msi_preprocessing: msi_preprocessing.xml comparison

comparison msi_preprocessing.xml @ 9:4d5578b57a77 draft

planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_preprocessing commit 5feaf3d0e0da8cef1241fecc1f4d6f81324594e6

author	galaxyp
date	Wed, 22 Aug 2018 13:43:04 -0400
parents	d77c5228fd1a
children	df8d7f6f210b

comparison

equal deleted inserted replaced

-:d77c5228fd1a
+:4d5578b57a77
-<tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.10.0.5">
+<tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.10.0.6">
 <description>
 mass spectrometry imaging preprocessing
 </description>
 <requirements>
 <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement>
 msidata <- readImzML('infile', attach.only=TRUE)
 #end if
 #elif $infile.ext == 'analyze75'
 msidata = readAnalyze('infile', attach.only=TRUE)
 #else
-load('infile.RData')
+loadRData <- function(fileName){
+load(fileName)
+get(ls()[ls() != "fileName"])
+}
+msidata = loadRData('infile.RData')
 #end if
 print(paste0("Number of NA in input file: ",sum(is.na(spectra(msidata)[]))))
 ## function to later read RData reference files in
 ######################### preparations for QC report #################
 maxfeatures = length(features(msidata))
 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
 medint = round(median(spectra(msidata)[],na.rm=TRUE), digits=2)
-TICs = round(mean(colSums(spectra(msidata)[], na.rm=TRUE)), digits=1)
+minmz = round(min(mz(msidata)), digits=2)
-QC_numbers= data.frame(inputdata = c(maxfeatures, medianpeaks, medint, TICs))
+maxmz = round(max(mz(msidata)), digits=2)
+QC_numbers= data.frame(inputdata = c(minmz, maxmz,maxfeatures, medianpeaks, medint))
 vectorofactions = "inputdata"
 ############################### Preprocessing steps ###########################
 ###############################################################################
 ############################### QC ###########################
 maxfeatures = length(features(msidata))
 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE),)
 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
-TICs = round(mean(colSums(spectra(msidata)[], na.rm=TRUE)), digits=1)
+minmz = round(min(mz(msidata)), digits=2)
-normalized = c(maxfeatures, medianpeaks, medint, TICs)
+maxmz = round(max(mz(msidata)), digits=2)
+normalized = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
 QC_numbers= cbind(QC_numbers, normalized)
 vectorofactions = append(vectorofactions, "normalized")
 ############################### Baseline reduction ###########################
 ############################### QC ###########################
 maxfeatures = length(features(msidata))
 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
-TICs = round(mean(colSums(spectra(msidata)[], na.rm=TRUE)), digits=1)
+minmz = round(min(mz(msidata)), digits=2)
-baseline= c(maxfeatures, medianpeaks, medint, TICs)
+maxmz = round(max(mz(msidata)), digits=2)
+baseline = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
 QC_numbers= cbind(QC_numbers, baseline)
 vectorofactions = append(vectorofactions, "baseline red.")
 ############################### Smoothing ###########################
 ############################### QC ###########################
 maxfeatures = length(features(msidata))
 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
-TICs = round(mean(colSums(spectra(msidata)[], na.rm=TRUE)), digits=1)
+minmz = round(min(mz(msidata)), digits=2)
-smoothed= c(maxfeatures, medianpeaks, medint, TICs)
+maxmz = round(max(mz(msidata)), digits=2)
+smoothed = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
 QC_numbers= cbind(QC_numbers, smoothed)
 vectorofactions = append(vectorofactions, "smoothed")
 ############################### Peak picking ###########################
 #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_picking':
 print('Peak_picking')
 ## Peakpicking
 ## remove duplicated coordinates, otherwise peak picking will fail
 print(paste0(sum(duplicated(coord(msidata))), " coordinates were removed"))
 msidata <- msidata[,!duplicated(coord(msidata))]
 #if str( $method.methods_conditional.methods_for_picking.picking_method) == 'adaptive':
 ############################### QC ###########################
 maxfeatures = length(features(msidata))
 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
-TICs = round(mean(colSums(spectra(msidata)[], na.rm=TRUE)), digits=1)
+minmz = round(min(mz(msidata)), digits=2)
-picked= c(maxfeatures, medianpeaks, medint, TICs)
+maxmz = round(max(mz(msidata)), digits=2)
+picked = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
 QC_numbers= cbind(QC_numbers, picked)
 vectorofactions = append(vectorofactions, "picked")
 ############################### Peak alignment ###########################
 ############################### QC ###########################
 maxfeatures = length(features(msidata))
 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
-TICs = round(mean(colSums(spectra(msidata)[], na.rm=TRUE)), digits=1)
+minmz = round(min(mz(msidata)), digits=2)
-aligned= c(maxfeatures, medianpeaks, medint, TICs)
+maxmz = round(max(mz(msidata)), digits=2)
+aligned = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
 QC_numbers= cbind(QC_numbers, aligned)
 vectorofactions = append(vectorofactions, "aligned")
 ############################### Peak filtering ###########################
 ############################### QC ###########################
 maxfeatures = length(features(msidata))
 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
-TICs = round(mean(colSums(spectra(msidata)[], na.rm=TRUE)), digits=1)
+minmz = round(min(mz(msidata)), digits=2)
-filtered= c(maxfeatures, medianpeaks, medint, TICs)
+maxmz = round(max(mz(msidata)), digits=2)
+filtered = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
 QC_numbers= cbind(QC_numbers, filtered)
 vectorofactions = append(vectorofactions, "filtered")
 ############################### Data reduction ###########################
 ############################### QC ###########################
 maxfeatures = length(features(msidata))
 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
-TICs = round(mean(colSums(spectra(msidata)[], na.rm=TRUE)), digits=1)
+minmz = round(min(mz(msidata)), digits=2)
-reduced= c(maxfeatures, medianpeaks, medint, TICs)
+maxmz = round(max(mz(msidata)), digits=2)
+reduced = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
 QC_numbers= cbind(QC_numbers, reduced)
 vectorofactions = append(vectorofactions, "reduced")
 ############################### Transformation ###########################
 ############################### QC ###########################
 maxfeatures = length(features(msidata))
 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
-TICs = round(mean(colSums(spectra(msidata)[], na.rm=TRUE)), digits=1)
+minmz = round(min(mz(msidata)), digits=2)
-transformed= c(maxfeatures, medianpeaks, medint, TICs)
+maxmz = round(max(mz(msidata)), digits=2)
+transformed = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
 QC_numbers= cbind(QC_numbers, transformed)
 vectorofactions = append(vectorofactions, "transformed")
 #end if
 #end for
 ## save QC report
 pdf("Preprocessing.pdf", fonts = "Times", pointsize = 12)
 plot(0,type='n',axes=FALSE,ann=FALSE)
 title(main=paste("Quality control during preprocessing \n", "Filename:", "$infile.display_name"))
-rownames(QC_numbers) = c("# features", "median # peaks", "median intensity", "median TIC")
+rownames(QC_numbers) = c("min m/z", "max mz", "# features", "median\n# peaks", "median\nintensity")
 grid.table(t(QC_numbers))
 #if str($tabular_annotation.load_annotation) == 'yes_annotation':
 ## the more annotation groups a file has the smaller will be the legend
 Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets. `More information on Cardinal <http://cardinalmsi.org//>`_
 This tool provides provides multiple Cardinal functions to preprocess mass spectrometry imaging data.
-Input data: 3 types of input data can be used:
+Input data: 3 types of MSI 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/>`_
 - Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
 - Cardinal "MSImageSet" data (with variable name "msidata", saved as .RData)
+- Coordinates stored as decimals rather than integers will be rounded to obtain a regular pixel grid. This might lead to duplicated coordinates which will be automatically removed before peak picking.
 - optional: tabular file with pixel annotations: x and y values in separate columns and the corresponding annotation in a third column
 Options:
 - Normalization: Normalization of intensities to total ion current (TIC)
-- Baseline reduction: Baseline  reduction removes backgroundintensity generated by chemical noise (common in MALDI datasets)
+- Baseline reduction: Baseline  reduction removes background intensity generated by chemical noise (common in MALDI datasets)
-- Smoothening: Smoothing of the peaks reduces noise and improves peak detection
+- Smoothing: Smoothing of the peaks reduces noise and improves peak detection
 - Peak picking: relevant peaks are picked while noise-peaks are removed (needs peak alignment afterwards)
 - Peak alignment: only possible after peak picking, m/z inaccuracies are removed by alignment of same peaks to a common m/z value
 - Peak filtering: works only on centroided data (after peak picking and alignment or data reduction with peak filtering), removes peaks that occur only in a small proportion of pixels. If not sure which cutoff to chose run qualitycontrol first and decide according to the zero value plot.
 - Data reduction: binning, resampling or peak filtering to reduce data
 - Transformation: log2 or squareroot transformation of all intensities; when using log2 transformation zero intensities will become NA, this can lead to compatibility problems.
 Output:
 - imzML file, preprocessed
 - pdf with key values after each processing step, in case of loaded annotations file overview plot of pixel annotations
-- optional: intensity matrix as tabular file (intensities for m/z in rows and pixel in columns)
+- optional: intensity matrix as tabular file (m/z in rows and pixel in columns, filled with intensity values)
 - optional: summarized intensity matrix: mean, median or standard deviation for each m/z feature; in case pixel annotations are provided the intensity values are summarized for each pixel group
 Tip:
 - Peak alignment works only after peak picking

Mercurial > repos > galaxyp > msi_preprocessing

comparison msi_preprocessing.xml @ 9:4d5578b57a77 draft