Mercurial > repos > galaxyp > cardinal_filtering
view filtering.xml @ 8:a8157342d9a3 draft
"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit d008f6ea0f5c8435fb975a34cb99ea4d42c5ebd2"
author | galaxyp |
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date | Wed, 13 May 2020 14:11:34 -0400 |
parents | 1d4a6fe69a40 |
children | e92efed83208 |
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<tool id="cardinal_filtering" name="MSI filtering" version="@VERSION@.0"> <description>tool for filtering 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="3.2.1">r-ggplot2</requirement> </expand> <expand macro="print_version"/> <command detect_errors="exit_code"> <![CDATA[ @INPUT_LINKING@ cat '${MSI_subsetting}' && Rscript '${MSI_subsetting}' && mkdir $outfile_imzml.files_path && mv ./out.imzML "${os.path.join($outfile_imzml.files_path, 'imzml')}" | true && mv ./out.ibd "${os.path.join($outfile_imzml.files_path, 'ibd')}" | true && echo "imzML file:" > $outfile_imzml && ls -l "$outfile_imzml.files_path" >> $outfile_imzml ]]> </command> <configfiles> <configfile name="MSI_subsetting"><![CDATA[ ################################# load libraries and read file ################# library(Cardinal) library(ggplot2) library(gridExtra) @READING_MSIDATA_FULLY_COMPATIBLE@ ########################### QC numbers ######################## ## Number of features (m/z) maxfeatures = nrow(msidata) ## Range m/z minmz = round(min(mz(msidata)), digits=2) maxmz = round(max(mz(msidata)), digits=2) ## Number of spectra (pixels) pixelcount = ncol(msidata) ## Range x coordinates minimumx = min(coord(msidata)\$x) maximumx = max(coord(msidata)\$x) ## Range y coordinates minimumy = min(coord(msidata)\$y) maximumy = max(coord(msidata)\$y) ## Store features for QC plot featuresinfile = mz(msidata) all_df = data.frame(coord(msidata)\$x, coord(msidata)\$y, rep("removed pixels", times=ncol(msidata))) colnames(all_df) = c("x", "y", "annotation") ## Next steps will only run if there are more than 0 pixels/features in the file if (ncol(msidata)>0 & nrow(msidata) >0) { ###################################### Filtering of pixels ##################### ################################################################################ ############ Pixels in two columns format: x and y in different columns ############# #if str($pixels_cond.pixel_filtering) == "two_columns": print("two columns") ## read tabular file input_list = read.delim("$pixels_cond.annotation_file", header = $pixels_cond.tabular_header, stringsAsFactors = FALSE) inputpixels = input_list[,c($pixels_cond.column_x, $pixels_cond.column_y)] input_pixels = paste(inputpixels[,1], inputpixels[,2], sep="_") dataset_pixels = paste(coord(msidata)\$x, coord(msidata)\$y, sep="_") pixelsofinterest = dataset_pixels %in% input_pixels tryCatch( { msidata = msidata[,pixelsofinterest] if (ncol(msidata) == 0) { stop(call.=FALSE) } }, error=function(cond) { ## in case all coordinates were outside the dataset leading to zero pixels, tool is stopped to avoid continuing with wrong data message("Error during pixel filtering") message("Possible problems: Forgot to set 'Tabular file contains a header line' = Yes, wrong columns selected, columns with coordinates contain empty fields or letters, all coordinates were outside the range of the dataset - this can be checked with the 'MSI qualitycontrol' tool") stop(call.=FALSE) } ) ## QC values: numberpixels = nrow(input_list) validpixels=ncol(msidata) ########### Pixels wihin x and y minima and maxima are kept ################# #elif str($pixels_cond.pixel_filtering) == "pixel_range": print("pixel range") ## QC values: numberpixels = "range" validpixels = "range" tryCatch( { msidata = msidata[, coord(msidata)\$x <= $pixels_cond.max_x_range & coord(msidata)\$x >= $pixels_cond.min_x_range] msidata = msidata[, coord(msidata)\$y <= $pixels_cond.max_y_range & coord(msidata)\$y >= $pixels_cond.min_y_range] if (ncol(msidata) == 0) { stop(call.=FALSE) } }, error=function(cond) { ## in case one of the ranges was outside the dataset leading to zero pixels, tool is stopped to avoid continuing with wrong data message("Error during pixel filtering") message("Check that both x and y ranges were inside the dataset coordinates (can be checked with the 'MSI qualitycontrol' tool) or if any not numeric character was entered into the input fields") stop(call.=FALSE) } ) ######################## no pixel filtering ################################ #elif str($pixels_cond.pixel_filtering) == "none": print("no pixel filtering") ## QC values: numberpixels = 0 validpixels = 0 #end if ############################# QC data ##################################### ## dataframe for QC of pixel distribution remaining_df = data.frame(as.numeric(coord(msidata)\$x), as.numeric(coord(msidata)\$y), rep("remaining pixels", times=ncol(msidata))) colnames(remaining_df) = c("x", "y", "annotation") position_df = rbind(all_df, remaining_df) position_df[row.names(unique(position_df[,c("x", "y")])),] position_df\$annotation = factor(position_df\$annotation) gc() }else{ print("Inputfile has no intensities > 0") } ################################# filtering of features ###################### ############################################################################## ####################### Keep m/z from tabular file ######################### ## feature filtering only when pixels/features/intensities are left if (ncol(msidata) > 0){ if (nrow(msidata) > 0) { #if str($features_cond.features_filtering) == "features_list": print("feature list") ## read tabular file, define starting row, extract and count valid features input_features = read.delim("$mz_tabular", header = $features_cond.feature_header, stringsAsFactors = FALSE) extracted_features = input_features[,$features_cond.feature_column] numberfeatures = length(extracted_features) if (class(extracted_features) == "numeric"){ ### max digits given in the input file will be used to match m/z but the maximum is 4 max_digits = max(nchar(sapply(strsplit(as.character(extracted_features), "\\."),`[`,2)), na.rm=TRUE) if (max_digits >4) { max_digits = 4 } validfeatures = round(extracted_features, max_digits) %in% round(mz(msidata),max_digits) featuresofinterest = features(msidata)[round(mz(msidata), digits = max_digits) %in% round(extracted_features[validfeatures], max_digits)] validmz = length(unique(featuresofinterest)) }else{ validmz = 0 featuresofinterest = 0} ### filter msidata for valid features tryCatch( { msidata = msidata[featuresofinterest,] ## does not throw error when processed file has no features left, therefore create error to avoid continuing with wrong data if (nrow(msidata) == 0) { stop(call.=FALSE) } }, error=function(cond) { ## in case all provided m/z values were outside the m/z range ## tool is stopped to avoid continuing with wrong data message("Error during m/z filtering") message("Possible problems: Forgot to set 'Tabular file contains a header line' = Yes, wrong columns selected, column with m/z features contains empty fields or letters, all m/z features s were outside the range of the dataset (this can be checked with the 'MSI qualitycontrol' tool) or did not match any m/z feature of the dataset (see help section for more information on that)") stop(call.=FALSE) } ) ############### features within a given range are kept ##################### #elif str($features_cond.features_filtering) == "features_range": print("feature range") numberfeatures = "range" validmz = "range" tryCatch( { msidata = msidata[mz(msidata) >= $features_cond.min_mz & mz(msidata) <= $features_cond.max_mz,] ## does not throw error when processed file has no features left, therefore create error to avoid continuing with wrong data if (nrow(msidata) == 0) { stop(call.=FALSE) } }, error=function(cond) { ## in case all m/z features were outside the dataset leading to zero m/z features, tool is stopped to avoid continuing with wrong data message("Error during m/z filtering") message("Check that the entered m/z range is inside the dataset coordinates (can be checked with the 'MSI qualitycontrol' tool) or if any not numeric character was entered into the input fields") stop(call.=FALSE) } ) ############### Remove m/z from tabular file ######################### #elif str($features_cond.features_filtering) == "remove_features": print("remove features") ## read tabular file, define starting row, extract and count valid features input_features = read.delim("$mz_tabular", header = $features_cond.feature_header, stringsAsFactors = FALSE) extracted_features = input_features[,$features_cond.feature_column] numberfeatures = length(extracted_features) if (class(extracted_features) == "numeric"){ print("input is numeric") featuresofinterest = extracted_features }else{featuresofinterest = 0} ### Here starts removal of features: plusminus = $features_cond.removal_plusminus tryCatch( { mass_to_remove = numeric() for (masses in featuresofinterest){ #if str($features_cond.units_removal) == "ppm": plusminus = masses * $features_cond.removal_plusminus/1000000 #end if current_mass = which(c(mz(msidata) <= masses + plusminus & mz(msidata) >= masses - plusminus)) mass_to_remove = append(mass_to_remove, current_mass)} mass_to_keep = setdiff(1:nrow(msidata),mass_to_remove) msidata= msidata[mass_to_keep, ] validmz = maxfeatures - nrow(msidata) ## does not throw error when processed file has no features left, therefore create error to avoid continuing with wrong data if (nrow(msidata) == 0) { stop(call.=FALSE) } }, error=function(cond) { message("Error during removal of m/z features") stop(call.=FALSE) } ) ######################## No m/z filtering ############################## #elif str($features_cond.features_filtering) == "none": print("no feature filtering") validmz = 0 numberfeatures = 0 #end if }else{ print("Inputfile has no m/z features") numberfeatures = NA validmz = NA } }else{ print("Inputfile or file filtered for pixels has no pixels") numberfeatures = NA validmz = NA } gc() #################### QC numbers ####################### ## Number of features (m/z) maxfeatures2 = nrow(msidata) ## Range m/z minmz2 = round(min(mz(msidata)), digits=2) maxmz2 = round(max(mz(msidata)), digits=2) ## Number of spectra (pixels) pixelcount2 = ncol(msidata) ## Range x coordinates minimumx2 = min(coord(msidata)\$x) maximumx2 = max(coord(msidata)\$x) ## Range y coordinates minimumy2 = min(coord(msidata)\$y) maximumy2 = max(coord(msidata)\$y) properties = c("Number of m/z features", "Range of m/z values", "Number of pixels", "Range of x coordinates", "Range of y coordinates", "pixel overview", "feature overview") before = c(paste0(maxfeatures), paste0(minmz, " - ", maxmz), paste0(pixelcount), paste0(minimumx, " - ", maximumx), paste0(minimumy, " - ", maximumy), paste0("input pixels: ", numberpixels), paste0("input mz: ", numberfeatures)) filtered = c(paste0(maxfeatures2), paste0(minmz2, " - ", maxmz2), paste0(pixelcount2), paste0(minimumx2, " - ", maximumx2), paste0(minimumy2, " - ", maximumy2), paste0("valid pixels: ", validpixels), paste0("valid mz: ", validmz)) property_df = data.frame(properties, before, filtered) ########################### PDF QC and MSI output ########################### pdf("filtertool_QC.pdf", fonts = "Times", pointsize = 12) plot(0,type='n',axes=FALSE,ann=FALSE) title(main=paste0("Qualitycontrol of filtering tool for file: \n\n", "$infile.element_identifier")) grid.table(property_df, rows= NULL) ## QC report only when pixels/features are left if (ncol(msidata)>0 & nrow(msidata) >0) { ### visual pixel control pixel_image = ggplot(position_df, aes(x=x, y=y, fill=annotation))+ geom_tile(height = 1, width=1)+ coord_fixed()+ ggtitle("Spatial orientation of filtered pixels")+ theme_bw()+ theme(plot.title = element_text(hjust = 0.5))+ theme(legend.position="bottom",legend.direction="vertical")+ guides(fill=guide_legend(ncol=5,byrow=TRUE)) print(pixel_image) ### visual mz feature control kept_df = data.frame(mz(msidata), rep("remaining m/z", nrow(msidata))) colnames(kept_df) = c("mz", "legend") mz_removed = setdiff(featuresinfile, mz(msidata)) removed_df = data.frame(mz_removed, rep("removed m/z", length(mz_removed))) colnames(removed_df) = c("mz", "legend") histogram_df = rbind(removed_df,kept_df) histogram_mz= ggplot(histogram_df, aes(x=mz, fill=legend)) + geom_histogram()+ theme_bw()+ theme(plot.title = element_text(hjust = 0.5))+ theme(legend.position="bottom",legend.direction="vertical")+ labs(title="Overview of filtered m/z", x="m/z", y = "count") + guides(fill=guide_legend(ncol=5,byrow=TRUE)) print(histogram_mz) dev.off() ## save msidata as imzML file, will only work if there is at least 1 m/z left if (maxfeatures2 > 0){ ## make sure that coordinates are integers coord(msidata)\$y = as.integer(coord(msidata)\$y) coord(msidata)\$x = as.integer(coord(msidata)\$x) writeImzML(msidata, "out")} }else{ print("Inputfile or filtered file has no intensities > 0") dev.off() } ]]></configfile> </configfiles> <inputs> <expand macro="reading_msidata"/> <conditional name="pixels_cond"> <param name="pixel_filtering" type="select" label="Select pixel filtering option"> <option value="none" selected="True">none</option> <option value="two_columns">coordinates from tabular file</option> <option value="pixel_range">ranges for x and y (manually)</option> </param> <when value="none"/> <when value="two_columns"> <param name="annotation_file" type="data" format="tabular" label="Tabular file with pixel coordinates" help="Tabular file with two columns: x values and y 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="tabular_header" type="boolean" label="Tabular file contains a header line" truevalue="TRUE" falsevalue="FALSE"/> </when> <when value="pixel_range"> <param name="min_x_range" type="integer" value="0" label="Minimum value for x"/> <param name="max_x_range" type="integer" value="100" label="Maximum value for x"/> <param name="min_y_range" type="integer" value="0" label="Minimum value for y"/> <param name="max_y_range" type="integer" value="100" label="Maximum value for y"/> </when> </conditional> <conditional name="features_cond"> <param name="features_filtering" type="select" label="Select m/z feature filtering option"> <option value="none" selected="True">none</option> <option value="features_list">keep m/z (tabular file)</option> <option value="features_range">m/z range (manually)</option> <option value="remove_features">remove m/z (tabular file)</option> </param> <when value="none"/> <when value="features_list"> <expand macro="reading_1_column_mz_tabular" label="Tabular file with m/z features to keep"/> </when> <when value="features_range"> <param name="min_mz" type="float" value="1" label="Minimum value for m/z"/> <param name="max_mz" type="float" value="100" label="Maximum value for m/z"/> </when> <when value="remove_features"> <expand macro="reading_1_column_mz_tabular" label="Tabular file with m/z features to remove"/> <param name="removal_plusminus" type="float" value="20" label="Window in which all m/z will be removed" help="This value is the half window size, it will be added and substracted from the given input value"/> <param name="units_removal" type="select" display="radio" optional ="False" label="units"> <option value="ppm" selected="True">ppm</option> <option value="Da">Da</option> </param> </when> </conditional> </inputs> <outputs> <data format="imzml" name="outfile_imzml" label="${tool.name} on ${on_string}: imzML"/> <data format="pdf" name="QC_overview" from_work_dir="filtertool_QC.pdf" label = "${tool.name} on ${on_string}: QC"/> </outputs> <tests> <test> <expand macro="infile_imzml"/> <param name="pixel_filtering" value="pixel_range"/> <param name="min_x_range" value="1"/> <param name="max_x_range" value="20"/> <param name="min_y_range" value="2"/> <param name="max_y_range" value="2"/> <param name="features_filtering" value="features_range"/> <param name="min_mz" value="350"/> <param name="max_mz" value="500"/> <output name="QC_overview" file="imzml_filtered3.pdf" compare="sim_size"/> <output name="outfile_imzml" ftype="imzml" file="out3.imzml.txt" compare="sim_size"> <extra_files type="file" file="out3.imzml" name="imzml" lines_diff="6"/> <extra_files type="file" file="out3.ibd" name="ibd" compare="sim_size"/> </output> </test> <test> <expand macro="infile_imzml"/> <param name="pixel_filtering" value="two_columns"/> <param name="annotation_file" ftype="tabular" value="inputpixels_2column.tabular"/> <param name="column_x" value="1"/> <param name="column_y" value="3"/> <output name="QC_overview" file="imzml_filtered4.pdf" compare="sim_size"/> <output name="outfile_imzml" ftype="imzml" file="out4.imzml.txt" compare="sim_size"> <extra_files type="file" file="out4.imzml" name="imzml" lines_diff="6"/> <extra_files type="file" file="out4.ibd" name="ibd" compare="sim_size"/> </output> </test> <test> <expand macro="infile_imzml"/> <param name="pixel_filtering" value="pixel_range"/> <param name="min_x_range" value="0"/> <param name="max_x_range" value="10"/> <param name="min_y_range" value="2"/> <param name="max_y_range" value="20"/> <param name="features_filtering" value="features_list"/> <param name="mz_tabular" ftype="tabular" value = "featuresofinterest5.tabular"/> <param name="feature_column" value="1"/> <param name="feature_header" value="0"/> <output name="QC_overview" file="imzml_filtered5.pdf" compare="sim_size"/> <output name="outfile_imzml" ftype="imzml" file="out5.imzml.txt" compare="sim_size"> <extra_files type="file" file="out5.imzml" name="imzml" lines_diff="6"/> <extra_files type="file" file="out5.ibd" name="ibd" compare="sim_size"/> </output> </test> <test> <expand macro="infile_analyze75"/> <output name="QC_overview" file="analyze75_filtered2.pdf" compare="sim_size"/> <output name="outfile_imzml" ftype="imzml" file="out6.imzml.txt" compare="sim_size"> <extra_files type="file" file="out6.imzml" name="imzml" lines_diff="6"/> <extra_files type="file" file="out6.ibd" name="ibd" compare="sim_size"/> </output> </test> <test> <param name="infile" value="preprocessed.RData" ftype="rdata"/> <output name="QC_overview" file="rdata_notfiltered.pdf" compare="sim_size" /> <output name="outfile_imzml" ftype="imzml" file="out7.imzml.txt" compare="sim_size"> <extra_files type="file" file="out7.imzml" name="imzml" lines_diff="6"/> <extra_files type="file" file="out7.ibd" name="ibd" compare="sim_size"/> </output> </test> <test> <expand macro="processed_infile_imzml"/> <conditional name="processed_cond"> <param name="processed_file" value="processed"/> <param name="accuracy" value="100"/> <param name="units" value="ppm"/> </conditional> <param name="pixel_filtering" value="two_columns"/> <param name="annotation_file" ftype="tabular" value="inputpixels_2column.tabular"/> <param name="column_x" value="1"/> <param name="column_y" value="3"/> <param name="features_filtering" value="remove_features"/> <param name="mz_tabular" ftype="tabular" value = "featuresofinterest5.tabular"/> <param name="feature_column" value="1"/> <param name="feature_header" value="0"/> <param name="removal_plusminus" value="100"/> <param name="units_removal" value="ppm"/> <output name="QC_overview" file="imzml_filtered8.pdf" compare="sim_size"/> <output name="outfile_imzml" ftype="imzml" file="out8.imzml.txt" compare="sim_size"> <extra_files type="file" file="out8.imzml" name="imzml" lines_diff="6"/> <extra_files type="file" file="out8.ibd" name="ibd" compare="sim_size"/> </output> </test> </tests> <help> <![CDATA[ @CARDINAL_DESCRIPTION@ ----- This tool provides options to filter (subset) pixels and m/z features of mass spectrometry imaging data. @MSIDATA_INPUT_DESCRIPTION@ - Optional file with pixel coordinates and annotation: - Tabular file: One column with x values, one column with y values - The file is allowed to have any column names as header (in this case set "Tabular file contains a header line" to yes) - Pixel with coordinates outside the coordinates of the input file are ignored :: x_coord y_coord 1 1 2 1 3 1 ... ... @MZ_TABULAR_INPUT_DESCRIPTION@ **Options** - Pixel filtering/annotation: - Either with a tabular file containing x and y coordinates or by entering x-min, x-max, y-min, y-max manually - Pixel that are not present in the dataset are ignored - An error occurs if the input for filtering (tabular file, x-range or y-range) contains not a single coordinate that occurs in the input dataset - m/z feature filtering: - Either with a tabular file containing m/z values or by entering m/z-min and m/z-max manually - m/z values that are not present in the dataset are ignored - An error occurs if the input for filtering (tabular file or mz-range) contains not a single m/z feature that occurs in the dataset - m/z feature removing: - Perturbing m/z features such as matrix contaminants, tryptic peptides and internal calibrants can be removed by specifying their m/z value in a tabular file, optionally with a half window size in ppm or m/z for the window in which peaks should be removed **Tips** - m/z feautre filtering with a tabular file: - For matching the m/z features of the input dataset are rounded to the number of decimal points of the m/z values from the tabular file. In case the input had more than 4 digits m/z values of dataset and tabular file are rounded to 4 digits. - Therefore, it is recommended to use the filtering tool only for m/z features which have been extracted from the same dataset. If the m/z values are from a different dataset, the tool 'Join two files on column allowing a small difference' should be used to find corresponding m/z values, which can then be used for filtering. - Problems to select tabular file: - In case tabular file cannot be selected in drop-down menu: Datatype in Galaxy might be different from 'tabular' - datatype can be changed by pressing the pen button of the dataset (edit attributes) **Output** - MSI data as (continuous) imzML file - pdf with heatmap showing the pixels that are removed and kept as well as histograms of kept and removed m/z ]]> </help> <expand macro="citations"/> </tool>