comparison msi_qualitycontrol.xml @ 16:ed23ae226cdc draft default tip

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

15:2d69460669ae

<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.10.0.6">
    <description>
        mass spectrometry imaging QC
    </description>
    <requirements>
        <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement>

        msidata <- readImzML('infile')
    #end if
#elif $infile.ext == 'analyze75'
    msidata = readAnalyze('infile')
#else
    load('infile.RData')
#end if

## create full matrix to make processed imzML files compatible with segmentation and other steps
iData(msidata) <- iData(msidata)[]

## remove duplicated coordinates
print(paste0(sum(duplicated(coord(msidata))), " duplicated coordinates were removed"))
msidata <- msidata[,!duplicated(coord(msidata))]

## optional annotation from tabular file to obtain pixel groups (otherwise all pixels are considered to be one sample)

#if str($tabular_annotation.load_annotation) == 'yes_annotation':

        spectrum_list[[list_count]] = position_df
        list_count = list_count+1

        colnames(position_df)[3] = "annotation"

print(position_df)
print(class(position_df\$x))
print(class(position_df\$annotation))

        combine_plot = ggplot(position_df, aes(x=x, y=y, fill=annotation))+
               geom_tile() +
               coord_fixed()+
               ggtitle("Spatial orientation of pixel annotations")+
               theme_bw()+
               theme(plot.title = element_text(hjust = 0.5))+
               theme(text=element_text(family="ArialMT", face="bold", size=12))+
               theme(legend.position="bottom",legend.direction="vertical")+
               theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = legend_size))+
               guides(fill=guide_legend(ncol=5,byrow=TRUE))

        print(combine_plot)

        ### order pixels according to annotation - this gives a new pixel/spectra index order according to the annotation groups 
        pixel_name_df = data.frame(pixels(msidata), msidata\$annotation)

        <![CDATA[
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 uses Cardinal to read files and create a quality control report with descriptive plots for mass spectrometry imaging data. 

Input data: 3 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/>`_
- Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
- Cardinal "MSImageSet" data (with variable name "msidata", saved as .RData)
- optional: tabular file with pixel annotations: x and y values in separate columns and the corresponding annotation in a third column

Options: 

- internal calibrants are used for m/z heatmaps (x-y grid), heatmap of number of calibrants per spectrum (x-y grid), zoomed in mass spectra, m/z accuracy
- optional fold change plot: draws a heatmap (x-y grid) for the fold change of two m/z (log2(intensity ratio))

16:ed23ae226cdc

<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.10.0.7">
    <description>
        mass spectrometry imaging QC
    </description>
    <requirements>
        <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement>

        msidata <- readImzML('infile')
    #end if
#elif $infile.ext == 'analyze75'
    msidata = readAnalyze('infile')
#else
    loadRData <- function(fileName){
    load(fileName)
    get(ls()[ls() != "fileName"])
    }
    msidata = loadRData('infile.RData')
#end if

## remove duplicated coordinates
print(paste0(sum(duplicated(coord(msidata))), " duplicated coordinates were removed"))
msidata <- msidata[,!duplicated(coord(msidata))]

## create full matrix to make processed imzML files compatible with segmentation and other steps
iData(msidata) <- iData(msidata)[]

## optional annotation from tabular file to obtain pixel groups (otherwise all pixels are considered to be one sample)

#if str($tabular_annotation.load_annotation) == 'yes_annotation':

        spectrum_list[[list_count]] = position_df
        list_count = list_count+1

        colnames(position_df)[3] = "annotation"

        combine_plot = ggplot(position_df, aes(x=x, y=y, fill=annotation))+
               geom_tile() +
               coord_fixed()+
               ggtitle("Spatial orientation of pixel annotations")+
               theme_bw()+
               theme(plot.title = element_text(hjust = 0.5))+
               theme(text=element_text(family="ArialMT", face="bold", size=12))+
               theme(legend.position="bottom",legend.direction="vertical")+
               theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = legend_size))+
               guides(fill=guide_legend(ncol=4,byrow=TRUE))

        print(combine_plot)

        ### order pixels according to annotation - this gives a new pixel/spectra index order according to the annotation groups 
        pixel_name_df = data.frame(pixels(msidata), msidata\$annotation)

        <![CDATA[
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 uses Cardinal to read files and create a quality control report with descriptive plots for mass spectrometry imaging data. 

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 the tools analysis starts. 
- optional: tabular file with pixel annotations: x and y values in separate columns and the corresponding annotation in a third column


Options: 

- internal calibrants are used for m/z heatmaps (x-y grid), heatmap of number of calibrants per spectrum (x-y grid), zoomed in mass spectra, m/z accuracy
- optional fold change plot: draws a heatmap (x-y grid) for the fold change of two m/z (log2(intensity ratio))