Mercurial > repos > galaxyp > cardinal_classification
view classification.xml @ 18:0a18ac48ac53 draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit eeeb69463a2037a6ee620b9223cb152fcc39f1b0
| author | galaxyp |
|---|---|
| date | Wed, 19 Apr 2023 22:50:47 +0000 |
| parents | eddc2ae2db80 |
| children | 4c177985028a |
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<tool id="cardinal_classification" name="MSI classification" version="@VERSION@.0"> <description>spatial classification of mass spectrometry imaging data</description> <macros> <import>macros.xml</import> </macros> <expand macro="requirements"> <requirement type="package" version="2.3">r-gridextra</requirement> <requirement type="package" version="3.3.5">r-ggplot2</requirement> </expand> <command detect_errors="exit_code"> <![CDATA[ @INPUT_LINKING@ cat '${MSI_segmentation}' && Rscript '${MSI_segmentation}' ]]> </command> <configfiles> <configfile name="MSI_segmentation"><![CDATA[ ################################# load libraries and read file ######################### library(Cardinal) library(gridExtra) library(ggplot2) library(scales) @READING_MSIDATA@ msidata = as(msidata, "MSImageSet") ##coercion to MSImageSet ## remove duplicated coordinates msidata <- msidata[,!duplicated(coord(msidata))] @DATA_PROPERTIES_INRAM@ ######################################## PDF ################################### ################################################################################ ################################################################################ Title = "Prediction" #if str( $type_cond.type_method) == "training": Title = "$type_cond.method_cond.class_method" #end if pdf("classificationpdf.pdf", fonts = "Times", pointsize = 12) plot(0,type='n',axes=FALSE,ann=FALSE) title(main=paste0(Title," for file: \n\n", "$infile.display_name")) ##################### I) numbers and control plots ############################# ################################################################################ ## table with values grid.table(property_df, rows= NULL) if (npeaks > 0 && sum(is.na(spectra(msidata)))==0){ opar <- par() ######################## II) Training ####################################### ############################################################################# #if str( $type_cond.type_method) == "training": print("training") ## load y response (will be needed in every training scenario) y_tabular = read.delim("$type_cond.annotation_file", header = $type_cond.tabular_header, stringsAsFactors = FALSE) #if str($type_cond.column_fold) == "None": y_input = y_tabular[,c($type_cond.column_x, $type_cond.column_y, $type_cond.column_response)] #else y_input = y_tabular[,c($type_cond.column_x, $type_cond.column_y, $type_cond.column_response, $type_cond.column_fold)] #end if colnames(y_input)[1:2] = c("x", "y") ## merge with coordinate information of msidata msidata_coordinates = cbind(coord(msidata)[,1:2], c(1:ncol(msidata))) colnames(msidata_coordinates)[3] = "pixel_index" merged_response = merge(msidata_coordinates, y_input, by=c("x", "y"), all.x=TRUE) merged_response[is.na(merged_response)] = "NA" merged_response = merged_response[order(merged_response\$pixel_index),] conditions = as.factor(merged_response[,4]) y_vector = conditions ## colours selection: #if str($colour_conditional.colour_type) == "manual_colour" #set $color_string = ','.join(['"%s"' % $color.annotation_color for $color in $colour_conditional.colours]) colourvector = c($color_string) #elif str($colour_conditional.colour_type) == "colourpalette" number_levels = (length(levels(conditions))) colourvector = noquote($colour_conditional.palettes)(number_levels) #end if ## plot of y vector position_df = cbind(coord(msidata)[,1:2], conditions) y_plot = ggplot(position_df, aes(x=x, y=y, fill=conditions))+ geom_tile() + coord_fixed()+ ggtitle("Distribution of the conditions")+ theme_bw()+ theme( plot.background = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(text=element_text(family="ArialMT", face="bold", size=15))+ theme(legend.position="bottom",legend.direction="vertical")+ guides(fill=guide_legend(ncol=4,byrow=TRUE))+ scale_discrete_manual(aesthetics = c("colour", "fill"), values = colourvector) coord_labels = aggregate(cbind(x,y)~conditions, data=position_df, mean, na.rm=TRUE, na.action="na.pass") coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$conditions) print(y_plot) ## plot of folds #if str($type_cond.column_fold) != "None": fold_vector = as.factor(merged_response[,5]) position_df = cbind(coord(msidata)[,1:2], fold_vector) fold_plot = ggplot(position_df, aes(x=x, y=y, fill=fold_vector))+ geom_tile() + coord_fixed()+ ggtitle("Distribution of the fold variable")+ theme_bw()+ theme( plot.background = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(text=element_text(family="ArialMT", face="bold", size=15))+ theme(legend.position="bottom",legend.direction="vertical")+ guides(fill=guide_legend(ncol=4,byrow=TRUE)) coord_labels = aggregate(cbind(x,y)~fold_vector, data=position_df, mean, na.rm=TRUE, na.action="na.pass") coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$fold_vector) print(fold_plot) #end if ######################## PLS ############################# #if str( $type_cond.method_cond.class_method) == "PLS": print("PLS") ######################## PLS - CV ############################# #if str( $type_cond.method_cond.analysis_cond.PLS_method) == "cvapply": print("PLS cv") ## set variables for components and number of response groups components = c($type_cond.method_cond.analysis_cond.plscv_comp) number_groups = length(levels(y_vector)) ## PLS-cvApply: msidata.cv.pls <- cvApply(msidata, .y = y_vector, .fold = fold_vector, .fun = "PLS", ncomp = components) ## remove msidata to clean up RAM space rm(msidata) gc() ## create table with summary count = 1 summary_plscv = list() accuracy_vector = numeric() for (iteration in components){ summary_iteration = summary(msidata.cv.pls)\$accuracy[[paste0("ncomp = ", iteration)]] ## change class of numbers into numeric to round and calculate mean summary_iteration2 = round(as.numeric(summary_iteration), digits=2) summary_matrix = matrix(summary_iteration2, nrow=4, ncol=number_groups) accuracy_vector[count] = mean(summary_matrix[1,]) ## vector with accuracies to find later maximum for plot summary_iteration3 = cbind(rownames(summary_iteration), summary_matrix) ## include rownames in table summary_iteration4 = t(summary_iteration3) summary_iteration5 = cbind(c(paste0("ncomp = ", iteration), colnames(summary_iteration)), summary_iteration4) summary_plscv[[count]] = summary_iteration5 count = count+1} ## create list with summary table for each component summary_plscv = do.call(rbind, summary_plscv) summary_df = as.data.frame(summary_plscv) colnames(summary_df) = NULL ## plots ## plot to find ncomp with highest accuracy plot(components, accuracy_vector, ylab = "mean accuracy",type="o", main="Mean accuracy of PLS classification") ncomp_max = components[which.max(accuracy_vector)] ## find ncomp with max. accuracy ## one image for each sample/fold, 4 images per page minimumy = min(coord(msidata.cv.pls)[,2]) maximumy = max(coord(msidata.cv.pls)[,2]) 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) ## print table with summary in pdf par(opar) plot(0,type='n',axes=FALSE,ann=FALSE) title(main="Summary for the different components\n", adj=0.5) ## 20 rows fits in one page: if (nrow(summary_df)<=20){ grid.table(summary_df, rows= NULL) }else{ grid.table(summary_df[1:20,], rows= NULL) mincount = 21 maxcount = 40 for (count20 in 1:(ceiling(nrow(summary_df)/20)-1)){ plot(0,type='n',axes=FALSE,ann=FALSE) if (maxcount <= nrow(summary_df)){ grid.table(summary_df[mincount:maxcount,], rows= NULL) mincount = mincount+20 maxcount = maxcount+20 }else{### stop last page with last sample otherwise NA in table grid.table(summary_df[mincount:nrow(summary_df),], rows= NULL)} } } ## optional output as .RData #if $output_rdata: save(msidata.cv.pls, file="$classification_rdata") #end if ######################## PLS - analysis ########################### #elif str( $type_cond.method_cond.analysis_cond.PLS_method) == "PLS_analysis": print("PLS analysis") ## set variables for components and number of response groups component = c($type_cond.method_cond.analysis_cond.pls_comp) number_groups = length(levels(y_vector)) ### stop if multiple values for PLS components are selected what sets component to 0 tryCatch( { if (component==0) { stop(call.=FALSE) } }, error=function(cond) { ## in case user used multiple inputs for component - this is only possible in cv apply message("Error during PLS training") message("Possible problems: Multiple values for component were selected - this is only possible in cvapply but not for PLS analysis or component was set to 0 but minimum for component is 1)") stop(call.=FALSE) } ) ### pls analysis and coefficients plot msidata.pls <- PLS(msidata, y = y_vector, ncomp = component, scale=$type_cond.method_cond.analysis_cond.pls_scale) plot(msidata.pls, main="PLS coefficients per m/z", col=colourvector) ### summary table of PLS summary_table = summary(msidata.pls)\$accuracy[[paste0("ncomp = ",component)]] summary_table2 = round(as.numeric(summary_table), digits=2) summary_matrix = matrix(summary_table2, nrow=4, ncol=number_groups) summary_table3 = cbind(rownames(summary_table), summary_matrix) ## include rownames in table summary_table4 = t(summary_table3) summary_table5 = cbind(c(paste0("ncomp = ", component), colnames(summary_table)), summary_table4) plot(0,type='n',axes=FALSE,ann=FALSE) grid.table(summary_table5, rows= NULL) ### image of the best m/z minimumy = min(coord(msidata)[,2]) maximumy = max(coord(msidata)[,2]) print(image(msidata, mz = topFeatures(msidata.pls)[1,1], normalize.image = "linear", contrast.enhance = "histogram",ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), smooth.image="gaussian", main="best m/z heatmap")) ### m/z and pixel information output pls_classes = data.frame(msidata.pls\$classes[[1]]) ## pixel names and coordinates ## to remove potential sample names and z dimension, split at comma and take only x and y x_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 1)) y_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 2)) x_coordinates = gsub("x = ","",x_coords) y_coordinates = gsub(" y = ","",y_coords) pixel_names = paste0("xy_", x_coordinates, "_", y_coordinates) ## remove msidata to clean up RAM space rm(msidata) gc() pls_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, pls_classes) colnames(pls_classes2) = c("pixel names", "x", "y","predicted condition") pls_toplabels = topFeatures(msidata.pls, n=Inf) pls_toplabels[,4:6] <-round(pls_toplabels[,4:6],6) write.table(pls_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t") write.table(pls_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t") ## image with predicted classes prediction_df = cbind(coord(msidata.pls)[,1:2], pls_classes) colnames(prediction_df) = c("x", "y", "predicted_classes") prediction_plot = ggplot(prediction_df, aes(x=x, y=y, fill=predicted_classes))+ geom_tile() + coord_fixed()+ ggtitle("Predicted condition for each pixel")+ theme_bw()+ theme( plot.background = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(text=element_text(family="ArialMT", face="bold", size=15))+ theme(legend.position="bottom",legend.direction="vertical")+ guides(fill=guide_legend(ncol=4,byrow=TRUE))+ scale_discrete_manual(aesthetics = c("colour", "fill"), values = colourvector) coord_labels = aggregate(cbind(x,y)~predicted_classes, data=prediction_df, mean, na.rm=TRUE, na.action="na.pass") coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$predicted_classes) print(prediction_plot) ### optional output as .RData #if $output_rdata: save(msidata.pls, file="$classification_rdata") #end if #end if ######################## OPLS ############################# #elif str( $type_cond.method_cond.class_method) == "OPLS": print("OPLS") ######################## OPLS -CV ############################# #if str( $type_cond.method_cond.opls_analysis_cond.opls_method) == "opls_cvapply": print("OPLS cv") ## set variables for components and number of response groups components = c($type_cond.method_cond.opls_analysis_cond.opls_cvcomp) number_groups = length(levels(y_vector)) ## OPLS-cvApply: msidata.cv.opls <- cvApply(msidata, .y = y_vector, .fold = fold_vector, .fun = "OPLS", ncomp = components) ## for use to reduce msidata: keep.Xnew = $type_cond.method_cond.opls_analysis_cond.xnew_cv ## remove msidata to clean up RAM space rm(msidata) gc() ## create table with summary count = 1 summary_oplscv = list() accuracy_vector = numeric() for (iteration in components){ summary_iteration = summary(msidata.cv.opls)\$accuracy[[paste0("ncomp = ", iteration)]] ## change class of numbers into numeric to round and calculate mean summary_iteration2 = round(as.numeric(summary_iteration), digits=2) summary_matrix = matrix(summary_iteration2, nrow=4, ncol=number_groups) accuracy_vector[count] = mean(summary_matrix[1,]) ## vector with accuracies to find later maximum for plot summary_iteration3 = cbind(rownames(summary_iteration), summary_matrix) ## include rownames in table summary_iteration4 = t(summary_iteration3) summary_iteration5 = cbind(c(paste0("ncomp = ", iteration), colnames(summary_iteration)), summary_iteration4) summary_oplscv[[count]] = summary_iteration5 count = count+1} ## create list with summary table for each component summary_oplscv = do.call(rbind, summary_oplscv) summary_df = as.data.frame(summary_oplscv) colnames(summary_df) = NULL ## plots ## plot to find ncomp with highest accuracy plot(components, accuracy_vector, ylab = "mean accuracy", type="o", main="Mean accuracy of OPLS classification") ncomp_max = components[which.max(accuracy_vector)] ## find ncomp with max. accuracy ## one image for each sample/fold, 4 images per page minimumy = min(coord(msidata.cv.opls)[,2]) maximumy = max(coord(msidata.cv.opls)[,2]) image(msidata.cv.opls, model = list(ncomp = ncomp_max),ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy),layout = c(1, 1), col=colourvector) ## print table with summary in pdf par(opar) plot(0,type='n',axes=FALSE,ann=FALSE) title(main="Summary for the different components\n", adj=0.5) ## 20 rows fits in one page: if (nrow(summary_df)<=20){ grid.table(summary_df, rows= NULL) }else{ grid.table(summary_df[1:20,], rows= NULL) mincount = 21 maxcount = 40 for (count20 in 1:(ceiling(nrow(summary_df)/20)-1)){ plot(0,type='n',axes=FALSE,ann=FALSE) if (maxcount <= nrow(summary_df)){ grid.table(summary_df[mincount:maxcount,], rows= NULL) mincount = mincount+20 maxcount = maxcount+20 }else{### stop last page with last sample otherwise NA in table grid.table(summary_df[mincount:nrow(summary_df),], rows= NULL)} } } ## optional output as .RData #if $output_rdata: save(msidata.cv.opls, file="$classification_rdata") #end if ######################## OPLS -analysis ########################### #elif str( $type_cond.method_cond.opls_analysis_cond.opls_method) == "opls_analysis": print("OPLS analysis") ## set variables for components and number of response groups component = c($type_cond.method_cond.opls_analysis_cond.opls_comp) number_groups = length(levels(y_vector)) ### stop if multiple values for OPLS components are selected what sets component to 0 tryCatch( { if (component==0) { stop(call.=FALSE) } }, error=function(cond) { ## in case user used multiple inputs for component - this is only possible in cv apply message("Error during OPLS training") message("Possible problems: Multiple values for component were selected - this is only possible in cvapply but not for OPLS analysis or component was set to 0 but minimum for component is 1)") stop(call.=FALSE) } ) ### opls analysis and coefficients plot msidata.opls <- PLS(msidata, y = y_vector, ncomp = component, scale=$type_cond.method_cond.opls_analysis_cond.opls_scale) ## to reduce msidata: keep.Xnew = $type_cond.method_cond.opls_analysis_cond.xnew plot(msidata.opls, main="OPLS coefficients per m/z", col=colourvector) ### summary table of OPLS summary_table = summary(msidata.opls)\$accuracy[[paste0("ncomp = ",component)]] summary_table2 = round(as.numeric(summary_table), digits=2) summary_matrix = matrix(summary_table2, nrow=4, ncol=number_groups) summary_table3 = cbind(rownames(summary_table), summary_matrix) ## include rownames in table summary_table4 = t(summary_table3) summary_table5 = cbind(c(paste0("ncomp = ", component), colnames(summary_table)), summary_table4) plot(0,type='n',axes=FALSE,ann=FALSE) grid.table(summary_table5, rows= NULL) ### image of the best m/z minimumy = min(coord(msidata)[,2]) maximumy = max(coord(msidata)[,2]) print(image(msidata, mz = topFeatures(msidata.opls)[1,1], normalize.image = "linear", contrast.enhance = "histogram",smooth.image="gaussian", ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), main="best m/z heatmap")) opls_classes = data.frame(msidata.opls\$classes[[1]]) ## pixel names and coordinates ## to remove potential sample names and z dimension, split at comma and take only x and y x_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 1)) y_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 2)) x_coordinates = gsub("x = ","",x_coords) y_coordinates = gsub(" y = ","",y_coords) pixel_names = paste0("xy_", x_coordinates, "_", y_coordinates) opls_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, opls_classes) colnames(opls_classes2) = c("pixel names", "x", "y","predicted condition") ## remove msidata to clean up RAM space rm(msidata) gc() opls_toplabels = topFeatures(msidata.opls, n=Inf) opls_toplabels[,4:6] <-round(opls_toplabels[,4:6],6) write.table(opls_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t") write.table(opls_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t") ## image with predicted classes prediction_df = cbind(coord(msidata.opls)[,1:2], opls_classes) colnames(prediction_df) = c("x", "y", "predicted_classes") prediction_plot = ggplot(prediction_df, aes(x=x, y=y, fill=predicted_classes))+ geom_tile() + coord_fixed()+ ggtitle("Predicted condition for each pixel")+ theme_bw()+ theme( plot.background = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(text=element_text(family="ArialMT", face="bold", size=15))+ theme(legend.position="bottom",legend.direction="vertical")+ guides(fill=guide_legend(ncol=4,byrow=TRUE))+ scale_discrete_manual(aesthetics = c("colour", "fill"), values = colourvector) coord_labels = aggregate(cbind(x,y)~predicted_classes, data=prediction_df, mean, na.rm=TRUE, na.action="na.pass") coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$predicted_classes) print(prediction_plot) ## optional output as .RData #if $output_rdata: save(msidata.opls, file="$classification_rdata") #end if #end if ######################## SSC ############################# #elif str( $type_cond.method_cond.class_method) == "spatialShrunkenCentroids": print("SSC") ######################## SSC - CV ############################# #if str( $type_cond.method_cond.ssc_analysis_cond.ssc_method) == "ssc_cvapply": print("SSC cv") ## set variables for components and number of response groups number_groups = length(levels(y_vector)) ## SSC-cvApply: msidata.cv.ssc <- cvApply(msidata, .y = y_vector,.fold = fold_vector,.fun = "spatialShrunkenCentroids", r = c($type_cond.method_cond.ssc_r), s = c($type_cond.method_cond.ssc_s), method = "$type_cond.method_cond.ssc_kernel_method") ## remove msidata to clean up RAM space rm(msidata) gc() ## create table with summary count = 1 summary_ssccv = list() accuracy_vector = numeric() iteration_vector = character() for (iteration in names(msidata.cv.ssc@resultData[[1]][,1])){ summary_iteration = summary(msidata.cv.ssc)\$accuracy[[iteration]] ## change class of numbers into numeric to round and calculate mean summary_iteration2 = round(as.numeric(summary_iteration), digits=2) summary_matrix = matrix(summary_iteration2, nrow=4, ncol=number_groups) accuracy_vector[count] = mean(summary_matrix[1,]) ## vector with accuracies to find later maximum for plot summary_iteration3 = cbind(rownames(summary_iteration), summary_matrix) ## include rownames in table summary_iteration4 = t(summary_iteration3) summary_iteration5 = cbind(c(iteration, colnames(summary_iteration)), summary_iteration4) summary_ssccv[[count]] = summary_iteration5 iteration_vector[count] = unlist(strsplit(iteration, "[,]"))[3] count = count+1} ## create list with summary table for each component summary_ssccv = do.call(rbind, summary_ssccv) summary_df = as.data.frame(summary_ssccv) colnames(summary_df) = NULL ## plot to find parameters with highest accuracy plot(c($type_cond.method_cond.ssc_s),accuracy_vector[!duplicated(iteration_vector)], type="o",ylab="Mean accuracy", xlab = "Shrinkage parameter (s)", main="Mean accuracy of SSC classification") best_params = names(msidata.cv.ssc@resultData[[1]][,1])[which.max(accuracy_vector)] ## find parameters with max. accuracy r_value = as.numeric(substring(unlist(strsplit(best_params, ","))[1], 4)) s_value = as.numeric(substring(unlist(strsplit(best_params, ","))[3], 5)) ## remove space minimumy = min(coord(msidata.cv.ssc)[,2]) maximumy = max(coord(msidata.cv.ssc)[,2]) image(msidata.cv.ssc, model = list( r = r_value, s = s_value ), ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy),layout=c(1,1), col=colourvector) #if $type_cond.method_cond.ssc_analysis_cond.write_best_params: write.table(r_value, file="$best_r", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") write.table(s_value, file="$best_s", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") #end if ## print table with summary in pdf par(opar) plot(0,type='n',axes=FALSE,ann=FALSE) title(main="Summary for the different parameters\n", adj=0.5) ## 20 rows fits in one page: if (nrow(summary_df)<=20){ grid.table(summary_df, rows= NULL) }else{ grid.table(summary_df[1:20,], rows= NULL) mincount = 21 maxcount = 40 for (count20 in 1:(ceiling(nrow(summary_df)/20)-1)){ plot(0,type='n',axes=FALSE,ann=FALSE) if (maxcount <= nrow(summary_df)){ grid.table(summary_df[mincount:maxcount,], rows= NULL) mincount = mincount+20 maxcount = maxcount+20 }else{### stop last page with last sample otherwise NA in table grid.table(summary_df[mincount:nrow(summary_df),], rows= NULL)} } } ## optional output as .RData #if $output_rdata: save(msidata.cv.ssc, file="$classification_rdata") #end if ######################## SSC -analysis ########################### #elif str( $type_cond.method_cond.ssc_analysis_cond.ssc_method) == "ssc_analysis": print("SSC analysis") ## set variables for components and number of response groups number_groups = length(levels(y_vector)) ## SSC analysis and plot msidata.ssc <- spatialShrunkenCentroids(msidata, y = y_vector, r = c($type_cond.method_cond.ssc_r), s = c($type_cond.method_cond.ssc_s), method = "$type_cond.method_cond.ssc_kernel_method") plot(msidata.ssc, mode = "tstatistics", model = list("r" = c($type_cond.method_cond.ssc_r), "s" = c($type_cond.method_cond.ssc_s)), col=colourvector, lwd=2) ### summary table SSC ##############summary_table = summary(msidata.ssc) ### stop if multiple values for r and s were used as input tryCatch( { if (length(names(msidata.ssc@resultData))>1) { stop(call.=FALSE) } }, error=function(cond) { ## in case user used multiple inputs for r or s stop - this is only possible in cv apply message("Error during SSC training") message("Possible problem: multiple values for r or s selected - this is only possible in cvapply but not for spatial shrunken centroid analysis)") stop(call.=FALSE) } ) summary_table = summary(msidata.ssc)\$accuracy[[names(msidata.ssc@resultData)]] summary_table2 = round(as.numeric(summary_table), digits=2) summary_matrix = matrix(summary_table2, nrow=4, ncol=number_groups) summary_table3 = cbind(rownames(summary_table), summary_matrix) ## include rownames in table summary_table4 = t(summary_table3) summary_table5 = cbind(c(names(msidata.ssc@resultData),colnames(summary_table)), summary_table4) plot(0,type='n',axes=FALSE,ann=FALSE) grid.table(summary_table5, rows= NULL) ### image of the best m/z minimumy = min(coord(msidata)[,2]) maximumy = max(coord(msidata)[,2]) print(image(msidata, mz = topFeatures(msidata.ssc)[1,1], normalize.image = "linear", contrast.enhance = "histogram",smooth.image="gaussian", ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), main="best m/z heatmap")) ## m/z and pixel information output ssc_classes = data.frame(msidata.ssc\$classes[[1]]) ssc_probabilities = data.frame(msidata.ssc\$probabilities[[1]]) ## pixel names and coordinates ## to remove potential sample names and z dimension, split at comma and take only x and y x_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 1)) y_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 2)) x_coordinates = gsub("x = ","",x_coords) y_coordinates = gsub(" y = ","",y_coords) pixel_names = paste0("xy_", x_coordinates, "_", y_coordinates) ## remove msidata to clean up RAM space rm(msidata) gc() ssc_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, ssc_classes, ssc_probabilities) colnames(ssc_classes2) = c("pixel names", "x", "y","predicted condition", levels(msidata.ssc\$classes[[1]])) ssc_toplabels = topFeatures(msidata.ssc, n=Inf) ssc_toplabels[,6:9] <-round(ssc_toplabels[,6:9],6) write.table(ssc_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t") write.table(ssc_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t") image(msidata.ssc, model=list(r = c($type_cond.method_cond.ssc_r), s = c($type_cond.method_cond.ssc_s)), ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), col=colourvector, mode="classes", layout=c(1,1), main="Class Prediction") image(msidata.ssc, model=list(r = c($type_cond.method_cond.ssc_r), s = c($type_cond.method_cond.ssc_s)), ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), col=colourvector, mode="probabilities", layout=c(1,1), main="Class probabilities") ## image with right and wrong classes: prediction_df = cbind(coord(msidata.ssc)[,1:2], ssc_classes) colnames(prediction_df) = c("x", "y", "predicted_classes") comparison_df = cbind(prediction_df, y_vector) comparison_df\$correct<- ifelse(comparison_df\$predicted_classes==comparison_df\$y_vector, T, F) correctness = round(sum(comparison_df\$correct)/length(comparison_df\$correct)*100,2) correctness_plot = ggplot(comparison_df, aes(x=x, y=y, fill=correct))+ geom_tile() + coord_fixed()+ ggtitle(paste0("Correctness of classification: ",correctness, "%"))+ scale_fill_manual(values = c("TRUE" = "orange","FALSE" = "darkblue"))+ theme_bw()+ theme( plot.background = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(text=element_text(family="ArialMT", face="bold", size=15))+ theme(legend.position="bottom",legend.direction="vertical")+ guides(fill=guide_legend(ncol=2,byrow=TRUE)) ## coord_labels = aggregate(cbind(x,y)~correct, data=comparison_df, mean, na.rm=TRUE, na.action="na.pass") ##coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$predicted_classes) print(correctness_plot) ## optional output as .RData #if $output_rdata: save(msidata.ssc, file="$classification_rdata") #end if #end if #end if ######################## II) Prediction ############################# ############################################################################# #elif str($type_cond.type_method) == "prediction": print("prediction") training_data = loadRData("$type_cond.training_result") #if str($type_cond.new_y_values_cond.new_y_values) == "new_response": print("new response") new_y_tabular = read.delim("$type_cond.new_y_values_cond.new_response_file", header = $type_cond.new_y_values_cond.new_tabular_header, stringsAsFactors = FALSE) new_y_input = new_y_tabular[,c($type_cond.new_y_values_cond.column_new_x, $type_cond.new_y_values_cond.column_new_y, $type_cond.new_y_values_cond.column_new_response)] colnames(new_y_input)[1:2] = c("x", "y") ## merge with coordinate information of msidata msidata_coordinates = cbind(coord(msidata)[,1:2], c(1:ncol(msidata))) colnames(msidata_coordinates)[3] = "pixel_index" merged_response = merge(msidata_coordinates, new_y_input, by=c("x", "y"), all.x=TRUE) merged_response[is.na(merged_response)] = "NA" merged_response = merged_response[order(merged_response\$pixel_index),] new_y_vector = as.factor(merged_response[,4]) prediction = predict(training_data,msidata, newy = new_y_vector) ##numbers of levels for colour selection number_levels = length(levels(new_y_vector)) ## Summary table prediction summary_table = summary(prediction)\$accuracy[[names(prediction@resultData)]] summary_table2 = round(as.numeric(summary_table), digits=2) summary_matrix = matrix(summary_table2, nrow=4, ncol=ncol(summary_table)) summary_table3 = cbind(rownames(summary_table), summary_matrix) ## include rownames in table summary_table4 = t(summary_table3) summary_table5 = cbind(c(names(prediction@resultData),colnames(summary_table)), summary_table4) plot(0,type='n',axes=FALSE,ann=FALSE) grid.table(summary_table5, rows= NULL) #else prediction = predict(training_data,msidata) number_levels = length(levels(training_data\$y[[1]])) #end if ## colours selection: #if str($colour_conditional.colour_type) == "manual_colour" #set $color_string = ','.join(['"%s"' % $color.annotation_color for $color in $colour_conditional.colours]) colourvector = c($color_string) #elif str($colour_conditional.colour_type) == "colourpalette" colourvector = noquote($colour_conditional.palettes)(number_levels) #end if ## m/z and pixel information output predicted_classes = data.frame(prediction\$classes[[1]]) pixel_names = gsub(", y = ", "_", names(pixels(msidata))) pixel_names = gsub(" = ", "y_", pixel_names) x_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,2] y_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,3] predicted_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, predicted_classes) colnames(predicted_classes2) = c("pixel names", "x", "y","predicted condition") predicted_toplabels = topFeatures(prediction, n=Inf) if (colnames(predicted_toplabels)[4] == "coefficients"){ predicted_toplabels[,4:6] <-round(predicted_toplabels[,4:6],5) }else{ predicted_toplabels[,6:9] <-round(predicted_toplabels[,6:9],5)} ##predicted classes prediction_df = cbind(coord(prediction)[,1:2], predicted_classes) colnames(prediction_df) = c("x", "y", "predicted_classes") #if str($type_cond.classification_type) == "SSC_classifier": ## this seems to work only for SSC, therefore overwrite tables predicted_probabilities = data.frame(prediction\$probabilities[[1]]) predicted_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, predicted_classes, predicted_probabilities) colnames(predicted_classes2) = c("pixel names", "x", "y","predicted condition", levels(prediction\$classes[[1]])) ## also image modes are specific to SSC image(prediction, mode="classes", layout=c(1,1), main="Class", ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), col=colourvector) image(prediction, mode="probabilities", layout=c(1,1), main="Class probabilities",ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), col=colourvector) #else prediction_plot = ggplot(prediction_df, aes(x=x, y=y, fill=predicted_classes))+ geom_tile()+ coord_fixed()+ ggtitle("Predicted condition for each spectrum")+ theme_bw()+ theme( plot.background = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank())+ theme(text=element_text(family="ArialMT", face="bold", size=15))+ theme(legend.position="bottom", legend.direction="vertical")+ guides(fill=guide_legend(ncol=4, byrow=TRUE))+ scale_discrete_manual(aesthetics = c("colour", "fill"), values = colourvector) coord_labels = aggregate(cbind(x,y)~predicted_classes, data=prediction_df, mean, na.rm=TRUE, na.action="na.pass") coord_labels\$file_number = gsub( "_.*ยง", "", coord_labels\$predicted_classes) print(prediction_plot) #end if write.table(predicted_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t") write.table(predicted_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t") #if str($type_cond.new_y_values_cond.new_y_values) == "new_response": ## image with right and wrong classes: comparison_df = cbind(prediction_df, new_y_vector) comparison_df\$correct<- ifelse(comparison_df\$predicted_classes==comparison_df\$new_y_vector, T, F) correctness = round(sum(comparison_df\$correct)/length(comparison_df\$correct)*100,2) correctness_plot = ggplot(comparison_df, aes(x=x, y=y, fill=correct))+ geom_tile()+ scale_fill_manual(values = c("TRUE" = "orange","FALSE" = "darkblue"))+ coord_fixed()+ ggtitle(paste0("Correctness of classification: ",correctness, "%"))+ theme_bw()+ theme(text=element_text(family="ArialMT", face="bold", size=15))+ theme(legend.position="bottom",legend.direction="vertical")+ guides(fill=guide_legend(ncol=2,byrow=TRUE)) print(correctness_plot) #end if ## optional output as .RData #if $output_rdata: msidata = prediction save(msidata, file="$classification_rdata") #end if #end if dev.off() }else{ print("Inputfile has no intensities > 0 or contains NA values") dev.off() } ]]></configfile> </configfiles> <inputs> <expand macro="reading_msidata"/> <conditional name="type_cond"> <param name="type_method" type="select" label="Analysis step to perform"> <option value="training" selected="True">training</option> <option value="prediction">prediction</option> </param> <when value="training"> <param name="annotation_file" type="data" format="tabular" label="Load tabular file with pixel coordinates and their classes" help="Three or four columns: x values, y values, response values, optionally fold values"/> <param name="column_x" data_ref="annotation_file" label="Column with x values" type="data_column"/> <param name="column_y" data_ref="annotation_file" label="Column with y values" type="data_column"/> <param name="column_response" data_ref="annotation_file" label="Column with response (condition) values" type="data_column" help="This is the condition (pixel group) which will be classified"/> <param name="column_fold" data_ref="annotation_file" optional="True" label="Column with fold values - only neccessary for cvapply" type="data_column" help="Each fold must contain pixels of all response groups and is used for cross validation"/> <param name="tabular_header" type="boolean" label="Tabular files contain a header line" truevalue="TRUE" falsevalue="FALSE"/> <conditional name="method_cond"> <param name="class_method" type="select" label="Select the method for classification"> <option value="PLS" selected="True">PLS-DA</option> <option value="OPLS">OPLS-DA</option> <option value="spatialShrunkenCentroids">spatial shrunken centroids</option> </param> <when value="PLS"> <conditional name="analysis_cond"> <param name="PLS_method" type="select" label="Crossvalidation or analysis"> <option value="cvapply" selected="True">cvApply</option> <option value="PLS_analysis">PLS-DA analysis</option> </param> <when value="cvapply"> <param name="plscv_comp" type="text" value="1:2" label="The number of PLS-DA components" help="For cvapply multiple values are allowed (e.g. 1,2,3 or 2:5). Mininum is 1."> <expand macro="sanitizer_multiple_digits"/> </param> </when> <when value="PLS_analysis"> <param name="pls_comp" type="integer" value="5" label="The optimal number of PLS-DA components as indicated by cross-validations (minimum is 1)" help="Run cvApply first to optain optimal number of PLS-DA components"/> <param name="pls_scale" type="boolean" label="Data scaling" truevalue="TRUE" falsevalue="FALSE"/> <param name="pls_toplabels" type="integer" value="100" label="Number of toplabels (m/z features) which should be written in tabular output"/> </when> </conditional> </when> <when value="OPLS"> <conditional name="opls_analysis_cond"> <param name="opls_method" type="select" label="Analysis step to perform"> <option value="opls_cvapply" selected="True">cvApply</option> <option value="opls_analysis">OPLS-DA analysis</option> </param> <when value="opls_cvapply"> <param name="opls_cvcomp" type="text" value="1:2" label="The number of OPLS-DA components" help="For cvapply multiple values are allowed (e.g. 1,2,3 or 2:5). Minimum is 1."> <expand macro="sanitizer_multiple_digits"/> </param> <!--param name="xnew_cv" type="boolean" truevalue="TRUE" falsevalue="FALSE" label="Keep new matrix"/--> </when> <when value="opls_analysis"> <param name="opls_comp" type="integer" value="5" label="The optimal number of OPLS-DA components as indicated by cross-validations (minimum is 1)" help="Run cvApply first to optain optimal number of OPLS-DA components"/> <!--param name="xnew" type="boolean" truevalue="TRUE" falsevalue="FALSE" label="Keep new matrix"/--> <param name="opls_scale" type="boolean" truevalue="TRUE" falsevalue="FALSE" label="Data scaling"/> <!--param name="opls_toplabels" type="integer" value="100" label="Number of toplabels (m/z features) which should be written in tabular output"/--> </when> </conditional> </when> <when value="spatialShrunkenCentroids"> <conditional name="ssc_analysis_cond"> <param name="ssc_method" type="select" label="Analysis step to perform"> <option value="ssc_cvapply" selected="True">cvApply</option> <option value="ssc_analysis">spatial shrunken centroids analysis</option> </param> <when value="ssc_cvapply"> <param name="write_best_params" type="boolean" label="Write out best r and s values" help="Can be used to generate automatic classification workflow"/> </when> <when value="ssc_analysis"> <!--param name="ssc_toplabels" type="integer" value="100" label="Number of toplabels (m/z features) which should be written in tabular output"/--> </when> </conditional> <param name="ssc_r" type="text" value="2" label="The spatial neighborhood radius of nearby pixels to consider (r)" help="For cvapply multiple values are allowed (e.g. 0,1,2,3 or 2:5)"> <expand macro="sanitizer_multiple_digits"/> </param> <param name="ssc_s" type="text" value="2" label="The sparsity thresholding parameter by which to shrink the t-statistics (s)." help="For cvapply multiple values are allowed (e.g. 0,1,2 or 2:5)"> <expand macro="sanitizer_multiple_digits"/> </param> <param name="ssc_kernel_method" type="select" display="radio" label = "The method to use to calculate the spatial smoothing kernels for the embedding. The 'gaussian' method refers to spatially-aware (SA) weights, and 'adaptive' refers to spatially-aware structurally-adaptive (SASA) weights"> <option value="gaussian">gaussian</option> <option value="adaptive" selected="True">adaptive</option> </param> </when> </conditional> </when> <when value="prediction"> <param name="training_result" type="data" format="rdata" label="Result from previous classification training"/> <!--param name="predicted_toplabels" type="integer" value="100" label="Number of toplabels (m/z features) which should be written in tabular output"/--> <param name="classification_type" type="select" display="radio" optional="False" label="Which classification method was used"> <option value="PLS_classifier" selected="True" >PLS classifier</option> <option value="OPLS_classifier">OPLS classifier</option> <option value="SSC_classifier">SSC_classifier</option> </param> <conditional name="new_y_values_cond"> <param name="new_y_values" type="select" label="Load annotations (optional, but allows accuracy calculations)"> <option value="no_new_response" selected="True">no</option> <option value="new_response">use annotations</option> </param> <when value="no_new_response"/> <when value="new_response"> <param name="new_response_file" type="data" format="tabular" label="Load tabular file with pixel coordinates and the new response"/> <param name="column_new_x" data_ref="new_response_file" label="Column with x values" type="data_column"/> <param name="column_new_y" data_ref="new_response_file" label="Column with y values" type="data_column"/> <param name="column_new_response" data_ref="new_response_file" label="Column with new response values" type="data_column"/> <param name="new_tabular_header" type="boolean" label="Tabular files contain a header line" truevalue="TRUE" falsevalue="FALSE"/> </when> </conditional> </when> </conditional> <conditional name="colour_conditional"> <param name="colour_type" type="select" label="Choose a colour scheme"> <option value="colourpalette" selected="True" >Colour palette</option> <option value="manual_colour">Manual selection</option> </param> <when value="manual_colour"> <repeat name="colours" title="Colours for the plots" min="1" max="50"> <param name="annotation_color" type="color" label="Colours" value="#ff00ff" help="Numbers of colours should be the same as number of components"> <sanitizer> <valid initial="string.letters,string.digits"> <add value="#" /> </valid> </sanitizer> </param> </repeat> </when> <when value="colourpalette"> <param name="palettes" type="select" display="radio" label="Select a colourpalette"> <option value="hue_pal()" selected="True">hue</option> <option value="rainbow">rainbow</option> <option value="heat.colors">heat colors</option> <option value="terrain.colors">terrain colors</option> <option value="topo.colors">topo colors</option> <option value="cm.colors">cm colors</option> </param> </when> </conditional> <param name="output_rdata" type="boolean" label="Results as .RData output" help="Can be used to generate a classification prediction on new data"/> </inputs> <outputs> <data format="pdf" name="classification_images" from_work_dir="classificationpdf.pdf" label = "${tool.name} on ${on_string}: results"/> <data format="tabular" name="mzfeatures" label="${tool.name} on ${on_string}: features"/> <data format="tabular" name="pixeloutput" label="${tool.name} on ${on_string}: pixels"/> <data format="txt" name="best_r" label="${tool.name} on ${on_string}:best r"> <filter>type_cond['type_method'] == 'training' and type_cond['method_cond']['class_method'] == 'spatialShrunkenCentroids' and type_cond['method_cond']['ssc_analysis_cond']['ssc_method'] == 'ssc_cvapply' and type_cond['method_cond']['ssc_analysis_cond']['write_best_params']</filter> </data> <data format="txt" name="best_s" label="${tool.name} on ${on_string}:best s"> <filter>type_cond['type_method'] == 'training' and type_cond['method_cond']['class_method'] == 'spatialShrunkenCentroids' and type_cond['method_cond']['ssc_analysis_cond']['ssc_method'] == 'ssc_cvapply' and type_cond['method_cond']['ssc_analysis_cond']['write_best_params']</filter> </data> <data format="rdata" name="classification_rdata" label="${tool.name} on ${on_string}: results.RData"> <filter>output_rdata</filter> </data> </outputs> <tests> <test expect_num_outputs="3"> <param name="infile" value="testfile_squares.rdata" ftype="rdata"/> <conditional name="type_cond"> <param name="type_method" value="training"/> <param name="annotation_file" value= "pixel_annotation_file1.tabular" ftype="tabular"/> <param name="column_x" value="1"/> <param name="column_y" value="2"/> <param name="column_response" value="4"/> <param name="column_fold" value="3"/> <param name="tabular_header" value="False"/> <conditional name="method_cond"> <param name="class_method" value="PLS"/> <conditional name="analysis_cond"> <param name="PLS_method" value="cvapply"/> <param name="plscv_comp" value="2:4"/> </conditional> </conditional> </conditional> <output name="mzfeatures" file="features_test1.tabular"/> <output name="pixeloutput" file="pixels_test1.tabular"/> <output name="classification_images" file="test1.pdf" compare="sim_size" delta="2000"/> </test> <test expect_num_outputs="4"> <param name="infile" value="testfile_squares.rdata" ftype="rdata"/> <conditional name="type_cond"> <param name="type_method" value="training"/> <param name="annotation_file" value= "pixel_annotation_file1.tabular" ftype="tabular"/> <param name="column_x" value="1"/> <param name="column_y" value="2"/> <param name="column_response" value="4"/> <param name="tabular_header" value="False"/> <conditional name="method_cond"> <param name="class_method" value="PLS"/> <conditional name="analysis_cond"> <param name="PLS_method" value="PLS_analysis"/> <param name="pls_comp" value="2"/> <param name="pls_scale" value="TRUE"/> <!--param name="pls_toplabels" value="100"/--> </conditional> </conditional> </conditional> <param name="output_rdata" value="True"/> <output name="mzfeatures" file="features_test2.tabular"/> <output name="pixeloutput" file="pixels_test2.tabular"/> <output name="classification_images" file="test2.pdf" compare="sim_size"/> <output name="classification_rdata" file="test2.rdata" compare="sim_size"/> </test> <test expect_num_outputs="3"> <param name="infile" value="testfile_squares.rdata" ftype="rdata"/> <conditional name="type_cond"> <param name="type_method" value="training"/> <param name="annotation_file" value= "random_factors.tabular" ftype="tabular"/> <param name="column_x" value="1"/> <param name="column_y" value="2"/> <param name="column_response" value="4"/> <param name="column_fold" value="3"/> <param name="tabular_header" value="False"/> <conditional name="method_cond"> <param name="class_method" value="OPLS"/> <conditional name="opls_analysis_cond"> <param name="opls_method" value="opls_cvapply"/> <param name="opls_cvcomp" value="1:2"/> <param name="xnew_cv" value="FALSE"/> </conditional> </conditional> </conditional> <output name="mzfeatures" file="features_test3.tabular"/> <output name="pixeloutput" file="pixels_test3.tabular"/> <output name="classification_images" file="test3.pdf" compare="sim_size"/> </test> <test expect_num_outputs="4"> <param name="infile" value="testfile_squares.rdata" ftype="rdata"/> <conditional name="type_cond"> <param name="type_method" value="training"/> <param name="annotation_file" value= "random_factors.tabular" ftype="tabular"/> <param name="column_x" value="1"/> <param name="column_y" value="2"/> <param name="column_response" value="4"/> <param name="tabular_header" value="False"/> <conditional name="method_cond"> <param name="class_method" value="OPLS"/> <conditional name="opls_analysis_cond"> <param name="opls_method" value="opls_analysis"/> <param name="opls_comp" value="3"/> <param name="xnew" value="FALSE"/> <param name="opls_scale" value="FALSE"/> <!--param name="opls_toplabels" value="100"/--> </conditional> </conditional> </conditional> <param name="output_rdata" value="True"/> <output name="mzfeatures" file="features_test4.tabular"/> <output name="pixeloutput" file="pixels_test4.tabular"/> <output name="classification_images" file="test4.pdf" compare="sim_size"/> <output name="classification_rdata" file="test4.rdata" compare="sim_size"/> </test> <test expect_num_outputs="3"> <param name="infile" value="testfile_squares.rdata" ftype="rdata"/> <conditional name="type_cond"> <param name="type_method" value="training"/> <param name="annotation_file" value= "pixel_annotation_file1.tabular" ftype="tabular"/> <param name="column_x" value="1"/> <param name="column_y" value="2"/> <param name="column_response" value="3"/> <param name="column_fold" value="4"/> <param name="tabular_header" value="False"/> <conditional name="method_cond"> <param name="class_method" value="spatialShrunkenCentroids"/> <conditional name="ssc_analysis_cond"> <param name="ssc_method" value="ssc_cvapply"/> <param name="ssc_r" value="1:2"/> <param name="ssc_s" value="2:3"/> <param name="ssc_kernel_method" value="adaptive"/> </conditional> </conditional> </conditional> <output name="mzfeatures" file="features_test5.tabular"/> <output name="pixeloutput" file="pixels_test5.tabular"/> <output name="classification_images" file="test5.pdf" compare="sim_size"/> </test> <test expect_num_outputs="4"> <param name="infile" value="testfile_squares.rdata" ftype="rdata"/> <conditional name="type_cond"> <param name="type_method" value="training"/> <param name="annotation_file" value= "random_factors.tabular" ftype="tabular"/> <param name="column_x" value="1"/> <param name="column_y" value="2"/> <param name="column_response" value="4"/> <conditional name="method_cond"> <param name="class_method" value="spatialShrunkenCentroids"/> <conditional name="ssc_analysis_cond"> <param name="ssc_method" value="ssc_analysis"/> <!--param name="ssc_toplabels" value="20"/--> </conditional> <param name="ssc_r" value="2"/> <param name="ssc_s" value="2"/> <param name="ssc_kernel_method" value="adaptive"/> </conditional> </conditional> <param name="output_rdata" value="True"/> <output name="mzfeatures" file="features_test6.tabular"/> <output name="pixeloutput" file="pixels_test6.tabular"/> <output name="classification_images" file="test6.pdf" compare="sim_size"/> <output name="classification_rdata" file="test6.rdata" compare="sim_size" delta="15000"/> </test> <test expect_num_outputs="4"> <param name="infile" value="testfile_squares.rdata" ftype="rdata"/> <conditional name="type_cond"> <param name="type_method" value="prediction"/> <param name="training_result" value="test2.rdata" ftype="rdata"/> <conditional name="new_y_values_cond"> <param name="new_y_values" value="new_response"/> <param name="new_response_file" value="pixel_annotation_file1.tabular" ftype="tabular"/> <param name="column_new_x" value="1"/> <param name="column_new_y" value="2"/> <param name="column_new_response" value="4"/> <param name="new_tabular_header" value="False"/> </conditional> </conditional> <param name="output_rdata" value="True"/> <output name="mzfeatures" file="features_test7.tabular"/> <output name="pixeloutput" file="pixels_test7.tabular"/> <output name="classification_images" file="test7.pdf" compare="sim_size"/> <output name="classification_rdata" file="test7.rdata" compare="sim_size" /> </test> </tests> <help> <![CDATA[ @CARDINAL_DESCRIPTION@ ----- This tool provides three different Cardinal functions for supervised classification of mass-spectrometry imaging data. @MSIDATA_INPUT_DESCRIPTION@ - NA intensities are not allowed - duplicated coordinates will be removed - For training: tabular file with condition and fold for each pixel: Two columns for pixel coordinates (x and y values); one column with the condition for the pixel, which will be used for classification; for the cross validation (cvapply) another column with a fold is necessary, each fold must contain pixels of all response groups and is used for cross validation. Condition and fold columns are treated as factor to perform discriminant analysis (also when numeric values are provided). :: x_coord y_coord condition fold 1 1 A f1 2 1 A f2 3 1 A f3 1 2 B f1 2 2 B f2 3 2 B f3 ... ... - For prediction: RData output from previous classification run is needed as input, optionally new response values can be loaded with a tabular file containing x values, y values and the response **Options** - PLS-DA: partial least square discriminant analysis - O-PLS-DA: Orthogonal partial least squares discriminant analysis - Spatial shrunken centroids (more details in `Bemis et al. <https://doi.org/10.1074/mcp.O115.053918>`_) - training and prediction - training can be done with cvapply that uses cross validation to find the best value for s, this requires not only a condition for each spectrum but also a fold (each fold should contain spectra of all conditions) - training with the best value for r and s gives the top m/z features for each condition and the predicted classification group for each spectrum - training result can be saved as RData file that can be reused for prediction of further samples - prediction can calculate accuracies when the annotations are known and provided .. image:: $PATH_TO_IMAGES/classification_overview.png :width: 1000 :height: 465 **Tips** - The classification function will only run on files with valid intensity values (NA are not allowed) - Only a single input file is accepted, several files have to be combined previously, for example with the MSI combine tool. **Output** - Pdf with the heatmaps and plots for the classification - Tabular file with information on m/z features and pixels: toplabels/classes - Optional: RData output that can be used to predict new data or to explore the results more deeply with the Cardinal package in R ]]> </help> <expand macro="citations"/> </tool>