| Previous changeset 14:ece627528a78 (2021-05-07) Next changeset 16:eddc2ae2db80 (2021-08-29) |
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Commit message:
"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit d0dc9303d449c63a6ffe8fbfe195951d5db9cb89-dirty" |
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modified:
classification.xml test-data/test1.pdf test-data/test2.pdf test-data/test3.pdf test-data/test4.pdf test-data/test5.pdf test-data/test6.pdf test-data/test6.rdata test-data/test7.pdf test-data/test7.rdata |
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| diff -r ece627528a78 -r f28ad96b76dc classification.xml --- a/classification.xml Fri May 07 10:10:35 2021 +0000 +++ b/classification.xml Mon Jun 21 07:35:58 2021 +0000 |
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| b'@@ -1,4 +1,4 @@\n-<tool id="cardinal_classification" name="MSI classification" version="@VERSION@.1">\n+<tool id="cardinal_classification" name="MSI classification" version="@VERSION@.2">\n <description>spatial classification of mass spectrometry imaging data</description>\n <macros>\n <import>macros.xml</import>\n@@ -92,6 +92,18 @@\n merged_response = merged_response[order(merged_response\\$pixel_index),]\n conditions = as.factor(merged_response[,4])\n y_vector = conditions\n+ \n+ ## colours selection:\n+\n+\t#if str($colour_conditional.colour_type) == "manual_colour"\n+\t #set $color_string = \',\'.join([\'"%s"\' % $color.annotation_color for $color in $colour_conditional.colours])\n+\t colourvector = c($color_string)\n+\n+\t#elif str($colour_conditional.colour_type) == "colourpalette"\n+\t number_levels = (length(levels(conditions)))\n+\t colourvector = noquote($colour_conditional.palettes)(number_levels)\n+\n+\t#end if\n \n ## plot of y vector\n \n@@ -107,7 +119,8 @@\n \t panel.grid.minor = element_blank())+\n theme(text=element_text(family="ArialMT", face="bold", size=15))+\n theme(legend.position="bottom",legend.direction="vertical")+\n- guides(fill=guide_legend(ncol=4,byrow=TRUE))\n+ guides(fill=guide_legend(ncol=4,byrow=TRUE))+\n+ scale_discrete_manual(aesthetics = c("colour", "fill"), values = colourvector)\n coord_labels = aggregate(cbind(x,y)~conditions, data=position_df, mean, na.rm=TRUE, na.action="na.pass")\n coord_labels\\$file_number = gsub( "_.*$", "", coord_labels\\$conditions)\n print(y_plot)\n@@ -183,7 +196,7 @@\n ## one image for each sample/fold, 4 images per page\n minimumy = min(coord(msidata.cv.pls)[,2])\n maximumy = max(coord(msidata.cv.pls)[,2])\n- image(msidata.cv.pls, model = list(ncomp = ncomp_max),ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy),layout = c(1, 1))\n+ image(msidata.cv.pls, model = list(ncomp = ncomp_max),ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy),layout = c(1, 1), col=colourvector)\n \n ## print table with summary in pdf\n par(opar)\n@@ -240,7 +253,7 @@\n \n ### pls analysis and coefficients plot\n msidata.pls <- PLS(msidata, y = y_vector, ncomp = component, scale=$type_cond.method_cond.analysis_cond.pls_scale)\n- plot(msidata.pls, main="PLS coefficients per m/z")\n+ plot(msidata.pls, main="PLS coefficients per m/z", col=colourvector)\n \n ### summary table of PLS\n summary_table = summary(msidata.pls)\\$accuracy[[paste0("ncomp = ",component)]]\n@@ -292,7 +305,8 @@\n \t\t panel.grid.minor = element_blank())+\n theme(text=element_text(family="ArialMT", face="bold", size=15))+\n theme(legend.position="bottom",legend.direction="vertical")+\n- guides(fill=guide_legend(ncol=4,byrow=TRUE))\n+ guides(fill=guide_legend(ncol=4,byrow=TRUE))+\n+ scale_discrete_manual(aesthetics = c("colour", "fill"), values = colourvector)\n coord_labels = aggregate(cbind(x,y)~predicted_classes, data=prediction_df, mean, na.rm=TRUE, na.action="na.pass")\n coord_labels\\$file_number = gsub( "_.*$", "", coord_labels\\$predicted_classes)\n print(prediction_plot)\n@@ -351,7 +365,7 @@\n ## one image for each sample/fold, 4 images per page\n minimumy = min(coord(msidata.cv.opls)[,2])\n maximumy = max(coord(msidata.cv.opls)[,2])\n- image(msidata.cv.opls, model = list(ncomp = ncomp_max),ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy),layout = c(1, 1))\n+ image(msidata.cv.opls, model = list(ncomp = ncomp_max),ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy),layout = c(1, 1), col=colourvec'..b'accuracy[[names(prediction@resultData)]]\n \t summary_table2 = round(as.numeric(summary_table), digits=2)\n@@ -704,7 +720,19 @@\n \n #else\n prediction = predict(training_data,msidata)\n+ number_levels = length(levels(training_data\\$y[[1]]))\n #end if\n+ \n+ ## colours selection:\n+\n+\t#if str($colour_conditional.colour_type) == "manual_colour"\n+\t #set $color_string = \',\'.join([\'"%s"\' % $color.annotation_color for $color in $colour_conditional.colours])\n+\t colourvector = c($color_string)\n+\n+\t#elif str($colour_conditional.colour_type) == "colourpalette"\n+\t colourvector = noquote($colour_conditional.palettes)(number_levels)\n+\n+\t#end if\n \n ## m/z and pixel information output\n predicted_classes = data.frame(prediction\\$classes[[1]])\n@@ -730,8 +758,8 @@\n predicted_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, predicted_classes, predicted_probabilities)\n colnames(predicted_classes2) = c("pixel names", "x", "y","predicted condition", levels(prediction\\$classes[[1]]))\n ## also image modes are specific to SSC\n- image(prediction, mode="classes", layout=c(1,1), main="Class", col=hue_pal()(length(unique(prediction\\$classes[[1]]))))\n- image(prediction, mode="probabilities", layout=c(1,1), main="Class probabilities", col=hue_pal()(length(unique(prediction\\$classes[[1]]))))\n+ image(prediction, mode="classes", layout=c(1,1), main="Class", ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), col=colourvector)\n+ image(prediction, mode="probabilities", layout=c(1,1), main="Class probabilities",ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), col=colourvector)\n \n \t#else\n \n@@ -746,7 +774,8 @@\n \tpanel.grid.minor = element_blank())+\n \ttheme(text=element_text(family="ArialMT", face="bold", size=15))+\n \ttheme(legend.position="bottom", legend.direction="vertical")+\n- \tguides(fill=guide_legend(ncol=4, byrow=TRUE))\n+ \tguides(fill=guide_legend(ncol=4, byrow=TRUE))+\n+ \tscale_discrete_manual(aesthetics = c("colour", "fill"), values = colourvector)\n \tcoord_labels = aggregate(cbind(x,y)~predicted_classes, data=prediction_df, mean, na.rm=TRUE, na.action="na.pass")\n \tcoord_labels\\$file_number = gsub( "_.*\xc2\xa7", "", coord_labels\\$predicted_classes)\n \tprint(prediction_plot)\n@@ -924,6 +953,33 @@\n </conditional>\n </when>\n </conditional>\n+ <conditional name="colour_conditional">\n+\t <param name="colour_type" type="select" label="Choose a colour scheme">\n+\t <option value="colourpalette" selected="True" >Colour palette</option>\n+\t <option value="manual_colour">Manual selection</option>\n+\t </param>\n+\t <when value="manual_colour">\n+\t <repeat name="colours" title="Colours for the plots" min="1" max="50">\n+\t <param name="annotation_color" type="color" label="Colours" value="#ff00ff" help="Numbers of colours should be the same as number of components">\n+\t <sanitizer>\n+\t <valid initial="string.letters,string.digits">\n+\t <add value="#" />\n+\t </valid>\n+\t </sanitizer>\n+\t </param>\n+\t </repeat>\n+\t </when>\n+\t <when value="colourpalette">\n+\t <param name="palettes" type="select" display="radio" label="Select a colourpalette">\n+\t\t <option value="hue_pal()" selected="True">hue</option>\n+\t\t <option value="rainbow">rainbow</option>\n+\t\t <option value="heat.colors">heat colors</option>\n+\t\t <option value="terrain.colors">terrain colors</option>\n+\t\t <option value="topo.colors">topo colors</option>\n+\t\t <option value="cm.colors">cm colors</option>\n+\t </param>\n+\t </when>\n+ </conditional>\n <param name="output_rdata" type="boolean" label="Results as .RData output" help="Can be used to generate a classification prediction on new data"/>\n </inputs>\n <outputs>\n' |
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