Mercurial > repos > galaxyp > msi_filtering
changeset 3:d51c3c814d57 draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_filtering commit a7be47698f53eb4f00961192327d93e8989276a7
| author | galaxyp |
|---|---|
| date | Mon, 11 Jun 2018 17:33:40 -0400 |
| parents | 22db5eb94e50 |
| children | bf61fc662615 |
| files | msi_filtering.xml test-data/Example_Continuous.ibd test-data/Example_Continuous.imzML test-data/analyze75_filtered2.pdf test-data/analyze_filtered.RData test-data/analyze_filtered.pdf test-data/analyze_filteredoutside.RData test-data/featuresofinterest5.tabular test-data/imzml_filtered.RData test-data/imzml_filtered.pdf test-data/imzml_filtered2.RData test-data/imzml_filtered2.pdf test-data/imzml_filtered3.RData test-data/imzml_filtered3.pdf test-data/imzml_filtered4.RData test-data/imzml_filtered4.pdf test-data/imzml_filtered5.RData test-data/imzml_filtered5.pdf test-data/imzml_matrix3.tabular |
| diffstat | 19 files changed, 541 insertions(+), 4852 deletions(-) [+] |
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--- a/msi_filtering.xml Mon May 28 12:36:24 2018 -0400 +++ b/msi_filtering.xml Mon Jun 11 17:33:40 2018 -0400 @@ -1,4 +1,4 @@ -<tool id="mass_spectrometry_imaging_filtering" name="MSI filtering" version="1.10.0.0"> +<tool id="mass_spectrometry_imaging_filtering" name="MSI filtering" version="1.10.0.1"> <description>tool for filtering mass spectrometry imaging data</description> <requirements> <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement> @@ -41,12 +41,13 @@ load('infile.RData') #end if -##################################### QC: inputfile properties in numbers ###### +########################### optional QC numbers ######################## #if $outputs.outputs_select == "quality_control": - ## Number of features (mz) + + ## Number of features (m/z) maxfeatures = length(features(msidata)) - ## Range mz + ## Range m/z minmz = round(min(mz(msidata)), digits=2) maxmz = round(max(mz(msidata)), digits=2) ## Number of spectra (pixels) @@ -59,7 +60,7 @@ maximumy = max(coord(msidata)[,2]) ## Number of intensities > 0 npeaks= sum(spectra(msidata)[]>0) - ## Spectra multiplied with mz (potential number of peaks) + ## Spectra multiplied with m/z (potential number of peaks) numpeaks = ncol(spectra(msidata)[])*nrow(spectra(msidata)[]) ## Percentage of intensities > 0 percpeaks = round(npeaks/numpeaks*100, digits=2) @@ -70,66 +71,55 @@ medint = round(median(TICs), digits=2) ## Store features for QC plot featuresinfile = mz(msidata) + #end if +###################################### Filtering of pixels ##################### +################################################################################ -###################################### Filtering of pixels ##################### - -### Pixels in the one column format "x=,y=" +#################### Pixels in the one column format "x=,y=" ##################### #if str($pixels_cond.pixel_filtering) == "single_column": print("single column") - input_list = read.delim("$pixels_cond.single_pixels", header = FALSE, stringsAsFactors = FALSE) - numberpixels = length(input_list[,$pixels_cond.pixel_column]) - valid_entries = input_list[,$pixels_cond.pixel_column] %in% names(pixels(msidata)) - validpixels = sum(valid_entries) + input_list = read.delim("$pixels_cond.single_pixels", header = FALSE, stringsAsFactors = FALSE) + numberpixels = length(input_list[,$pixels_cond.pixel_column]) + valid_entries = input_list[,$pixels_cond.pixel_column] %in% names(pixels(msidata)) + validpixels = sum(valid_entries) - if (validpixels != 0) - { - pixelsofinterest = pixels(msidata)[names(pixels(msidata)) %in% input_list[valid_entries,$pixels_cond.pixel_column]] - msidata = msidata[,pixelsofinterest] - }else{ - msidata = msidata[,0] - validpixels=0 - } + if (validpixels != 0){ + pixelsofinterest = pixels(msidata)[names(pixels(msidata)) %in% input_list[valid_entries,$pixels_cond.pixel_column]] + msidata = msidata[,pixelsofinterest] + }else{ + msidata = msidata[,0] + validpixels=0} - -### Pixels in two columns format: x and y in different columns +############ Pixels in two columns format: x and y in different columns ############# #elif str($pixels_cond.pixel_filtering) == "two_columns": print("two columns") - input_list = read.delim("$pixels_cond.two_columns_pixel", header = FALSE, - stringsAsFactors = FALSE) - numberpixels = length(input_list[,$pixels_cond.pixel_column_x]) - - inputpixel_x = input_list[,$pixels_cond.pixel_column_x] - inputpixel_y = input_list[,$pixels_cond.pixel_column_y] + input_list = read.delim("$pixels_cond.two_columns_pixel", header = FALSE, + stringsAsFactors = FALSE) + numberpixels = length(input_list[,$pixels_cond.pixel_column_x]) - inputpixels = cbind(inputpixel_x, inputpixel_y) - colnames(inputpixels) = c("x", "y") - valid_rows = merge(inputpixels, coord(msidata)[,1:2]) - validpixels = nrow(valid_rows) - - if (validpixels != 0) - { + inputpixel_x = input_list[,$pixels_cond.pixel_column_x] + inputpixel_y = input_list[,$pixels_cond.pixel_column_y] + inputpixels = cbind(inputpixel_x, inputpixel_y) + colnames(inputpixels) = c("x", "y") + valid_rows = merge(inputpixels, coord(msidata)[,1:2]) + validpixels = nrow(valid_rows) - pixelvector = character() - - for (pixel in 1:nrow(valid_rows)) - { - pixelvector[pixel] = paste0("x = ", valid_rows[pixel,1],", ", "y = ", valid_rows[pixel,2]) - } + if (validpixels != 0){ + pixelvector = character() + for (pixel in 1:nrow(valid_rows)){ + pixelvector[pixel] = paste0("x = ", valid_rows[pixel,1],", ", "y = ", valid_rows[pixel,2])} + pixelsofinterest= pixels(msidata)[names(pixels(msidata)) %in% pixelvector] + msidata = msidata[,pixelsofinterest] + }else{ + validpixels=0} - pixelsofinterest= pixels(msidata)[names(pixels(msidata)) %in% pixelvector] - msidata = msidata[,pixelsofinterest] - }else{ - validpixels=0 - } - - -### Pixels wihin x and y minima and maxima are kept: +########### Pixels wihin x and y minima and maxima are kept ################### #elif str($pixels_cond.pixel_filtering) == "pixel_range": print("pixel range") @@ -139,69 +129,60 @@ ## only filter pixels if at least one pixel will be left - if (sum(coord(msidata)\$x <= $pixels_cond.max_x_range & coord(msidata)\$x >= $pixels_cond.min_x_range) > 0 & sum(coord(msidata)\$y <= $pixels_cond.max_y_range & coord(msidata)\$y >= $pixels_cond.min_y_range) > 0) - { + if (sum(coord(msidata)\$x <= $pixels_cond.max_x_range & coord(msidata)\$x >= $pixels_cond.min_x_range) > 0 & sum(coord(msidata)\$y <= $pixels_cond.max_y_range & coord(msidata)\$y >= $pixels_cond.min_y_range) > 0){ 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 >= $pixels_cond.min_y_range] - }else{ + }else{ msidata = msidata[,0] - print("no valid pixel found") - } - - + print("no valid pixel found")} #elif str($pixels_cond.pixel_filtering) == "none": print("no pixel filtering") + numberpixels = 0 validpixels = 0 #end if +###################################### filtering of features ###################### +################################################################################## -###################################### filtering of features ###################### - -### Tabular file contains mz either as numbers or in the format mz=800.01 +######################## Keep m/z from tabular file ######################### #if str($features_cond.features_filtering) == "features_list": print("feature list") input_features = read.delim("$inputfeatures", header = FALSE, stringsAsFactors = FALSE) - startingrow = $features_cond.feature_header+1 extracted_features = input_features[startingrow:nrow(input_features),$features_cond.feature_column] numberfeatures = length(extracted_features) - if (grepl("m/z = ", input_features[startingrow,$features_cond.feature_column])==FALSE) + if (grepl("m/z = ", input_features[startingrow,$features_cond.feature_column])==FALSE){ ### if input is in numeric format - { - - if (class(extracted_features) == "numeric") - { - charactervector = rep("m/z = ", numberfeatures) - mz_added = paste0(charactervector, round(extracted_features,digits=2)) - validfeatures = mz_added %in% names(features(msidata)) - featuresofinterest = features(msidata)[names(features(msidata)) %in% mz_added[validfeatures]] - validmz = sum(validfeatures) + if (class(extracted_features) == "numeric"){ + ### max digits given in the input file will be used to match m/z + max_digits = max(nchar(matrix(unlist(strsplit(as.character(extracted_features), "\\.")), ncol=2, byrow=TRUE)[,2])) + validfeatures = extracted_features %in% round(mz(msidata),max_digits) + featuresofinterest = features(msidata)[round(mz(msidata), digits = max_digits) %in% extracted_features[validfeatures]] + validmz = length(unique(featuresofinterest)) }else{ - validmz = 0 - featuresofinterest = 0 - } + validmz = 0 + featuresofinterest = 0} ### if input is already in character format (m/z = 800.01) - }else{ + }else{ validfeatures = extracted_features %in% names(features(msidata)) featuresofinterest = features(msidata)[names(features(msidata)) %in% extracted_features[validfeatures]] - validmz = sum(validfeatures) - } + validmz = sum(validfeatures)} ### filter msidata for valid features + msidata = msidata[featuresofinterest,] - -### Only features within a given minimum and maximum value are kept: +############### features within a given range are kept ######################### #elif str($features_cond.features_filtering) == "features_range": print("feature range") @@ -209,146 +190,168 @@ numberfeatures = "range" validmz = "range" - if (sum(mz(msidata) >= $features_cond.min_mz & mz(msidata) <= $features_cond.max_mz)> 0) - { - msidata = msidata[mz(msidata) >= $features_cond.min_mz & mz(msidata) <= $features_cond.max_mz,] + if (sum(mz(msidata) >= $features_cond.min_mz & mz(msidata) <= $features_cond.max_mz)> 0){ + msidata = msidata[mz(msidata) >= $features_cond.min_mz & mz(msidata) <= $features_cond.max_mz,] }else{ msidata = msidata[0,] - print("no valid mz range") + print("no valid mz range")} + +############### Remove m/z from tabular file ######################### + +#elif str($features_cond.features_filtering) == "remove_features": + print("remove features") + +### Tabular file contains mz either as numbers or in the format mz = 800.01 + + input_features = read.delim("$inputfeatures_removal", header = FALSE, stringsAsFactors = FALSE) + startingrow = $features_cond.removal_header+1 + extracted_features = input_features[startingrow:nrow(input_features),$features_cond.removal_column] + numberfeatures = length(extracted_features) + + if (grepl("m/z = ", input_features[startingrow,$features_cond.removal_column])==TRUE){ + +### if input is mz = 800 character format + print("input is in format mz = 400") + validfeatures = extracted_features %in% names(features(msidata)) + validmz = sum(validfeatures) + filtered_features = features(msidata)[names(features(msidata)) %in% extracted_features[validfeatures]] + featuresofinterest = mz(msidata)[filtered_features] + +### if input is numeric: + }else{ + if (class(extracted_features) == "numeric"){ + print("input is numeric") + featuresofinterest = extracted_features + validmz = sum(featuresofinterest <= max(mz(msidata))& featuresofinterest >= min(mz(msidata))) + }else{featuresofinterest = 0 + validmz = 0} } +### Here starts removal of features: + + plusminus = $features_cond.removal_plusminus + + mass_to_remove = numeric() + if (sum(featuresofinterest) > 0){ + for (masses in featuresofinterest){ + #if str($features_cond.units_removal) == "ppm": + plusminus = masses * $features_cond.removal_plusminus/1000000 + #end if + current_mass = which(c(mz(msidata) <= masses + plusminus & mz(msidata) >= masses - plusminus)) + mass_to_remove = append(mass_to_remove, current_mass)} + msidata= msidata[-mass_to_remove, ] + }else{print("No features were removed as they were not fitting to m/z values and/or range")} + #elif str($features_cond.features_filtering) == "none": print("no feature filtering") validmz = 0 numberfeatures = 0 + #end if - - -# save msidata as Rfile +## save msidata as Rfile save(msidata, file="$msidata_filtered") -###################################### outputfile properties in numbers ######## +#################### optional QC numbers ####################### #if $outputs.outputs_select == "quality_control": -## Number of features (mz) -maxfeatures2 = length(features(msidata)) -## Range mz -minmz2 = round(min(mz(msidata)), digits=2) -maxmz2 = round(max(mz(msidata)), digits=2) -## Number of spectra (pixels) -pixelcount2 = length(pixels(msidata)) -## Range x coordinates -minimumx2 = min(coord(msidata)[,1]) -maximumx2 = max(coord(msidata)[,1]) -## Range y coordinates -minimumy2 = min(coord(msidata)[,2]) -maximumy2 = max(coord(msidata)[,2]) -## Number of intensities > 0 -npeaks2= sum(spectra(msidata)[]>0) -## Spectra multiplied with mz (potential number of peaks) -numpeaks2 = ncol(spectra(msidata)[])*nrow(spectra(msidata)[]) -## Percentage of intensities > 0 -percpeaks2 = round(npeaks2/numpeaks2*100, digits=2) -## Number of empty TICs -TICs2 = colSums(spectra(msidata)[]) -NumemptyTIC2 = sum(TICs2 == 0) -## median TIC -medint2 = round(median(TICs2), digits=2) - + ## Number of features (m/z) + maxfeatures2 = length(features(msidata)) + ## Range m/z + minmz2 = round(min(mz(msidata)), digits=2) + maxmz2 = round(max(mz(msidata)), digits=2) + ## Number of spectra (pixels) + pixelcount2 = length(pixels(msidata)) + ## Range x coordinates + minimumx2 = min(coord(msidata)[,1]) + maximumx2 = max(coord(msidata)[,1]) + ## Range y coordinates + minimumy2 = min(coord(msidata)[,2]) + maximumy2 = max(coord(msidata)[,2]) + ## Number of intensities > 0 + npeaks2= sum(spectra(msidata)[]>0) + ## Spectra multiplied with m/z (potential number of peaks) + numpeaks2 = ncol(spectra(msidata)[])*nrow(spectra(msidata)[]) + ## Percentage of intensities > 0 + percpeaks2 = round(npeaks2/numpeaks2*100, digits=2) + ## Number of empty TICs + TICs2 = colSums(spectra(msidata)[]) + NumemptyTIC2 = sum(TICs2 == 0) + ## median TIC + medint2 = round(median(TICs2), digits=2) -properties = c("Number of mz features", - "Range of mz values [Da]", - "Number of pixels", - "Range of x coordinates", - "Range of y coordinates", - "Intensities > 0", - "Median TIC per pixel", - "Number of zero TICs", - "pixel overview", - "feature overview") + properties = c("Number of m/z features", + "Range of m/z values [Da]", + "Number of pixels", + "Range of x coordinates", + "Range of y coordinates", + "Intensities > 0", + "Median TIC per pixel", + "Number of zero TICs", + "pixel overview", + "feature overview") -before = c(paste0(maxfeatures), - paste0(minmz, " - ", maxmz), - paste0(pixelcount), - paste0(minimumx, " - ", maximumx), - paste0(minimumy, " - ", maximumy), - paste0(percpeaks, " %"), - paste0(medint), - paste0(NumemptyTIC), - paste0("input pixels: ", numberpixels), - paste0("input mz: ", numberfeatures)) + before = c(paste0(maxfeatures), + paste0(minmz, " - ", maxmz), + paste0(pixelcount), + paste0(minimumx, " - ", maximumx), + paste0(minimumy, " - ", maximumy), + paste0(percpeaks, " %"), + paste0(medint), + paste0(NumemptyTIC), + paste0("input pixels: ", numberpixels), + paste0("input mz: ", numberfeatures)) -filtered = c(paste0(maxfeatures2), - paste0(minmz2, " - ", maxmz2), - paste0(pixelcount2), - paste0(minimumx2, " - ", maximumx2), - paste0(minimumy2, " - ", maximumy2), - paste0(percpeaks2, " %"), - paste0(medint2), - paste0(NumemptyTIC2), - paste0("valid pixels: ", validpixels), - paste0("valid mz: ", validmz)) + filtered = c(paste0(maxfeatures2), + paste0(minmz2, " - ", maxmz2), + paste0(pixelcount2), + paste0(minimumx2, " - ", maximumx2), + paste0(minimumy2, " - ", maximumy2), + paste0(percpeaks2, " %"), + paste0(medint2), + paste0(NumemptyTIC2), + paste0("valid pixels: ", validpixels), + paste0("valid mz: ", validmz)) - -property_df = data.frame(properties, before, filtered) + property_df = data.frame(properties, before, filtered) - - -######################################## PDF QC ################################ +############################### optional PDF QC ################################ 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.display_name")) - - - grid.table(property_df, rows= NULL) ### heatmap image as visual pixel control - -if (length(features(msidata))> 0 & length(pixels(msidata)) > 0) -{ - - - image(msidata, mz=$outputs.inputmz, plusminus = $outputs.plusminus_dalton, contrast.enhance = "none", + if (length(features(msidata))> 0 & length(pixels(msidata)) > 0){ + image(msidata, mz=$outputs.inputmz, plusminus = $outputs.plusminus_dalton, contrast.enhance = "none", main= paste0($outputs.inputmz," ± ", $outputs.plusminus_dalton, " Da"), ylim = c(maximumy2+0.2*maximumy2,minimumy2-0.2*minimumy2)) - ### control features which are left - - plot(featuresinfile, rep(1,length(featuresinfile)), yaxt="n", ylab=NA, xlab="m/z values", col="red", ylim=c(0.8, 1.1), main="Distribution of m/z values") - lines(mz(msidata),rep(0.9, length(mz(msidata))), col="green", type="p") - legend("top", horiz=TRUE, - legend = c("before", "filtered"), - fill = c("red", "green")) - -}else{ - print("file has no features or pixels left") -} + ### control features which are left + plot(featuresinfile, rep(1,length(featuresinfile)), yaxt="n", ylab=NA, xlab="m/z values", col="red", ylim=c(0.8, 1.1), main="Distribution of m/z values") + lines(mz(msidata),rep(0.9, length(mz(msidata))), col="green", type="p") + legend("top", horiz=TRUE, legend = c("before", "filtered"), fill = c("red", "green")) + }else{ + print("file has no features or pixels left")} dev.off() #end if -######################################## intensity matrix ###################### +############################### optional intensity matrix ###################### #if $output_matrix: -if (length(features(msidata))> 0 & length(pixels(msidata)) > 0) -{ - +if (length(features(msidata))> 0 & length(pixels(msidata)) > 0){ spectramatrix = spectra(msidata) rownames(spectramatrix) = mz(msidata) newmatrix = rbind(pixels(msidata), spectramatrix) write.table(newmatrix[2:nrow(newmatrix),], file="$matrixasoutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") - }else{ - print("file has no features or pixels left") -} + print("file has no features or pixels left")} #end if @@ -356,7 +359,7 @@ ]]></configfile> </configfiles> <inputs> - <param name="infile" type="data" format="imzml, rdata, analyze75" + <param name="infile" type="data" format="imzml,rdata,analyze75" label="Inputfile as imzML, Analyze7.5 or Cardinal MSImageSet saved as RData" help="Upload composite datatype imzML (ibd+imzML) or analyze75 (hdr+img+t2m) or regular upload .RData (Cardinal MSImageSet)"/> <conditional name="pixels_cond"> @@ -388,18 +391,29 @@ <conditional name="features_cond"> <param name="features_filtering" type="select" label="Select feature filtering option"> <option value="none" selected="True">none</option> - <option value="features_list">tabular file with features (data type: 800.12 or m/z = 800.12)</option> - <option value="features_range">range of features</option> + <option value="features_list">keep features (tabular input)</option> + <option value="features_range">keep features within a range (manual input)</option> + <option value="remove_features">remove features (tabular input)</option> </param> <when value="none"/> <when value="features_list"> - <param name="inputfeatures" type="data" format="tabular" label="Features for filtering of MSI data" help="tabular file with masses of interest either as numbers (800.05) or in the form m/z = 800.05"/> + <param name="inputfeatures" type="data" format="tabular" label="Features for filtering of MSI data" help="tabular file with m/z of interest either as numbers (800.05) or in the form m/z = 800.05"/> <param name="feature_column" data_ref="inputfeatures" label="Column with features" type="data_column"/> <param name="feature_header" label="Number of header lines to skip" value="0" type="integer"/> </when> <when value="features_range"> - <param name="min_mz" type="float" value="1" label="Minimum value for mz (in Dalton)"/> - <param name="max_mz" type="float" value="100" label="Maximum value for mz (in Dalton)"/> + <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"> + <param name="inputfeatures_removal" type="data" format="tabular" label="Features for filtering of MSI data" help="tabular file with m/z to be removed either as numbers (800.05) or in the form m/z = 800.05"/> + <param name="removal_column" data_ref="inputfeatures_removal" label="Column with features" type="data_column"/> + <param name="removal_header" label="Number of header lines to skip" value="0" type="integer"/> + <param name="removal_plusminus" type="float" value="20" label="Window in which m/z will be removed" help="This value 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> <conditional name="outputs"> @@ -408,19 +422,19 @@ <option value="no_quality_control">no</option> </param> <when value="quality_control"> - <param name="inputmz" type="float" value="1296.7" label="Mass for which a heatmap image will be drawn" help="Use a mass which is still present in all pixels to control if the pixel filtering went well"/> - <param name="plusminus_dalton" value="0.25" type="float" label="mass range for mz value" help="plusminus mass window in Dalton"/> + <param name="inputmz" type="float" value="1296.7" label="M/z for which a heatmap image will be drawn" help="Use a m/z which is still present in all pixels to control if the pixel filtering went well"/> + <param name="plusminus_dalton" value="0.25" type="float" label="Range for m/z value" help="plusminus m/z window"/> </when> <when value="no_quality_control"/> </conditional> <param name="output_matrix" type="boolean" display="radio" label="Intensity matrix output"/> </inputs> <outputs> - <data format="rdata" name="msidata_filtered" label="${tool.name} ${on_string}"/> - <data format="pdf" name="filtering_qc" from_work_dir="filtertool_QC.pdf" label = "QC ${tool.name} ${on_string}"> + <data format="rdata" name="msidata_filtered" label="$infile.display_name filtered"/> + <data format="pdf" name="filtering_qc" from_work_dir="filtertool_QC.pdf" label = "$infile.display_name filtered_QC"> <filter>outputs["outputs_select"] == "quality_control"</filter> </data> - <data format="tabular" name="matrixasoutput" label="Matrix ${tool.name} ${on_string}"> + <data format="tabular" name="matrixasoutput" label="$infile.display_name filtered_matrix"> <filter>output_matrix</filter> </data> </outputs> @@ -470,7 +484,7 @@ <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="0" /> + <param name="min_mz" value="350" /> <param name="max_mz" value="500"/> <param name="outputs_select" value="quality_control"/> <param name="inputmz" value="328.9"/> @@ -509,9 +523,10 @@ <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_range"/> - <param name="min_mz" value="500" /> - <param name="max_mz" value="700"/> + <param name="features_filtering" value="features_list"/> + <param name="inputfeatures" ftype="tabular" value = "featuresofinterest5.tabular"/> + <param name="feature_column" value="1"/> + <param name="feature_header" value="0"/> <param name="outputs_select" value="quality_control"/> <param name="inputmz" value="328.9"/> <param name="plusminus_dalton" value="0.25"/> @@ -569,7 +584,7 @@ Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets. `More information on Cardinal <http://cardinalmsi.org//>`_ -This tool provides provides options to filter (subset) pixels and masses of mass-spectrometry imaging data. +This tool provides provides options to filter (subset) pixels and m/z features of mass spectrometry imaging data. Input data: 3 types of input data can be used: @@ -577,20 +592,24 @@ - Analyze7.5 (upload hdr, img and t2m file via the "composite" function) - Cardinal "MSImageSet" data (with variable name "msidata", saved as .RData) + Options: - pixel filtering: can use a tabular file containing x and y coordinates or by defining a range for x and y by hand -- mass filtering: can use a tabular file containing masses of interest or by defining a range for the mass values +- m/z feature filtering: can use a tabular file containing m/z of interest or by defining a range for the m/z values (! numeric input will be rounded to 2 digits before matching to m/z!) +- m/z feature removing: infering m/z such as matrix contaminants can be removed by specifying their m/z in a tabular file and optionally set a window (window in ppm or Da in which peaks should be removed) + Output: -- imzML file filtered for pixels and/or masses -- optional: pdf with heatmap showing the pixels that are left after filtering and plot of masses before and after filtering -- optional: intensity matrix as tabular file (intensities for masses in rows and pixel in columns) +- imzML file filtered for pixels and/or m/z +- optional: pdf with heatmap showing the pixels that are left after filtering and plot of m/z before and after filtering +- optional: intensity matrix as tabular file (intensities for m/z in rows and pixel in columns) + Tip: -- It is recommended to use the filtering tool only for masses which have been extracted from the same dataset. If you have masses from dataset A and you want to use them to filter dataset B, first find the corresponding (closest) features in dataset B by using the tool "Join two files on column allowing a small difference". Afterwards use the corresponding feature masses from dataset A to filter dataset B. +- It is recommended to use the filtering tool only for m/z which have been extracted from the same dataset. If you have m/z from dataset A and you want to use them to filter dataset B, first find the corresponding (closest) features in dataset B by using the tool "Join two files on column allowing a small difference". Afterwards use the corresponding feature m/z from dataset A to filter dataset B. ]]>
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