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comparison preprocessing.xml @ 0:01d12e7e48d3 draft

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planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415
author galaxyp
date Mon, 01 Oct 2018 01:01:04 -0400
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children c8ab1b6f3834
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1 <tool id="cardinal_preprocessing" name="MSI preprocessing" version="@VERSION@.0">
2 <description>
3 mass spectrometry imaging preprocessing
4 </description>
5 <macros>
6 <import>macros.xml</import>
7 </macros>
8 <expand macro="requirements">
9 <requirement type="package" version="2.2.1">r-gridextra</requirement>
10 <requirement type="package" version="0.20-35">r-lattice</requirement>
11 <requirement type="package" version="2.2.1">r-ggplot2</requirement>
12 </expand>
13 <command detect_errors="exit_code">
14 <![CDATA[
15
16 @INPUT_LINKING@
17 cat '${cardinal_preprocessing}' &&
18 Rscript '${cardinal_preprocessing}'
19
20 ]]>
21 </command>
22 <configfiles>
23 <configfile name="cardinal_preprocessing"><![CDATA[
24
25 ################################# load libraries and read file #################
26
27 library(Cardinal)
28 library(gridExtra)
29 library(lattice)
30 library(ggplot2)
31
32 @READING_MSIDATA@
33
34 print(paste0("Number of NA in input file: ",sum(is.na(spectra(msidata)[]))))
35
36
37 if (sum(spectra(msidata)[]>0, na.rm=TRUE)> 0){
38 ######################### preparations for QC report #################
39
40 maxfeatures = length(features(msidata))
41 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
42 medint = round(median(spectra(msidata)[],na.rm=TRUE), digits=2)
43 minmz = round(min(mz(msidata)), digits=2)
44 maxmz = round(max(mz(msidata)), digits=2)
45 QC_numbers= data.frame(inputdata = c(minmz, maxmz,maxfeatures, medianpeaks, medint))
46 vectorofactions = "inputdata"
47
48 ############################### Preprocessing steps ###########################
49 ###############################################################################
50
51 #for $method in $methods:
52
53 ############################### Normalization ###########################
54
55 #if str( $method.methods_conditional.preprocessing_method ) == 'Normalization':
56 print('Normalization')
57 ##normalization
58
59 msidata = normalize(msidata, method="tic")
60
61 ############################### QC ###########################
62
63 maxfeatures = length(features(msidata))
64 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE),)
65 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
66 minmz = round(min(mz(msidata)), digits=2)
67 maxmz = round(max(mz(msidata)), digits=2)
68 normalized = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
69 QC_numbers= cbind(QC_numbers, normalized)
70 vectorofactions = append(vectorofactions, "normalized")
71
72 ############################### Baseline reduction ###########################
73
74 #elif str( $method.methods_conditional.preprocessing_method ) == 'Baseline_reduction':
75 print('Baseline_reduction')
76 ##baseline reduction
77
78 msidata = reduceBaseline(msidata, method="median", blocks=$method.methods_conditional.blocks_baseline, spar=$method.methods_conditional.spar_baseline)
79
80 ############################### QC ###########################
81
82 maxfeatures = length(features(msidata))
83 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
84 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
85 minmz = round(min(mz(msidata)), digits=2)
86 maxmz = round(max(mz(msidata)), digits=2)
87 baseline = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
88 QC_numbers= cbind(QC_numbers, baseline)
89 vectorofactions = append(vectorofactions, "baseline red.")
90
91 ############################### Smoothing ###########################
92
93 #elif str( $method.methods_conditional.preprocessing_method ) == 'Smoothing':
94 print('Smoothing')
95 ## Smoothing
96
97 #if str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'gaussian':
98 print('gaussian smoothing')
99
100 msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, sd = $method.methods_conditional.methods_for_smoothing.sd_gaussian)
101
102 #elif str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'sgolay':
103 print('sgolay smoothing')
104
105 msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, order = $method.methods_conditional.methods_for_smoothing.order_of_filters)
106 #elif str($method.methods_conditional.methods_for_smoothing.smoothing_method) == 'ma':
107 print('moving average smoothing')
108
109 msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, coef = $method.methods_conditional.methods_for_smoothing.coefficients_ma_filter)
110
111 #end if
112
113 ############################### QC ###########################
114
115 maxfeatures = length(features(msidata))
116 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
117 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
118 minmz = round(min(mz(msidata)), digits=2)
119 maxmz = round(max(mz(msidata)), digits=2)
120 smoothed = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
121 QC_numbers= cbind(QC_numbers, smoothed)
122 vectorofactions = append(vectorofactions, "smoothed")
123
124 ############################### Peak picking ###########################
125
126 #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_picking':
127 print('Peak_picking')
128 ## Peakpicking
129
130 ## remove duplicated coordinates, otherwise peak picking will fail
131 print(paste0(sum(duplicated(coord(msidata))), " duplicated coordinates were removed"))
132 msidata <- msidata[,!duplicated(coord(msidata))]
133
134 #if str( $method.methods_conditional.methods_for_picking.picking_method) == 'adaptive':
135 print('adaptive peakpicking')
136
137 msidata = peakPick(msidata, window = $method.methods_conditional.window_picking, blocks = $method.methods_conditional.blocks_picking, method='$method.methods_conditional.methods_for_picking.picking_method', SNR=$method.methods_conditional.SNR_picking_method, spar=$method.methods_conditional.methods_for_picking.spar_picking)
138
139 #elif str( $method.methods_conditional.methods_for_picking.picking_method) == 'limpic':
140 print('limpic peakpicking')
141
142 msidata = peakPick(msidata, window = $method.methods_conditional.window_picking, blocks = $method.methods_conditional.blocks_picking, method='$method.methods_conditional.methods_for_picking.picking_method', SNR=$method.methods_conditional.SNR_picking_method, thresh=$method.methods_conditional.methods_for_picking.tresh_picking)
143
144 #elif str( $method.methods_conditional.methods_for_picking.picking_method) == 'simple':
145 print('simple peakpicking')
146
147 msidata = peakPick(msidata, window = $method.methods_conditional.window_picking, blocks = $method.methods_conditional.blocks_picking, method='$method.methods_conditional.methods_for_picking.picking_method', SNR=$method.methods_conditional.SNR_picking_method)
148
149 #end if
150
151 ############################### QC ###########################
152
153 maxfeatures = length(features(msidata))
154 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
155 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
156 minmz = round(min(mz(msidata)), digits=2)
157 maxmz = round(max(mz(msidata)), digits=2)
158 picked = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
159 QC_numbers= cbind(QC_numbers, picked)
160 vectorofactions = append(vectorofactions, "picked")
161
162 ############################### Peak alignment ###########################
163
164 #elif str( $method.methods_conditional.preprocessing_method ) == 'Peak_alignment':
165 print('Peak_alignment')
166 ## Peakalignment
167
168 #if str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_noref':
169
170 align_peak_reference = msidata
171
172 #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_table':
173
174 align_reference_table = read.delim("$method.methods_conditional.align_ref_type.mz_tabular", header = $method.methods_conditional.align_ref_type.align_mass_header, stringsAsFactors = FALSE)
175 align_reference_column = align_reference_table[,$method.methods_conditional.align_ref_type.align_mass_column]
176 align_peak_reference = align_reference_column[align_reference_column>=min(mz(msidata)) & align_reference_column<=max(mz(msidata))]
177 if (length(align_peak_reference) == 0)
178 {align_peak_reference = 0}
179
180 #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_msidata_ref':
181
182 align_peak_reference = loadRData('$method.methods_conditional.align_ref_type.align_peaks_msidata')
183
184 #end if
185
186 #if str( $method.methods_conditional.methods_for_alignment.alignment_method) == 'diff':
187 print('diff peakalignment')
188
189 msidata = peakAlign(msidata, method='$method.methods_conditional.methods_for_alignment.alignment_method',diff.max =$method.methods_conditional.methods_for_alignment.value_diffalignment, units = "$method.methods_conditional.methods_for_alignment.units_diffalignment", ref=align_peak_reference)
190
191 #elif str( $method.methods_conditional.methods_for_alignment.alignment_method) == 'DP':
192 print('DPpeakalignment')
193
194 msidata = peakAlign(msidata, method='$method.methods_conditional.methods_for_alignment.alignment_method',gap = $method.methods_conditional.methods_for_alignment.gap_DPalignment, ref=align_peak_reference)
195
196 #end if
197
198 ############################### QC ###########################
199
200 maxfeatures = length(features(msidata))
201 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
202 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
203 minmz = round(min(mz(msidata)), digits=2)
204 maxmz = round(max(mz(msidata)), digits=2)
205 aligned = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
206 QC_numbers= cbind(QC_numbers, aligned)
207 vectorofactions = append(vectorofactions, "aligned")
208
209 ############################### Peak filtering ###########################
210
211 #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_filtering':
212 print('Peak_filtering')
213
214 msidata = peakFilter(msidata, method='freq', freq.min = $method.methods_conditional.frequ_filtering)
215
216 ############################### QC ###########################
217
218 maxfeatures = length(features(msidata))
219 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
220 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
221 minmz = round(min(mz(msidata)), digits=2)
222 maxmz = round(max(mz(msidata)), digits=2)
223 filtered = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
224 QC_numbers= cbind(QC_numbers, filtered)
225 vectorofactions = append(vectorofactions, "filtered")
226
227 ############################### Data reduction ###########################
228
229 #elif str( $method.methods_conditional.preprocessing_method) == 'Data_reduction':
230 print('Data_reduction')
231
232 #if str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'bin':
233 print('bin reduction')
234
235 msidata = reduceDimension(msidata, method="bin", width=$method.methods_conditional.methods_for_reduction.bin_width, units="$method.methods_conditional.methods_for_reduction.bin_units", fun=$method.methods_conditional.methods_for_reduction.bin_fun)
236
237 #elif str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'resample':
238 print('resample reduction')
239
240 msidata = reduceDimension(msidata, method="resample", step=$method.methods_conditional.methods_for_reduction.resample_step)
241
242 #elif str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'peaks':
243 print('peaks reduction')
244
245 #if str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'table':
246
247 reference_table = read.delim("$method.methods_conditional.methods_for_reduction.ref_type.mz_tabular", header = $method.methods_conditional.methods_for_reduction.ref_type.mass_header, stringsAsFactors = FALSE)
248 reference_column = reference_table[,$method.methods_conditional.methods_for_reduction.ref_type.mass_column]
249 peak_reference = reference_column[reference_column>min(mz(msidata)) & reference_column<max(mz(msidata))]
250
251 #elif str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'msidata_ref':
252
253 peak_reference = loadRData('$method.methods_conditional.methods_for_reduction.ref_type.peaks_msidata')
254
255 #end if
256
257 msidata = reduceDimension(msidata, method="peaks", ref=peak_reference, type="$method.methods_conditional.methods_for_reduction.peaks_type")
258 #end if
259 ############################### QC ###########################
260
261 maxfeatures = length(features(msidata))
262 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
263 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
264 minmz = round(min(mz(msidata)), digits=2)
265 maxmz = round(max(mz(msidata)), digits=2)
266 reduced = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
267 QC_numbers= cbind(QC_numbers, reduced)
268 vectorofactions = append(vectorofactions, "reduced")
269
270 ############################### Transformation ###########################
271
272 #elif str( $method.methods_conditional.preprocessing_method) == 'Transformation':
273 print('Transformation')
274
275 #if str( $method.methods_conditional.transf_conditional.trans_type) == 'log2':
276 print('log2 transformation')
277
278 spectra(msidata)[][spectra(msidata)[] ==0] = NA
279 print(paste0("Number of 0 which were converted into NA:",sum(is.na(spectra(msidata)[]))))
280 spectra(msidata)[] = log2(spectra(msidata)[])
281
282 #elif str( $method.methods_conditional.transf_conditional.trans_type) == 'sqrt':
283 print('squareroot transformation')
284
285 spectra(msidata)[] = sqrt(spectra(msidata)[])
286
287 #end if
288
289 ############################### QC ###########################
290
291 maxfeatures = length(features(msidata))
292 medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))
293 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
294 minmz = round(min(mz(msidata)), digits=2)
295 maxmz = round(max(mz(msidata)), digits=2)
296 transformed = c(minmz, maxmz,maxfeatures, medianpeaks, medint)
297 QC_numbers= cbind(QC_numbers, transformed)
298 vectorofactions = append(vectorofactions, "transformed")
299
300 #end if
301 #end for
302
303 ############# Outputs: RData and QC report #############
304 ################################################################################
305
306 print(paste0("Number of NA in output file: ",sum(is.na(spectra(msidata)[]))))
307
308 ## save as (.RData)
309 save(msidata, file="$msidata_preprocessed")
310
311 ## save QC report
312
313 pdf("Preprocessing.pdf", fonts = "Times", pointsize = 12)
314 plot(0,type='n',axes=FALSE,ann=FALSE)
315 title(main=paste("Quality control during preprocessing \n", "Filename:", "$infile.display_name"))
316 rownames(QC_numbers) = c("min m/z", "max mz", "# features", "median\n# peaks", "median\nintensity")
317 grid.table(t(QC_numbers))
318
319 dev.off()
320
321 }else{
322 print("inputfile has no intensities > 0")
323 }
324
325 ]]></configfile>
326 </configfiles>
327 <inputs>
328 <expand macro="reading_msidata"/>
329 <repeat name="methods" title="Preprocessing" min="1" max="50">
330 <conditional name="methods_conditional">
331 <param name="preprocessing_method" type="select" label="Preprocessing methods">
332 <option value="Normalization" selected="True">Normalization</option>
333 <option value="Baseline_reduction">Baseline Reduction</option>
334 <option value="Smoothing">Peak smoothing</option>
335 <option value="Peak_picking">Peak picking</option>
336 <option value="Peak_alignment">Peak alignment</option>
337 <option value="Peak_filtering">Peak filtering</option>
338 <option value="Data_reduction">Data reduction</option>
339 <option value="Transformation">Transformation</option>
340 </param>
341 <when value="Normalization"/>
342 <when value="Baseline_reduction">
343 <param name="blocks_baseline" type="integer" value="500"
344 label="Blocks"/>
345 <param name="spar_baseline" type="float" value="1.0" label="Spar value"
346 help = "Smoothing parameter for the spline smoothing
347 applied to the spectrum in order to decide the cutoffs
348 for throwing away false noise spikes that might occur inside peaks"/>
349 </when>
350 <when value="Smoothing">
351 <conditional name="methods_for_smoothing">
352 <param name="smoothing_method" type="select" label="Smoothing method">
353 <option value="gaussian" selected="True">gaussian</option>
354 <option value="sgolay">Savitsky-Golay</option>
355 <option value="ma">moving average</option>
356 </param>
357 <when value="gaussian">
358 <param name="sd_gaussian" type="float" value="2"
359 label="The standard deviation for the Gaussian kernel (window/sd)"/>
360 </when>
361 <when value="sgolay">
362 <param name="order_of_filters" type="integer" value="3"
363 label="The order of the smoothing filter, must be smaller than window size"/>
364 </when>
365 <when value="ma">
366 <param name="coefficients_ma_filter" type="float" value="1"
367 label="The coefficients for the moving average filter"/>
368 </when>
369 </conditional>
370 <param name="window_smoothing" type="float" value="8"
371 label="Window size"/>
372 </when>
373 <when value="Peak_picking">
374 <param name="SNR_picking_method" type="integer" value="6"
375 label="Signal to noise ratio"
376 help="The minimal signal to noise ratio for peaks to be considered as a valid peak."/>
377 <param name="blocks_picking" type="integer" value="100" label = "Number of blocks"
378 help="Number of blocks in which to divide mass spectrum to calculate noise"/>
379 <param name="window_picking" type="float" value="5" label= "Window size" help="Window width for seeking local maxima"/>
380 <conditional name="methods_for_picking">
381 <param name="picking_method" type="select" label="Peak picking method" help="only simple works for processed imzML files">
382 <option value="adaptive" selected="True">adaptive</option>
383 <option value="limpic">limpic</option>
384 <option value="simple">simple</option>
385 </param>
386 <when value="adaptive">
387 <param name="spar_picking" type="float" value="1.0"
388 label="Spar value"
389 help = "Smoothing parameter for the spline smoothing
390 applied to the spectrum in order to decide the cutoffs
391 for throwing away false noise spikes that might occur inside peaks"/>
392 </when>
393 <when value="limpic">
394 <param name="tresh_picking" type="float" value="0.75"
395 label="thresh value" help="The thresholding quantile to use when comparing slopes in order to throw away peaks that are too flat"/>
396 </when>
397 <when value="simple"/>
398 </conditional>
399 </when>
400 <when value="Peak_alignment">
401 <conditional name="methods_for_alignment">
402 <param name="alignment_method" type="select" label="Alignment method">
403 <option value="diff" selected="True">diff</option>
404 <option value="DP">DP</option>
405 </param>
406 <when value="diff">
407 <param name="value_diffalignment" type="float" value="200"
408 label="diff.max" help="Peaks that differ less than this value will be aligned together"/>
409 <param name="units_diffalignment" type="select" display="radio" optional="False" label="units">
410 <option value="ppm" selected="True">ppm</option>
411 <option value="Da">m/z</option>
412 </param>
413 </when>
414 <when value="DP">
415 <param name="gap_DPalignment" type="float" value="0"
416 label="Gap" help="The gap penalty for the dynamic programming sequence alignment"/>
417 </when>
418 </conditional>
419 <conditional name="align_ref_type">
420 <param name="align_reference_datatype" type="select" label="Choose reference">
421 <option value="align_noref" selected="True">no reference</option>
422 <option value="align_table" >tabular file as reference</option>
423 <option value="align_msidata_ref">msidata file as reference</option>
424 </param>
425 <when value="align_noref"/>
426 <when value="align_table">
427 <expand macro="reading_1_column_mz_tabular" label="Tabular file with m/z features to use for alignment. Only the m/z values from the tabular file will be kept."/>
428 </when>
429 <when value="align_msidata_ref">
430 <param name="align_peaks_msidata" type="data" format="rdata," label="Picked and aligned Cardinal MSImageSet saved as RData"/>
431 </when>
432 </conditional>
433 </when>
434 <when value="Peak_filtering">
435 <param name="frequ_filtering" type="integer" value="1000"
436 label="Freq.min" help="Peaks that occur in the dataset fewer times than this will be removed. Number should be between 1 (no filtering) and number of spectra (pixel)"/>
437 </when>
438 <when value="Data_reduction">
439 <conditional name="methods_for_reduction">
440 <param name="reduction_method" type="select" label="Reduction method">
441 <option value="bin" selected="True">bin</option>
442 <option value="resample">resample</option>
443 <option value="peaks">peaks</option>
444 </param>
445 <when value="bin">
446 <param name="bin_width" type="float" value="1"
447 label="The width of a bin in m/z or ppm" help="Width must be greater than range of m/z values divided by number of m/z features"/>
448 <param name="bin_units" type="select" display="radio"
449 label="Unit for bin">
450 <option value="mz" selected="True">mz</option>
451 <option value="ppm">ppm</option>
452 </param>
453 <param name="bin_fun" type="select" display="radio"
454 label="Calculate sum or mean intensity for ions of the same bin">
455 <option value="mean" selected="True">mean</option>
456 <option value="sum">sum</option>
457 </param>
458 </when>
459 <when value="resample">
460 <param name="resample_step" type="float" value="1"
461 label="The step size in m/z" help="Step size must be greater than range of m/z values divided by number of m/z features"/>
462 </when>
463 <when value="peaks">
464 <param name="peaks_type" type="select" display="radio"
465 label="Should the peak height or area under the curve be taken as the intensity value?">
466 <option value="height" selected="True">height</option>
467 <option value="area">area</option>
468 </param>
469 <conditional name="ref_type">
470 <param name="reference_datatype" type="select" label="Choose reference datatype">
471 <option value="table" selected="True">tabular file</option>
472 <option value="msidata_ref">msidata file</option>
473 </param>
474 <when value="table">
475 <expand macro="reading_1_column_mz_tabular" label="Tabular file with m/z features to extract from input file"/>
476 </when>
477 <when value="msidata_ref">
478 <param name="peaks_msidata" type="data" format="rdata," label="Picked and aligned Cardinal MSImageSet saved as RData"/>
479 </when>
480 </conditional>
481 </when>
482 </conditional>
483 </when>
484 <when value="Transformation">
485 <conditional name="transf_conditional">
486 <param name="trans_type" type="select" label="Intensity transformations" help="logarithm base 2 (log2) or squareroot (sqrt)">
487 <option value="log2" selected="True">log2</option>
488 <option value="sqrt">sqrt</option>
489 </param>
490 <when value="log2"/>
491 <when value="sqrt"/>
492 </conditional>
493 </when>
494 </conditional>
495 </repeat>
496 </inputs>
497 <outputs>
498 <data format="rdata" name="msidata_preprocessed" label="${tool.name} on ${on_string}"/>
499 <data format="pdf" name="QC_overview" from_work_dir="Preprocessing.pdf" label = "${tool.name} on ${on_string}: QC"/>
500 </outputs>
501 <tests>
502 <test>
503 <expand macro="infile_imzml"/>
504 <repeat name="methods">
505 <conditional name="methods_conditional">
506 <param name="preprocessing_method" value="Normalization"/>
507 <conditional name="methods_for_normalization">
508 <param name="normalization_method" value="median"/>
509 </conditional>
510 </conditional>
511 </repeat>
512 <repeat name="methods">
513 <conditional name="methods_conditional">
514 <param name="preprocessing_method" value="Smoothing"/>
515 <conditional name="methods_for_smoothing">
516 <param name="smoothing_method" value="gaussian"/>
517 <param name="sd_gaussian" value="4"/>
518 </conditional>
519 <param name="window_smoothing" value="9"/>
520 </conditional>
521 </repeat>
522 <repeat name="methods">
523 <conditional name="methods_conditional">
524 <param name="preprocessing_method" value="Peak_picking"/>
525 <conditional name="methods_for_picking">
526 <param name="picking_method" value="adaptive"/>
527 </conditional>
528 <param name="blocks_picking" value="3"/>
529 <param name="window_picking" value="3"/>
530 <param name="SNR_picking_method" value="3"/>
531 </conditional>
532 </repeat>
533 <repeat name="methods">
534 <conditional name="methods_conditional">
535 <param name="preprocessing_method" value="Peak_alignment"/>
536 <conditional name="methods_for_alignment">
537 <param name="alignment_method" value="diff"/>
538 </conditional>
539 </conditional>
540 </repeat>
541 <repeat name="methods">
542 <conditional name="methods_conditional">
543 <param name="preprocessing_method" value="Peak_filtering"/>
544 <param name="frequ_filtering" value="2"/>
545 </conditional>
546 </repeat>
547 <repeat name="methods">
548 <conditional name="methods_conditional">
549 <param name="preprocessing_method" value="Transformation"/>
550 <conditional name="transf_conditional">
551 <param name="trans_type" value="sqrt"/>
552 </conditional>
553 </conditional>
554 </repeat>
555 <output name="msidata_preprocessed" file="preprocessing_results1.RData" compare="sim_size"/>
556 <output name="QC_overview" file="preprocessing_results1.pdf" compare="sim_size"/>
557 </test>
558 <test>
559 <param name="infile" value="3_files_combined.RData" ftype="rdata"/>
560 <repeat name="methods">
561 <conditional name="methods_conditional">
562 <param name="preprocessing_method" value="Peak_picking"/>
563 <param name="blocks_picking" value="3"/>
564 <param name="window_picking" value="5"/>
565 <param name="SNR_picking_method" value="2"/>
566 <conditional name="methods_for_picking">
567 <param name="picking_method" value="adaptive"/>
568 </conditional>
569 </conditional>
570 </repeat>
571 <repeat name="methods">
572 <conditional name="methods_conditional">
573 <param name="preprocessing_method" value="Peak_alignment"/>
574 <conditional name="methods_for_alignment">
575 <param name="alignment_method" value="DP"/>
576 </conditional>
577 </conditional>
578 </repeat>
579 <output name="msidata_preprocessed" file="preprocessing_results2.RData" compare="sim_size"/>
580 <output name="QC_overview" file="preprocessing_results2.pdf" compare="sim_size"/>
581 </test>
582 <test>
583 <expand macro="infile_analyze75"/>
584 <repeat name="methods">
585 <conditional name="methods_conditional">
586 <param name="preprocessing_method" value="Normalization"/>
587 <conditional name="methods_for_normalization">
588 <param name="normalization_method" value="median"/>
589 </conditional>
590 </conditional>
591 </repeat>
592 <repeat name="methods">
593 <conditional name="methods_conditional">
594 <param name="preprocessing_method" value="Peak_picking"/>
595 <param name="blocks_picking" value="100"/>
596 <param name="window_picking" value="5"/>
597 <param name="SNR_picking_method" value="3"/>
598 <param name="picking_method" value="limpic"/>
599 </conditional>
600 </repeat>
601 <repeat name="methods">
602 <conditional name="methods_conditional">
603 <param name="preprocessing_method" value="Peak_alignment"/>
604 <conditional name="methods_for_alignment">
605 <param name="alignment_method" value="diff"/>
606 </conditional>
607 </conditional>
608 </repeat>
609 <output name="msidata_preprocessed" file="preprocessing_results3.RData" compare="sim_size"/>
610 <output name="QC_overview" file="preprocessing_results3.pdf" compare="sim_size"/>
611 </test>
612 <test>
613 <expand macro="infile_analyze75"/>
614 <repeat name="methods">
615 <conditional name="methods_conditional">
616 <param name="preprocessing_method" value="Normalization"/>
617 </conditional>
618 </repeat>
619 <repeat name="methods">
620 <conditional name="methods_conditional">
621 <param name="preprocessing_method" value="Data_reduction"/>
622 <param name="bin_width" value="0.1"/>
623 </conditional>
624 </repeat>
625 <output name="msidata_preprocessed" file="preprocessing_results4.RData" compare="sim_size"/>
626 <output name="QC_overview" file="preprocessing_results4.pdf" compare="sim_size"/>
627 </test>
628 <test>
629 <expand macro="infile_imzml"/>
630 <repeat name="methods">
631 <conditional name="methods_conditional">
632 <param name="preprocessing_method" value="Data_reduction"/>
633 <conditional name="methods_for_reduction">
634 <param name="reduction_method" value="resample"/>
635 <param name="step_width" value="0.1"/>
636 </conditional>
637 </conditional>
638 </repeat>
639 <output name="msidata_preprocessed" file="preprocessing_results5.RData" compare="sim_size"/>
640 <output name="QC_overview" file="preprocessing_results5.pdf" compare="sim_size"/>
641 </test>
642 </tests>
643 <help>
644 <![CDATA[
645
646 @CARDINAL_DESCRIPTION@
647
648 -----
649
650 This tool provides multiple Cardinal functions to preprocess mass spectrometry imaging data.
651
652 @MSIDATA_INPUT_DESCRIPTION@
653 - Coordinates stored as decimals rather than integers will be rounded to obtain a regular pixel grid. This might lead to duplicated coordinates which will be automatically removed before peak picking.
654 @MZ_TABULAR_INPUT_DESCRIPTION@
655
656 **Options**
657
658 - Normalization: Normalization of intensities to total ion current (TIC)
659 - Baseline reduction: Baseline reduction removes background intensity generated by chemical noise (common in MALDI datasets)
660 - Smoothing: Smoothing of the peaks reduces noise and improves peak detection
661 - Peak picking: relevant peaks are picked while noise-peaks are removed (needs peak alignment afterwards)
662 - Peak alignment: only possible after peak picking, m/z inaccuracies are removed by alignment of same peaks to a common m/z value; if no reference is given the peaks are aligned to the local maxima of the mean spectrum of the current dataset; external reference data can be used from another MSI data file or a tabular file with m/z values, but then only the m/z from the reference will be kept
663 - Peak filtering: removes peaks that occur only in a small proportion of pixels. If not sure which cut off to choose run quality control tool first and decide according to the number of peaks per m/z plot
664 - Data reduction: binning, resampling or peak filtering to reduce data
665 - Transformation: log2 or squareroot transformation of all intensities; when using log2 transformation zero intensities will become NA, this can lead to compatibility problems.
666
667 **Tips**
668
669 - Peak alignment works only after peak picking
670 - Peak filtering works only on centroided data (peak picking and alignment or Data reduction peaks
671
672 **Output**
673
674 - imzML file, preprocessed
675 - pdf with key values after each processing step
676
677 ]]>
678 </help>
679 <expand macro="citations"/>
680 </tool>