jcapelladesto / george Goto Github PK

Hello, I am having an issue running basepeak_finder, even on the example dataset. I keep getting the error:

Error in xj[i] : only 0's may be mixed with negative subscripts
In addition: Warning messages:
1: In temp$mzmed - mz0 :
longer object length is not a multiple of shorter object length
2: In abs(temp$mzmed - mz0) * 1e+06/mz0 :
longer object length is not a multiple of shorter object length'

running the following code:

library(xcms)
library(geoRge)
data(mtbls213)

s1 <- PuInc_seeker(XCMSet=mtbls213,ULtag="CELL_Glc12",Ltag="CELL_Glc13",sep.pos.front = TRUE)
s2 <- basepeak_finder(PuIncR=s1,XCMSet=mtbls213,UL.atomM=12.0,L.atomM=13.003355,
  ppm.s=6.5,Basepeak.minInt=2000)

Hi Jordi,

Is there a way to run geoRge independent of XCMSet?

I was trying to use apLCMS and XCMS to run geoRge but found out it accepts XCMSet (which I have no idea how can I create it without all the peak picking, retcor and so on in xcms().

And all my samples were run 3 times and need to do average/mean to summarize them which I was not aware of a way to do within XCMSet.

I was learning your codes but found it kind of difficult to crack down all the details. Do you think if I can modify the script to adapt to just a feature table which contains mz (mzmed), rtime (rtmed) and feature intensities. I recognized your script will take advantage of rtime_min, rtime_max but I don't think it is a very important paramter right? Just need to have a specified minimum retention time window.

Let me know your suggestion. And if you can, give me any suggestions. Greatly appreciate your help!

Best,
Minghao Gong, PhD candidate
University of Florida

Hi Jordi,

Thank you for this fantastic software tool. I had a question/ comment about the statistical tests used for differential labeling analysis. T-tests make perfect sense for comparing labelled to unlabelled specimens. For differential labelling experiments, however, it would be fantastic to support ANOVA-like comparisons across more than two experimental groups. Is this for any reason unfeasible with the current implementation of geoRge? Am I missing an existing framework to carry out this type of analysis? I would worry about false discoveries emerging from carrying out all pairwise comparisons across all experimental groups. Thanks!

-Tom

Dear

I was simply following it but it was returned with the error message following the script

s1 <- PuInc_seeker(XCMSet=xset3,ULtag="CELL_Glc12",Ltag="CELL_Glc13",sep.pos="f")

Error in if (sep.pos.front) { : argument is not interpretable as logical

Would you help me figure it out? thanks

Hi Jordi,

first of all, thanks for providing geoRge.

I am currently facing the following issue:
Using the sample data, everything works fine, but when I try to use our own data, I get the following error message:
"Error in meanitensities[grep(ULtag, rownames(meanintensities)), ]: wrong number of dimensions"

In both cases, the data has been preprocessed using xcms. However, our own data is generated by GC-MS.

Comparing the two datasets, I get two similar outputs:

Sample data:

str(xset3)
Formal class 'xcmsSet' [package "xcms"] with 14 slots
..@ peaks : num [1:205594, 1:11] 169 169 169 169 173 ...
.. ..- attr(, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:11] "mz" "mzmin" "mzmax" "rt" ...
..@ groups : num [1:14607, 1:11] 100 100 100 100 101 ...
.. ..- attr(, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:11] "mzmed" "mzmin" "mzmax" "rtmed" ...
..@ groupidx :List of 14607
.. ..$ : int [1:12] 4940 14904 26709 36721 67803 77949 88441 152196 159043 186197 ...
.. ..$ : int [1:12] 4755 14624 67601 88127 138047 144931 152197 159044 173004 186198 ...
.. ..$ : int [1:12] 38144 69353 90704 125328 132229 138048 152198 159045 173005 186199 ...
.. ..$ : int [1:12] 5068 15106 36889 57691 67969 88675 120335 138049 152199 173006 ...
.. ..$ : int [1:14] 9396 21253 31405 31406 42532 52271 62412 72478 83263 94923 ...
.. ..$ : int [1:12] 9218 21088 31235 42394 52107 62228 72241 83148 94767 104835 ...
.. ..$ : int [1:12] 13344 35746 46953 56390 76017 99296 109542 119009 125329 138050 ...
.. ..$ : int [1:12] 4950 14920 26721 36757 47901 57537 67820 77967 88453 100453 ...
.. ..$ : int [1:12] 4418 26136 36082 47229 56807 67181 77127 87460 109777 119396 ...
.. ..$ : int [1:12] 5820 16852 27711 48892 58728 68909 79465 90177 101660 111464 ...
.. ..$ : int [1:12] 5930 17100 27845 58863 69060 79627 90338 111609 121477 144933 ...
.. ..$ : int [1:12] 6587 17952 38733 59656 69875 80444 122249 138051 152201 180074 ...
.. ..$ : int [1:13] 26984 48233 78435 100949 101056 120517 125330 132231 144934 159046 ...
.. ..$ : int [1:12] 16685 58602 79328 90043 125331 138052 144935 152202 166655 186204 ...
.. ..$ : int [1:12] 50787 81495 92233 103525 125332 132232 138053 144936 159047 166656 ...
.. ..$ : int [1:12] 18732 29302 125333 144937 152203 159048 166657 173007 180076 186205 ...
.. ..$ : int [1:15] 9397 31407 31408 42533 42534 52272 83264 104989 104990 132233 ...
.. ..$ : int [1:12] 4947 14913 26719 36753 47898 57531 67815 77960 88448 100448 ...
.. ..$ : int [1:12] 26404 47560 57170 125334 132234 144938 166659 173008 180078 186206 ...
.. ..$ : int [1:12] 3456 13284 25241 35507 46196 56247 66458 76636 86653 98635 ...
.. ..$ : int [1:12] 21254 31412 42536 52275 62414 72479 83265 104992 114676 124728 ...
.. ..$ : int [1:13] 31410 42535 52274 62413 104991 114675 114677 124727 125336 132235 ...
.. ..$ : int [1:12] 31409 52273 114674 124726 125337 132236 144939 159050 166661 173010 ...
.. ..$ : int [1:12] 8321 19794 30227 61357 71484 82116 92973 144940 152204 186208 ...
.. ..$ : int [1:12] 8317 19791 30224 41403 51361 61354 71481 82113 92970 104072 ...
.. ..$ : int [1:12] 8318 19792 30225 41404 51362 61355 71482 82114 92971 104073 ...
.. ..$ : int [1:12] 8319 19793 30226 41405 51363 61356 71483 82115 92972 104074 ...
.. ..$ : int [1:12] 6971 18405 29019 39417 50176 60101 70332 80885 91576 102949 ...
.. ..$ : int [1:12] 12861 66024 86316 118418 125338 138054 144941 152205 159051 173011 ...
.. ..$ : int [1:12] 3015 12748 24456 34895 45747 76018 98458 108576 159052 166662 ...
.. ..$ : int [1:12] 3139 13139 24098 34554 45748 55385 65894 76086 86713 97835 ...
.. ..$ : int [1:12] 4656 14490 26424 36463 47587 57189 67479 77522 87960 100068 ...
.. ..$ : int [1:12] 5125 15167 26863 48099 57841 78345 100770 110657 144942 166663 ...
.. ..$ : int [1:12] 67173 77107 125339 132237 138055 144943 152206 159053 180084 186210 ...
.. ..$ : int [1:12] 7095 18531 29119 39557 50295 60209 70453 81004 91681 103057 ...
.. ..$ : int [1:12] 7096 39558 50296 91682 103058 132238 138056 159054 166664 173012 ...
.. ..$ : int [1:12] 3457 13407 25052 35670 46149 56121 66328 76412 86780 98515 ...
.. ..$ : int [1:12] 3901 13464 25795 35436 46409 56248 66663 76413 86902 99002 ...
.. ..$ : int [1:12] 2429 12813 34712 45572 55386 65961 76019 86576 98516 108625 ...
.. ..$ : int [1:12] 4667 14502 26452 36469 47609 57215 67496 77528 87989 100075 ...
.. ..$ : int [1:12] 7079 18505 29099 39535 50282 60206 70417 80981 91661 103027 ...
.. ..$ : int [1:12] 7080 39536 50283 70418 91662 103028 132239 138058 159055 173013 ...
.. ..$ : int [1:13] 6537 17887 28550 38611 49720 59579 69811 80379 91064 102476 ...
.. ..$ : int [1:12] 9401 21257 94926 138059 144944 152207 159056 166665 173014 186211 ...
.. ..$ : int [1:25] 9398 9402 21255 21259 21260 31413 31417 42537 42539 42540 ...
.. ..$ : int [1:12] 9400 21258 31416 42538 52278 62417 72482 83268 94927 104995 ...
.. ..$ : int [1:12] 9399 21256 31414 52279 62418 72481 83267 94925 104996 114681 ...
.. ..$ : int [1:12] 31415 52277 62416 104994 125340 132240 144946 166666 173015 180085 ...
.. ..$ : int [1:14] 4670 14516 26434 36476 36477 47615 57227 67513 77563 77564 ...
.. ..$ : int [1:15] 7003 18432 29050 29051 39460 50189 60144 60145 70333 80942 ...
.. ..$ : int [1:12] 9268 21194 31318 42478 52225 62387 72411 83195 94888 104888 ...
.. ..$ : int [1:12] 4162 25819 35747 46894 66928 77021 99003 109339 132241 159057 ...
.. ..$ : int [1:12] 3389 12862 25053 46098 55721 66078 76020 86394 144947 186212 ...
.. ..$ : int [1:12] 6978 18400 29038 50190 60116 70325 80866 91563 102956 112635 ...
.. ..$ : int [1:12] 16191 78937 89601 125341 138060 144949 152208 159058 166667 186213 ...
.. ..$ : int [1:12] 48310 58046 125342 132242 138061 144950 166668 173016 180087 186214 ...
.. ..$ : int [1:12] 4638 14448 26388 36442 47563 57168 67467 77488 87928 100047 ...
.. ..$ : int [1:12] 6500 17845 38577 49676 80334 91032 112193 122142 138062 159059 ...
.. ..$ : int [1:13] 6994 18420 29042 39447 50201 60102 60103 70334 80916 91569 ...
.. ..$ : int [1:12] 36016 56643 67058 87322 99477 125343 132243 138063 152209 173017 ...
.. ..$ : int [1:13] 8617 30577 71713 71714 123985 132244 144951 152210 159060 173018 ...
.. ..$ : int [1:12] 9403 21261 31418 42455 52262 62391 72483 83238 94930 104970 ...
.. ..$ : int [1:12] 3390 12913 25160 35115 46197 55670 66133 76147 86503 98562 ...
.. ..$ : int [1:12] 3391 13408 25425 35116 46254 55784 66329 76276 86781 98636 ...
.. ..$ : int [1:12] 5893 17092 27831 58812 69020 79599 90304 111584 121455 144952 ...
.. ..$ : int [1:12] 15545 48259 100950 110841 120587 125344 138064 144953 159061 166670 ...
.. ..$ : int [1:12] 4283 13890 26096 36041 47220 56730 67113 77060 87395 99529 ...
.. ..$ : int [1:12] 12080 33705 54783 64876 74983 85694 97464 117082 125345 138065 ...
.. ..$ : int [1:12] 3459 13071 25161 35117 46332 55854 66257 76277 86851 98563 ...
.. ..$ : int [1:12] 72463 94842 125346 132245 138066 144954 152213 159062 173020 186218 ...
.. ..$ : int [1:12] 3728 13564 25584 35578 46547 56055 66536 76637 87125 98708 ...
.. ..$ : int [1:12] 5692 16653 27584 37592 48775 58571 68765 79297 90008 111352 ...
.. ..$ : int [1:12] 15595 37186 58010 68243 78506 125347 138067 152214 180090 186220 ...
.. ..$ : int [1:12] 5708 16679 27599 37582 48790 58592 68788 79313 90035 101539 ...
.. ..$ : int [1:12] 5213 15497 27027 37094 48245 57960 68199 78478 88921 100883 ...
.. ..$ : int [1:12] 4342 14021 26104 36042 47230 56792 67153 77061 87416 99641 ...
.. ..$ : int [1:18] 6995 18406 29032 39421 39422 50191 50192 60124 60125 70326 ...
.. ..$ : int [1:12] 9127 31118 42383 52007 62179 72314 83026 94817 114440 124539 ...
.. ..$ : int [1:12] 34352 55481 125348 132247 138068 152215 166671 173021 180091 186222 ...
.. ..$ : int [1:12] 3857 13523 25513 35437 46792 56197 66664 76638 87233 99073 ...
.. ..$ : int [1:12] 5053 15086 26826 36867 48025 57672 67951 78130 88633 100584 ...
.. ..$ : int [1:12] 32094 43161 125349 132248 138069 159063 166672 173022 180092 186223 ...
.. ..$ : int [1:12] 4960 14937 26736 36773 47913 57546 67833 77984 88469 100462 ...
.. ..$ : int [1:12] 5214 15115 26926 37072 48234 57933 68146 78259 100615 110729 ...
.. ..$ : int [1:12] 14553 26477 36542 57254 67533 77589 88023 100152 125350 152216 ...
.. ..$ : int [1:12] 5702 27595 48784 68783 79314 90031 101531 121219 132249 144955 ...
.. ..$ : int [1:12] 11833 44780 54657 85557 117175 125351 138070 144956 166673 173023 ...
.. ..$ : int [1:12] 9404 21262 31419 42541 52281 62421 72484 83270 94931 104997 ...
.. ..$ : int [1:12] 4333 14044 26157 36141 47308 56821 67166 77134 87492 99612 ...
.. ..$ : int [1:12] 4063 13600 25820 35777 46926 55722 66726 76877 87361 98564 ...
.. ..$ : int [1:12] 82 10153 21979 32108 53077 73198 95642 105752 115445 152217 ...
.. ..$ : int [1:12] 47948 57585 125352 132250 138071 144957 166675 173024 180095 186225 ...
.. ..$ : int [1:12] 4824 14753 26605 36647 47785 57406 67691 77795 88264 100330 ...
.. ..$ : int [1:12] 4397 14059 26152 36116 47250 56822 67189 77162 87474 99684 ...
.. ..$ : int [1:12] 5065 15102 26845 36881 48038 57685 67964 78152 88667 100610 ...
.. ..$ : int [1:12] 4311 14060 26127 36183 47231 56847 67202 77081 87461 99575 ...
.. ..$ : int [1:12] 5011 15004 26793 47983 57621 67895 78057 88546 100534 110515 ...
.. ..$ : int [1:12] 16039 27253 58223 68432 78818 89495 101193 120827 125353 144959 ...
.. ..$ : int [1:12] 5704 16678 27596 37577 48785 58589 68787 79316 90029 101538 ...
.. .. [list output truncated]
..@ filled : int [1:80267] 125328 125329 125330 125331 125332 125333 125334 125335 125336 125337 ...
..@ phenoData :'data.frame': 12 obs. of 1 variable:
.. ..$ class: Factor w/ 4 levels "CELL_Glc12_05mM_Normo",..: 1 1 1 2 2 2 3 3 3 4 ...
..@ rt :List of 2
.. ..$ raw :List of 12
.. .. ..$ : num [1:5023] 2.23 2.48 2.73 2.98 3.23 ...
.. .. ..$ : num [1:5023] 2.21 2.46 2.71 2.97 3.22 ...
.. .. ..$ : num [1:5023] 2.33 2.58 2.83 3.08 3.33 ...
.. .. ..$ : num [1:5023] 2.32 2.57 2.82 3.07 3.32 ...
.. .. ..$ : num [1:5023] 2.31 2.56 2.81 3.06 3.31 ...
.. .. ..$ : num [1:5023] 2.2 2.45 2.7 2.95 3.2 ...
.. .. ..$ : num [1:5023] 2.25 2.5 2.75 3 3.25 ...
.. .. ..$ : num [1:5024] 2.09 2.34 2.59 2.84 3.09 ...
.. .. ..$ : num [1:5023] 2.21 2.46 2.71 2.96 3.21 ...
.. .. ..$ : num [1:5023] 2.2 2.45 2.7 2.96 3.21 ...
.. .. ..$ : num [1:5023] 2.18 2.43 2.68 2.93 3.18 ...
.. .. ..$ : num [1:5024] 2.15 2.4 2.65 2.9 3.15 ...
.. ..$ corrected:List of 12
.. .. ..$ : num [1:5023] 2.21 2.46 2.72 2.97 3.22 ...
.. .. ..$ : num [1:5023] 2.21 2.46 2.71 2.97 3.22 ...
.. .. ..$ : num [1:5023] 2.21 2.46 2.72 2.97 3.22 ...
.. .. ..$ : num [1:5023] 2.21 2.46 2.72 2.96 3.22 ...
.. .. ..$ : num [1:5023] 2.21 2.47 2.72 2.97 3.22 ...
.. .. ..$ : num [1:5023] 2.21 2.47 2.72 2.97 3.22 ...
.. .. ..$ : num [1:5023] 2.21 2.46 2.71 2.97 3.22 ...
.. .. ..$ : num [1:5024] 2.21 2.47 2.72 2.97 3.22 ...
.. .. ..$ : num [1:5023] 2.21 2.46 2.72 2.97 3.22 ...
.. .. ..$ : num [1:5023] 2.21 2.47 2.72 2.97 3.22 ...
.. .. ..$ : num [1:5023] 2.21 2.47 2.72 2.97 3.22 ...
.. .. ..$ : num [1:5024] 2.21 2.46 2.72 2.97 3.22 ...
..@ filepaths : chr [1:12] "L:/MCTP/Hefe 13C-Test R/Test_Bernd/CELL_Glc12_05mM_Normo/CELL_Glc12_05mM_Normo_04.mzXML" "L:/MCTP/Hefe 13C-Test R/Test_Bernd/CELL_Glc12_05mM_Normo/CELL_Glc12_05mM_Normo_05.mzXML" "L:/MCTP/Hefe 13C-Test R/Test_Bernd/CELL_Glc12_05mM_Normo/CELL_Glc12_05mM_Normo_06.mzXML" "L:/MCTP/Hefe 13C-Test R/Test_Bernd/CELL_Glc12_25mM_Normo/CELL_Glc12_25mM_Normo_16.mzXML" ...
..@ profinfo :List of 2
.. ..$ method: chr "bin"
.. ..$ step : num 0.1
..@ dataCorrection : int(0)
..@ polarity : chr(0)
..@ progressInfo :List of 12
.. ..$ group.density : num 0
.. ..$ group.mzClust : num 0
.. ..$ group.nearest : num 0
.. ..$ findPeaks.centWave : num 0
.. ..$ findPeaks.massifquant : num 0
.. ..$ findPeaks.matchedFilter: num 0
.. ..$ findPeaks.MS1 : num 0
.. ..$ findPeaks.MSW : num 0
.. ..$ retcor.obiwarp : num 1
.. ..$ retcor.peakgroups : num 0
.. ..$ fillPeaks.chrom : num 0
.. ..$ fillPeaks.MSW : num 0
..@ progressCallback:function (progress)
..@ mslevel : num(0)
..@ scanrange : num(0)

Own data:

str(xset6)
Formal class 'xcmsSet' [package "xcms"] with 14 slots
..@ peaks : num [1:3486, 1:11] 57.1 74.1 99.2 58 46 ...
.. ..- attr(, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:11] "mz" "mzmin" "mzmax" "rt" ...
..@ groups : num [1:286, 1:11] 41.1 42 42 42 42 ...
.. ..- attr(, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:11] "mzmed" "mzmin" "mzmax" "rtmed" ...
..@ groupidx :List of 286
.. ..$ : int [1:12] 161 370 579 788 997 1206 2983 3067 3151 3235 ...

.. ..$ : int [1:12] 96 305 514 723 932 1141 1344 1552 1760 1968 ...
.. ..$ : int [1:12] 7 216 425 634 843 1052 1259 1467 1675 1883 ...
.. ..$ : int [1:12] 108 317 526 735 944 1153 2984 3068 3152 3236 ...

.. ..$ : int [1:12] 54 263 472 681 890 1099 2985 3069 3153 3237 ...
.. ..$ : int [1:12] 1399 1607 1815 2023 2231 2439 2503 2583 2663 2743 ...
.. ..$ : int [1:12] 1276 1484 1692 1900 2108 2316 2504 2584 2664 2744 ...
.. ..$ : int [1:18] 145 159 354 368 563 577 772 786 981 995 ...
.. ..$ : int [1:12] 13 222 431 640 849 1058 1265 1473 1681 1889 ...
.. ..$ : int [1:12] 120 329 538 747 956 1165 2987 3071 3155 3239 ...

.. ..$ : int [1:12] 63 272 481 690 899 1108 2988 3072 3156 3240 ...
.. ..$ : int [1:12] 87 296 505 714 923 1132 2989 3073 3157 3241 ...
.. ..$ : int [1:12] 109 318 527 736 945 1154 2990 3074 3158 3242 ...

.. ..$ : int [1:12] 1277 1485 1693 1901 2109 2317 2505 2585 2665 2745 ...
.. ..$ : int [1:18] 111 320 529 738 947 1156 1357 1358 1565 1566 ...

.. ..$ : int [1:12] 52 261 470 679 888 1097 1304 1512 1720 1928 ...
.. ..$ : int [1:12] 130 339 548 757 966 1175 1380 1588 1796 2004 ...

.. ..$ : int [1:12] 92 301 510 719 928 1137 1342 1550 1758 1966 ...
.. ..$ : int [1:12] 69 278 487 696 905 1114 1322 1530 1738 1946 ...
.. ..$ : int [1:12] 175 384 593 802 1011 1220 1423 1631 1839 2047 ...
.. ..$ : int [1:12] 158 367 576 785 994 1203 1401 1609 1817 2025 ...

.. ..$ : int [1:12] 61 270 479 688 897 1106 1314 1522 1730 1938 ...
.. ..$ : int [1:12] 85 294 503 712 921 1130 1336 1544 1752 1960 ...
.. ..$ : int [1:12] 14 223 432 641 850 1059 1258 1466 1674 1882 ...
.. ..$ : int [1:12] 141 350 559 768 977 1186 1387 1595 1803 2011 ...

.. ..$ : int [1:12] 1285 1493 1701 1909 2117 2325 2506 2586 2666 2746 ...
.. ..$ : int [1:12] 50 259 468 677 886 1095 1295 1503 1711 1919 ...
.. ..$ : int [1:12] 124 333 542 751 960 1169 1376 1584 1792 2000 ...

.. ..$ : int [1:12] 155 364 573 782 991 1200 1392 1600 1808 2016 ...

.. ..$ : int [1:12] 84 293 502 711 920 1129 1333 1541 1749 1957 ...
.. ..$ : int [1:12] 5 214 423 632 841 1050 1263 1471 1679 1887 ...
.. ..$ : int [1:12] 1318 1526 1734 1942 2150 2358 2507 2587 2667 2747 ...
.. ..$ : int [1:12] 59 268 477 686 895 1104 2991 3075 3159 3243 ...
.. ..$ : int [1:12] 1353 1561 1769 1977 2185 2393 2508 2588 2668 2748 ...
.. ..$ : int [1:12] 1367 1575 1783 1991 2199 2407 2509 2589 2669 2749 ...
.. ..$ : int [1:12] 138 347 556 765 974 1183 2992 3076 3160 3244 ...

.. ..$ : int [1:12] 1330 1538 1746 1954 2162 2370 2510 2590 2670 2750 ...
.. ..$ : int [1:12] 125 334 543 752 961 1170 2993 3077 3161 3245 ...

.. ..$ : int [1:12] 162 371 580 789 998 1207 2994 3078 3162 3246 ...

.. ..$ : int [1:12] 1257 1465 1673 1881 2089 2297 2511 2591 2671 2751 ...
.. ..$ : int [1:12] 163 372 581 790 999 1208 1405 1613 1821 2029 ...

.. ..$ : int [1:12] 118 327 536 745 954 1163 2995 3079 3163 3247 ...

.. ..$ : int [1:12] 156 365 574 783 992 1201 1407 1615 1823 2031 ...

.. ..$ : int [1:12] 1 210 419 628 837 1046 2996 3080 3164 3248 ...
.. ..$ : int [1:12] 140 349 558 767 976 1185 1390 1598 1806 2014 ...

.. ..$ : int [1:12] 4 213 422 631 840 1049 2997 3081 3165 3249 ...
.. ..$ : int [1:12] 153 362 571 780 989 1198 2998 3082 3166 3250 ...

.. ..$ : int [1:12] 1366 1574 1782 1990 2198 2406 2512 2592 2672 2752 ...
.. ..$ : int [1:12] 81 290 499 708 917 1126 2999 3083 3167 3251 ...
.. ..$ : int [1:12] 1400 1608 1816 2024 2232 2440 2513 2593 2673 2753 ...
.. ..$ : int [1:18] 104 313 522 731 940 1149 1350 1351 1558 1559 ...

.. ..$ : int [1:12] 131 340 549 758 967 1176 1374 1582 1790 1998 ...

.. ..$ : int [1:12] 46 255 464 673 882 1091 1293 1501 1709 1917 ...
.. ..$ : int [1:12] 68 277 486 695 904 1113 1317 1525 1733 1941 ...
.. ..$ : int [1:12] 11 220 429 638 847 1056 1260 1468 1676 1884 ...
.. ..$ : int [1:12] 154 363 572 781 990 1199 3000 3084 3168 3252 ...

.. ..$ : int [1:12] 25 234 443 652 861 1070 3001 3085 3169 3253 ...
.. ..$ : int [1:12] 80 289 498 707 916 1125 3002 3086 3170 3254 ...
.. ..$ : int [1:12] 150 359 568 777 986 1195 1393 1601 1809 2017 ...

.. ..$ : int [1:12] 1332 1540 1748 1956 2164 2372 2514 2594 2674 2754 ...
.. ..$ : int [1:12] 1309 1517 1725 1933 2141 2349 2515 2595 2675 2755 ...
.. ..$ : int [1:12] 1310 1518 1726 1934 2142 2350 2516 2596 2676 2756 ...
.. ..$ : int [1:12] 1429 1637 1845 2053 2261 2469 2517 2597 2677 2757 ...
.. ..$ : int [1:12] 1319 1527 1735 1943 2151 2359 2518 2598 2678 2758 ...
.. ..$ : int [1:12] 1255 1463 1671 1879 2087 2295 2519 2599 2679 2759 ...
.. ..$ : int [1:12] 1300 1508 1716 1924 2132 2340 2520 2600 2680 2760 ...
.. ..$ : int [1:18] 193 209 402 418 611 627 820 836 1029 1045 ...
.. ..$ : int [1:18] 121 330 539 748 957 1166 1368 1370 1576 1578 ...

.. ..$ : int [1:12] 137 346 555 764 973 1182 1382 1590 1798 2006 ...

.. ..$ : int [1:12] 172 381 590 799 1008 1217 1415 1623 1831 2039 ...
.. ..$ : int [1:12] 100 309 518 727 936 1145 1346 1554 1762 1970 ...

.. ..$ : int [1:12] 116 325 534 743 952 1161 1363 1571 1779 1987 ...

.. ..$ : int [1:12] 76 285 494 703 912 1121 1329 1537 1745 1953 ...
.. ..$ : int [1:12] 178 387 596 805 1014 1223 1417 1625 1833 2041 ...
.. ..$ : int [1:12] 157 366 575 784 993 1202 1406 1614 1822 2030 ...

.. ..$ : int [1:12] 190 399 608 817 1026 1235 1436 1644 1852 2060 ...
.. ..$ : int [1:12] 21 230 439 648 857 1066 1274 1482 1690 1898 ...
.. ..$ : int [1:12] 27 236 445 654 863 1072 1282 1490 1698 1906 ...
.. ..$ : int [1:12] 88 297 506 715 924 1133 1335 1543 1751 1959 ...
.. ..$ : int [1:12] 12 221 430 639 848 1057 1261 1469 1677 1885 ...
.. ..$ : int [1:12] 180 389 598 807 1016 1225 1428 1636 1844 2052 ...
.. ..$ : int [1:12] 144 353 562 771 980 1189 1391 1599 1807 2015 ...

.. ..$ : int [1:12] 38 247 456 665 874 1083 3003 3087 3171 3255 ...
.. ..$ : int [1:12] 66 275 484 693 902 1111 3004 3088 3172 3256 ...
.. ..$ : int [1:18] 97 306 515 724 933 1142 1339 1345 1547 1553 ...
.. ..$ : int [1:18] 19 22 228 231 437 440 646 649 855 858 ...
.. ..$ : int [1:12] 135 344 553 762 971 1180 1377 1585 1793 2001 ...

.. ..$ : int [1:12] 168 377 586 795 1004 1213 1413 1621 1829 2037 ...
.. ..$ : int [1:12] 117 326 535 744 953 1162 1364 1572 1780 1988 ...

.. ..$ : int [1:12] 196 405 614 823 1032 1241 1460 1668 1876 2084 ...
.. ..$ : int [1:12] 67 276 485 694 903 1112 1328 1536 1744 1952 ...
.. ..$ : int [1:12] 164 373 582 791 1000 1209 1409 1617 1825 2033 ...
.. ..$ : int [1:12] 179 388 597 806 1015 1224 1424 1632 1840 2048 ...
.. ..$ : int [1:12] 122 331 540 749 958 1167 1369 1577 1785 1993 ...

.. ..$ : int [1:12] 188 397 606 815 1024 1233 1432 1640 1848 2056 ...
.. ..$ : int [1:12] 28 237 446 655 864 1073 1279 1487 1695 1903 ...
.. ..$ : int [1:12] 82 291 500 709 918 1127 1338 1546 1754 1962 ...
.. ..$ : int [1:12] 16 225 434 643 852 1061 1270 1478 1686 1894 ...
.. ..$ : int [1:12] 142 351 560 769 978 1187 1388 1596 1804 2012 ...

.. .. [list output truncated]
..@ filled : int [1:984] 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 ...
..@ phenoData :'data.frame': 12 obs. of 1 variable:
.. ..$ class: Factor w/ 4 levels "CELL_Glc12_05mM_Normo",..: 1 1 1 2 2 2 3 3 3 4 ...
..@ rt :List of 2
.. ..$ raw :List of 12
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. ..$ corrected:List of 12
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
.. .. ..$ : num [1:7680] 504 504 504 505 505 ...
..@ filepaths : chr [1:12] "L:/MCTP/Hefe 13C-Test R/Test_mzxml/CELL_Glc12_05mM_Normo/CELL_Glc12_05mM_Normo_01.mzXML" "L:/MCTP/Hefe 13C-Test R/Test_mzxml/CELL_Glc12_05mM_Normo/CELL_Glc12_05mM_Normo_02.mzXML" "L:/MCTP/Hefe 13C-Test R/Test_mzxml/CELL_Glc12_05mM_Normo/CELL_Glc12_05mM_Normo_03.mzXML" "L:/MCTP/Hefe 13C-Test R/Test_mzxml/CELL_Glc12_25mM_Normo/CELL_Glc12_25mM_Normo_07.mzXML" ...
..@ profinfo :List of 2
.. ..$ method: chr "bin"
.. ..$ step : num 0.1
..@ dataCorrection : int(0)
..@ polarity : chr(0)
..@ progressInfo :List of 12
.. ..$ group.density : num 0
.. ..$ group.mzClust : num 0
.. ..$ group.nearest : num 0
.. ..$ findPeaks.centWave : num 0
.. ..$ findPeaks.massifquant : num 0
.. ..$ findPeaks.matchedFilter: num 0
.. ..$ findPeaks.MS1 : num 0
.. ..$ findPeaks.MSW : num 0
.. ..$ retcor.obiwarp : num 1
.. ..$ retcor.peakgroups : num 0
.. ..$ fillPeaks.chrom : num 0
.. ..$ fillPeaks.MSW : num 0
..@ progressCallback:function (progress)
..@ mslevel : num(0)
..@ scanrange : num(0)

Any hint to solve this problem would be highly appreciated.

Many thanks in advance!
Anne

Dear Jordi

Thanks for your help the other day.

I have question on Welch's ttest between different condition. Because my own data is not working I have tried it with example data "mtbl231". But I was not able to have p value and fold change between two condition.

Here is the script I am using. I have tried it with different combination of control.con = (e.g. "05mM_Nor)

s4 <- label_compare(geoRgeR = s2, XCMSet = xset3, ppm.s = NULL,rt.win.min = 1, control.cond = NULL, fc.vs.Control = 1, p.value.vs.Control = 0.05, Show.bp = T)

str(s4)
'data.frame': 1105 obs. of 5 variables:
$ Comparison : Factor w/ 2 levels "","Base peak": 2 1 2 1 1 2 1 2 1 1 ...
$ X05mM_Normo_MEAN: num 61.89 38.11 81.43 7.36 11.21 ...
$ X25mM_Normo_MEAN: num 67.12 32.88 82.01 7.92 10.07 ...
$ X05mM_Normo_SD : num 7.17 7.17 1.6 0.37 1.92 ...
$ X25mM_Normo_SD : num 0.87 0.87 0.723 0.437 1.142 ...

Here is the file name that I downloaded for mtbl231
CELL_Glc12_05mM_Normo_04
CELL_Glc12_05mM_Normo_05
CELL_Glc12_05mM_Normo_06
CELL_Glc12_25mM_Normo_16
CELL_Glc12_25mM_Normo_17
CELL_Glc12_25mM_Normo_18
CELL_Glc13_05mM_Normo_01
CELL_Glc13_05mM_Normo_02
CELL_Glc13_05mM_Normo_03
CELL_Glc13_25mM_Normo_13
CELL_Glc13_25mM_Normo_14
CELL_Glc13_25mM_Normo_15

Thanks in advance.

Hi,

I processed my raw MS data using XCMS (functions xcmsSet, retcor, group) as you describe in the paper. In the first line of PuInc_seeker, xcms::groupval creates a matrix with NA values. This is different from the groupval matrix created using your test data mtbls213 which seems to have no missing values. XCMS documentation say that NA values occur when there is no peak for that specific sample, which doesn't seem surprising to me. These NA values, though, I think cause issues for me when filtsampsint with a lot of NAs is used to select in the Welch's test.

Do you know this to be a problem? If yes how do I go about this?

Any help would be appreciated!

It looks like geoRge is the perfect tool for me project so I really hope there is a solution for this issue.

Thanks in advance!
Arjana Begzati

Hi, the following code triggers an Error in apply(meanintensities, 2, function(x) all(x < PuInc.int.lim)) : dim(X) must have a positive length .
Jordi can reproduce the issue and is working on it.
Yours, Steffen

library(xcms)
library(geoRge)
data(mtbls213)

sampclass(mtbls213) <- c(rep("CELL_Glc12_05mM_Normo", 6), rep("CELL_Glc13_05mM_Normo", 6))
s1 <- PuInc_seeker(XCMSet=mtbls213,ULtag="CELL_Glc12",Ltag="CELL_Glc13",sep.pos="f")

s2 <- basepeak_finder(PuIncR=s1,XCMSet=mtbls213,ULtag="CELL_Glc12",Ltag="CELL_Glc13",
  sep.pos="f",UL.atomM=12.0,L.atomM=13.003355,
  ppm.s=6.5,Basepeak.minInt=2000)

I have xcms installed and I am running RStudio as admin, but I keep receiving the following messages:
`> library(devtools)
Loading required package: usethis
Warning messages:
1: package ‘devtools’ was built under R version 4.0.4
2: package ‘usethis’ was built under R version 4.0.4

install_github("jcapelladesto/geoRge")
Downloading GitHub repo jcapelladesto/geoRge@HEAD
Skipping 1 packages not available: xcms
√ checking for file 'C:\Users\madis\AppData\Local\Temp\RtmpgTGnAD\remotes374462ce7705\jcapelladesto-geoRge-450f8df/DESCRIPTION' ...

• preparing 'geoRge':
  √ checking DESCRIPTION meta-information ...
• checking for LF line-endings in source and make files and shell scripts
• checking for empty or unneeded directories
• building 'geoRge_1.0.tar.gz'

Installing package into ‘C:/Users/madis/Documents/R/win-library/4.0’
(as ‘lib’ is unspecified)

• installing source package 'geoRge' ...
  ** using staged installation
  ** R
  ** data
  *** moving datasets to lazyload DB
  Warning in fun(libname, pkgname) :
  mzR has been built against a different Rcpp version (1.0.5)
  than is installed on your system (1.0.6). This might lead to errors
  when loading mzR. If you encounter such issues, please send a report,
  including the output of sessionInfo() to the Bioc support forum at
  https://support.bioconductor.org/. For details see also
  https://github.com/sneumann/mzR/wiki/mzR-Rcpp-compiler-linker-issue.
  Warning: namespace 'xcms' is not available and has been replaced
  by .GlobalEnv when processing object 'mtbls213'
  Warning: namespace 'xcms' is not available and has been replaced
  by .GlobalEnv when processing object 'mtbls213'
  ** inst
  ** byte-compile and prepare package for lazy loading
  Error: (converted from warning) package 'Rcpp' was built under R version 4.0.4
  Execution halted
  ERROR: lazy loading failed for package 'geoRge'
• removing 'C:/Users/madis/Documents/R/win-library/4.0/geoRge'
  Error: Failed to install 'geoRge' from GitHub:
  (converted from warning) installation of package ‘C:/Users/madis/AppData/Local/Temp/RtmpgTGnAD/file37446bb523b4/geoRge_1.0.tar.gz’ had non-zero exit status

traceback()
10: stop(remote_install_error(remotes[[i]], e))
9: value[3L]
8: tryCatchOne(expr, names, parentenv, handlers[[1L]])
7: tryCatchList(expr, classes, parentenv, handlers)
6: tryCatch(res[[i]] <- install_remote(remotes[[i]], ...), error = function(e) {
stop(remote_install_error(remotes[[i]], e))
})
5: install_remotes(remotes, auth_token = auth_token, host = host,
dependencies = dependencies, upgrade = upgrade, force = force,
quiet = quiet, build = build, build_opts = build_opts, build_manual = build_manual,
build_vignettes = build_vignettes, repos = repos, type = type,
...)
4: force(code)
3: withr::with_path(rtools_path(), code)
2: pkgbuild::with_build_tools({
ellipsis::check_dots_used(action = getOption("devtools.ellipsis_action",
rlang::warn))
{
remotes <- lapply(repo, github_remote, ref = ref, subdir = subdir,
auth_token = auth_token, host = host)
install_remotes(remotes, auth_token = auth_token, host = host,
dependencies = dependencies, upgrade = upgrade, force = force,
quiet = quiet, build = build, build_opts = build_opts,
build_manual = build_manual, build_vignettes = build_vignettes,
repos = repos, type = type, ...)
}
}, required = FALSE)
1: install_github("jcapelladesto/geoRge")`

Any advice on how to continue?

Hi, I am trying to run the example script with the MTBLS213 data,
and get the following error in PuInc_seeker(). Very often this is due
to a missing DROP=FALSE in some access, so that a matrix/dataframe
gets "helpfully" changed from 2D to just a vector by R.

Yours, Steffen

> xset3
An "xcmsSet" object with 12 samples

Time range: 2.5-1259.8 seconds (0-21 minutes)
Mass range: 100.0112-1499.59 m/z
Peaks: 125327 (about 10444 per sample)
Peak Groups: 7588 
Sample classes: MTBLS213 

Peak picking was performed on MS1.
Profile settings: method = bin
                  step = 0.1

Memory usage: 12.6 MB
> s1 <- PuInc_seeker(XCMSet=xset3,ULtag="CELL_Glc12",Ltag="CELL_Glc13",sep.pos="f")
Error in meanintensities[grep(ULtag, rownames(meanintensities)), ] : 
  incorrect number of dimensions

Hi,
I could run script for geoRge with partial success for sample data (mtbls213). This is what I am trying
->library(xcms)

library(geoRge)

data(mtbls213)

sampclass(mtbls213) <- c(rep("CELL_Glc12_05mM_Normo", 3), rep("CELL_Glc13_05mM_Normo", 3),rep("CELL_Glc13_25mM_Normo", 3), rep("CELL_Glc13_25mM_Normo", 3))

s1 <- PuInc_seeker(XCMSet=mtbls213,ULtag="CELL_Glc12",Ltag="CELL_Glc13",sep.pos.front=TRUE ,fc.threshold=1.5,p.value.threshold=.05,PuInc.int.lim = 4000)

s2 <- basepeak_finder(PuIncR = s1, XCMSet = mtbls213, UL.atomM=12.0,L.atomM=13.003355,ppm.s=6.5,Basepeak.minInt=2000)

s4 <- label_compare(geoRgeR = s2, XCMSet = mtbls213, ppm.s = 6.5,rt.win.min = 1, control.cond = "05mM_Normo", fc.vs.Control = 1, p.value.vs.Control = 0.05, Show.bp = T)

str (s4)

s5 <- database_query(geoRgeR = s2, adducts = M-H, db = "C:/Users/Admin/Downloads/Compressed/geoRge-master/inst/extdata/ExampleDatabase.csv", ppm.db = 10)

1. I do not see comparison between 5 and 25 mM conditions in label compare file i.e, s4. Please suggest.
2. s4 table only has Base peak, X05mM_Normo_MEAN and X05mM_Normo_SD.
   mzmed and rtmed columns are missing.
3. I am not able to run database query command (s5) which I think generates the file (Supplementary table 1- ac5b03628_si_001 from the manuscript). The problem is -
   Error in DatabaseSearch(input, db, ppm.db, adducts) :
   object 'M' not found
4. Do we also get putative id's for labeled features from the given HMDB database like they are shown in ac5b03628_si_001

I am newbie in this type of analysis. It would be of immense help to me if code given here is rectified.
Thanks a lot.