Mercurial > repos > galaxyp > maldi_quant_preprocessing
changeset 1:0892a051eb17 draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/MALDIquant commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
| author | galaxyp |
|---|---|
| date | Mon, 01 Oct 2018 01:09:28 -0400 |
| parents | e2aa05746a69 |
| children | e754c2b545a9 |
| files | maldi_macros.xml maldi_quant_preprocessing.xml test-data/int1.tabular test-data/int2.tabular test-data/intensity_matrix3.tabular test-data/masspeaks1_forinput.tabular test-data/masspeaks2.tabular test-data/masspeaks3.tabular test-data/masspeaks3_forinput.tabular test-data/peakdetection1_QC.pdf test-data/peakdetection2_QC.pdf test-data/peakdetection3_QC.pdf |
| diffstat | 12 files changed, 846 insertions(+), 468 deletions(-) [+] |
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--- a/maldi_macros.xml Wed Aug 22 11:49:06 2018 -0400 +++ b/maldi_macros.xml Mon Oct 01 01:09:28 2018 -0400 @@ -1,20 +1,28 @@ <macros> <token name="@R_IMPORTS@"><![CDATA[ - ## Libraries library (Cardinal) library (MALDIquantForeign) library (MALDIquant) library (ggplot2) - + library(gridExtra) ]]> </token> + <token name="@MADLI_QUANT_DESCRIPTION@"><![CDATA[ +MALDIquant_ provides a complete analysis pipeline for MALDI-TOF and other mass spectrometry data. +So far we have only implemented the functionalities for mass spectrometry imaging data. + ]]> + </token> + + <token name="@VERSION@">1.18.0</token> + <xml name="requirements"> <requirements> <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement> <requirement type="package" version="0.11.5">r-maldiquantforeign</requirement> <requirement type="package" version="1.18">r-maldiquant</requirement> <requirement type="package" version="2.2.1">r-ggplot2</requirement> + <requirement type="package" version="2.2.1">r-gridextra</requirement> </requirements> </xml>
--- a/maldi_quant_preprocessing.xml Wed Aug 22 11:49:06 2018 -0400 +++ b/maldi_quant_preprocessing.xml Mon Oct 01 01:09:28 2018 -0400 @@ -1,4 +1,4 @@ -<tool id="maldi_quant_preprocessing" name="MALDIquant preprocessing" version="1.18.0.0"> +<tool id="maldi_quant_preprocessing" name="MALDIquant preprocessing" version="@VERSION@.1"> <description> Preprocessing of mass-spectrometry imaging data </description> @@ -39,7 +39,7 @@ print('Reading mask region') ## Import imzML file - coordinate_matrix = as.matrix(read.delim("$restriction_conditional.coordinates_file", header = FALSE, stringsAsFactors = FALSE))[,1:2] + coordinate_matrix = as.matrix(read.delim("$restriction_conditional.coordinates_file", header = $restriction_conditional.coordinates_header, stringsAsFactors = FALSE))[,1:2] maldi_data = importImzMl('infile.imzML', coordinates = coordinate_matrix) @@ -51,9 +51,11 @@ #if $infile.ext == 'imzml' ## Import imzML file maldi_data = import( 'infile.imzML', type="imzML" ) + coordinates_info = cbind(coordinates(maldi_data)[,1:2], c(1:length(maldi_data))) #elif $infile.ext == 'analyze75' ## Import analyze7.5 file maldi_data = import( 'infile.hdr' ) + coordinates_info = cbind(coordinates(maldi_data)[,1:2], c(1:length(maldi_data))) #else loadRData <- function(fileName){ #loads an RData file, and returns it @@ -61,7 +63,6 @@ get(ls()[ls() != "fileName"]) } msidata = loadRData('infile.RData') - ## save coordinates cardinal_coordinates = as.matrix(Cardinal::coord(msidata)[,1:2]) ## save mz values @@ -70,13 +71,14 @@ maldi_data = list() for(number_spectra in 1:ncol(msidata)){ maldi_data[[number_spectra]] = createMassSpectrum(mass = cardinal_mzs, intensity = iData(msidata)[,number_spectra]) + coordinates_info = cbind(cardinal_coordinates, c(1:length(maldi_data))) } #end if #end if -## Quality control plots during preprocessing +## Quality control plots during preprocessing pdf("prepro_qc_plot.pdf", fonts = "Times", pointsize = 12) plot(0,type='n',axes=FALSE,ann=FALSE) @@ -87,15 +89,15 @@ #if str($tabular_annotation.load_annotation) == 'yes_annotation': print("use annotation file") + ## read and extract x,y,annotation information input_tabular = read.delim("$tabular_annotation.annotation_file", header = $tabular_annotation.tabular_header, stringsAsFactors = FALSE) annotation_input = input_tabular[,c($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 - coordinates_st = cbind(coordinates(maldi_data)[,1:2], c(1:length(maldi_data))) - colnames(coordinates_st)[3] = "pixel_index" - merged_annotation = merge(coordinates_st, annotation_input, by=c("x", "y"), all.x=TRUE) + ## merge with coordinate information (from above) 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" merged_annotation = merged_annotation[order(merged_annotation\$pixel_index),] samples = as.factor(merged_annotation\$annotation) @@ -133,10 +135,20 @@ #################### Preprocessing methods ##################################### -## QC plot +## QC plot on input file avgSpectra = averageMassSpectra(maldi_data,method="mean") plot(avgSpectra, main="Average spectrum for input file") +pixel_number = length(maldi_data) +minmz = round(min(unlist(lapply(maldi_data,mass))), digits=4) +maxmz = round(max(unlist(lapply(maldi_data,mass))), digits=4) +maxfeatures = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2) +medint = round(median(unlist(lapply(maldi_data,intensity))), digits=2) +inputdata = c(minmz, maxmz,maxfeatures, medint) +QC_numbers= data.frame(inputdata = c(minmz, maxmz,maxfeatures, medint)) +vectorofactions = "inputdata" + + #for $method in $methods: #if str( $method.methods_conditional.method ) == 'Transformation': @@ -144,9 +156,17 @@ print('transforming') ##transformation maldi_data = transformIntensity(maldi_data, method="$method.methods_conditional.transform_method") - ## QC plot + ## QC plot and numbers avgSpectra = averageMassSpectra(maldi_data,method="mean") plot(avgSpectra, main="Average spectrum after transformation") + pixel_number = length(maldi_data) + minmz = round(min(unlist(lapply(maldi_data,mass))), digits=4) + maxmz = round(max(unlist(lapply(maldi_data,mass))), digits=4) + maxfeatures = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2) + medint = round(median(unlist(lapply(maldi_data,intensity))), digits=2) + transformed = c(minmz, maxmz,maxfeatures, medint) + QC_numbers= cbind(QC_numbers, transformed) + vectorofactions = append(vectorofactions, "transformed") #elif str( $method.methods_conditional.method ) == 'Smoothing': @@ -170,9 +190,17 @@ #end if - ## QC plot + ## QC plot and numbers avgSpectra = averageMassSpectra(maldi_data,method="mean") - plot(avgSpectra, main="Average spectrum after smoothing") + plot(avgSpectra, main="Average spectrum after smoothing", sub="") + pixel_number = length(maldi_data) + minmz = round(min(unlist(lapply(maldi_data,mass))), digits=4) + maxmz = round(max(unlist(lapply(maldi_data,mass))), digits=4) + maxfeatures = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2) + medint = round(median(unlist(lapply(maldi_data,intensity))), digits=2) + smoothed = c(minmz, maxmz,maxfeatures, medint) + QC_numbers= cbind(QC_numbers, smoothed) + vectorofactions = append(vectorofactions, "smoothed") #elif str( $method.methods_conditional.method ) == 'Baseline': @@ -180,12 +208,54 @@ print('baseline removing') ## Remove baseline - maldi_data = removeBaseline(maldi_data, - method="$method.methods_conditional.baseline_method", - iterations=$method.methods_conditional.iterations) - ## QC plot + #if str($method.methods_conditional.methods_for_baseline.baseline_method ) == 'SNIP': + print('SNIP') + random_spectra = sample(1:length(maldi_data), 4, replace=FALSE) + par(mfrow = c(2,2)) + for (random_sample in random_spectra){ + maldi_data_baseline = estimateBaseline(maldi_data[[random_sample]], + method="SNIP", iterations=$method.methods_conditional.methods_for_baseline.iterations) + plot(maldi_data[[random_sample]], sub="", main=paste0("Estimated baseline for spectrum ", random_sample)) + lines(maldi_data_baseline, col="blue", lwd=2)} + + maldi_data = removeBaseline(maldi_data, + method="SNIP", + iterations=$method.methods_conditional.methods_for_baseline.iterations) + + #elif str($method.methods_conditional.methods_for_baseline.baseline_method ) == 'TopHat': + print('TopHat') + + maldi_data = removeBaseline(maldi_data, + method="TopHat", + halfWindowSize=$method.methods_conditional.methods_for_baseline.tophat_halfWindowSize) + + #elif str($method.methods_conditional.methods_for_baseline.baseline_method ) == 'ConvexHull': + print('ConvexHull') + + maldi_data = removeBaseline(maldi_data, + method="ConvecHull") + + #elif str($method.methods_conditional.methods_for_baseline.baseline_method ) == 'median': + print('median') + + maldi_data = removeBaseline(maldi_data, + method="TopHat", + halfWindowSize=$method.methods_conditional.methods_for_baseline.median_halfWindowSize) + + #end if + + ## QC plot and numbers + par(mfrow = c(1,1)) avgSpectra = averageMassSpectra(maldi_data,method="mean") plot(avgSpectra, main="Average spectrum after baseline removal") + pixel_number = length(maldi_data) + minmz = round(min(unlist(lapply(maldi_data,mass))), digits=4) + maxmz = round(max(unlist(lapply(maldi_data,mass))), digits=4) + maxfeatures = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2) + medint = round(median(unlist(lapply(maldi_data,intensity))), digits=2) + baseline_removed = c(minmz, maxmz,maxfeatures, medint) + QC_numbers= cbind(QC_numbers, baseline_removed) + vectorofactions = append(vectorofactions, "baseline_removed") #elif str( $method.methods_conditional.method ) == 'Calibrate': @@ -202,9 +272,17 @@ maldi_data = calibrateIntensity(maldi_data, method="$method.methods_conditional.calibrate_method") #end if - ## QC plot + ## QC plot and numbers avgSpectra = averageMassSpectra(maldi_data,method="mean") plot(avgSpectra, main="Average spectrum after normalization") + pixel_number = length(maldi_data) + minmz = round(min(unlist(lapply(maldi_data,mass))), digits=4) + maxmz = round(max(unlist(lapply(maldi_data,mass))), digits=4) + maxfeatures = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2) + medint = round(median(unlist(lapply(maldi_data,intensity))), digits=2) + intensity_calibrated = c(minmz, maxmz,maxfeatures, medint) + QC_numbers= cbind(QC_numbers, intensity_calibrated ) + vectorofactions = append(vectorofactions, "intensity_calibrated ") #elif str( $method.methods_conditional.method ) == 'Align': @@ -213,16 +291,14 @@ ##align spectra #if str($method.methods_conditional.reference_for_alignment.align_ref) == 'no_reference': - maldi_data = alignSpectra(maldi_data, halfWindowSize=$method.methods_conditional.halfWindowSize, - SNR=$method.methods_conditional.snr, - tolerance=$method.methods_conditional.tolerance, + SNR=$method.methods_conditional.snr, tolerance=$method.methods_conditional.tolerance, + allowNoMatches =$method.methods_conditional.allow_nomatch, emptyNoMatches = $method.methods_conditional.empty_nomatch, warpingMethod="$method.methods_conditional.warping_method") #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 = FALSE, stringsAsFactors = FALSE)[,1] + mass_vector = read.delim("$method.methods_conditional.reference_for_alignment.reference_file", header = $method.methods_conditional.reference_for_alignment.reference_header, stringsAsFactors = FALSE)[,1] int_vector = rep(1,length(mass_vector)) mass_list = createMassPeaks(mass_vector, int_vector) @@ -230,20 +306,23 @@ SNR=$method.methods_conditional.snr, tolerance=$method.methods_conditional.tolerance, warpingMethod="$method.methods_conditional.warping_method", - reference = mass_list, allowNoMatches =$method.methods_conditional.reference_for_alignment.allow_nomatch, emptyNoMatches = $method.methods_conditional.reference_for_alignment.empty_nomatch) + reference = mass_list, allowNoMatches =$method.methods_conditional.allow_nomatch, emptyNoMatches = $method.methods_conditional.empty_nomatch) - #if $method.methods_conditional.reference_for_alignment.remove_empty: + #end if + + #if $method.methods_conditional.remove_empty: + print("remove empty spectra") - #if $infile.ext == 'rdata' - cardinal_coordinates = cardinal_coordinates[-findEmptyMassObjects(maldi_data),] ## remove coordinates of empty spectra for Cardinal RData input - #end if - #if str($tabular_annotation.load_annotation) == 'yes_annotation': - merged_annotation = merged_annotation[-findEmptyMassObjects(maldi_data),] ## remove coordinate annotations for empty spectra - #end if - maldi_data = removeEmptyMassObjects(maldi_data) + #if $infile.ext == 'rdata' + cardinal_coordinates = cardinal_coordinates[-findEmptyMassObjects(maldi_data),] ## remove coordinates of empty spectra for Cardinal RData input #end if + #if str($tabular_annotation.load_annotation) == 'yes_annotation': + merged_annotation = merged_annotation[-findEmptyMassObjects(maldi_data),] ## remove coordinate annotations for empty spectra + #end if + maldi_data = removeEmptyMassObjects(maldi_data) #end if + ## QC plot if (length(maldi_data)>0){ @@ -251,9 +330,22 @@ plot(avgSpectra, main="Average spectrum after alignment") }else{"All spectra are empty"} + pixel_number = length(maldi_data) + minmz = round(min(unlist(lapply(maldi_data,mass))), digits=4) + maxmz = round(max(unlist(lapply(maldi_data,mass))), digits=4) + maxfeatures = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2) + medint = round(median(unlist(lapply(maldi_data,intensity))), digits=2) + spectra_aligned = c(minmz, maxmz,maxfeatures, medint) + QC_numbers= cbind(QC_numbers, spectra_aligned ) + vectorofactions = append(vectorofactions, "spectra_aligned") #end if + #end for +rownames(QC_numbers) = c("min m/z", "max mz", "# features", "median\nintensity") +plot(0,type='n',axes=FALSE,ann=FALSE) +grid.table(t(QC_numbers)) + dev.off() ## export imzML file @@ -274,19 +366,20 @@ </configfile> </configfiles> <inputs> - <param name="infile" type="data" format="imzml,rdata" label="MS metadata" help="This file is in imzML format or Cardinal MSImageSet saved as RData"/> + <param name="infile" type="data" format="imzml,rdata" label="Inputfile as imzML or Cardinal MSImageSet saved as RData" help="This file is in imzML format or Cardinal MSImageSet saved as RData. The file must be in profile mode, not centroided"/> <conditional name="restriction_conditional"> <param name="restriction" type="select" label="Read in only spectra of interest" help="This option only works for imzML files"> <option value="no_restriction" selected="True">Calculate on entire file</option> <option value="restrict">Restrict to coordinates of interest</option> </param> <when value="restrict"> - <param name="coordinates_file" type="data" format="tabular" label="Tabular file with coordinates which should be read" help="x-values in first column, y-values in second column"/> + <param name="coordinates_file" type="data" format="tabular" label="Tabular file with coordinates" help="x-values in first column, y-values in second column"/> + <param name="coordinates_header" type="boolean" label="Tabular file contains a header line" truevalue="TRUE" falsevalue="FALSE"/> </when> <when value="no_restriction"/> </conditional> <conditional name="tabular_annotation"> - <param name="load_annotation" type="select" label="Use pixel annotation from tabular file to have updated annotation information in case empty spectra will be removed"> + <param name="load_annotation" type="select" label="For Cardinal RData only: Use pixel annotation from tabular file to have updated annotation information in case empty spectra will be removed"> <option value="no_annotation" selected="True">use no annotation</option> <option value="yes_annotation">use pixel annotation from a tabular file</option> </param> @@ -302,7 +395,7 @@ </conditional> <repeat name="methods" title="Method" min="1"> <conditional name="methods_conditional"> - <param name="method" type="select" label="Select the method you want to apply"> + <param name="method" type="select" label="Select a method"> <option value="Transformation" selected="True">Transformation</option> <option value="Smoothing">Smoothing</option> <option value="Baseline">Baseline removal</option> @@ -311,7 +404,7 @@ <validator type="empty_field" /> </param> <when value="Transformation"> - <param name="transform_method" type="select" label="Select your transfprormation method"> + <param name="transform_method" type="select" label="Select the transfprormation method"> <option value="sqrt" selected="True">sqrt</option> <option value="log">log</option> <option value="log2">log2</option> @@ -340,16 +433,30 @@ The best size differs depending on the selected smoothing method."/> </when> <when value="Baseline"> - <param name="baseline_method" type="select" label="Baseline removal method"> - <option value="SNIP" selected="True">SNIP</option> - <option value="TopHat">TopHat</option> - <option value="ConvexHull">ConvexHull</option> - <option value="median">median</option> - <validator type="empty_field" /> - </param> - <param name="iterations" type="integer" value="100" - label="Number of iterations" - help=""/> + <conditional name="methods_for_baseline"> + <param name="baseline_method" type="select" label="Baseline removal method"> + <option value="SNIP" selected="True">SNIP</option> + <option value="TopHat">TopHat</option> + <option value="ConvexHull">ConvexHull</option> + <option value="median">median</option> + <validator type="empty_field" /> + </param> + <when value="SNIP"> + <param name="iterations" type="integer" value="100" + label="Number of iterations" help="Corresponds to half window size: The resulting window reaches from mass[cur_index-iterations] to mass[cur_index+iterations]"/> + </when> + <when value="TopHat"> + <param name="tophat_halfWindowSize" type="integer" value="10" + label="Half window size" help="The resulting window reaches from + mass[currentIndex-halfWindowSize] to mass[currentIndex+halfWindowSize]"/> + </when> + <when value="ConvexHull"/> + <when value="median"> + <param name="median_halfWindowSize" type="integer" value="10" + label="Half window size" help="The resulting window reaches from + mass[currentIndex-halfWindowSize] to mass[currentIndex+halfWindowSize]"/> + </when> + </conditional> </when> <when value="Calibrate"> <param name="calibrate_method" type="select" label="Calibration method"> @@ -360,10 +467,9 @@ </param> <param name="mass_start" type="integer" value="0" label="Start of m/z range, has to be inside m/z range" - help="Scaling factor is calculated on the mass range and applied to the whole spectrum"/> + help="Scaling factor is calculated on the mass range and applied to the whole spectrum. Start and end are not allowed to be 0"/> <param name="mass_end" type="integer" value="0" - label="End of m/z range, has to be inside m/z range" - help="The Start and End value needs to be different from 0 to be taken into account and."/> + label="End of m/z range, has to be inside m/z range"/> </when> <when value="Align"> <param name="warping_method" type="select" label="Warping methods"> @@ -384,9 +490,10 @@ (window size is 2*halfWindowSize+1). The best size differs depending on the selected smoothing method."/> - <param name="snr" type="integer" value="2" - label="Signal-to-noise-ratio" - help=""/> + <param name="snr" type="integer" value="2" label="Signal-to-noise-ratio"/> + <param name="allow_nomatch" type="boolean" label="Don't throw an error when less than 2 reference m/z were found in a spectrum" truevalue="TRUE" falsevalue="FALSE"/> + <param name="empty_nomatch" type="boolean" label="logical, if TRUE the intensity values of MassSpectrum or MassPeaks objects with missing (NA) warping functions are set to zero" truevalue="TRUE" falsevalue="FALSE"/> + <param name="remove_empty" type="boolean" label="Should empty spectra be removed" truevalue="TRUE" falsevalue="FALSE" help="For Cardinal RData files this step can only be performed if pixel annotations were provided"/> <conditional name="reference_for_alignment"> <param name="align_ref" type="select" label="Reference to which the samples should be aligned" help="Use internal calibrants to perform m/z calibration"> @@ -398,9 +505,7 @@ <param name="reference_file" type="data" format="tabular" label="Tabular file with m/z of internal calibrants (MassPeaks) which should be used for spectra alignment" help="calibration of m/z values to internal calibrants, at least 2 m/z per spectrum are needed"/> - <param name="allow_nomatch" type="boolean" label="Don't throw an error when less than 2 reference m/z were found in a spectrum" truevalue="TRUE" falsevalue="FALSE"/> - <param name="empty_nomatch" type="boolean" label="logical, if TRUE the intensity values of MassSpectrum or MassPeaks objects with missing (NA) warping functions are set to zero" truevalue="TRUE" falsevalue="FALSE"/> - <param name="remove_empty" type="boolean" label="Should empty spectra be removed" truevalue="TRUE" falsevalue="FALSE"/> + <param name="reference_header" type="boolean" label="Tabular file contains a header line" truevalue="TRUE" falsevalue="FALSE"/> </when> </conditional> </when> @@ -409,7 +514,7 @@ <param name="export_processed" type="boolean" label="Export file as processed imzML" help="otherwise continuous imzML will be exported" truevalue="TRUE" falsevalue="FALSE"/> </inputs> <outputs> - <data format="imzml" name="outfile_imzml" label="$infile.display_name processed" /> + <data format="imzml" name="outfile_imzml" label="$infile.display_name preprocessed" /> <data format="pdf" name="plots" from_work_dir="prepro_qc_plot.pdf" label="$infile.display_name preprocessed QC"/> <data format="tabular" name="annotation_output" label="$infile.display_name annotations"> <filter>tabular_annotation["load_annotation"] == 'yes_annotation'</filter> @@ -464,12 +569,12 @@ <param name="method" value="Align"/> <param name="warping_method" value="linear"/> <param name="halfWindowSize" value="1"/> + <param name="allow_nomatch" value="TRUE"/> + <param name="remove_empty" value="TRUE"/> + <param name="empty_nomatch" value="TRUE"/> <conditional name="reference_for_alignment"> <param name="align_ref" value="yes_reference"/> <param name="reference_file" value="align_reference_test2.tabular" ftype="tabular"/> - <param name="allow_nomatch" value="TRUE"/> - <param name="remove_empty" value="TRUE"/> - <param name="empty_nomatch" value="TRUE"/> </conditional> </conditional> <output name="outfile_imzml" file="outfile3.imzML" compare="sim_size"/> @@ -480,26 +585,62 @@ </tests> <help><![CDATA[ -MALDIquant_ provides a complete analysis pipeline for MALDI-TOF and other mass spectrometry data. So far we have only implemented the functionalities for mass spectrometry imaging data. +@MADLI_QUANT_DESCRIPTION@ + +----- + +**Input data** -Input data: +- MSI data: 2 types of input data can be used: + + - imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/wp/imzml/>`_ + - Cardinal "MSImageSet" data saved as .RData +- Only for Cardinal RData files and when remove empty spectra is chosen: Tabular file with coordinates annotations. Separate columns for x and y coordinates and a third column with pixel annotations. 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. 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. + + :: -- MSI data as imzML file (upload via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/wp/imzml/>`_ -- optinal tabular file with pixel coordinates to restrict reading of imzML file to coordinates of interest + x_coord y_coord + 1 1 + 2 1 + 3 1 + ... + ... + +- Optional: Tabular file with reference m/z for the spectra align function. At least 2 m/z values of the input list must be present in every spectrum to peform the alignment. First column must contain m/z values, without empty fields or letters. Tabular files with any header name or no header at all are supported. + + :: -Options: + + m/z + 100.0 + 100.01 + 100.02 + ... + ... + + + +**Options** - Transformation: transformation of intensities with log, log2, log10 and squareroot - Smoothing: Smoothing of the peaks reduces noise and improves peak detection. Available smoothing methods are SavitzkyGolay and Moving Average -- Baseline reduction: Baseline reduction removes background intensity generated by chemical noise (common in MALDI datasets). Available methods are SNIP, TopHat,ConvexHull and median. +- Baseline reduction: Baseline reduction removes background intensity generated by chemical noise (common in MALDI datasets). + + - Available methods are SNIP, TopHat,ConvexHull and median: + - SNIP is the default baseline reduction method in MALDIquant. + - ConvexHull cannot be used for MALDI-TOF baseline removal. + - The moving median may generate negative intensities. + - Except for the ConvexHull all methods have a parameter for the 'Half window size' (in SNIP it is called 'iterations'). The smaller the window the more baseline will be removed but also parts of the peaks. Wider windows preserve the peak height better and produce a smoother baseline, but some local background variation will remain. + - Intensity calibration (normalization): Normalization of intensities to Total Ion Current (TIC), median spectrum, Probabilistic Quotient Normalization (PQN) -- Spectra alignment (warping):alignment for (re)calibration of m/z values +- Spectra alignment (warping): alignment for (re)calibration of m/z values, 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 "logical, 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. -Output: +**Output** - imzML file (imzML format can be continuous or processed) -- pdf with average mass spectra after each preprocessing step +- PDF with average mass spectra after each preprocessing step .. _MALDIquant: http://strimmerlab.org/software/maldiquant/
--- a/test-data/int1.tabular Wed Aug 22 11:49:06 2018 -0400 +++ b/test-data/int1.tabular Mon Oct 01 01:09:28 2018 -0400 @@ -1,4 +1,4 @@ -mz | spectra col1 col2 col3 +mz col1 col2 col3 300.083343505859 0 0.0878976186116536 0 304 9.438736829163e-11 0.137228806813558 0.203098436196645 304.166687011719 0.104457606871923 0.0169352528949116 0.00765286510187286
--- a/test-data/int2.tabular Wed Aug 22 11:49:06 2018 -0400 +++ b/test-data/int2.tabular Mon Oct 01 01:09:28 2018 -0400 @@ -0,0 +1,10 @@ +mz xy_1_1 xy_2_1 xy_3_1 xy_1_2 xy_2_2 xy_3_2 xy_1_3 xy_2_3 xy_3_3 +308.266668701172 NA NA NA 0.399492412805557 2.67954346782062e-05 0.630028009414673 NA NA NA +329.152786254883 2.47849035263062 1.6380660533905 0.519839584827423 NA NA NA NA NA NA +329.697925567627 NA NA NA NA 2.56914954661625e-05 NA 4.62229545519222e-05 NA 6.18351987213828e-05 +343.642865862165 NA 0.662226140499115 NA NA NA NA NA NA NA +344.611124674479 0.638258039951324 NA NA NA NA NA NA NA NA +366.562515258789 NA 2.64647725956024e-09 2.61009136011126e-05 NA NA NA NA NA NA +367.645835876465 0.235070616006851 NA NA NA NA NA NA NA NA +387.850006103516 NA NA NA NA NA NA 2.8698808819172e-05 0.197833687067032 4.42154814663809e-05 +394.791679382324 NA NA NA NA NA NA NA 1.05953142589779e-21 0.310508847236633
--- a/test-data/intensity_matrix3.tabular Wed Aug 22 11:49:06 2018 -0400 +++ b/test-data/intensity_matrix3.tabular Mon Oct 01 01:09:28 2018 -0400 @@ -1,4 +1,4 @@ -mz | spectra x = 1, y = 1 x = 2, y = 1 x = 3, y = 1 x = 1, y = 2 x = 2, y = 2 x = 3, y = 2 x = 1, y = 3 x = 2, y = 3 x = 3, y = 3 +mz xy_1_1 xy_2_1 xy_3_1 xy_1_2 xy_2_2 xy_3_2 xy_1_3 xy_2_3 xy_3_3 304.595254080636 1.76512676262064e-05 0 0.327420804051165 1.17177887401856e-13 2.65423709869989e-05 0 0.279548346996307 0.256212419994475 0.226718190873036 305.119057791574 1.62699820283052e-09 0 1.36897748140363e-05 5.69736818276873e-18 1.71781823336396e-09 0 0.716848015785217 1.44706262158933e-05 9.11607707771881e-06 305.66667175293 1.02237303638199e-13 0 0 5.6757509669499e-22 1.38465480219223e-13 0 6.01442479819525e-05 0.104363307437671 0
--- a/test-data/masspeaks1_forinput.tabular Wed Aug 22 11:49:06 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,99 +0,0 @@ -snr mass intensity spectrum -5.34196717869329 304.166687011719 0.104457606871923 x = 1, y = 1 -12.0922480027988 305 0.238949338595072 x = 1, y = 1 -6.80380283473404 306.25 0.133792817751516 x = 1, y = 1 -11.9239943102966 306.666687011719 0.232789536317284 x = 1, y = 1 -16.7672721943661 306.916687011719 0.322907487551371 x = 1, y = 1 -14.5727405573344 315.916687011719 0.134740673005581 x = 1, y = 1 -12.6587975530526 316.25 0.117780188719432 x = 1, y = 1 -15.5891513371263 320.916687011719 0.0873032609621684 x = 1, y = 1 -8.28066653191209 322.75 0.107258200645447 x = 1, y = 1 -8.64305080588796 327.083343505859 0.593451981743177 x = 1, y = 1 -22.5976205263238 329 1.80600585540136 x = 1, y = 1 -6.62986417925206 343.083343505859 0.319934318463008 x = 1, y = 1 -5.76872573536526 343.75 0.314895849686906 x = 1, y = 1 -7.23012875909998 344 0.41009783744812 x = 1, y = 1 -12.5923211364761 345 0.777459700902303 x = 1, y = 1 -5.61154187261139 346.083343505859 0.311080597071943 x = 1, y = 1 -11.6128628366043 353.083343505859 0.0853602389494578 x = 1, y = 1 -8.93926402677033 355.166687011719 0.093819797039032 x = 1, y = 1 -11.4518402499656 355.916687011719 0.124494592348735 x = 1, y = 1 -12.2687590055812 356.916687011719 0.131409923235575 x = 1, y = 1 -17.1598526473503 360.916687011719 0.159461249907811 x = 1, y = 1 -19.5606964350667 364.333343505859 0.279228508472443 x = 1, y = 1 -13.4363791597757 365.583343505859 0.189359684795294 x = 1, y = 1 -7.2659142337533 366.083343505859 0.0986319063122271 x = 1, y = 1 -6.78882554537553 367.583343505859 0.0783568720022837 x = 1, y = 1 -19.2913990161673 370.083343505859 0.149130453666051 x = 1, y = 1 -22.9258781818056 374.5 0.10046042005221 x = 1, y = 1 -32.553974512381 395.916687011719 0.170605540275574 x = 1, y = 1 -20.0044568991975 397.083343505859 0.0962067842483521 x = 1, y = 1 -52.5251008144488 398.75 0.214157521724701 x = 1, y = 1 -6.53932310180994 300.083343505859 0.0878976186116536 x = 2, y = 1 -8.1332053348611 304 0.137228806813558 x = 2, y = 1 -8.87376047659255 304.666687011719 0.15201666439801 x = 2, y = 1 -9.0031548908286 305.75 0.155222187439613 x = 2, y = 1 -12.1483037475036 305.916687011719 0.20923868753016 x = 2, y = 1 -5.50393277232579 306.916687011719 0.0935481031580518 x = 2, y = 1 -8.92694410124806 307.083343505859 0.151374835480731 x = 2, y = 1 -8.53366295883023 307.75 0.14276859164238 x = 2, y = 1 -20.8363523568967 308.25 0.34317347407341 x = 2, y = 1 -9.03952553159105 311.5 0.126103460788727 x = 2, y = 1 -9.21792692425819 313.166687011719 0.11177615324661 x = 2, y = 1 -10.2836388640006 314 0.114434331655502 x = 2, y = 1 -14.5366950159814 315.833343505859 0.110506186882656 x = 2, y = 1 -29.6030745455647 321.25 0.0977287689844767 x = 2, y = 1 -5.12133326721085 327.916687011719 0.406224122348552 x = 2, y = 1 -18.2260181460723 328.916687011719 1.60846790671349 x = 2, y = 1 -6.71691695849357 339.416687011719 0.214046796162923 x = 2, y = 1 -5.42044735697872 343.916687011719 0.369042471672098 x = 2, y = 1 -9.90109899068147 344.916687011719 0.751941124598185 x = 2, y = 1 -5.7761050136785 346 0.407967170079549 x = 2, y = 1 -9.53380778579935 350.083343505859 0.103204836448034 x = 2, y = 1 -22.3739163562105 357.333343505859 0.132146000862122 x = 2, y = 1 -17.1123310736305 359.916687011719 0.0798314611117045 x = 2, y = 1 -21.5296474065392 365.833343505859 0.247531970342 x = 2, y = 1 -17.6598326813686 367.916687011719 0.225095887978872 x = 2, y = 1 -14.4139559364593 369.083343505859 0.167913814385732 x = 2, y = 1 -13.4612234580055 370.416687011719 0.129989445209503 x = 2, y = 1 -10.0270809962262 376 0.0839582482973735 x = 2, y = 1 -13.7957864133576 378.75 0.090022732814153 x = 2, y = 1 -21.7167106106545 382.583343505859 0.111637224753698 x = 2, y = 1 -37.803872450968 383.166687011719 0.194509585698446 x = 2, y = 1 -19.3443105357995 386.75 0.0984363655249278 x = 2, y = 1 -40.2946953152003 393.583343505859 0.136588126420975 x = 2, y = 1 -10.9042784289401 304 0.203098436196645 x = 3, y = 1 -14.2536155186416 304.666687011719 0.269343495368958 x = 3, y = 1 -9.73428286587393 305.916687011719 0.186377684291186 x = 3, y = 1 -25.326392266369 307.166687011719 0.48292171999357 x = 3, y = 1 -5.77081370276002 308 0.107646773258845 x = 3, y = 1 -9.71607704287468 309.833343505859 0.161644687255224 x = 3, y = 1 -12.3286648153297 314.75 0.168087442715963 x = 3, y = 1 -10.0188114825306 314.916687011719 0.136831253767014 x = 3, y = 1 -9.98544088935621 318.083343505859 0.131532917420069 x = 3, y = 1 -15.9567928309838 318.416687011719 0.202652255694071 x = 3, y = 1 -5.45721434599321 326.916687011719 0.25384642680486 x = 3, y = 1 -6.94450239587698 328 0.340036729971568 x = 3, y = 1 -26.3506501037786 328.916687011719 1.28705859184265 x = 3, y = 1 -21.9755116973922 329.083343505859 1.05529999732971 x = 3, y = 1 -6.26698769334215 342.333343505859 0.19008461634318 x = 3, y = 1 -7.40453698942461 343.916687011719 0.2857492963473 x = 3, y = 1 -5.47629632143471 344.333343505859 0.225824773311615 x = 3, y = 1 -24.2489760518392 345.083343505859 1.09834182262421 x = 3, y = 1 -7.55360848799134 346 0.326915502548218 x = 3, y = 1 -31.8141872712877 355.333343505859 0.218277891476949 x = 3, y = 1 -28.500421447754 359.916687011719 0.0977983176708221 x = 3, y = 1 -35.9263674883478 370.333343505859 0.318489452203115 x = 3, y = 1 -17.9023349585808 376.833343505859 0.119955976804097 x = 3, y = 1 -14.165837350632 379.5 0.138252675533295 x = 3, y = 1 -6.1026956808649 380.75 0.063951442639033 x = 3, y = 1 -6.66805606030542 386.666687011719 0.0939600268999736 x = 3, y = 1 -10.4761700777795 387 0.148616979519526 x = 3, y = 1 -14.9996173987781 387.166687011719 0.213376681009928 x = 3, y = 1 -9.49389038421466 387.5 0.135613699754079 x = 3, y = 1 -6.40597511255643 390.166687011719 0.0942935446898143 x = 3, y = 1 -6.91093965563744 391.083343505859 0.103488087654114 x = 3, y = 1 -12.1864531063779 392 0.184977193673452 x = 3, y = 1 -17.1091639921622 392.833343505859 0.264233609040578 x = 3, y = 1 -6.41767571506743 394.833343505859 0.103502949078878 x = 3, y = 1 -8.23619853479686 398.166687011719 0.142847468455633 x = 3, y = 1
--- a/test-data/masspeaks2.tabular Wed Aug 22 11:49:06 2018 -0400 +++ b/test-data/masspeaks2.tabular Mon Oct 01 01:09:28 2018 -0400 @@ -0,0 +1,20 @@ +snr mass intensity spectrum +Inf 329.152786254883 2.47849035263062 xy_1_1 +Inf 344.611124674479 0.638258039951324 xy_1_1 +Inf 367.645835876465 0.235070616006851 xy_1_1 +Inf 329.152786254883 1.6380660533905 xy_2_1 +Inf 343.642865862165 0.662226140499115 xy_2_1 +Inf 366.562515258789 2.64647725956024e-09 xy_2_1 +Inf 329.152786254883 0.519839584827423 xy_3_1 +Inf 366.562515258789 2.61009136011126e-05 xy_3_1 +Inf 308.266668701172 0.399492412805557 xy_1_2 +Inf 308.266668701172 2.67954346782062e-05 xy_2_2 +Inf 329.697925567627 2.56914954661625e-05 xy_2_2 +Inf 308.266668701172 0.630028009414673 xy_3_2 +Inf 329.697925567627 4.62229545519222e-05 xy_1_3 +Inf 387.850006103516 2.8698808819172e-05 xy_1_3 +Inf 387.850006103516 0.197833687067032 xy_2_3 +Inf 394.791679382324 1.05953142589779e-21 xy_2_3 +Inf 329.697925567627 6.18351987213828e-05 xy_3_3 +Inf 387.850006103516 4.42154814663809e-05 xy_3_3 +Inf 394.791679382324 0.310508847236633 xy_3_3
--- a/test-data/masspeaks3.tabular Wed Aug 22 11:49:06 2018 -0400 +++ b/test-data/masspeaks3.tabular Mon Oct 01 01:09:28 2018 -0400 @@ -1,298 +1,298 @@ snr mass intensity spectrum -Inf 304.595254080636 1.76512676262064e-05 x = 1, y = 1 -Inf 305.119057791574 1.62699820283052e-09 x = 1, y = 1 -Inf 305.66667175293 1.02237303638199e-13 x = 1, y = 1 -Inf 306.32292175293 7.71168242810122e-18 x = 1, y = 1 -Inf 307.031265258789 5.54882387838857e-22 x = 1, y = 1 -Inf 307.614593505859 3.61475171997316e-26 x = 1, y = 1 -Inf 308.718757629395 1.67789168240286e-34 x = 1, y = 1 -Inf 309.250011444092 1.43981749664202e-38 x = 1, y = 1 -Inf 321.050012207031 1.5556590571819e-38 x = 1, y = 1 -Inf 328.000010172526 0.481155782938004 x = 1, y = 1 -Inf 329.152786254883 2.47849035263062 x = 1, y = 1 -Inf 330.197929382324 1.09462394703996e-08 x = 1, y = 1 -Inf 330.712968614366 5.96421675068404e-13 x = 1, y = 1 -Inf 331.259270562066 5.44180783738833e-17 x = 1, y = 1 -Inf 331.796308729384 2.72964412068832e-21 x = 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