| Previous changeset 3:09b638ceee45 (2019-02-28) Next changeset 5:b16b64ca7b7c (2019-12-13) |
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Commit message:
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit ecdc3a64aa245d80dbc5487b2bf10a85a43adc6d |
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modified:
macros.xml segmentation.xml test-data/Example_Processed.imzML test-data/QC_analyze75.pdf test-data/QC_empty_spectra.pdf test-data/QC_imzml.pdf test-data/QC_rdata.pdf test-data/features_test2.tabular test-data/features_test4.tabular test-data/features_test6.tabular test-data/features_test7.tabular test-data/test1.pdf test-data/test2.pdf test-data/test2.rdata test-data/test3.pdf test-data/test4.pdf test-data/test4.rdata test-data/test5.pdf test-data/test6.pdf test-data/test6.rdata test-data/test7.pdf test-data/test7.rdata |
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added:
static/images/classification_overview.png |
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| diff -r 09b638ceee45 -r 9f7d1ec01767 macros.xml --- a/macros.xml Thu Feb 28 09:23:26 2019 -0500 +++ b/macros.xml Fri Mar 22 08:15:15 2019 -0400 |
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| @@ -99,6 +99,7 @@ #if str($processed_cond.processed_file) == "processed": msidata <- readImzML('infile', mass.accuracy=$processed_cond.accuracy, units.accuracy = "$processed_cond.units") centroided(msidata) = $centroids + iData(msidata) = iData(msidata)[] #else msidata <- readImzML('infile') centroided(msidata) = $centroids @@ -129,14 +130,16 @@ minimumy = min(coord(msidata)[,2]) maximumy = max(coord(msidata)[,2]) ## Range of intensities - minint = round(min(spectra(msidata)[], na.rm=TRUE), digits=2) - maxint = round(max(spectra(msidata)[], na.rm=TRUE), digits=2) + minint = round(min(spectra(msidata), na.rm=TRUE), digits=2) + maxint = round(max(spectra(msidata), na.rm=TRUE), digits=2) ## Number of intensities > 0, for if conditions - npeaks= sum(spectra(msidata)[]>0, na.rm=TRUE) + npeaks= sum(spectra(msidata)>0, na.rm=TRUE) + ## Number of NA in spectra matrix + NAcount = sum(is.na(spectra(msidata))) ## Number of NA in spectra matrix - NAcount = sum(is.na(spectra(msidata)[])) - ## Number of NA in spectra matrix - infcount = sum(is.infinite(spectra(msidata)[])) + infcount = sum(is.infinite(spectra(msidata))) + ## Number of duplicated coordinates + dupl_coord = sum(duplicated(coord(msidata))) properties = c("Number of m/z features", "Range of m/z values", @@ -145,7 +148,8 @@ "Range of y coordinates", "Range of intensities", "Number of NA intensities", - "Number of Inf intensities") + "Number of Inf intensities", + "Number of duplicated coordinates") values = c(paste0(maxfeatures), paste0(minmz, " - ", maxmz), @@ -154,7 +158,8 @@ paste0(minimumy, " - ", maximumy), paste0(minint, " - ", maxint), paste0(NAcount), - paste0(infcount)) + paste0(infcount), + paste0(dupl_coord)) property_df = data.frame(properties, values) ]]></token> @@ -229,7 +234,7 @@ <xml name="reading_msidata"> <param name="infile" type="data" format="imzml,rdata,analyze75" label="MSI data" - help="Input file as imzML (composite upload), or Cardinal MSImageSet saved as RData (regular upload)"/> + help="Input file as imzML (composite upload), Analyze7.5 (composite upload) or Cardinal MSImageSet saved as RData (regular upload)"/> <param name="centroids" type="boolean" label="Centroided input" help="Choose Yes if peak detection has already been done." truevalue="TRUE" falsevalue="FALSE"/> <conditional name="processed_cond"> <param name="processed_file" type="select" label="Processed imzML file" help="Choose no if your input is an Analyze7.5 or continuous imzML file"> |
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| diff -r 09b638ceee45 -r 9f7d1ec01767 segmentation.xml --- a/segmentation.xml Thu Feb 28 09:23:26 2019 -0500 +++ b/segmentation.xml Fri Mar 22 08:15:15 2019 -0400 |
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| b'@@ -1,4 +1,4 @@\n-<tool id="cardinal_segmentations" name="MSI segmentation" version="@VERSION@.2">\n+<tool id="cardinal_segmentations" name="MSI segmentation" version="@VERSION@.3">\n <description>mass spectrometry imaging spatial clustering</description>\n <macros>\n <import>macros.xml</import>\n@@ -30,15 +30,10 @@\n \n @READING_MSIDATA_INRAM@\n \n-## to make sure that processed files work as well: \n-iData(msidata) = iData(msidata)[]\n \n ## remove duplicated coordinates\n-print(paste0(sum(duplicated(coord(msidata))), " duplicated coordinates were removed"))\n msidata <- msidata[,!duplicated(coord(msidata))]\n \n-## count and print number of NAs, all methods are not compatible with NAs\n-print(paste0("Number of NA in dataset: ", sum(is.na(spectra(msidata)[])), " - segmentation does not work with NA values"))\n \n @DATA_PROPERTIES_INRAM@\n \n@@ -58,7 +53,7 @@\n #############################################################################\n grid.table(property_df, rows= NULL)\n \n-if (npeaks > 0 && sum(is.na(spectra(msidata)[]))==0)\n+if (npeaks > 0 && sum(is.na(spectra(msidata)))==0)\n {\n \n ######################## II) segmentation tools #############################\n@@ -67,13 +62,13 @@\n colourvector = c($color_string)\n \n ### preparation for images and plots:\n- #if str($image_cond.image_type) == "standard_image":\n+ #if str($image_type) == "standard_image":\n print("standard image")\n \n- strip_input = TRUE\n+ strip_input = FALSE\n lattice_input = FALSE\n \n- #elif str($image_cond.image_type) == "lattice_image":\n+ #elif str($image_type) == "lattice_image":\n print("lattice image")\n \n strip_input = strip.custom(bg="lightgrey", par.strip.text=list(col="black", cex=.9))\n@@ -112,11 +107,11 @@\n ### images in pdf file\n print(image(pca_result, main="PCA image", lattice=lattice_input, strip = strip_input, col=colourvector, ylim=c(maximumy+2, minimumy-2)))\n for (PCs in 1:$segm_cond.pca_ncomp){\n- print(image(pca_result, column = c(paste0("PC",PCs)), lattice=lattice_input, superpose = FALSE, col.regions = risk.colors(100), ylim=c(maximumy+2, minimumy-2)))}\n+ print(image(pca_result, column = c(paste0("PC",PCs)), lattice=lattice_input,strip = strip_input, superpose = FALSE, main=paste0("PC", PCs), col.regions = risk.colors(100), ylim=c(maximumy+2, minimumy-2)))}\n ### plots in pdf file\n print(plot(pca_result, main="PCA plot", lattice=lattice_input, col= colourvector, strip = strip_input))\n for (PCs in 1:$segm_cond.pca_ncomp){\n- print(plot(pca_result, column = c(paste0("PC",PCs)),superpose = FALSE))}\n+ print(plot(pca_result, column = c(paste0("PC",PCs)),main=paste0("PC", PCs),strip = FALSE,superpose = FALSE))}\n \n ### values in tabular files\n pcaloadings = formatC(pca_result@resultData\\$ncomp\\$loadings, format = "e", digits = 6)### loading for each m/z value\n@@ -155,7 +150,6 @@\n gc()\n \n print(image(skm, key=TRUE, main="K-means clustering", lattice=lattice_input, strip=strip_input, col= colourvector, layout=c(1,1), ylim=c(maximumy+2, minimumy-2)))\n-\n print(plot(skm, main="K-means plot", lattice=lattice_input, col= colourvector, strip=strip_input, layout=c(1,1)))\n \n skm_clusters = data.frame(matrix(NA, nrow = pixelcount, ncol = 0))\n@@ -194,9 +188,10 @@\n ## remove msidata to clean up RAM space\n rm(msidata)\n gc()\n- print(image(ssc, key=TRUE, main="Spatial shrunken centroids", lattice=lattice_input, strip = strip_input, col= colourvector,layout=c(1,1), ylim=c(maximumy+2, minimumy-2)))\n- print(plot(ssc, main="Spatial shrunken centroids plot", lattice=lattice_input, col= colourvector, strip = strip_input,layout=c(1,1)))\n+ print(image(ssc, key=TRUE, main="Spatial shrunken centroids", latti'..b'( $segm_cond.segmentationtool ) == \'kmeans\':\n+ coord(skm)\\$y <- max(coord(skm)\\$y) - coord(skm)\\$y + 1\n+ image(skm, key=FALSE, strip=FALSE, col= colourvector)\n+ #elif str( $segm_cond.segmentationtool ) == \'centroids\':\n+ coord(ssc)\\$y <- max(coord(ssc)\\$y) - coord(ssc)\\$y + 1\n+ image(ssc, key=FALSE, strip = FALSE, col= colourvector)\n+ #end if\n+ dev.off()\n+ #end if\n+\n+\n }else{\n print("Inputfile has no intensities > 0")\n dev.off()\n@@ -256,7 +273,6 @@\n </param>\n <param name="pca_scale" type="boolean" truevalue="TRUE" falsevalue="FALSE" label="Scaling of data before analysis"/>\n </when>\n-\n <when value="kmeans">\n <param name="kmeans_r" type="text" value="2"\n label="The spatial neighborhood radius of nearby pixels to consider (r)" help="Multiple values are allowed (e.g. 1,2,3 or 2:5)">\n@@ -297,14 +313,9 @@\n label="Number of toplabels (m/z) which should be written in tabular output"/>\n </when>\n </conditional>\n- <conditional name="image_cond">\n- <param name="image_type" type="select" label="Select the image type">\n- <option value="standard_image" selected="True">standard</option>\n- <option value="lattice_image">lattice</option>\n- </param>\n- <when value="standard_image"/>\n- <when value="lattice_image"/>\n- </conditional>\n+ <param name="image_type" type="boolean" checked="True" truevalue="standard_image" falsevalue="lattice_image" \n+ label="Standard image" help="No: lattice function is used to display image"/>\n+ <param name="svg_pixelimage" type="boolean" label="Export first segmentation image as svg"/>\n <repeat name="colours" title="Colours for the plots" min="1" max="50">\n <param name="feature_color" type="color" label="Colours" value="#ff00ff" help="Numbers of colours should be the same as number of components">\n <sanitizer>\n@@ -324,6 +335,9 @@\n <data format="rdata" name="segmentation_rdata" label="${tool.name} on ${on_string}: results.RData">\n <filter>output_rdata</filter>\n </data>\n+ <data format="svg" name="svg_output" from_work_dir="svg_pixel_output.svg" label="${tool.name} on ${on_string}: image.svg">\n+ <filter>svg_pixelimage</filter>\n+ </data>\n </outputs>\n <tests>\n <test>\n@@ -404,18 +418,22 @@\n This tool provides three different Cardinal functions for unsupervised clustering/spatial segmentation of mass spectrometry imaging data.\n \n @MSIDATA_INPUT_DESCRIPTION@\n+ - NA intensities are not allowed\n+ - duplicated coordinates will be removed\n+\n \n **Options**\n \n - PCA: principal component analysis\n-- k-means: spatially-aware k-means clustering\n-- spatial shrunken centroids: Allows the number of segments to decrease according to the data. This allows automatic selection of the number of clusters\n+- k-means: spatially-aware k-means clustering (adopted from `Alexandrov and Kobarg <https://doi.org/10.1093/bioinformatics/btr246>`_)\n+- spatial shrunken centroids: Allows the number of segments to decrease according to the data. This allows selection of the number of clusters (more details in `Bemis et al. <https://doi.org/10.1074/mcp.O115.053918>`_)\n \n **Output**\n \n - Pdf with the heatmaps and plots for the segmentation\n - Tabular file with information on m/z and pixels: loadings/scores (PCA), toplabels/clusters (k-means), toplabels/classes (spatial shrunken centroids)\n - Optional .RData file which contains the segmentation results and can be used for further exploration in R using the Cardinal package\n+- Optional: svg file with the first segmentation image\n \n ]]>\n </help>\n' |
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| diff -r 09b638ceee45 -r 9f7d1ec01767 test-data/Example_Processed.imzML --- a/test-data/Example_Processed.imzML Thu Feb 28 09:23:26 2019 -0500 +++ b/test-data/Example_Processed.imzML Fri Mar 22 08:15:15 2019 -0400 |
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| b'@@ -1,373 +1,380 @@\n-<?xml version="1.0" encoding="ISO-8859-1"?>\r\n-<mzML xmlns="http://psi.hupo.org/ms/mzml" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://psi.hupo.org/ms/mzml http://psidev.info/files/ms/mzML/xsd/mzML1.1.0_idx.xsd" version="1.1">\r\n- <cvList count="3">\r\n- <cv id="MS" fullName="Proteomics Standards Initiative Mass Spectrometry Ontology" version="1.3.1" URI="http://psidev.info/ms/mzML/psi-ms.obo"/>\r\n- <cv id="UO" fullName="Unit Ontology" version="1.15" URI="http://obo.cvs.sourceforge.net/obo/obo/ontology/phenotype/unit.obo"/>\r\n- <cv id="IMS" fullName="Imaging MS Ontology" version="0.9.1" URI="http://www.maldi-msi.org/download/imzml/imagingMS.obo"/>\r\n- </cvList>\r\n- <fileDescription>\r\n- <fileContent>\r\n- <cvParam cvRef="MS" accession="MS:1000579" name="MS1 spectrum" value=""/>\r\n- <cvParam cvRef="MS" accession="MS:1000128" name="profile spectrum" value=""/>\r\n- <cvParam cvRef="IMS" accession="IMS:1000080" name="universally unique identifier" value="{9D501BDC-5344-4916-B7E9-7E795B02C856}"/>\r\n- <cvParam cvRef="IMS" accession="IMS:1000091" name="ibd SHA-1" value="7E8FDB93053915D3EDB51B70AA0619AC209964DF"/>\r\n- <cvParam cvRef="IMS" accession="IMS:1000031" name="processed" value=""/>\r\n- </fileContent>\r\n- <sourceFileList count="1">\r\n- <sourceFile id="sf1" name="Example.raw" location="C:\\Users\\Thorsten Schramm\\Documents\\Promotion\\imzML\\Website\\files\\Beispiel-Dateien\\Example images\\">\r\n- <cvParam cvRef="MS" accession="MS:1000563" name="Thermo RAW file" value=""/>\r\n- <cvParam cvRef="MS" accession="MS:1000768" name="Thermo nativeID format" value=""/>\r\n- <cvParam cvRef="MS" accession="MS:1000569" name="SHA-1" value="7623BE263B25FF99FDF017154B86FAB742D4BB0B"/>\r\n- </sourceFile>\r\n- </sourceFileList>\r\n- <contact>\r\n- <cvParam cvRef="MS" accession="MS:1000586" name="contact name" value="Thorsten Schramm"/>\r\n- <cvParam cvRef="MS" accession="MS:1000590" name="contact organization" value="Institut f\xfcr Anorganische und Analytische Chemie"/>\r\n- <cvParam cvRef="MS" accession="MS:1000587" name="contact address" value="Schubertstra\xdfe 60, Haus 16, Gie\xdfen, Germany"/>\r\n- <cvParam cvRef="MS" accession="MS:1000589" name="contact email" value="thorsten.schramm@anorg.chemie.uni-.giessen.de"/>\r\n- </contact>\r\n- </fileDescription>\r\n- <referenceableParamGroupList count="4">\r\n- <referenceableParamGroup id="mzArray">\r\n- <cvParam cvRef="MS" accession="MS:1000576" name="no compression" value=""/>\r\n- <cvParam cvRef="MS" accession="MS:1000514" name="m/z array" value="" unitCvRef="MS" unitAccession="MS:1000040" unitName="m/z"/>\r\n- <cvParam cvRef="IMS" accession="IMS:1000101" name="external data" value="true"/>\r\n- <cvParam cvRef="MS" accession="MS:1000521" name="32-bit float" value=""/>\r\n- </referenceableParamGroup>\r\n- <referenceableParamGroup id="intensityArray">\r\n- <cvParam cvRef="MS" accession="MS:1000576" name="no compression" value=""/>\r\n- <cvParam cvRef="MS" accession="MS:1000515" name="intensity array" value="" unitCvRef="MS" unitAccession="MS:1000131" unitName="number of counts"/>\r\n- <cvParam cvRef="IMS" accession="IMS:1000101" name="external data" value="true"/>\r\n- <cvParam cvRef="MS" accession="MS:1000521" name="32-bit float" value=""/>\r\n- </referenceableParamGroup>\r\n- <referenceableParamGroup id="scan1">\r\n- <cvParam cvRef="MS" accession="MS:1000093" name="increasing m/z scan" value=""/>\r\n- <cvParam cvRef="MS" accession="MS:1000095" name="linear" value=""/>\r\n- <cvParam cvRef="MS" accession="MS:1000512" name="filter string" value="ITMS - 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| diff -r 09b638ceee45 -r 9f7d1ec01767 test-data/features_test2.tabular --- a/test-data/features_test2.tabular Thu Feb 28 09:23:26 2019 -0500 +++ b/test-data/features_test2.tabular Fri Mar 22 08:15:15 2019 -0400 |
| b |
| b'@@ -99,3 +99,8057 @@\n 946.450012207031\t2\tA\t0.003494\t0.042887\t0.032578\n 979.395385742188\t2\tC\t0.003494\t-0.009858\t-0.008482\n 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|
| b |
| diff -r 09b638ceee45 -r 9f7d1ec01767 test-data/features_test4.tabular --- a/test-data/features_test4.tabular Thu Feb 28 09:23:26 2019 -0500 +++ b/test-data/features_test4.tabular Fri Mar 22 08:15:15 2019 -0400 |
| b |
| b'@@ -99,3 +99,8057 @@\n 927.295166015625\t3\tA\t0.00081\t0.03242\t0.019578\n 957.467712402344\t3\tA\t0.000805\t0.034961\t0.029421\n 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|
| b |
| diff -r 09b638ceee45 -r 9f7d1ec01767 test-data/features_test6.tabular --- a/test-data/features_test6.tabular Thu Feb 28 09:23:26 2019 -0500 +++ b/test-data/features_test6.tabular Fri Mar 22 08:15:15 2019 -0400 |
| b |
| b'@@ -19,3 +19,8137 @@\n 900.434692382812\t2\t3\t2\tA\t30.041667\t0\t1\t1\n 900.470520019531\t2\t3\t2\tA\t19.166667\t0\t1\t1\n 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|
| b |
| diff -r 09b638ceee45 -r 9f7d1ec01767 test-data/features_test7.tabular --- a/test-data/features_test7.tabular Thu Feb 28 09:23:26 2019 -0500 +++ b/test-data/features_test7.tabular Fri Mar 22 08:15:15 2019 -0400 |
| b |
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