Closed mctphd closed 6 years ago
Dear Anne,
Sorry for the delay.
I am not sure of what is causing it, but it is strange that you have the same sample file names as in our shared sample data.
Could you please share the following:
Jordi
Hi Jordi,
many thanks for your answer! I really appreciate your support!
During our first tests with geoRge I simply put the same file names as in your sample data in order to avoid any problems caused by wrong file structures etc.
Following your comment, I did run the script with our data again and checked "xset@phenoData"
The output is the following:
class
CELL_Glc12_05mM_Normo_01 CELL_Glc12_05mM_Normo CELL_Glc12_05mM_Normo_02 CELL_Glc12_05mM_Normo CELL_Glc12_05mM_Normo_03 CELL_Glc12_05mM_Normo CELL_Glc12_25mM_Normo_07 CELL_Glc12_25mM_Normo CELL_Glc12_25mM_Normo_08 CELL_Glc12_25mM_Normo CELL_Glc12_25mM_Normo_09 CELL_Glc12_25mM_Normo CELL_Glc13_05mM_Normo_04 CELL_Glc13_05mM_Normo CELL_Glc13_05mM_Normo_05 CELL_Glc13_05mM_Normo CELL_Glc13_05mM_Normo_06 CELL_Glc13_05mM_Normo CELL_Glc13_25mM_Normo_11 CELL_Glc13_25mM_Normo CELL_Glc13_25mM_Normo_12 CELL_Glc13_25mM_Normo CELL_Glc13_25mM_Normo_13 CELL_Glc13_25mM_Normo
Error message is still the same:
Error in rownames<-
(*tmp*
, value = c(1L, 0L)) :
length of 'dimnames' [1] not equal to array extent
Regards, Anne
That part looks fine, please run this:
X1 <- xcms::groupval(xset6) head(X1)
and could you share the line of code that you run for PuInc_seeker() as well?
Jordi
Hi Jordi,
running
X1 <- xcms::groupval(xset6) head(X1)
gives:
CELL_Glc12_05mM_Normo_01 CELL_Glc12_05mM_Normo_02 CELL_Glc12_05mM_Normo_03
41.1/1205 161 370 579 42/983 96 305 514 42/617 7 216 425 42/1016 108 317 526 42/892 54 263 472 43/1204 2503 2583 2663 CELL_Glc12_25mM_Normo_07 CELL_Glc12_25mM_Normo_08 CELL_Glc12_25mM_Normo_09 41.1/1205 788 997 1206 42/983 723 932 1141 42/617 634 843 1052 42/1016 735 944 1153 42/892 681 890 1099 43/1204 2743 2823 2903 CELL_Glc13_05mM_Normo_04 CELL_Glc13_05mM_Normo_05 CELL_Glc13_05mM_Normo_06 41.1/1205 2983 3067 3151 42/983 1344 1552 1760 42/617 1259 1467 1675 42/1016 2984 3068 3152 42/892 2985 3069 3153 43/1204 1399 1607 1815 CELL_Glc13_25mM_Normo_11 CELL_Glc13_25mM_Normo_12 CELL_Glc13_25mM_Normo_13 41.1/1205 3235 3319 3403 42/983 1968 2176 2384 42/617 1883 2091 2299 42/1016 3236 3320 3404 42/892 3237 3321 3405 43/1204 2023 2231 2439
To start the PuInc_seeker function, I used following line of code
s1 <- PuInc_seeker(XCMSet=xset6,ULtag="CELL_Glc12",Ltag="CELL_Glc13",sep.pos.front=TRUE, fc.threshold=1.5,PuInc.int.lim=4000)
Kind regards, Anne
Hi Anne,
I think the solution would be to ignore the argument "PuInc.int.lim". Please, run:
s1 <- PuInc_seeker(XCMSet=xset6,ULtag="CELL_Glc12",Ltag="CELL_Glc13",sep.pos.front=TRUE, fc.threshold=1.5)
Let me know if it worked.
Yours, Jordi
Hi Jordi,
unfortunately ignoring the above mentioned argument still gives the same error message:
Error in rownames<-(tmp, value = c(1L, 0L)) : length of 'dimnames' [1] not equal to array extent
Yours, Anne
Hi Anne
First, sorry, this is frustrating. I am not sure what is going on, would you mind sharing your xcmSet object via email so I can take a look myself at the data and check what is wrong within the code, my email is j.capellades.to@gmail.com
Yours, Jordi
For future readers, that may happen to deal with the same error. If the p-value + fold-change filtering finds no significant features for "putative incorporations" this error will pop up.
Error in rownames<-(tmp, value = c(1L, 0L)) : length of 'dimnames' [1] not equal to array extent
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:
Own data:
.. ..$ : 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