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comparison msi_preprocessing.xml @ 4:ada9dee67b5d draft

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planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_preprocessing commit a7be47698f53eb4f00961192327d93e8989276a7
author galaxyp
date Mon, 11 Jun 2018 17:32:11 -0400
parents c17eb2cc0048
children 755d77066d4b
comparison
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3:c17eb2cc0048 4:ada9dee67b5d
1 <tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.10.0.0"> 1 <tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.10.0.1">
2 <description> 2 <description>
3 mass spectrometry imaging preprocessing 3 mass spectrometry imaging preprocessing
4 </description> 4 </description>
5 <requirements> 5 <requirements>
6 <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement> 6 <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement>
7 <requirement type="package" version="2.2.1">r-gridextra</requirement> 7 <requirement type="package" version="2.2.1">r-gridextra</requirement>
8 <requirement type="package" version="0.20-35">r-lattice</requirement> 8 <requirement type="package" version="0.20-35">r-lattice</requirement>
9 <requirement type="package" version="3.34.9">bioconductor-limma</requirement>
9 </requirements> 10 </requirements>
10 <command detect_errors="exit_code"> 11 <command detect_errors="exit_code">
11 <![CDATA[ 12 <![CDATA[
12 13
13 #if $infile.ext == 'imzml' 14 #if $infile.ext == 'imzml'
25 26
26 ]]> 27 ]]>
27 </command> 28 </command>
28 <configfiles> 29 <configfiles>
29 <configfile name="cardinal_preprocessing"><![CDATA[ 30 <configfile name="cardinal_preprocessing"><![CDATA[
31
32 ################################# load libraries and read file #################
33
30 library(Cardinal) 34 library(Cardinal)
31 library(gridExtra) 35 library(gridExtra)
32 library(lattice) 36 library(lattice)
37 library(limma)
33 38
34 #if $infile.ext == 'imzml' 39 #if $infile.ext == 'imzml'
35 msidata = readImzML('infile') 40 msidata = readImzML('infile')
36 #elif $infile.ext == 'analyze75' 41 #elif $infile.ext == 'analyze75'
37 msidata = readAnalyze('infile') 42 msidata = readAnalyze('infile')
38 #else 43 #else
39 load('infile.RData') 44 load('infile.RData')
40 #end if 45 #end if
41 46
42 47 ## function to later read RData reference files in
43 ##################################### Preparations for QC report ############### 48
49 loadRData <- function(fileName){
50 #loads an RData file, and returns it
51 load(fileName)
52 get(ls()[ls() != "fileName"])
53 }
54
55 ######################### preparations for optional QC report #################
44 56
45 #if $outputs.outputs_select == "quality_control": 57 #if $outputs.outputs_select == "quality_control":
46 58
47 ### values for QC table: 59 ### values for QC table:
60
48 maxfeatures = length(features(msidata)) 61 maxfeatures = length(features(msidata))
49 medianpeaks = median(colSums(spectra(msidata)[]>0)) 62 medianpeaks = median(colSums(spectra(msidata)[]>0))
50 medint = round(median(spectra(msidata)[]), digits=2) 63 medint = round(median(spectra(msidata)[]), digits=2)
51 TICs = round(mean(colSums(spectra(msidata)[])), digits=1) 64 TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
52 QC_numbers= data.frame(rawdata = c(maxfeatures, medianpeaks, medint, TICs)) 65 QC_numbers= data.frame(rawdata = c(maxfeatures, medianpeaks, medint, TICs))
53 vectorofactions = "rawdata" 66 vectorofactions = "rawdata"
54 67
55 68 ### Read tabular file with calibrant m/z:
56 ### Read tabular file with calibrant masses: 69
57 calibrant_list = read.delim("$outputs.calibrant_file", header = FALSE, stringsAsFactors = FALSE) 70 calibrant_list = read.delim("$outputs.calibrant_file", header = FALSE, stringsAsFactors = FALSE)
58 71
59 ### calculate how many input calibrant masses are valid: 72 ### calculate how many input calibrant m/z are valid:
60 inputcalibrants = calibrant_list[calibrant_list[,$outputs.calibrants_column]>min(mz(msidata)) & calibrant_list[,$outputs.calibrants_column]<max(mz(msidata)),$outputs.calibrants_column] 73
61 number_calibrants_in = length(calibrant_list[,$outputs.calibrants_column]) 74 inputcalibrants = calibrant_list[calibrant_list[,$outputs.calibrants_column]>min(mz(msidata)) & calibrant_list[,$outputs.calibrants_column]<max(mz(msidata)),$outputs.calibrants_column]
62 number_calibrants_valid = length(inputcalibrants) 75 number_calibrants_in = length(calibrant_list[,$outputs.calibrants_column])
63 76 number_calibrants_valid = length(inputcalibrants)
64
65
66 77
67 ### Quality control report 78 ### Quality control report
68
69 79
70 pdf("Preprocessing.pdf", fonts = "Times", pointsize = 12) 80 pdf("Preprocessing.pdf", fonts = "Times", pointsize = 12)
71 plot(0,type='n',axes=FALSE,ann=FALSE) 81 plot(0,type='n',axes=FALSE,ann=FALSE)
72 title(main=paste("Quality control during preprocessing \n", "Filename:", "$infile.display_name")) 82 title(main=paste("Quality control during preprocessing \n", "Filename:", "$infile.display_name"))
73 title(main=paste0("\n\n\n\n Number valid masses in ", "$outputs.calibrant_file.display_name",": ", number_calibrants_valid, "/", number_calibrants_in)) 83 title(main=paste0("\n\n\n\n Number valid m/z in ", "$outputs.calibrant_file.display_name",": ", number_calibrants_valid, "/", number_calibrants_in))
74
75
76 84
77 for (calibrant in inputcalibrants) 85 for (calibrant in inputcalibrants)
78 { 86 {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
79
80 currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
81 par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 87 par.strip.text=list(col="black", cex=.9)),lattice=TRUE,
82 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="raw") 88 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="raw")
83 89 assign(paste("rawdata",calibrant, sep="_"), currentimage)}
84 assign(paste("rawdata",calibrant, sep="_"), currentimage)
85 }
86
87 90
88 current_plot_raw = vector(length(inputcalibrants), mode='list') 91 current_plot_raw = vector(length(inputcalibrants), mode='list')
89 92
90
91 #end if 93 #end if
92 94
93 ############################### Preprocessing steps ########################### 95 ############################### Preprocessing steps ###########################
96 ###############################################################################
97
94 #for $method in $methods: 98 #for $method in $methods:
99
100 ############################### Normalization ###########################
95 101
96 #if str( $method.methods_conditional.preprocessing_method ) == 'Normalization': 102 #if str( $method.methods_conditional.preprocessing_method ) == 'Normalization':
97 print('Normalization') 103 print('Normalization')
98 ##normalization 104 ##normalization
99 105
100 msidata = normalize(msidata, method="tic") 106 msidata = normalize(msidata, method="tic")
101 107
108 ############################### optional QC ###########################
109
102 #if $outputs.outputs_select == "quality_control": 110 #if $outputs.outputs_select == "quality_control":
111
103 ### values for QC table: 112 ### values for QC table:
104 maxfeatures = length(features(msidata)) 113 maxfeatures = length(features(msidata))
105 medianpeaks = median(colSums(spectra(msidata)[]>0)) 114 medianpeaks = median(colSums(spectra(msidata)[]>0))
106 medint = round(median(spectra(msidata)[]), digits=2) 115 medint = round(median(spectra(msidata)[]), digits=2)
107 TICs = round(mean(colSums(spectra(msidata)[])), digits=1) 116 TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
108 normalized = c(maxfeatures, medianpeaks, medint, TICs) 117 normalized = c(maxfeatures, medianpeaks, medint, TICs)
109 QC_numbers= cbind(QC_numbers, normalized) 118 QC_numbers= cbind(QC_numbers, normalized)
119
110 ### preparation for QC plots 120 ### preparation for QC plots
111 vectorofactions = append(vectorofactions, "normalized") 121 vectorofactions = append(vectorofactions, "normalized")
112
113 for (calibrant in inputcalibrants) 122 for (calibrant in inputcalibrants)
114 { 123 {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
115 currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
116 par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 124 par.strip.text=list(col="black", cex=.9)),lattice=TRUE,
117 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="normalized") 125 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="normalized")
118 126 assign(paste("normalized",calibrant, sep="_"), currentimage)}
119 assign(paste("normalized",calibrant, sep="_"), currentimage) 127
120 } 128 #end if
121 129
122 130 ############################### Baseline reduction ###########################
123 #end if
124 131
125 #elif str( $method.methods_conditional.preprocessing_method ) == 'Baseline_reduction': 132 #elif str( $method.methods_conditional.preprocessing_method ) == 'Baseline_reduction':
126 print('Baseline_reduction') 133 print('Baseline_reduction')
127 ##baseline reduction 134 ##baseline reduction
135
128 msidata = reduceBaseline(msidata, method="median", blocks=$method.methods_conditional.blocks_baseline) 136 msidata = reduceBaseline(msidata, method="median", blocks=$method.methods_conditional.blocks_baseline)
129 137
138 ############################### optional QC ###########################
139
130 #if $outputs.outputs_select == "quality_control": 140 #if $outputs.outputs_select == "quality_control":
141
142 ### values for QC table:
131 maxfeatures = length(features(msidata)) 143 maxfeatures = length(features(msidata))
132 medianpeaks = median(colSums(spectra(msidata)[]>0)) 144 medianpeaks = median(colSums(spectra(msidata)[]>0))
133 medint = round(median(spectra(msidata)[]), digits=2) 145 medint = round(median(spectra(msidata)[]), digits=2)
134 TICs = round(mean(colSums(spectra(msidata)[])), digits=1) 146 TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
135 baseline= c(maxfeatures, medianpeaks, medint, TICs) 147 baseline= c(maxfeatures, medianpeaks, medint, TICs)
136 QC_numbers= cbind(QC_numbers, baseline) 148 QC_numbers= cbind(QC_numbers, baseline)
149
137 ### preparation for QC plots 150 ### preparation for QC plots
138 vectorofactions = append(vectorofactions, "baseline_rem") 151 vectorofactions = append(vectorofactions, "baseline_rem")
139 152
140 for (calibrant in inputcalibrants) 153 for (calibrant in inputcalibrants)
141 { 154 {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
142 currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
143 par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 155 par.strip.text=list(col="black", cex=.9)),lattice=TRUE,
144 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="baseline removed") 156 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="baseline removed")
145 157 assign(paste("baseline_rem",calibrant, sep="_"), currentimage)}
146 assign(paste("baseline_rem",calibrant, sep="_"), currentimage) 158
147 } 159 #end if
148 #end if 160
161 ############################### Smoothing ###########################
149 162
150 #elif str( $method.methods_conditional.preprocessing_method ) == 'Smoothing': 163 #elif str( $method.methods_conditional.preprocessing_method ) == 'Smoothing':
151 print('Smoothing') 164 print('Smoothing')
152 ## Smoothing 165 ## Smoothing
153 166
154 #if str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'gaussian': 167 #if str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'gaussian':
155 print('gaussian smoothing') 168 print('gaussian smoothing')
169
156 msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, sd = $method.methods_conditional.methods_for_smoothing.sd_gaussian) 170 msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, sd = $method.methods_conditional.methods_for_smoothing.sd_gaussian)
171
157 #elif str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'sgolay': 172 #elif str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'sgolay':
158 print('sgolay smoothing') 173 print('sgolay smoothing')
174
159 msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, order = $method.methods_conditional.methods_for_smoothing.order_of_filters) 175 msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, order = $method.methods_conditional.methods_for_smoothing.order_of_filters)
160 #elif str($method.methods_conditional.methods_for_smoothing.smoothing_method) == 'ma': 176 #elif str($method.methods_conditional.methods_for_smoothing.smoothing_method) == 'ma':
161 print('sgolay smoothing') 177 print('sgolay smoothing')
178
162 msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, coef = $method.methods_conditional.methods_for_smoothing.coefficients_ma_filter) 179 msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, coef = $method.methods_conditional.methods_for_smoothing.coefficients_ma_filter)
163 #end if 180
181 #end if
182
183 ############################### optional QC ###########################
164 184
165 #if $outputs.outputs_select == "quality_control": 185 #if $outputs.outputs_select == "quality_control":
186
187 ### values for QC table:
166 maxfeatures = length(features(msidata)) 188 maxfeatures = length(features(msidata))
167 medianpeaks = median(colSums(spectra(msidata)[]>0)) 189 medianpeaks = median(colSums(spectra(msidata)[]>0))
168 medint = round(median(spectra(msidata)[]), digits=2) 190 medint = round(median(spectra(msidata)[]), digits=2)
169 TICs = round(mean(colSums(spectra(msidata)[])), digits=1) 191 TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
170 smoothed= c(maxfeatures, medianpeaks, medint, TICs) 192 smoothed= c(maxfeatures, medianpeaks, medint, TICs)
171 QC_numbers= cbind(QC_numbers, smoothed) 193 QC_numbers= cbind(QC_numbers, smoothed)
194
172 ### preparation for QC plots 195 ### preparation for QC plots
173 vectorofactions = append(vectorofactions, "smoothed") 196 vectorofactions = append(vectorofactions, "smoothed")
174 197
175 for (calibrant in inputcalibrants) 198 for (calibrant in inputcalibrants)
176 { 199 {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
177 currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
178 par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 200 par.strip.text=list(col="black", cex=.9)),lattice=TRUE,
179 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="smoothed") 201 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="smoothed")
180 202 assign(paste("smoothed",calibrant, sep="_"), currentimage)}
181 assign(paste("smoothed",calibrant, sep="_"), currentimage) 203
182 } 204 #end if
183 #end if 205
206 ############################### Peak picking ###########################
184 207
185 #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_picking': 208 #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_picking':
186 print('Peak_picking') 209 print('Peak_picking')
187 ## Peakpicking 210 ## Peakpicking
188 211
189 #if str( $method.methods_conditional.methods_for_picking.picking_method) == 'adaptive': 212 #if str( $method.methods_conditional.methods_for_picking.picking_method) == 'adaptive':
190 print('adaptive peakpicking') 213 print('adaptive peakpicking')
214
191 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) 215 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)
192 216
193 #elif str( $method.methods_conditional.methods_for_picking.picking_method) == 'limpic': 217 #elif str( $method.methods_conditional.methods_for_picking.picking_method) == 'limpic':
194 print('limpic peakpicking') 218 print('limpic peakpicking')
219
195 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) 220 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)
196 221
197 #elif str( $method.methods_conditional.methods_for_picking.picking_method) == 'simple': 222 #elif str( $method.methods_conditional.methods_for_picking.picking_method) == 'simple':
198 print('simple peakpicking') 223 print('simple peakpicking')
224
199 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) 225 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)
200 #end if 226
227 #end if
228
229 ############################### optional QC ###########################
201 230
202 #if $outputs.outputs_select == "quality_control": 231 #if $outputs.outputs_select == "quality_control":
232
233 ### values for QC table:
203 maxfeatures = length(features(msidata)) 234 maxfeatures = length(features(msidata))
204 medianpeaks = median(colSums(spectra(msidata)[]>0)) 235 medianpeaks = median(colSums(spectra(msidata)[]>0))
205 medint = round(median(spectra(msidata)[]), digits=2) 236 medint = round(median(spectra(msidata)[]), digits=2)
206 TICs = round(mean(colSums(spectra(msidata)[])), digits=1) 237 TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
207 picked= c(maxfeatures, medianpeaks, medint, TICs) 238 picked= c(maxfeatures, medianpeaks, medint, TICs)
208 QC_numbers= cbind(QC_numbers, picked) 239 QC_numbers= cbind(QC_numbers, picked)
240
209 ### preparation for QC plots 241 ### preparation for QC plots
210 vectorofactions = append(vectorofactions, "picked") 242 vectorofactions = append(vectorofactions, "picked")
211 243
212 for (calibrant in inputcalibrants) 244 for (calibrant in inputcalibrants)
213 { 245 {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
214 currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
215 par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 246 par.strip.text=list(col="black", cex=.9)),lattice=TRUE,
216 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="picked") 247 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="picked")
217 248 assign(paste("picked",calibrant, sep="_"), currentimage)}
218 assign(paste("picked",calibrant, sep="_"), currentimage) 249
219 } 250 #end if
220 #end if 251
252 ############################### Peak alignment ###########################
221 253
222 #elif str( $method.methods_conditional.preprocessing_method ) == 'Peak_alignment': 254 #elif str( $method.methods_conditional.preprocessing_method ) == 'Peak_alignment':
223 print('Peak_alignment') 255 print('Peak_alignment')
224 ## Peakalignment 256 ## Peakalignment
225 257
226 #if str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_noref': 258 #if str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_noref':
259
227 align_peak_reference = msidata 260 align_peak_reference = msidata
228 261
229 #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_table': 262 #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_table':
263
230 align_reference_table = read.delim("$method.methods_conditional.align_ref_type.align_peaks_table", header = FALSE, stringsAsFactors = FALSE) 264 align_reference_table = read.delim("$method.methods_conditional.align_ref_type.align_peaks_table", header = FALSE, stringsAsFactors = FALSE)
231
232 align_reference_column = align_reference_table[,$method.methods_conditional.align_ref_type.align_mass_column] 265 align_reference_column = align_reference_table[,$method.methods_conditional.align_ref_type.align_mass_column]
233
234 align_peak_reference = align_reference_column[align_reference_column>=min(mz(msidata)) & align_reference_column<=max(mz(msidata))] 266 align_peak_reference = align_reference_column[align_reference_column>=min(mz(msidata)) & align_reference_column<=max(mz(msidata))]
235 if (length(align_peak_reference) == 0) 267 if (length(align_peak_reference) == 0)
236 {align_peak_reference = 0 268 {align_peak_reference = 0}
237 } 269
238
239
240
241 #### if length <0: align_peak_reference = 0
242
243
244 #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_msidata_ref': 270 #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_msidata_ref':
245 loadRData <- function(fileName){ 271
246 #loads an RData file, and returns it
247 load(fileName)
248 get(ls()[ls() != "fileName"])
249 }
250 align_peak_reference = loadRData('$method.methods_conditional.align_ref_type.align_peaks_msidata') 272 align_peak_reference = loadRData('$method.methods_conditional.align_ref_type.align_peaks_msidata')
273
251 #end if 274 #end if
252 275
253 #if str( $method.methods_conditional.methods_for_alignment.alignment_method) == 'diff': 276 #if str( $method.methods_conditional.methods_for_alignment.alignment_method) == 'diff':
254 print('diff peakalignment') 277 print('diff peakalignment')
278
255 msidata = peakAlign(msidata, method='$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) 279 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)
256 280
257 #elif str( $method.methods_conditional.methods_for_alignment.alignment_method) == 'DP': 281 #elif str( $method.methods_conditional.methods_for_alignment.alignment_method) == 'DP':
258 print('DPpeakalignment') 282 print('DPpeakalignment')
283
259 msidata = peakAlign(msidata, method='$method.methods_conditional.methods_for_alignment.alignment_method',gap = $method.methods_conditional.methods_for_alignment.gap_DPalignment, ref=align_peak_reference) 284 msidata = peakAlign(msidata, method='$method.methods_conditional.methods_for_alignment.alignment_method',gap = $method.methods_conditional.methods_for_alignment.gap_DPalignment, ref=align_peak_reference)
285
260 #end if 286 #end if
261 287
288 ############################### optional QC ###########################
262 #if $outputs.outputs_select == "quality_control": 289 #if $outputs.outputs_select == "quality_control":
290
291 ### values for QC table:
263 maxfeatures = length(features(msidata)) 292 maxfeatures = length(features(msidata))
264 medianpeaks = median(colSums(spectra(msidata)[]>0)) 293 medianpeaks = median(colSums(spectra(msidata)[]>0))
265 medint = round(median(spectra(msidata)[]), digits=2) 294 medint = round(median(spectra(msidata)[]), digits=2)
266 TICs = round(mean(colSums(spectra(msidata)[])), digits=1) 295 TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
267 aligned= c(maxfeatures, medianpeaks, medint, TICs) 296 aligned= c(maxfeatures, medianpeaks, medint, TICs)
268 QC_numbers= cbind(QC_numbers, aligned) 297 QC_numbers= cbind(QC_numbers, aligned)
298
269 ### preparation for QC plots 299 ### preparation for QC plots
270 vectorofactions = append(vectorofactions, "aligned") 300 vectorofactions = append(vectorofactions, "aligned")
271 301
272 for (calibrant in inputcalibrants) 302 for (calibrant in inputcalibrants)
273 { 303 {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
274 currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
275 par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 304 par.strip.text=list(col="black", cex=.9)),lattice=TRUE,
276 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="aligned") 305 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="aligned")
277 306 assign(paste("aligned",calibrant, sep="_"), currentimage)}
278 assign(paste("aligned",calibrant, sep="_"), currentimage) 307
279 } 308 #end if
280 #end if 309
310 ############################### Peak filtering ###########################
281 311
282 #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_filtering': 312 #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_filtering':
283 print('Peak_filtering') 313 print('Peak_filtering')
314
284 msidata = peakFilter(msidata, method='freq', freq.min = $method.methods_conditional.frequ_filtering) 315 msidata = peakFilter(msidata, method='freq', freq.min = $method.methods_conditional.frequ_filtering)
285 316
317 ############################### optional QC ###########################
318
286 #if $outputs.outputs_select == "quality_control": 319 #if $outputs.outputs_select == "quality_control":
320
321 ### values for QC table:
287 maxfeatures = length(features(msidata)) 322 maxfeatures = length(features(msidata))
288 medianpeaks = median(colSums(spectra(msidata)[]>0)) 323 medianpeaks = median(colSums(spectra(msidata)[]>0))
289 medint = round(median(spectra(msidata)[]), digits=2) 324 medint = round(median(spectra(msidata)[]), digits=2)
290 TICs = round(mean(colSums(spectra(msidata)[])), digits=1) 325 TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
291 filtered= c(maxfeatures, medianpeaks, medint, TICs) 326 filtered= c(maxfeatures, medianpeaks, medint, TICs)
292 QC_numbers= cbind(QC_numbers, filtered) 327 QC_numbers= cbind(QC_numbers, filtered)
328
293 ### preparation for QC plots 329 ### preparation for QC plots
294 vectorofactions = append(vectorofactions, "filtered") 330 vectorofactions = append(vectorofactions, "filtered")
295 331
296 for (calibrant in inputcalibrants) 332 for (calibrant in inputcalibrants)
297 { 333 {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
298 currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
299 par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 334 par.strip.text=list(col="black", cex=.9)),lattice=TRUE,
300 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="filtered") 335 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="filtered")
301 336 assign(paste("filtered",calibrant, sep="_"), currentimage)}
302 assign(paste("filtered",calibrant, sep="_"), currentimage) 337
303 } 338 #end if
304 #end if 339
340 ############################### Data reduction ###########################
305 341
306 #elif str( $method.methods_conditional.preprocessing_method) == 'Data_reduction': 342 #elif str( $method.methods_conditional.preprocessing_method) == 'Data_reduction':
307 print('Data_reduction') 343 print('Data_reduction')
308 344
309 #if str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'bin': 345 #if str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'bin':
310 print('bin reduction') 346 print('bin reduction')
347
311 msidata = reduceDimension(msidata, method="bin", width=$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) 348 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)
312 349
313 #elif str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'resample': 350 #elif str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'resample':
314 print('resample reduction') 351 print('resample reduction')
352
315 msidata = reduceDimension(msidata, method="resample", step=$method.methods_conditional.methods_for_reduction.resample_step) 353 msidata = reduceDimension(msidata, method="resample", step=$method.methods_conditional.methods_for_reduction.resample_step)
316 354
317 #elif str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'peaks': 355 #elif str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'peaks':
318 print('peaks reduction') 356 print('peaks reduction')
319 357
320 #if str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'table': 358 #if str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'table':
359
321 reference_table = read.delim("$method.methods_conditional.methods_for_reduction.ref_type.peaks_table", header = FALSE, stringsAsFactors = FALSE) 360 reference_table = read.delim("$method.methods_conditional.methods_for_reduction.ref_type.peaks_table", header = FALSE, stringsAsFactors = FALSE)
322 reference_column = reference_table[,$method.methods_conditional.methods_for_reduction.ref_type.mass_column] 361 reference_column = reference_table[,$method.methods_conditional.methods_for_reduction.ref_type.mass_column]
323 peak_reference = reference_column[reference_column>min(mz(msidata)) & reference_column<max(mz(msidata))] 362 peak_reference = reference_column[reference_column>min(mz(msidata)) & reference_column<max(mz(msidata))]
324 363
325 #elif str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'msidata_ref': 364 #elif str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'msidata_ref':
326 loadRData <- function(fileName){ 365
327 #loads an RData file, and returns it
328 load(fileName)
329 get(ls()[ls() != "fileName"])
330 }
331 peak_reference = loadRData('$method.methods_conditional.methods_for_reduction.ref_type.peaks_msidata') 366 peak_reference = loadRData('$method.methods_conditional.methods_for_reduction.ref_type.peaks_msidata')
367
332 #end if 368 #end if
369
333 msidata = reduceDimension(msidata, method="peaks", ref=peak_reference, type="$method.methods_conditional.methods_for_reduction.peaks_type") 370 msidata = reduceDimension(msidata, method="peaks", ref=peak_reference, type="$method.methods_conditional.methods_for_reduction.peaks_type")
334 #end if 371
372 #end if
373
374 ############################### optional QC ###########################
335 375
336 #if $outputs.outputs_select == "quality_control": 376 #if $outputs.outputs_select == "quality_control":
377
378 ### values for QC table:
337 maxfeatures = length(features(msidata)) 379 maxfeatures = length(features(msidata))
338 medianpeaks = median(colSums(spectra(msidata)[]>0)) 380 medianpeaks = median(colSums(spectra(msidata)[]>0))
339 medint = round(median(spectra(msidata)[]), digits=2) 381 medint = round(median(spectra(msidata)[]), digits=2)
340 TICs = round(mean(colSums(spectra(msidata)[])), digits=1) 382 TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
341 reduced= c(maxfeatures, medianpeaks, medint, TICs) 383 reduced= c(maxfeatures, medianpeaks, medint, TICs)
342 QC_numbers= cbind(QC_numbers, reduced) 384 QC_numbers= cbind(QC_numbers, reduced)
385
343 ### preparation for QC plots 386 ### preparation for QC plots
344 vectorofactions = append(vectorofactions, "reduced") 387 vectorofactions = append(vectorofactions, "reduced")
345 388
346 for (calibrant in inputcalibrants) 389 for (calibrant in inputcalibrants)
347 { 390 {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
348 currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
349 par.strip.text=list(col="black", cex=.9)),lattice=TRUE, 391 par.strip.text=list(col="black", cex=.9)),lattice=TRUE,
350 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="reduced") 392 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="reduced")
351 393 assign(paste("reduced",calibrant, sep="_"), currentimage)}
352 assign(paste("reduced",calibrant, sep="_"), currentimage) 394
353 } 395 #end if
354 #end if 396
355 397 ############################### Transformation ###########################
356 #elif str( $method.methods_conditional.preprocessing_method) == 'Transformation': 398
357 print('Transformation') 399 ####elif str( $method.methods_conditional.preprocessing_method) == 'Transformation':
358 400 ###print('Transformation')
359 #if str( $method.methods_conditional.transf_conditional.trans_type) == 'log2': 401
360 print('log2 transformation') 402 ####if str( $method.methods_conditional.transf_conditional.trans_type) == 'log2':
361 spectra(msidata)[spectra(msidata) ==0] = NA 403 ####print('log2 transformation')
362 print(paste0("Number of 0 which were converted into NA:",sum(is.na(spectra(msidata))))) 404
363 spectra(msidata) = log2(spectra(msidata)) 405 ###spectra(msidata)[spectra(msidata) ==0] = NA
364 406 ###print(paste0("Number of 0 which were converted into NA:",sum(is.na(spectra(msidata)))))
365 #elif str( $method.methods_conditional.transf_conditional.trans_type) == 'sqrt': 407 ###spectra(msidata) = log2(spectra(msidata))
366 print('squareroot transformation') 408
367 spectra(msidata) = sqrt(spectra(msidata)) 409 ####elif str( $method.methods_conditional.transf_conditional.trans_type) == 'sqrt':
368 410 ###print('squareroot transformation')
369 #end if 411
412 ###spectra(msidata) = sqrt(spectra(msidata))
413
414 ###end if
415
416 ############################### optional QC ###########################
417
418 #if $outputs.outputs_select == "quality_control":
419
420 ### values for QC table:
421 maxfeatures = length(features(msidata))
422 medianpeaks = median(colSums(spectra(msidata)[]>0), na.rm=TRUE)
423 medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2)
424 TICs = round(mean(colSums(spectra(msidata)[]), na.rm=TRUE), digits=1)
425 transformed= c(maxfeatures, medianpeaks, medint, TICs)
426 QC_numbers= cbind(QC_numbers, transformed)
427
428 ### preparation for QC plots
429 vectorofactions = append(vectorofactions, "transformed")
430
431 for (calibrant in inputcalibrants)
432 {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
433 par.strip.text=list(col="black", cex=.9)),lattice=TRUE,
434 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="transformed")
435 assign(paste("transformed",calibrant, sep="_"), currentimage)}
436
437 #end if
438
439 ############################### optional QC ###########################
440
441 #if $outputs.outputs_select == "quality_control":
442
443 ### values for QC table:
444 maxfeatures = length(features(msidata))
445 medianpeaks = median(colSums(spectra(msidata)[]>0))
446 medint = round(median(spectra(msidata)[]), digits=2)
447 TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
448 sample_norm= c(maxfeatures, medianpeaks, medint, TICs)
449 QC_numbers= cbind(QC_numbers, sample_norm)
450
451 ### preparation for QC plots
452 vectorofactions = append(vectorofactions, "sample_norm")
453
454 for (calibrant in inputcalibrants)
455 {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey",
456 par.strip.text=list(col="black", cex=.9)),lattice=TRUE,
457 scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="sample normalized")
458 assign(paste("sample_norm",calibrant, sep="_"), currentimage)}
459
460 #end if
461
370 #end if 462 #end if
371 #end for 463 #end for
372 464
373 ###################### Outputs: RData, tabular and QC report ################### 465 ###################### Outputs: RData, tabular and QC report ###################
374 466 ###############################################################################
375 467
376 ## save as (.RData) 468 ## save as (.RData)
377 save(msidata, file="$msidata_preprocessed") 469 save(msidata, file="$msidata_preprocessed")
378 470
379 471 print(paste0("Number of NAs in intensity matrix: ", sum(is.na(spectra(msidata)))))
472
473 ## save output matrix
380 #if $output_matrix: 474 #if $output_matrix:
381 475
382 476
383 if (length(features(msidata))> 0) 477 if (length(features(msidata))> 0)
384 { 478 {
385
386 ## save as intensity matrix 479 ## save as intensity matrix
387
388 spectramatrix = spectra(msidata) 480 spectramatrix = spectra(msidata)
389 rownames(spectramatrix) = mz(msidata) 481 rownames(spectramatrix) = mz(msidata)
390 newmatrix = rbind(pixels(msidata), spectramatrix) 482 newmatrix = rbind(pixels(msidata), spectramatrix)
391 write.table(newmatrix[2:nrow(newmatrix),], file="$matrixasoutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") 483 write.table(newmatrix[2:nrow(newmatrix),], file="$matrixasoutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
392 484
393 }else{ 485 }else{
394 print("file has no features left") 486 print("file has no features left")
395 write.table(matrix(rownames(coord(msidata)), ncol=ncol(msidata), nrow=1), file="$matrixasoutput", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") 487 write.table(matrix(rownames(coord(msidata)), ncol=ncol(msidata), nrow=1), file="$matrixasoutput", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
396 } 488 }
397 489
398 #end if 490 #end if
399 491
400 492 ## save QC report
401 #if $outputs.outputs_select == "quality_control": 493 #if $outputs.outputs_select == "quality_control":
402 494
403 rownames(QC_numbers) = c("# features", "median # peaks", "median intensity", "median TIC") 495 rownames(QC_numbers) = c("# features", "median # peaks", "median intensity", "median TIC")
404 grid.table(t(QC_numbers)) 496 grid.table(t(QC_numbers))
405 497
406 for (calibrant in inputcalibrants) 498 for (calibrant in inputcalibrants)
407 { 499 {imagelist = list()
408 imagelist = list()
409
410 for (numberprepro in 1:length(vectorofactions)){ 500 for (numberprepro in 1:length(vectorofactions)){
411 501 imagelist[[numberprepro]] = get(paste(vectorofactions[numberprepro],calibrant, sep="_"))}
412 imagelist[[numberprepro]] = get(paste(vectorofactions[numberprepro],calibrant, sep="_")) 502 do.call(grid.arrange,imagelist)}
413
414
415 }
416
417 do.call(grid.arrange,imagelist)
418 }
419
420 503
421 dev.off() 504 dev.off()
422 505
423 #end if 506 #end if
424 507
437 <option value="Smoothing">Peak smoothing</option> 520 <option value="Smoothing">Peak smoothing</option>
438 <option value="Peak_picking">Peak picking</option> 521 <option value="Peak_picking">Peak picking</option>
439 <option value="Peak_alignment">Peak alignment</option> 522 <option value="Peak_alignment">Peak alignment</option>
440 <option value="Peak_filtering">Peak filtering</option> 523 <option value="Peak_filtering">Peak filtering</option>
441 <option value="Data_reduction">Data reduction</option> 524 <option value="Data_reduction">Data reduction</option>
442 <option value="Transformation">Transformation</option> 525 <!--option value="Transformation">Transformation</option-->
526
443 </param> 527 </param>
444 <when value="Normalization"/> 528 <when value="Normalization"/>
445 <when value="Baseline_reduction"> 529 <when value="Baseline_reduction">
446 <param name="blocks_baseline" type="integer" value="50" 530 <param name="blocks_baseline" type="integer" value="50"
447 label="Blocks"/> 531 label="Blocks"/>
504 </param> 588 </param>
505 <when value="diff"> 589 <when value="diff">
506 <param name="value_diffalignment" type="integer" value="200" 590 <param name="value_diffalignment" type="integer" value="200"
507 label="diff.max" help="Peaks that differ less than this value will be aligned together"/> 591 label="diff.max" help="Peaks that differ less than this value will be aligned together"/>
508 <param name="units_diffalignment" type="select" display = "radio" optional = "False" 592 <param name="units_diffalignment" type="select" display = "radio" optional = "False"
509 label="units" help="The coefficients for the moving average filter"> 593 label="units">
510 <option value="ppm" selected="True">ppm</option> 594 <option value="ppm" selected="True">ppm</option>
511 <option value="Da">Da</option> 595 <option value="Da">Da</option>
512 </param> 596 </param>
513 </when> 597 </when>
514 <when value="DP"> 598 <when value="DP">
523 <option value="align_msidata_ref">msidata file as reference</option> 607 <option value="align_msidata_ref">msidata file as reference</option>
524 </param> 608 </param>
525 <when value="align_noref"/> 609 <when value="align_noref"/>
526 <when value="align_table"> 610 <when value="align_table">
527 <param name="align_peaks_table" type="data" format="tabular" 611 <param name="align_peaks_table" type="data" format="tabular"
528 label="Reference mz values to use for alignment - only these will be kept" help="One column with mz values (without empty cells or letters)"/> 612 label="Reference m/z values to use for alignment - only these will be kept" help="One column with m/z values (without empty cells or letters)"/>
529 <param name="align_mass_column" data_ref="align_peaks_table" label="Column with reference mz" type="data_column"/> 613 <param name="align_mass_column" data_ref="align_peaks_table" label="Column with reference m/z" type="data_column"/>
530 </when> 614 </when>
531 <when value="align_msidata_ref"> 615 <when value="align_msidata_ref">
532 <param name="align_peaks_msidata" type="data" format="rdata," label="Picked and aligned Cardinal MSImageSet saved as RData"/> 616 <param name="align_peaks_msidata" type="data" format="rdata," label="Picked and aligned Cardinal MSImageSet saved as RData"/>
533 </when> 617 </when>
534 </conditional> 618 </conditional>
544 <option value="resample">resample</option> 628 <option value="resample">resample</option>
545 <option value="peaks">peaks</option> 629 <option value="peaks">peaks</option>
546 </param> 630 </param>
547 <when value="bin"> 631 <when value="bin">
548 <param name="bin_width" type="float" value="1" 632 <param name="bin_width" type="float" value="1"
549 label="The width of a bin in mz or ppm" help="Width must be greater than range of mz values divided by number of mz features"/> 633 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"/>
550 <param name="bin_units" type="select" display="radio" 634 <param name="bin_units" type="select" display="radio"
551 label="Unit for bin"> 635 label="Unit for bin">
552 <option value="mz" selected="True">mz</option> 636 <option value="mz" selected="True">mz</option>
553 <option value="ppm">ppm</option> 637 <option value="ppm">ppm</option>
554 </param> 638 </param>
558 <option value="sum">sum</option> 642 <option value="sum">sum</option>
559 </param> 643 </param>
560 </when> 644 </when>
561 <when value="resample"> 645 <when value="resample">
562 <param name="resample_step" type="float" value="1" 646 <param name="resample_step" type="float" value="1"
563 label="The step size in mz" help="Step size must be greater than range of mz values divided by number of mz features"/> 647 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"/>
564 </when> 648 </when>
565 <when value="peaks"> 649 <when value="peaks">
566 <param name="peaks_type" type="select" display="radio" 650 <param name="peaks_type" type="select" display="radio"
567 label="Should the peak height or area under the curve be taken as the intensity value?"> 651 label="Should the peak height or area under the curve be taken as the intensity value?">
568 <option value="height" selected="True">height</option> 652 <option value="height" selected="True">height</option>
573 <option value="table" selected="True">tabular file</option> 657 <option value="table" selected="True">tabular file</option>
574 <option value="msidata_ref">msidata file</option> 658 <option value="msidata_ref">msidata file</option>
575 </param> 659 </param>
576 <when value="table"> 660 <when value="table">
577 <param name="peaks_table" type="data" format="tabular" 661 <param name="peaks_table" type="data" format="tabular"
578 label="Reference mz values to use to reduce the dimension" help="One column with mz values (without empty cells or letters, mz outside mz range are not used for filtering)"/> 662 label="Reference m/z values to use to reduce the dimension" help="One column with m/z values (without empty cells or letters, m/z outside m/z range are not used for filtering)"/>
579 <param name="mass_column" data_ref="peaks_table" label="Column with reference mz" type="data_column"/> 663 <param name="mass_column" data_ref="peaks_table" label="Column with reference m/z" type="data_column"/>
580 </when> 664 </when>
581 <when value="msidata_ref"> 665 <when value="msidata_ref">
582 <param name="peaks_msidata" type="data" format="rdata," label="Picked and aligned Cardinal MSImageSet saved as RData"/> 666 <param name="peaks_msidata" type="data" format="rdata," label="Picked and aligned Cardinal MSImageSet saved as RData"/>
583 </when> 667 </when>
584 </conditional> 668 </conditional>
585 </when> 669 </when>
586 </conditional> 670 </conditional>
587 </when> 671 </when>
588 <when value="Transformation"> 672 <!--when value="Transformation">
589 <conditional name="transf_conditional"> 673 <conditional name="transf_conditional">
590 <param name="trans_type" type="select" label="Choose which intensity transformation you want to apply" help="logarithm base 2 (log2) or squareroot (sqrt)"> 674 <param name="trans_type" type="select" label="Choose which intensity transformation you want to apply" help="logarithm base 2 (log2) or squareroot (sqrt)">
591 <option value="log2" selected="True">log2</option> 675 <option value="log2" selected="True">log2</option>
592 <option value="sqrt">sqrt</option> 676 <option value="sqrt">sqrt</option>
593 </param> 677 </param>
594 <when value="log2"/> 678 <when value="log2"/>
595 <when value="sqrt"/> 679 <when value="sqrt"/>
596 </conditional> 680 </conditional>
597 </when> 681 </when-->
598 </conditional> 682 </conditional>
599 </repeat> 683 </repeat>
600 <conditional name="outputs"> 684 <conditional name="outputs">
601 <param name="outputs_select" type="select" label="Quality control output"> 685 <param name="outputs_select" type="select" label="Quality control output">
602 <option value="quality_control" selected="True">yes</option> 686 <option value="quality_control" selected="True">yes</option>
603 <option value="no_quality_control">no</option> 687 <option value="no_quality_control">no</option>
604 </param> 688 </param>
605 <when value="quality_control"> 689 <when value="quality_control">
606 <param name="calibrant_file" type="data" format="tabular" 690 <param name="calibrant_file" type="data" format="tabular"
607 label="Provide a list of masses which will be plotted in the quality control report" 691 label="Provide a list of m/z, which will be plotted in the quality control report"
608 help="Use internal calibrant masses"/> 692 help="Use internal calibrant m/z"/>
609 <param name="calibrants_column" data_ref="calibrant_file" label="Column with masses" type="data_column"/> 693 <param name="calibrants_column" data_ref="calibrant_file" label="Column with m/z" type="data_column"/>
610 <param name="plusminus_dalton" value="0.25" type="text" label="Mass range" help="Plusminus mass window in Dalton"/> 694 <param name="plusminus_dalton" value="0.25" type="text" label="M/z range" help="Plusminus m/z window in Dalton"/>
611 </when> 695 </when>
612 <when value="no_quality_control"/> 696 <when value="no_quality_control"/>
613 </conditional> 697 </conditional>
614 <param name="output_matrix" type="boolean" display="radio" label="Intensity matrix output"/> 698 <param name="output_matrix" type="boolean" display="radio" label="Intensity matrix output"/>
615 </inputs> 699 </inputs>
616 <outputs> 700 <outputs>
617 <data format="rdata" name="msidata_preprocessed" label="Preprocessed ${on_string}"/> 701 <data format="rdata" name="msidata_preprocessed" label="$infile.display_name preprocessed"/>
618 <data format="pdf" name="QC_plots" from_work_dir="Preprocessing.pdf" label = "QC preprocessing report on ${on_string}"> 702 <data format="pdf" name="QC_plots" from_work_dir="Preprocessing.pdf" label = "$infile.display_name preprocessed_QC">
619 <filter>outputs["outputs_select"] == "quality_control"</filter> 703 <filter>outputs["outputs_select"] == "quality_control"</filter>
620 </data> 704 </data>
621 <data format="tabular" name="matrixasoutput" label="Intensity matrix ${on_string}"> 705 <data format="tabular" name="matrixasoutput" label="$infile.display_name preprocessed_matrix">
622 <filter>output_matrix</filter> 706 <filter>output_matrix</filter>
623 </data> 707 </data>
624 </outputs> 708 </outputs>
625 <tests> 709 <tests>
626 <test expect_num_outputs="2"> 710 <test expect_num_outputs="2">
663 <conditional name="methods_conditional"> 747 <conditional name="methods_conditional">
664 <param name="preprocessing_method" value="Peak_filtering"/> 748 <param name="preprocessing_method" value="Peak_filtering"/>
665 <param name="frequ_filtering" value="2"/> 749 <param name="frequ_filtering" value="2"/>
666 </conditional> 750 </conditional>
667 </repeat> 751 </repeat>
668 <repeat name="methods"> 752 <!--repeat name="methods">
669 <conditional name="methods_conditional"> 753 <conditional name="methods_conditional">
670 <param name="preprocessing_method" value="Transformation"/> 754 <param name="preprocessing_method" value="Transformation"/>
671 <conditional name="transf_conditional"> 755 <conditional name="transf_conditional">
672 <param name="trans_type" value="sqrt"/> 756 <param name="trans_type" value="sqrt"/>
673 </conditional> 757 </conditional>
674 </conditional> 758 </conditional>
675 </repeat> 759 </repeat-->
676 <param name="outputs_select" value="no_quality_control"/> 760 <param name="outputs_select" value="no_quality_control"/>
677 <param name="output_matrix" value="True"/> 761 <param name="output_matrix" value="True"/>
678 <output name="msidata_preprocessed" file="preprocessing_results1.RData" compare="sim_size"/> 762 <output name="msidata_preprocessed" file="preprocessing_results1.RData" compare="sim_size"/>
679 <output name="matrixasoutput" file="preprocessing_results1.txt"/> 763 <output name="matrixasoutput" file="preprocessing_results1.txt"/>
680 </test> 764 </test>
681 <test expect_num_outputs="3"> 765 <test expect_num_outputs="3">
682 <param name="infile" value="example_continous.RData" ftype="rdata"/> 766 <param name="infile" value="preprocessed.RData" ftype="rdata"/>
683 <repeat name="methods"> 767 <repeat name="methods">
684 <conditional name="methods_conditional"> 768 <conditional name="methods_conditional">
685 <param name="preprocessing_method" value="Peak_picking"/> 769 <param name="preprocessing_method" value="Peak_picking"/>
686 <param name="blocks_picking" value="3"/> 770 <param name="blocks_picking" value="3"/>
687 <param name="window_picking" value="5"/> 771 <param name="window_picking" value="5"/>
772 <param name="SNR_picking_method" value="2"/>
688 <conditional name="methods_for_picking"> 773 <conditional name="methods_for_picking">
689 <param name="picking_method" value="simple"/> 774 <param name="picking_method" value="adaptive"/>
690 </conditional> 775 </conditional>
691 </conditional> 776 </conditional>
692 </repeat> 777 </repeat>
693 <repeat name="methods"> 778 <repeat name="methods">
694 <conditional name="methods_conditional"> 779 <conditional name="methods_conditional">
697 <param name="alignment_method" value="DP"/> 782 <param name="alignment_method" value="DP"/>
698 </conditional> 783 </conditional>
699 </conditional> 784 </conditional>
700 </repeat> 785 </repeat>
701 <param name="outputs_select" value="quality_control"/> 786 <param name="outputs_select" value="quality_control"/>
702 <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile2.tabular"/> 787 <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile1.tabular"/>
703 <param name="calibrants_column" value="1"/> 788 <param name="calibrants_column" value="1"/>
704 <param name="plusminus_dalton" value="0.25"/> 789 <param name="plusminus_dalton" value="0.25"/>
705 <param name="output_matrix" value="True"/> 790 <param name="output_matrix" value="True"/>
706 <output name="msidata_preprocessed" file="preprocessing_results2.RData" compare="sim_size"/> 791 <output name="msidata_preprocessed" file="preprocessing_results2.RData" compare="sim_size"/>
707 <output name="matrixasoutput" file="preprocessing_results2.txt" lines_diff="2"/> 792 <output name="matrixasoutput" file="preprocessing_results2.txt" lines_diff="2"/>
721 <repeat name="methods"> 806 <repeat name="methods">
722 <conditional name="methods_conditional"> 807 <conditional name="methods_conditional">
723 <param name="preprocessing_method" value="Peak_picking"/> 808 <param name="preprocessing_method" value="Peak_picking"/>
724 <param name="blocks_picking" value="100"/> 809 <param name="blocks_picking" value="100"/>
725 <param name="window_picking" value="5"/> 810 <param name="window_picking" value="5"/>
726 <conditional name="methods_for_picking">
727 <param name="picking_method" value="limpic"/> 811 <param name="picking_method" value="limpic"/>
728 </conditional>
729 </conditional> 812 </conditional>
730 </repeat> 813 </repeat>
731 <repeat name="methods"> 814 <repeat name="methods">
732 <conditional name="methods_conditional"> 815 <conditional name="methods_conditional">
733 <param name="preprocessing_method" value="Peak_alignment"/> 816 <param name="preprocessing_method" value="Peak_alignment"/>
777 <param name="reduction_method" value="resample"/> 860 <param name="reduction_method" value="resample"/>
778 <param name="step_width" value="0.1"/> 861 <param name="step_width" value="0.1"/>
779 </conditional> 862 </conditional>
780 </conditional> 863 </conditional>
781 </repeat> 864 </repeat>
782 <repeat name="methods">
783 <conditional name="methods_conditional">
784 <param name="preprocessing_method" value="Transformation"/>
785 <conditional name="transf_conditional">
786 <param name="trans_type" value="log2"/>
787 </conditional>
788 </conditional>
789 </repeat>
790 <param name="outputs_select" value="quality_control"/> 865 <param name="outputs_select" value="quality_control"/>
791 <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile1.tabular"/> 866 <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile1.tabular"/>
792 <param name="calibrants_column" value="1"/> 867 <param name="calibrants_column" value="1"/>
793 <param name="plusminus_dalton" value="0.25"/> 868 <param name="plusminus_dalton" value="0.25"/>
794 <param name="output_matrix" value="True"/> 869 <param name="output_matrix" value="True"/>
800 <help> 875 <help>
801 <![CDATA[ 876 <![CDATA[
802 877
803 Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets. `More information on Cardinal <http://cardinalmsi.org//>`_ 878 Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets. `More information on Cardinal <http://cardinalmsi.org//>`_
804 879
805 This tool provides provides multiple Cardinal functions to preprocess mass-spectrometry imaging data. 880 This tool provides provides multiple Cardinal functions to preprocess mass spectrometry imaging data.
806 881
807 Input data: 3 types of input data can be used: 882 Input data: 3 types of input data can be used:
808 883
809 - imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/wp/imzml/>`_ 884 - imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/wp/imzml/>`_
810 - Analyze7.5 (upload hdr, img and t2m file via the "composite" function) 885 - Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
812 887
813 Options: 888 Options:
814 889
815 - Normalization: Normalization of intensities to total ion current (TIC) 890 - Normalization: Normalization of intensities to total ion current (TIC)
816 - Baseline reduction: Baseline reduction removes backgroundintensity generated by chemical noise (common in MALDI datasets) 891 - Baseline reduction: Baseline reduction removes backgroundintensity generated by chemical noise (common in MALDI datasets)
817 - Smoothening: Smoothing of the mass peaks reduces noise and improves peak detection 892 - Smoothening: Smoothing of the peaks reduces noise and improves peak detection
818 - Peak picking: relevant peaks are picked while noise-peaks are removed (needs peak alignment afterwards) 893 - Peak picking: relevant peaks are picked while noise-peaks are removed (needs peak alignment afterwards)
819 - Peak alignment: only possible after peak picking, mz inaccuracies are removed by alignment of same peaks to a common mz value 894 - Peak alignment: only possible after peak picking, m/z inaccuracies are removed by alignment of same peaks to a common m/z value
820 - Peak filtering: works only on centroided data (after peak picking and alignment or data reduction with peak filtering), removes peaks that occur only in a small proportion of pixels. If not sure which cutoff to chose run qualitycontrol first and decide according to the zero value plot. 895 - Peak filtering: works only on centroided data (after peak picking and alignment or data reduction with peak filtering), removes peaks that occur only in a small proportion of pixels. If not sure which cutoff to chose run qualitycontrol first and decide according to the zero value plot.
821 - Data reduction: binning, resampling or peak filtering to reduce data 896 - Data reduction: binning, resampling or peak filtering to reduce data
822 - Transformation: log2 or squareroot transformation of all intensities 897
823 898
824 Output: 899 Output:
825 900
826 - imzML file, preprocessed 901 - imzML file, preprocessed
827 - optional: pdf with heatmap of masses of interest after each preprocessing step 902 - optional: pdf with heatmap of m/z of interest after each preprocessing step
828 - optional: intensity matrix as tabular file (intensities for masses in rows and pixel in columns) 903 - optional: intensity matrix as tabular file (intensities for m/z in rows and pixel in columns)
829 904
830 Tip: 905 Tip:
831 906
832 - Peak alignment works only after peak picking 907 - Peak alignment works only after peak picking
833 - Peak filtering works only on centroided data (peak picking and alignment or Data reduction peaks) 908 - Peak filtering works only on centroided data (peak picking and alignment or Data reduction peaks)