[R] Three way correspondence analyses?
Suparna Mitra
suparna.mitra.sm at gmail.com
Fri Aug 5 11:00:59 CEST 2016
Sorry somehow the mail was buried in my spam folder and I was waiting for
any reply. Now when I searched specifically then found in spam. Sorry about
this.
This is my data format.
I have three data matrix. Samples are matched
> dput(Cytok_and_ProInf)
structure(list(IFN._ = c(3.412082432, 3.052252998, 5.142508722,
12.70932318, 1.861206813, 0.993497776, 0.836846636, 4.125564372,
1.385344616, 1.292459442, 0.11649863, 0.150193815, 27.86121845,
1.725385265, 1.715598671, 0.017175222, 1e-06, 1e-06, 6.668275976,
0.790970336, 4.03583889, 0.971457745, 1.059011154, 0.637639199,
0.48875513, 0.301263118, 0.272641165, 0.343154282, 1e-06, 1e-06,
1.282052844, 1.080656696, 1.302848316, 6.22346499, 0.329317838,
1e-06, 0.437037978, 0.287027959, 0.960397988, 0.098872923, 1.06984553,
0.836846636, 1.302848316, 0.904683816), IL.10 = c(0.115021123,
0.150136084, 0.205984417, 0.16364998, 0.099053965, 0.152406978,
0.107718618, 0.180196098, 0.073236511, 0.101546531, 0.233120615,
0.097802351, 0.67071499, 0.159174453, 0.226924759, 0.042082686,
1e-06, 1e-06, 0.242345366, 0.250478861, 0.170311925, 0.079862061,
0.083777663, 0.062337816, 0.026139707, 0.088935013, 0.158051134,
0.178010445, 0.19103657, 0.178010445, 0.186717539, 0.066471894,
0.263570447, 0.403324556, 1e-06, 0.15467023, 0.096547094, 0.131672017,
0.085073597, 0.1877994, 0.182375762, 0.115021123, 0.117431784,
0.158051134), IL.12p70 = c(0.070763998, 0.090748695, 0.208540497,
1e-06, 0.100363261, 1e-06, 0.049381659, 0.278572877, 0.359093222,
0.236327042, 1e-06, 1e-06, 1.730678237, 1e-06, 1e-06, 1e-06,
1e-06, 1e-06, 1e-06, 0.228467277, 0.355528037, 0.150149937, 1e-06,
0.100363261, 1e-06, 1e-06, 0.351954745, 0.236327042, 0.167289445,
1e-06, 0.297291961, 0.208540497, 1e-06, 1e-06, 0.240234706, 0.025530181,
0.114409102, 1e-06, 1e-06, 0.031847909, 0.228467277, 1e-06, 0.212559242,
0.30100342), IL.13 = c(1.704419932, 1.112298247, 2.285765956,
4.633806398, 0.642126976, 0.746932456, 0.363434771, 2.340450899,
2.074555897, 1.244106163, 1e-06, 1.820132354, 74.41151063, 2.034099156,
20.68036347, 1e-06, 1e-06, 4.101483243, 0.794749267, 1e-06, 2.805396078,
1.077152785, 1.179818983, 1.581359427, 1.077152785, 1.529718601,
1e-06, 1e-06, 0.58364863, 1e-06, 1.421542399, 0.965068178, 2.836027955,
5.571883643, 1e-06, 1e-06, 1e-06, 1e-06, 1e-06, 1e-06, 1.449249978,
1.146494964, 1e-06, 1e-06), IL.1_ = c(1e-06, 2.307704109, 25.26088067,
572.801725, 0.510013312, 0.362017284, 0.031608863, 3.870488003,
0.01290693, 1.838427599, 7.097086101, 3.272835372, 10406.43981,
1e-06, 81.64973722, 1.070281402, 9.682079245, 10.80856769, 167.0831603,
0.397080631, 128.7969178, 0.995448576, 14.26930517, 0.69205361,
2.304314695, 0.579468482, 1e-06, 1.304363973, 3.759936213, 0.589889298,
0.299325951, 0.291769643, 15.20223699, 271.2112448, 17.88589268,
0.377847524, 0.142551711, 2.042925614, 17.63920898, 0.954063427,
0.841909578, 0.791637687, 2.719932082, 0.612547139), IL.2 = c(0.310017477,
0.639550623, 0.364921535, 0.90788638, 1e-06, 1e-06, 1e-06, 0.479461553,
0.153405415, 0.456098215, 0.659233077, 0.019421531, 2.581092035,
0.60647104, 0.374719897, 0.198939483, 1e-06, 1e-06, 0.420590306,
0.637356204, 0.650500136, 0.187772403, 0.234414214, 0.135640615,
0.167896217, 0.043668, 0.543715428, 0.491057054, 0.104904788,
0.268833496, 0.394164323, 0.153405415, 0.511791782, 1.40318585,
0.162131403, 0.386895823, 0.207232191, 0.234414214, 0.129616074,
0.465471829, 0.411020666, 0.374719897, 0.302383698, 0.266218696
), IL.4 = c(1e-06, 0.061134995, 0.033725716, 0.176628741, 0.036390669,
0.016385835, 0.02963346, 0.0912511, 0.044136184, 0.007841464,
1e-06, 1e-06, 0.555602008, 0.201038117, 1e-06, 0.016385835, 1e-06,
1e-06, 0.05520647, 0.014779363, 0.0453956, 0.019496483, 0.066933632,
1e-06, 1e-06, 1e-06, 0.040306406, 0.077093341, 1e-06, 0.02963346,
0.042868427, 0.088031622, 0.041591965, 0.039011321, 1e-06, 0.013131473,
1e-06, 1e-06, 1e-06, 1e-06, 0.009676617, 0.023961259, 1e-06,
0.025405987), IL.6 = c(0.132069931, 0.205121881, 0.266403938,
0.357044807, 0.175675816, 0.135299256, 0.160466529, 0.801623905,
0.219429811, 0.178675804, 1e-06, 1e-06, 1.946693297, 0.00336273,
0.260996547, 1e-06, 1e-06, 1e-06, 0.101959817, 0.148023004, 0.522793842,
0.166593645, 0.098477711, 0.122253955, 0.184636758, 0.076829582,
1e-06, 1e-06, 1e-06, 0.065400292, 0.144869779, 0.151158758, 0.175675816,
1.136760714, 0.031581939, 0.049271129, 1e-06, 0.036206361, 1e-06,
1e-06, 0.154277589, 0.163537592, 0.101959817, 0.166593645), IL.8 =
c(0.263813623,
0.176968743, 21.45511221, 41.02244667, 0.325779267, 0.19875696,
0.191549828, 5.874233467, 0.162143262, 0.254734152, 0.424914919,
0.83134713, 615.7282871, 0.222420019, 11.71507301, 0.254734152,
0.48778161, 0.459603466, 9.245098493, 0.937998793, 158.7036736,
1.052601593, 7.398795984, 0.517616924, 0.842129973, 0.049980916,
0.091283798, 0.703339445, 0.353738394, 0.12114101, 0.189135463,
0.272831114, 2.577264558, 1e-06, 6.92155151, 0.099598586, 0.126403393,
0.519593589, 2.999681278, 0.279555285, 4.425047545, 0.203532755,
0.829547294, 0.159646452), TNF._ = c(1e-06, 1e-06, 0.497412481,
2.502977176, 1e-06, 1e-06, 1e-06, 0.663152793, 0.115785465, 0.112196976,
3.296149665, 1.103455361, 13.43259911, 1e-06, 4.62646124, 0.17981792,
0.561262449, 1e-06, 6.979610188, 0.104932683, 3.077428626, 1e-06,
0.140201934, 0.256307354, 0.104932683, 1e-06, 1e-06, 0.014627633,
1.016512746, 1e-06, 1e-06, 1e-06, 0.086184523, 4.778283885, 0.46726225,
1e-06, 0.049369295, 0.049369295, 1e-06, 1e-06, 0.074442943, 0.070428387,
1e-06, 0.040326195), GM.CSF = c(0.540900573, 0.42223301, 0.202804186,
1.956298248, 0.06647775, 0.175295758, 0.123620468, 0.66961417,
1e-06, 0.010627992, 1e-06, 0.025094065, 1.791958029, 1e-06, 0.313611726,
0.129677511, 1e-06, 1e-06, 0.072451697, 0.472115537, 2.438178508,
1e-06, 0.341470499, 0.267309423, 0.016380207, 1e-06, 0.072451697,
1e-06, 1e-06, 1e-06, 0.416006909, 0.150926695, 1e-06, 0.86531761,
1e-06, 0.184454916, 1e-06, 0.004950137, 1e-06, 0.160055214, 0.057540307,
0.072451697, 0.484605677, 0.227328934), IL.12p40 = c(3.449523303,
1.253952318, 1.153628772, 24.66757728, 0.281211388, 0.45621453,
0.577190569, 6.035154458, 0.248565664, 0.303009219, 0.489162441,
0.335752283, 4.483277375, 1e-06, 7.754198316, 0.610258154, 0.259440413,
0.183488862, 7.811612719, 3.596931106, 39.86410464, 1e-06, 19.81040921,
0.687524342, 0.194312525, 1e-06, 0.303009219, 0.787068241, 0.401386775,
0.324832028, 1.187055113, 0.478175762, 1.533272067, 6.012282081,
0.313917608, 0.259440413, 0.08662882, 0.129536365, 2.263026136,
0.478175762, 0.248565664, 0.215988153, 1.120217824, 0.720681708
), IL.15 = c(3.663956797, 0.229437717, 0.949300626, 1.401070403,
0.287497045, 1e-06, 0.049717539, 3.216577943, 0.000801684, 0.039236284,
0.044490955, 0.000801684, 0.47768266, 1e-06, 0.548030419, 1e-06,
0.028624659, 1e-06, 1e-06, 2.722888088, 5.4735116, 1e-06, 0.548030419,
1e-06, 1e-06, 1e-06, 1e-06, 0.075522355, 0.150922499, 1e-06,
0.229437717, 0.378377969, 1e-06, 5.831002955, 1e-06, 1e-06, 0.439974635,
1e-06, 2.04484964, 1e-06, 1e-06, 1e-06, 0.18054264, 0.190365246
), IL.16 = c(20.5193362, 4.328836648, 8.255914035, 16.13264058,
1.268642287, 1e-06, 1e-06, 11.8242623, 1e-06, 1e-06, 1e-06, 1e-06,
4.092029806, 1e-06, 3.67041693, 1e-06, 1e-06, 1e-06, 0.301180818,
14.7673695, 20.3213016, 1e-06, 6.82449294, 1e-06, 1e-06, 1e-06,
1e-06, 0.401190562, 0.55732358, 1e-06, 1.444064455, 0.433320317,
1e-06, 28.06557331, 1e-06, 0.301180818, 2.015806139, 1e-06, 4.485194079,
1.59090159, 1e-06, 0.335201656, 0.873203993, 1.035154141), IL.17 =
c(4.977017713,
2.570719348, 2.015098932, 3.992319128, 1.06226368, 0.885436394,
1.3153313, 2.244935789, 0.874075666, 0.908175994, 0.896803194,
0.755173959, 4.459228138, 0.862721083, 1.142538479, 1.292234665,
0.806042993, 0.857046119, 1.09663731, 3.877447052, 12.35812507,
0.942329724, 1.570454586, 1.171265948, 1.200022785, 0.806042993,
0.970830673, 1.073716485, 2.52322014, 0.885436394, 1.587915828,
1.803905178, 0.789071362, 5.466413978, 0.755173959, 1.251857053,
0.698820169, 0.936633804, 1.20577762, 1.010790396, 0.930939325,
0.868397602, 1.523930998, 1.745417659), IL.1_.1 = c(118.208659,
114.7568995, 99.56142647, 517.2879232, 100.3722434, 96.93543486,
81.64662024, 70.27819582, 17.13820342, 82.52992205, 81.78590123,
92.43171819, 1053.37885, 65.5820605, 363.635418, 45.79361892,
33.21789883, 29.57769534, 311.8625878, 40.79704867, 851.3943098,
55.1196921, 239.9686999, 38.15064699, 45.53416821, 28.63008947,
18.15241505, 20.64297024, 32.5135396, 20.39305218, 28.46271279,
6.469080388, 41.24452118, 297.4003248, 102.2122555, 10.96668006,
22.48696243, 23.15105529, 25.04798574, 25.01122998, 77.30344367,
79.36764816, 45.54090693, 35.20462575), IL.5 = c(1.044231313,
0.573092875, 0.584075265, 0.598727834, 1e-06, 1e-06, 1e-06, 0.372977066,
1e-06, 0.291939134, 1e-06, 1e-06, 1e-06, 0.161982214, 1e-06,
1e-06, 0.048963445, 0.088502876, 1e-06, 0.232920333, 1.023752123,
1e-06, 0.04556126, 1e-06, 1e-06, 1e-06, 1e-06, 1e-06, 0.338690819,
0.067789042, 0.657440756, 0.282972479, 1e-06, 10.196604, 1e-06,
1e-06, 1e-06, 1e-06, 1e-06, 1e-06, 0.154932764, 0.086770774,
1e-06, 0.29373344), IL.7 = c(0.480830602, 0.669397614, 0.056955049,
0.645772623, 0.085416118, 0.157189302, 1e-06, 0.669397614, 0.037168535,
0.039986238, 1e-06, 1e-06, 0.054120296, 0.003790123, 0.162959026,
0.270215723, 1e-06, 0.028737802, 1e-06, 0.212128061, 0.947919977,
1e-06, 0.244047658, 1e-06, 0.296425864, 1e-06, 0.168732133, 0.157189302,
0.168732133, 0.111169917, 0.194750148, 0.678260571, 1e-06, 0.873693001,
1e-06, 0.082561887, 1e-06, 0.025936126, 1e-06, 0.174508512, 0.068316418,
0.206332705, 0.051287998, 0.572039147), TNF._.1 = c(3.419068944,
0.774614576, 1.778032483, 3.677555816, 0.09525377, 0.045152852,
0.203310769, 2.480647474, 0.035471915, 0.018965724, 0.064189143,
0.082908763, 0.91606112, 0.20625602, 0.264708971, 0.250170556,
0.393803487, 0.125749131, 0.235584535, 2.600828984, 3.532130347,
0.235584535, 0.728067967, 0.038713645, 1e-06, 1e-06, 0.086004142,
0.008673406, 0.921475662, 1e-06, 0.170745502, 0.012155505, 0.152838242,
4.753887357, 0.035471915, 0.155830622, 0.048352721, 0.045152852,
1.058973116, 0.09525377, 0.140835049, 0.250170556, 0.170745502,
0.110560669), VEGF = c(4087.981219, 3715.552662, 3944.604398,
3844.577145, 396.5302176, 299.8314501, 207.394422, 3933.745405,
14.78051963, 321.5135099, 297.4382786, 561.2753232, 3050.364368,
116.7615841, 3145.523924, 212.8841604, 44.27057474, 44.58276978,
2020.429602, 4128.947066, 4073.070344, 1691.91391, 3758.767261,
889.8387076, 644.4912406, 250.6067383, 590.6027003, 716.7750862,
404.9555583, 204.2231031, 71.38985829, 36.15359803, 1329.165521,
4009.740565, 2903.460055, 889.1034558, 1417.808231, 480.7677475,
3696.819676, 1455.555265, 998.5832238, 1637.63334, 1149.828041,
55.667355)), .Names = c("IFN._", "IL.10", "IL.12p70", "IL.13",
"IL.1_", "IL.2", "IL.4", "IL.6", "IL.8", "TNF._", "GM.CSF", "IL.12p40",
"IL.15", "IL.16", "IL.17", "IL.1_.1", "IL.5", "IL.7", "TNF._.1",
"VEGF"), class = "data.frame", row.names = c("I100A", "I100B",
"I100C", "I100D", "I100E", "I100F", "I100G", "I123A", "I143A",
"I143B", "I14A", "I14B", "I14C", "I14D", "I17A", "I17B", "I17C",
"I17D", "I17E", "I185A", "I185B", "I185C", "I185D", "I185E",
"I185F", "I185G", "I20A", "I20B", "I20C", "I215A", "I215B", "I215C",
"I215D", "I50A", "I50B", "I50C", "I50D", "I50E", "I78A", "I78B",
"I88A", "I88B", "I88C", "I88D"))
> dput(Microbiome_NEC)
structure(list(environmental.samples..Bacteria. = c(0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Rhizobiales = c(0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 8L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Burkholderiales = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L), Enterobacteriales = c(3636L,
3574L, 5908L, 5358L, 3067L, 2392L, 1876L, 40L, 109L, 1182L, 2741L,
3660L, 1716L, 5282L, 1242L, 3570L, 3065L, 3270L, 4023L, 67L,
8361L, 4743L, 10080L, 6857L, 6164L, 3580L, 11L, 3L, 1064L, 1L,
323L, 45L, 1730L, 32L, 5376L, 3002L, 2164L, 3111L, 586L, 4023L,
22L, 110L, 41L, 67L), Pasteurellales = c(0L, 0L, 0L, 0L, 0L,
0L, 0L, 5L, 46L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 172L, 425L, 1L, 2L, 0L, 0L, 0L,
0L, 10L, 0L, 0L, 0L, 5L, 6L, 121L, 831L), Pseudomonadales = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Actinomycetales = c(0L,
0L, 0L, 0L, 1L, 0L, 53L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 12L, 68L), Bifidobacteriales =
c(6L,
2L, 437L, 925L, 748L, 1569L, 1459L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Corynebacteriales
= c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 27L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 163L, 0L, 0L, 0L), Micrococcales = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 36L, 23L, 0L), Propionibacteriales = c(0L,
0L, 0L, 0L, 0L, 1L, 28L, 10L, 1205L, 24L, 0L, 2L, 0L, 3L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 30L,
0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 18L, 3L, 2166L, 441L, 5L),
Eggerthellales = c(0L, 0L, 1L, 0L, 1L, 4L, 3L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L), Bacillales = c(1573L, 1121L, 1077L, 366L,
304L, 136L, 3L, 2087L, 1378L, 91L, 1L, 8L, 6L, 19L, 732L,
130L, 2L, 0L, 1L, 5374L, 2L, 811L, 22L, 40L, 23L, 4L, 79L,
20L, 717L, 1285L, 503L, 1525L, 151L, 0L, 0L, 0L, 45L, 16L,
2778L, 249L, 3370L, 973L, 231L, 32L), Lactobacillales = c(0L,
0L, 93L, 2L, 2L, 38L, 12L, 596L, 318L, 38L, 1L, 2L, 14L,
18L, 1L, 47L, 1L, 1L, 24L, 13L, 27L, 1L, 335L, 96L, 321L,
444L, 1797L, 3668L, 714L, 2L, 2775L, 2830L, 1202L, 3224L,
465L, 605L, 57L, 92L, 1315L, 58L, 1L, 2L, 7L, 167L), Clostridiales =
c(0L,
0L, 0L, 0L, 1L, 0L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Negativicoccus =
c(0L,
0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 12L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 5L),
Veillonella = c(0L, 0L, 1L, 0L, 5L, 158L, 61L, 6L, 25L, 93L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 2037L, 327L, 108L, 910L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 2L, 33L), Tissierellales = c(0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 37L, 1L, 4L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 5L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3121L, 576L)), .Names =
c("environmental.samples..Bacteria.",
"Rhizobiales", "Burkholderiales", "Enterobacteriales", "Pasteurellales",
"Pseudomonadales", "Actinomycetales", "Bifidobacteriales",
"Corynebacteriales",
"Micrococcales", "Propionibacteriales", "Eggerthellales", "Bacillales",
"Lactobacillales", "Clostridiales", "Negativicoccus", "Veillonella",
"Tissierellales"), class = "data.frame", row.names = c("I100A",
"I100B", "I100C", "I100D", "I100E", "I100F", "I100G", "I123A",
"I143A", "I143B", "I14A", "I14B", "I14C", "I14D", "I17A", "I17B",
"I17C", "I17D", "I17E", "I185A", "I185B", "I185C", "I185D", "I185E",
"I185F", "I185G", "I20A", "I20B", "I20C", "I215A", "I215B", "I215C",
"I215D", "I50A", "I50B", "I50C", "I50D", "I50E", "I78A", "I78B",
"I88A", "I88B", "I88C", "I88D"))
> dput(Metab_NEC)
structure(list(fatty.acids = c(0.97, 1.96, 2.15, 10.06, 5.87,
3.57, 3, 18.36, 4.6, 3.55, 2.44, 1.69, 2.88, 0.76, 6.05, 1.93,
2.02, 1.99, 2.77, 1.76, 1.4, 3.48, 2.24, 0.95, 1.1, 2.32, 0.27,
1.16, 5.32, 4.37, 3.27, 3.02, 2.19, 2.37, 1.38, 0.95, 1.08, 2.79,
3.26, 2.7, 1.26, 5.14, 5.13, 11.56), aldehydes = c(0.25, 0, 0.36,
0.07, 0, 0, 0.18, 0, 0.08, 0.11, 0.13, 0.24, 0, 0.04, 1.45, 0.12,
0.03, 0.05, 0.02, 0.11, 0.02, 0, 0.12, 0.14, 0, 0.17, 0, 0.05,
0, 0.02, 0.03, 0.23, 0, 0.06, 0, 0.02, 0, 0, 0.59, 0.05, 0.19,
0.17, 0, 0.1), alcohol = c(1.91, 0.21, 0.86, 3.29, 0.9, 0.37,
0.39, 6.98, 1.31, 1.52, 1.34, 1.88, 4.31, 1.03, 4.85, 0.82, 0.44,
0.91, 1.32, 2.18, 1.01, 2.9, 4.68, 1.26, 1.6, 0.99, 1.39, 1.04,
2.86, 2.25, 1.71, 1.55, 1.01, 1.22, 1.04, 1.1, 1.64, 1.31, 4.53,
1.57, 1.69, 3.81, 3.32, 3.25), amines = c(0.06, 0.08, 0.01, 2.04,
6.06, 2.67, 4.04, 2.2, 0.75, 0.2, 0.94, 0.39, 0.51, 0.1, 0.11,
0.19, 0.16, 0.12, 0.16, 0, 0.16, 0, 0, 0.53, 0.13, 2.48, 0.29,
0.47, 0.19, 0.66, 0.09, 0.28, 1.25, 0, 0.26, 0.11, 0, 0.16, 0,
0, 0, 1.17, 1.17, 1.86), phenolic.acid = c(0.01, 0.01, 0.04,
0, 0, 0, 0.21, 0.22, 0.04, 0.65, 0, 0.01, 0.03, 0, 0.01, 0, 0.01,
0.01, 0.04, 0, 0, 0, 0.01, 0, 0, 0.02, 0, 0.09, 0.03, 0, 0, 0,
0.06, 0.03, 0, 0.06, 0, 0, 0, 0, 0.03, 0.01, 0, 0.48), sugars = c(50.98,
37.78, 18, 4.38, 23.55, 22.65, 15.63, 14.5, 15.89, 11.24, 8.37,
20.22, 10.21, 18.5, 61.92, 16.1, 30.24, 26.1, 3.09, 41.16, 34.96,
19.03, 28.68, 37.19, 41.9, 16.35, 43.77, 32.99, 15.54, 17.47,
31.21, 12.99, 21.59, 31.59, 51.64, 45.92, 47.17, 31.55, 45.51,
45.3, 56.08, 21.79, 31.22, 4.28), amino.acids = c(4.24, 4.31,
6.27, 9.1, 4.75, 2.31, 2.49, 13.22, 4.27, 5.34, 5.44, 2.35, 3.46,
2.05, 9.41, 4.23, 2.85, 2.65, 3.69, 5.67, 3.01, 4.33, 3.99, 1.84,
2.88, 2.52, 2.14, 5.25, 6.18, 9.02, 3.07, 1.4, 2.25, 9.52, 4.04,
2.94, 2.91, 4.84, 6.34, 4.88, 3.31, 6.29, 4.86, 4.23), osmolytes = c(7.1,
2.15, 1.59, 2.91, 0.56, 4.04, 0.3, 2.05, 0.24, 1.06, 4.54, 4.19,
1.16, 0.41, 8.52, 3.28, 7.27, 2.38, 2.65, 3.04, 1.52, 3.55, 2.69,
3.21, 0.67, 0.98, 1.6, 0.83, 0.99, 4.64, 3.38, 12.2, 0.42, 1.28,
4.64, 2.5, 3.36, 1.08, 5.32, 1.7, 2.01, 1.89, 2.72, 1.04),
energy.related.acid = c(0.55,
1.52, 0.6, 1.9, 0.63, 0.74, 0.46, 2.67, 1.08, 4.22, 7.69, 2.09,
7.31, 1.07, 1.19, 1.78, 0.43, 0.3, 11.11, 1.52, 1.67, 4.21, 1.76,
2.84, 3.06, 3.13, 0.04, 2.7, 3.01, 5.15, 2.31, 0.29, 2.16, 4.09,
1.21, 0.59, 0.55, 2.88, 1, 1.22, 0.7, 1.09, 1.5, 1.12), nucleobase = c(0,
0, 0.05, 0.11, 0.05, 0, 0.24, 0.2, 0.04, 0.04, 0.02, 0.02, 0.08,
0.01, 0.17, 0.02, 0.02, 0.03, 0.03, 0, 0.03, 0.09, 0.02, 0.02,
0.02, 0.01, 0.11, 0.03, 0.03, 0.05, 0.04, 0.02, 0.28, 0.06, 0.04,
0.03, 0, 0.01, 0.05, 0.05, 0.01, 0.07, 0.05, 0.09)), .Names =
c("fatty.acids",
"aldehydes", "alcohol", "amines", "phenolic.acid", "sugars",
"amino.acids", "osmolytes", "energy.related.acid", "nucleobase"
), class = "data.frame", row.names = c("I100A", "I100B", "I100C",
"I100D", "I100E", "I100F", "I100G", "I123A", "I143A", "I143B",
"I14A", "I14B", "I14C", "I14D", "I17A", "I17B", "I17C", "I17D",
"I17E", "I185A", "I185B", "I185C", "I185D", "I185E", "I185F",
"I185G", "I20A", "I20B", "I20C", "I215A", "I215B", "I215C", "I215D",
"I50A", "I50B", "I50C", "I50D", "I50E", "I78A", "I78B", "I88A",
"I88B", "I88C", "I88D"))
Now I normalised them all by % and tried cca in vegan package.
normCytok_and_ProInf <- (Cytok_and_ProInf/rowSums(Cytok_and_ProInf))*100
normMetab_NEC <- (Metab_NEC/rowSums(Metab_NEC))*100
normMicrobiome_NEC <- (Microbiome_NEC/rowSums(Microbiome_NEC))*100
#Now CCA
Metab.Cytok.Microb.cca <-
cca(normMicrobiome_NEC,normMetab_NEC,normCytok_and_ProInf)
plot(Metab.Cytok.Microb.cca )
Metab.Cytok.Microb.cca <- cca(normMicrobiome_NEC,normCytok_and_ProInf)
plot(Metab.Cytok.Microb.cca )
Metab.Cytok.Microb.cca <- cca(normMicrobiome_NEC,normMetab_NEC)
plot(Metab.Cytok.Microb.cca )
Any help will be really great.
Thank you very much.
Mitra
On 4 August 2016 at 14:25, Michael Friendly <friendly at yorku.ca> wrote:
> You haven't supplied any data, and we can only guess which cca() function
> you are using (ade4::cca, ..., vegan::cca(), yacca::cca), and the term
> 'cca' generally refers to canonical correspondence analysis,
> which is not quite the same thing as 'three-way correspondence analysis'.
>
> For three-way tables, there are several variations of standard
> correspondence analysis that generalize CA for two-way tables
> in reasonable, but different ways.
> You may find more joy using the mjca() in the ca package
> which provides these alternatives.
>
> best,
> -Michael
>
>
> On 8/2/2016 3:58 PM, Suparna Mitra wrote:
>
>> Hello R experts,
>> have some data for microbiome, metabolome and cytokine from the same
>> sample. Now I want to do a three-way correspondence analyses. From three
>> normalised data I was trying,
>> #Now CCA
>>
>> with two data it works good like:
>> Metab.Cytok.Microb.cca <- cca(normMicrobiome_NEC,normCytok_and_ProInf)
>> plot(Metab.Cytok.Microb.cca )
>> Metab.Cytok.Microb.cca <- cca(normMicrobiome_NEC,normMetab_NEC)
>> plot(Metab.Cytok.Microb.cca )
>>
>> But when I tried with three
>> Metab.Cytok.Microb.cca <-
>> cca(normMicrobiome_NEC,normMetab_NEC,normCytok_and_ProInf)
>> plot(Metab.Cytok.Microb.cca )
>> But this is not displaying all three variables.
>> Sorry, I am very new in this. Can anybody please help me?
>> Thanks a lot,
>> Mitra
>>
>> [[alternative HTML version deleted]]
>>
>>
>
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