c test-data/outfile1.imzML
--- a/test-data/outfile1.imzML Thu Oct 25 07:32:17 2018 -0400
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,4 +0,0 @@
-imzML file:
-total 84
--rw-r--r-- 1 meli meli 67160 Okt 24 10:12 ibd
--rw-r--r-- 1 meli meli 15071 Okt 24 10:12 imzml
diff -r 17c54820f3be -r 36d38d2cf88c test-data/outfile1.imzml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/outfile1.imzml Fri Feb 15 10:26:45 2019 -0500
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diff -r 17c54820f3be -r 36d38d2cf88c test-data/outfile2.ibd
Binary file test-data/outfile2.ibd has changed
diff -r 17c54820f3be -r 36d38d2cf88c test-data/outfile2.imzML
--- a/test-data/outfile2.imzML Thu Oct 25 07:32:17 2018 -0400
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,4 +0,0 @@
-imzML file:
-total 276
--rw-r--r-- 1 meli meli 268784 Okt 24 10:12 ibd
--rw-r--r-- 1 meli meli 9286 Okt 24 10:12 imzml
diff -r 17c54820f3be -r 36d38d2cf88c test-data/outfile2.imzml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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diff -r 17c54820f3be -r 36d38d2cf88c test-data/outfile3.ibd
Binary file test-data/outfile3.ibd has changed
diff -r 17c54820f3be -r 36d38d2cf88c test-data/outfile3.imzML
--- a/test-data/outfile3.imzML Thu Oct 25 07:32:17 2018 -0400
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,4 +0,0 @@
-imzML file:
-total 52
--rw-r--r-- 1 meli meli 38384 Okt 24 10:13 ibd
--rw-r--r-- 1 meli meli 9551 Okt 24 10:13 imzml
diff -r 17c54820f3be -r 36d38d2cf88c test-data/outfile3.imzml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/outfile3.imzml Fri Feb 15 10:26:45 2019 -0500
@@ -0,0 +1,333 @@
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diff -r 17c54820f3be -r 36d38d2cf88c test-data/peak_detection1.imzml.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/peak_detection1.imzml.txt Fri Feb 15 10:26:45 2019 -0500
@@ -0,0 +1,4 @@
+imzML file:
+total 120
+-rw-r--r-- 1 meli meli 95936 Feb 14 17:46 ibd
+-rw-r--r-- 1 meli meli 20580 Feb 14 17:46 imzml
diff -r 17c54820f3be -r 36d38d2cf88c test-data/peak_detection2.ibd
Binary file test-data/peak_detection2.ibd has changed
diff -r 17c54820f3be -r 36d38d2cf88c test-data/peak_detection2.imzml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/peak_detection2.imzml Fri Feb 15 10:26:45 2019 -0500
@@ -0,0 +1,319 @@
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diff -r 17c54820f3be -r 36d38d2cf88c test-data/peak_detection2.imzml.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/peak_detection2.imzml.txt Fri Feb 15 10:26:45 2019 -0500
@@ -0,0 +1,4 @@
+imzML file:
+total 24
+-rw-r--r-- 1 meli meli 320 Feb 14 18:44 ibd
+-rw-r--r-- 1 meli meli 19244 Feb 14 18:44 imzml
diff -r 17c54820f3be -r 36d38d2cf88c test-data/peak_detection3.ibd
Binary file test-data/peak_detection3.ibd has changed
diff -r 17c54820f3be -r 36d38d2cf88c test-data/peak_detection3.imzml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/peak_detection3.imzml Fri Feb 15 10:26:45 2019 -0500
@@ -0,0 +1,324 @@
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diff -r 17c54820f3be -r 36d38d2cf88c test-data/peak_detection3.imzml.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/peak_detection3.imzml.txt Fri Feb 15 10:26:45 2019 -0500
@@ -0,0 +1,4 @@
+imzML file:
+total 28
+-rw-r--r-- 1 meli meli 4768 Feb 14 18:44 ibd
+-rw-r--r-- 1 meli meli 19627 Feb 14 18:44 imzml
diff -r 17c54820f3be -r 36d38d2cf88c test-data/peak_detection4.ibd
Binary file test-data/peak_detection4.ibd has changed
diff -r 17c54820f3be -r 36d38d2cf88c test-data/peak_detection4.imzml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/peak_detection4.imzml Fri Feb 15 10:26:45 2019 -0500
@@ -0,0 +1,754 @@
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diff -r 17c54820f3be -r 36d38d2cf88c test-data/peak_detection4.imzml.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/peak_detection4.imzml.txt Fri Feb 15 10:26:45 2019 -0500
@@ -0,0 +1,4 @@
+imzML file:
+total 80
+-rw-r--r-- 1 meli meli 29792 Feb 14 18:45 ibd
+-rw-r--r-- 1 meli meli 45290 Feb 14 18:45 imzml
diff -r 17c54820f3be -r 36d38d2cf88c test-data/peakdetection1_QC.pdf
Binary file test-data/peakdetection1_QC.pdf has changed
diff -r 17c54820f3be -r 36d38d2cf88c test-data/peakdetection2_QC.pdf
Binary file test-data/peakdetection2_QC.pdf has changed
diff -r 17c54820f3be -r 36d38d2cf88c test-data/peakdetection3_QC.pdf
Binary file test-data/peakdetection3_QC.pdf has changed
diff -r 17c54820f3be -r 36d38d2cf88c test-data/peakdetection4_QC.pdf
Binary file test-data/peakdetection4_QC.pdf has changed
diff -r 17c54820f3be -r 36d38d2cf88c test-data/preprocessing1.imzml.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/preprocessing1.imzml.txt Fri Feb 15 10:26:45 2019 -0500
@@ -0,0 +1,4 @@
+imzML file:
+total 84
+-rw-r--r-- 1 meli meli 67160 Feb 14 17:45 ibd
+-rw-r--r-- 1 meli meli 15071 Feb 14 17:45 imzml
diff -r 17c54820f3be -r 36d38d2cf88c test-data/preprocessing2.imzml.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/preprocessing2.imzml.txt Fri Feb 15 10:26:45 2019 -0500
@@ -0,0 +1,4 @@
+imzML file:
+total 208
+-rw-r--r-- 1 meli meli 201592 Feb 14 17:45 ibd
+-rw-r--r-- 1 meli meli 7431 Feb 14 17:45 imzml
diff -r 17c54820f3be -r 36d38d2cf88c test-data/preprocessing3.imzml.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/preprocessing3.imzml.txt Fri Feb 15 10:26:45 2019 -0500
@@ -0,0 +1,4 @@
+imzML file:
+total 120
+-rw-r--r-- 1 meli meli 95936 Feb 14 17:46 ibd
+-rw-r--r-- 1 meli meli 20580 Feb 14 17:46 imzml