How to isolate group of values and determine correlation between them ?

-- Hi all,

I want to determine if some group of values are correlated between samples,
I have a set of values (val1,val2,val3,val4,val5) organized according to the dataframe (below)
and I'd like to identify groups of values that fluctuate in the same direction and determine whether they are sample-dependent or not.
What statistical methods should I use and how can I apply them to my dataframe ?
Do i need to use PCA analysis, correlation analysis....?

Thank you --

DF<-structure(list(samples = c("sample_1", "sample_2", "sample_3", 
"sample_4", "sample_5", "sample_6", "sample_7", "sample_8", "sample_9", 
"sample_10", "sample_11", "sample_12", "sample_13", "sample_14", 
"sample_15", "sample_16", "sample_17", "sample_18", "sample_19", 
"sample_20", "sample_21", "sample_22", "sample_23", "sample_24", 
"sample_25", "sample_26", "sample_27", "sample_28", "sample_29", 
"sample_30", "sample_31", "sample_32", "sample_33", "sample_34", 
"sample_35", "sample_36", "sample_37", "sample_38", "sample_39", 
"sample_40", "sample_41", "sample_42", "sample_43", "sample_44", 
"sample_45", "sample_46", "sample_47", "sample_48", "sample_49", 
"sample_50", "sample_51", "sample_52", "sample_53", "sample_54", 
"sample_55", "sample_56", "sample_57", "sample_58", "sample_59", 
"sample_60", "sample_61", "sample_62", "sample_63", "sample_64", 
"sample_65", "sample_66", "sample_67", "sample_68", "sample_69", 
"sample_70", "sample_71", "sample_72", "sample_73", "sample_74", 
"sample_75", "sample_76", "sample_77", "sample_78", "sample_79", 
"sample_80", "sample_81", "sample_82", "sample_83", "sample_84", 
"sample_85", "sample_86", "sample_87", "sample_88", "sample_89", 
"sample_90", "sample_91", "sample_92", "sample_93", "sample_94", 
"sample_95", "sample_96", "sample_97", "sample_98", "sample_99", 
"sample_100", "sample_101", "sample_102", "sample_103", "sample_104", 
"sample_105", "sample_106", "sample_107", "sample_108", "sample_109", 
"sample_110", "sample_111", "sample_112", "sample_113", "sample_114", 
"sample_115", "sample_116", "sample_117", "sample_118", "sample_119", 
"sample_120", "sample_121", "sample_122", "sample_123", "sample_124", 
"sample_125", "sample_126", "sample_127", "sample_128", "sample_129", 
"sample_130", "sample_131", "sample_132", "sample_133", "sample_134", 
"sample_135", "sample_136", "sample_137", "sample_138", "sample_139", 
"sample_140", "sample_141", "sample_142", "sample_143", "sample_144", 
"sample_145", "sample_146", "sample_147", "sample_148", "sample_149", 
"sample_150", "sample_151", "sample_152", "sample_153", "sample_154", 
"sample_155", "sample_156", "sample_157", "sample_158", "sample_159", 
"sample_160", "sample_161", "sample_162", "sample_163", "sample_164", 
"sample_165", "sample_166", "sample_167", "sample_168", "sample_169", 
"sample_170", "sample_171", "sample_172", "sample_173", "sample_174", 
"sample_175", "sample_176", "sample_177", "sample_178", "sample_179", 
"sample_180", "sample_181", "sample_182", "sample_183", "sample_184", 
"sample_185", "sample_186", "sample_187", "sample_188", "sample_189", 
"sample_190", "sample_191", "sample_192", "sample_193", "sample_194", 
"sample_195", "sample_196", "sample_197", "sample_198", "sample_199", 
"sample_200", "sample_201", "sample_202", "sample_203", "sample_204", 
"sample_205", "sample_206", "sample_207", "sample_208", "sample_209", 
"sample_210", "sample_211", "sample_212", "sample_213", "sample_214", 
"sample_215", "sample_216", "sample_217", "sample_218", "sample_219", 
"sample_220", "sample_221", "sample_222", "sample_223", "sample_224", 
"sample_225", "sample_226", "sample_227", "sample_228", "sample_229", 
"sample_230", "sample_231", "sample_232", "sample_233", "sample_234", 
"sample_235", "sample_236", "sample_237", "sample_238", "sample_239", 
"sample_240", "sample_241", "sample_242", "sample_243", "sample_244", 
"sample_245", "sample_246", "sample_247", "sample_248", "sample_249", 
"sample_250", "sample_251", "sample_252", "sample_253", "sample_254", 
"sample_255", "sample_256", "sample_257", "sample_258", "sample_259", 
"sample_260", "sample_261", "sample_262", "sample_263", "sample_264", 
"sample_265", "sample_266", "sample_267", "sample_268", "sample_269", 
"sample_270", "sample_271", "sample_272", "sample_273", "sample_274", 
"sample_275", "sample_276", "sample_277", "sample_278", "sample_279", 
"sample_280", "sample_281", "sample_282", "sample_283", "sample_284", 
"sample_285", "sample_286", "sample_287", "sample_288", "sample_289", 
"sample_290", "sample_291", "sample_292", "sample_293", "sample_294", 
"sample_295", "sample_296", "sample_297", "sample_298", "sample_299", 
"sample_300", "sample_301", "sample_302", "sample_303", "sample_304", 
"sample_305", "sample_306", "sample_307", "sample_308", "sample_309", 
"sample_310", "sample_311", "sample_312", "sample_313", "sample_314", 
"sample_315", "sample_316", "sample_317", "sample_318", "sample_319", 
"sample_320", "sample_321", "sample_322", "sample_323", "sample_324", 
"sample_325", "sample_326", "sample_327", "sample_328", "sample_329", 
"sample_330", "sample_331", "sample_332", "sample_333", "sample_334", 
"sample_335", "sample_336", "sample_337", "sample_338", "sample_339", 
"sample_340", "sample_341", "sample_342", "sample_343", "sample_344", 
"sample_345", "sample_346", "sample_347", "sample_348", "sample_349", 
"sample_350", "sample_351", "sample_352", "sample_353", "sample_354", 
"sample_355", "sample_356", "sample_357", "sample_358", "sample_359", 
"sample_360", "sample_361", "sample_362", "sample_363", "sample_364", 
"sample_365", "sample_366", "sample_367", "sample_368", "sample_369", 
"sample_370", "sample_371", "sample_372", "sample_373", "sample_374", 
"sample_375", "sample_376", "sample_377", "sample_378", "sample_379", 
"sample_380", "sample_381", "sample_382", "sample_383", "sample_384", 
"sample_385", "sample_386", "sample_387", "sample_388", "sample_389", 
"sample_390", "sample_391", "sample_392", "sample_393", "sample_394", 
"sample_395", "sample_396", "sample_397", "sample_398", "sample_399", 
"sample_400"), `val1` = c(2.082858, 2.122263, 2.088764, 2.071917, 
2.112873, 1.91232, 2.228338, 2.238842, 2.204705, 2.25799, 2.247922, 
2.264857, 2.284079, 2.184844, 2.219508, 2.123316, 2.198431, 2.210576, 
2.098665, 2.206564, 2.305151, 2.327899, 2.347045, 2.234165, 2.307019, 
2.156422, 2.183574, 2.07336, 2.248262, 2.316283, 2.160408, 2.046137, 
2.176502, 1.563847, 2.310179, 2.07625, 2.028296, 2.065215, 2.30437, 
2.092364, 1.935416, 1.985022, 2.010673, 2.013315, 2.046925, 2.148523, 
1.846122, 1.936764, 2.293144, 2.397894, 2.410375, 2.416399, 1.8967, 
1.94594, 1.909418, 1.971929, 2.019644, 1.965783, 2.231236, 2.269495, 
2.352585, 2.254097, 2.216921, 2.142253, 2.299506, 2.04482, 2.00557, 
2.271586, 2.195803, 2.255651, 1.873039, 1.946898, 1.991462, 2.146694, 
2.164778, 2.209621, 2.290843, 2.187884, 2.049942, 2.058623, 2.131342, 
2.152823, 2.284918, 2.266683, 2.180382, 2.01722, 2.029978, 2.082069, 
1.990474, 2.04705, 2.031689, 2.408628, 2.226449, 2.184436, 2.248969, 
2.276837, 2.295985, 2.143137, 2.153112, 1.901395, 1.977717, 2.002051, 
2.154496, 2.19486, 2.233862, 2.030338, 1.978764, 2.052828, 2.11602, 
2.320996, 2.088018, 1.921463, 2.100839, 2.027167, 2.053049, 1.873107, 
1.879176, 1.96511, 1.905438, 2.287117, 2.119682, 1.889649, 2.050932, 
2.062423, 2.147351, 1.993605, 2.179864, 2.305471, 2.221362, 2.120141, 
2.353358, 2.222375, 2.176474, 1.745787, 2.04053, 2.27677, 2.205036, 
1.952083, 1.967993, 2.132868, 1.949719, 2.257922, 2.334273, 2.332832, 
2.026935, 2.049107, 2.111323, 2.244565, 2.137639, 2.16603, 2.193296, 
2.225692, 2.25583, 2.275703, 2.290776, 2.296312, 1.888474, 2.024919, 
2.080257, 2.134283, 2.145789, 2.099064, 2.14684, 2.12732, 2.0526, 
2.240667, 2.15323, 2.053933, 2.167032, 2.259444, 2.203162, 2.214217, 
2.13477, 1.538202, 2.184755, 2.217369, 1.934981, 1.994553, 2.08068, 
2.011057, 2.105287, 1.967695, 2.109566, 2.026368, 2.290976, 2.255277, 
2.247285, 1.951347, 1.99945, 2.245651, 2.18856, 2.222482, 2.121424, 
2.109054, 2.082864, 2.362444, 2.013584, 2.05746, 2.166546, 2.168365, 
2.069021, 2.098348, 2.142036, 2.092707, 2.152859, 2.068626, 2.055397, 
2.056062, 2.175835, 2.188238, 2.038177, 2.138391, 2.276856, 2.174242, 
2.19482, 2.113511, 2.329332, 2.249665, 2.17901, 2.157453, 2.085514, 
2.162304, 1.983162, 2.09645, 2.086829, 2.069861, 2.094015, 2.037438, 
2.128538, 2.094458, 2.091278, 2.236437, 2.161954, 2.161966, 2.033872, 
2.124975, 2.036079, 2.15647, 2.230147, 2.048557, 2.151563, 2.16802, 
2.189068, 2.160613, 2.183146, 2.197435, 2.107876, 2.04596, 2.003809, 
2.088539, 2.095891, 2.238743, 2.129981, 2.053809, 2.093723, 2.114702, 
2.047264, 2.175869, 2.08047, 2.17311, 2.225797, 2.059016, 2.192365, 
2.084445, 2.088945, 2.18169, 2.146257, 2.311534, 2.141637, 2.175739, 
2.127526, 2.147002, 2.272443, 2.14763, 2.093103, 2.069417, 2.100754, 
2.229609, 2.179895, 2.24466, 2.061181, 2.252336, 2.132369, 2.118515, 
2.101917, 2.149535, 2.090827, 2.118568, 2.244184, 2.361944, 2.214365, 
2.259785, 2.338635, 2.046819, 2.142318, 2.12535, 2.048488, 2.030475, 
2.110566, 2.094643, 2.075998, 2.121076, 2.117321, 2.183068, 2.060518, 
2.075803, 2.097205, 2.115661, 2.120569, 2.047365, 2.172754, 2.109363, 
2.140297, 2.188512, 2.190935, 2.164103, 2.07223, 2.045483, 2.121717, 
2.110568, 2.036107, 2.035171, 2.119228, 2.141326, 2.037731, 2.119369, 
2.003983, 2.018401, 2.010878, 2.083425, 2.089139, 2.282455, 2.049371, 
2.076649, 2.135887, 2.22206, 2.145577, 2.105737, 2.101818, 2.079847, 
2.03853, 2.00578, 2.091156, 2.173219, 2.165171, 2.11761, 2.122339, 
2.010948, 2.074492, 2.163965, 2.149956, 2.056055, 2.105201, 2.068876, 
2.174275, 2.120809, 2.089455, 2.114321, 2.182982, 2.193953, 2.037804, 
2.208789, 2.068346, 2.057904, 2.055882, 2.169807, 2.054432, 2.183712, 
1.964441, 1.960812, 1.996795, 2.074152, 2.049355, 2.004009, 2.053112, 
1.995155, 2.304014, 2.263169, 2.289217, 2.073105, 2.173921, 2.130906, 
2.141058, 2.142085, 2.167612, 2.323677, 2.318744, 2.159492, 2.077183, 
2.10162, 2.072703, 2.085948, 1.926688, 2.115667, 2.159722, 2.151376, 
2.15546, 2.012882, 2.141457, 1.975143), `val2` = c(2.121346, 2.057013, 
2.111548, 2.158725, 2.124835, 1.8321, 2.123801, 2.55457, 2.32552, 
2.263824, 2.33237, 2.40342, 2.435667, 2.271651, 2.285832, 2.413729, 
2.347152, 2.456612, 2.446705, 2.376359, 2.354466, 2.428076, 2.398521, 
2.32005, 2.37856, 2.337184, 2.254457, 2.359102, 2.305856, 2.305669, 
1.80539, 2.337231, 2.366302, 1.668763, 2.279011, 2.041656, 2.045334, 
2.013131, 2.264024, 2.117566, 1.883608, 2.005024, 1.899453, 2.03688, 
2.193941, 2.165064, 1.877933, 1.656289, 2.206814, 2.306979, 2.348562, 
2.33883, 1.828998, 1.694623, 2.070877, 1.894557, 1.663993, 2.062695, 
2.222888, 2.203874, 2.396811, 2.333893, 2.195548, 2.314806, 2.393149, 
2.106452, 2.0867, 1.950008, 2.229766, 2.258149, 1.891405, 1.994098, 
1.973966, 2.146489, 2.194699, 2.249427, 2.200922, 2.144333, 2.019622, 
2.18366, 2.257231, 2.267063, 2.355385, 2.351872, 2.435147, 2.029042, 
2.098046, 2.105343, 1.912382, 2.110974, 2.040763, 2.159514, 2.13006, 
2.304408, 2.207029, 2.29645, 2.169954, 2.202413, 2.275843, 1.936519, 
2.042529, 2.029375, 2.161983, 2.195556, 2.189191, 1.978561, 1.747422, 
2.017418, 2.138009, 2.266166, 2.074008, 1.593988, 1.971115, 1.962473, 
2.087003, 1.84086, 1.912322, 1.968383, 1.838688, 2.256437, 2.268156, 
1.865337, 1.933451, 1.945718, 2.274528, 2.09925, 2.277204, 2.214034, 
2.278242, 2.329639, 2.275792, 2.388433, 2.272512, 1.872799, 1.995085, 
2.28755, 2.166229, 2.102435, 2.005053, 2.135626, 2.103744, 2.275947, 
2.279221, 2.258276, 1.931342, 2.168069, 2.183174, 2.337666, 2.160291, 
2.31235, 2.268757, 2.276785, 2.191121, 2.200755, 2.399039, 2.4622, 
1.889064, 1.97375, 2.138371, 2.178417, 2.229665, 2.134945, 2.358874, 
2.160434, 2.102112, 2.2501, 2.202255, 2.155681, 2.111324, 2.25234, 
2.24462, 2.314045, 2.228368, 1.393818, 2.239559, 2.240666, 2.169803, 
2.208392, 2.208392, 2.214578, 2.214578, 2.250664, 2.250664, 2.002537, 
2.452555, 2.172164, 2.267603, 2.120744, 1.963622, 2.315482, 2.038826, 
2.239879, 2.137838, 2.310075, 2.035209, 2.300045, 2.136338, 2.096093, 
2.309587, 2.289974, 2.136349, 2.071765, 2.081854, 2.068642, 2.08411, 
2.156214, 2.20417, 2.118596, 2.294891, 2.241429, 2.143121, 2.07384, 
2.315079, 2.228802, 2.198389, 2.237765, 2.287054, 2.207762, 2.117005, 
2.239277, 2.201032, 2.226966, 1.978663, 2.127048, 2.138229, 2.126905, 
2.147161, 2.198079, 2.159185, 2.146965, 2.174537, 2.30177, 2.21213, 
2.212304, 2.096243, 2.075516, 2.251111, 2.207776, 2.349764, 2.070651, 
2.303252, 2.277557, 2.352218, 2.278433, 2.241535, 2.308933, 2.199939, 
2.188046, 2.12897, 2.053481, 2.187801, 2.285338, 2.215279, 2.204896, 
2.145548, 2.134417, 2.161437, 2.219601, 2.168947, 2.209282, 2.189806, 
2.14961, 2.130051, 2.074155, 2.196938, 2.169139, 2.19535, 2.251364, 
2.071415, 2.08414, 2.146077, 2.123159, 2.211347, 2.007488, 2.197693, 
2.244524, 2.144347, 2.135782, 2.17264, 2.260589, 2.280875, 2.183601, 
2.035551, 2.14278, 2.136295, 2.30651, 2.035369, 2.245049, 2.157683, 
2.173291, 2.207225, 2.270673, 2.247661, 2.273533, 2.252527, 2.144356, 
1.85498, 2.017327, 2.131334, 2.154868, 2.098541, 2.159889, 2.133461, 
2.295671, 2.085562, 2.131564, 2.090968, 2.038269, 2.242191, 2.155241, 
2.218413, 2.189792, 2.061016, 2.208076, 2.122188, 2.131967, 2.118371, 
2.069602, 2.239564, 2.098603, 2.095114, 2.120751, 2.126101, 2.133297, 
2.085431, 2.036575, 2.123177, 2.118041, 2.149367, 2.137017, 2.096356, 
2.277791, 2.155334, 2.100648, 2.127216, 2.302333, 2.225221, 2.169416, 
2.007423, 2.111852, 2.048581, 2.115114, 2.089046, 2.285424, 2.17155, 
2.136776, 2.065787, 2.167453, 2.187317, 2.111283, 2.077909, 2.049304, 
2.024453, 2.16709, 2.095574, 2.250399, 2.109284, 2.063335, 2.184867, 
2.21021, 2.120943, 2.300302, 2.161862, 2.07172, 2.101225, 2.173641, 
2.255698, 2.126073, 1.982578, 1.990192, 1.990307, 2.093519, 2.121378, 
2.128941, 2.016535, 2.049717, 2.323625, 2.330499, 2.278661, 2.190421, 
2.249739, 2.313736, 2.206258, 2.222959, 2.238193, 2.334758, 2.295016, 
2.33971, 2.162662, 2.12201, 2.055884, 2.090801, 1.981376, 2.2449, 
2.258894, 2.164073, 2.228572, 2.099607, 2.209779, 2.092219), 
    `val3` = c(2.14252, 2.175419, 2.26789, 2.219875, 2.167182, 
    2.053002, 2.152574, 2.36973, 2.386524, 2.355951, 2.423532, 
    2.369754, 2.421104, 2.272194, 2.24334, 2.247397, 2.247935, 
    2.337053, 2.254528, 2.299579, 2.451648, 2.434791, 2.413604, 
    2.278367, 2.359631, 2.327937, 2.292264, 2.184793, 2.128214, 
    2.34826, 1.736369, 2.246826, 2.278744, 1.609359, 2.149199, 
    2.056187, 2.005308, 2.017998, 2.271404, 2.115661, 1.918609, 
    1.971176, 1.927277, 2.148639, 2.125553, 2.230468, 2.0303, 
    1.528465, 2.334077, 2.509969, 2.343552, 2.426268, 2.046001, 
    1.566295, 2.012493, 2.099083, 1.53598, 2.019948, 2.335971, 
    2.371226, 2.497284, 2.302614, 2.262051, 2.34778, 2.419204, 
    2.093739, 2.069555, 1.826686, 2.107145, 2.232015, 1.907773, 
    2.063211, 1.971781, 2.143656, 2.28016, 2.257669, 2.165872, 
    2.316845, 1.993204, 2.109138, 2.257096, 2.170264, 2.325351, 
    2.254235, 2.277522, 2.062624, 2.110956, 2.131972, 1.931915, 
    2.003313, 2.04408, 2.015764, 1.962285, 2.304109, 2.238633, 
    2.217348, 2.210052, 2.163779, 2.174652, 2.019867, 1.906523, 
    2.017825, 2.191424, 2.18117, 2.149712, 1.881857, 1.543816, 
    2.030026, 2.169016, 2.221381, 2.141331, 1.608621, 2.004654, 
    1.996462, 2.103756, 1.785617, 1.921303, 2.01878, 1.927096, 
    2.301642, 2.262258, 1.936385, 1.941799, 2.165343, 2.243224, 
    1.925497, 2.089144, 2.358042, 2.247775, 2.264547, 2.275754, 
    2.25868, 2.276655, 1.861224, 2.061538, 2.230054, 2.127053, 
    1.93639, 1.844573, 2.112442, 2.069124, 2.088666, 2.22327, 
    2.275378, 1.919877, 2.172298, 2.212157, 2.301012, 2.229858, 
    2.194098, 2.340829, 2.30437, 2.312016, 2.238267, 2.352278, 
    2.319287, 1.995792, 1.93857, 2.187406, 2.202112, 2.170208, 
    2.128147, 2.287111, 2.169452, 2.187433, 2.19519, 2.214937, 
    2.087365, 2.2268, 2.28623, 2.069005, 2.22988, 2.197564, 1.327704, 
    2.210598, 2.318935, 1.934981, 1.958112, 2.179368, 1.907111, 
    2.07291, 1.876928, 2.081677, 1.970865, 2.362311, 2.212368, 
    2.288541, 2.063169, 1.991251, 2.256495, 2.198519, 2.225588, 
    2.185618, 2.173044, 2.101828, 2.335278, 2.096587, 2.169129, 
    2.201262, 2.290541, 2.18592, 2.133513, 2.120704, 2.071174, 
    2.050728, 2.126838, 2.082943, 2.057391, 2.20872, 2.180407, 
    2.157854, 2.102841, 2.267875, 2.311124, 2.171999, 2.255905, 
    2.177938, 2.188296, 2.201336, 2.191709, 2.231804, 2.290071, 
    2.023881, 2.146732, 2.16778, 2.109588, 2.146173, 2.161353, 
    2.179975, 2.182263, 2.186609, 2.299938, 2.207546, 2.299751, 
    2.110938, 2.252099, 2.177418, 2.238984, 2.219852, 2.062572, 
    2.251983, 2.316414, 2.303087, 2.228346, 2.309663, 2.235903, 
    2.189952, 1.983696, 2.150872, 2.194775, 2.170584, 2.354886, 
    2.312899, 2.190148, 2.183438, 2.072558, 2.110178, 2.238394, 
    2.219896, 2.173578, 2.339692, 2.209672, 2.098955, 2.101192, 
    2.188244, 2.178494, 2.241643, 2.216229, 2.223225, 2.182035, 
    2.14844, 2.062796, 2.314615, 2.136511, 2.174501, 2.225644, 
    2.129476, 2.121252, 2.213425, 2.249062, 2.250093, 2.237232, 
    2.105991, 2.155478, 2.226418, 2.153588, 1.960591, 2.126968, 
    2.287284, 2.337094, 2.259087, 2.33551, 2.362881, 2.111644, 
    2.075585, 2.151435, 1.950531, 1.960602, 2.151787, 2.057674, 
    2.087935, 2.16013, 2.145974, 2.309717, 2.052284, 2.123123, 
    2.134674, 2.03777, 2.164606, 2.159854, 2.268756, 2.181091, 
    2.237057, 2.273092, 2.258321, 2.137125, 2.158171, 2.194724, 
    2.160183, 2.120599, 2.181107, 2.110795, 2.155562, 2.180645, 
    2.062989, 2.19967, 2.228301, 2.077007, 2.109296, 2.160272, 
    2.206313, 2.398574, 2.089601, 2.122303, 2.164323, 2.342606, 
    2.150247, 2.1706, 2.124876, 2.091632, 2.147148, 2.0212, 2.124492, 
    2.275959, 2.237386, 2.201155, 2.155138, 2.104636, 2.138844, 
    2.182284, 2.145762, 2.070256, 2.018307, 2.111384, 2.198714, 
    2.199193, 2.107418, 2.199925, 2.265293, 2.263555, 2.081988, 
    2.397868, 2.137808, 2.068976, 2.192084, 2.148425, 2.205134, 
    2.209922, 1.983753, 1.984189, 2.064658, 2.135486, 2.137695, 
    2.072517, 2.037692, 1.862418, 2.325198, 2.267791, 2.285763, 
    2.16696, 2.196881, 2.222214, 2.174961, 2.216402, 2.183686, 
    2.383081, 2.39431, 2.261765, 2.129212, 2.097548, 2.067722, 
    2.11232, 2.017724, 2.285934, 2.21157, 2.161602, 2.148854, 
    2.100749, 2.185951, 2.052716), `val4` = c(2.140233, 2.037519, 
    2.113521, 2.088845, 2.146646, 1.834525, 2.183151, 2.36086, 
    2.375638, 2.455928, 2.424402, 2.477603, 2.383812, 2.25989, 
    2.300623, 2.227262, 2.322886, 2.388468, 2.328027, 2.376393, 
    2.632898, 2.552, 2.371743, 2.379509, 2.403326, 2.36195, 2.385105, 
    2.302416, 2.17747, 2.467303, 1.891915, 2.303444, 2.401075, 
    1.669299, 2.190901, 2.119865, 2.132712, 2.015169, 2.257012, 
    3.332752, 1.942406, 2.970787, 2.662868, 2.265898, 2.190538, 
    2.178219, 2.201261, 1.693196, 2.270553, 2.409355, 3.208329, 
    3.288885, 1.94487, 1.800267, 2.099565, 2.114151, 1.711404, 
    1.917562, 2.326826, 2.345992, 2.507143, 2.398142, 2.283767, 
    2.253, 2.303071, 3.089826, 3.163045, 1.986025, 2.131257, 
    2.218043, 1.95166, 1.972309, 2.02333, 2.151578, 2.359243, 
    2.250042, 2.146358, 2.225975, 2.186238, 2.209661, 2.205924, 
    2.179038, 2.313613, 2.280705, 2.461195, 2.030174, 1.984316, 
    2.115094, 2.005554, 2.173414, 2.072978, 2.116258, 1.990868, 
    2.220333, 2.241729, 2.177197, 2.175097, 2.215843, 2.11971, 
    1.95506, 2.765252, 2.086681, 2.193199, 2.29085, 2.103523, 
    1.898235, 1.666698, 2.153971, 2.139013, 2.205495, 2.165776, 
    1.753286, 2.169395, 2.11318, 2.019268, 2.186592, 2.141843, 
    2.033839, 1.899277, 2.206061, 2.213252, 1.857455, 2.027472, 
    2.06853, 2.135313, 1.962058, 2.145372, 2.313626, 2.105825, 
    2.284117, 2.228288, 2.277833, 2.228736, 1.969638, 2.987737, 
    2.294886, 2.227587, 2.049362, 1.906014, 2.201817, 2.105977, 
    2.241837, 2.330335, 2.308066, 1.798105, 2.122765, 2.265536, 
    2.385606, 2.211229, 2.300095, 2.227938, 2.259108, 2.277109, 
    2.273571, 2.425302, 2.454249, 1.872756, 2.681021, 2.166747, 
    2.174513, 2.204329, 2.170444, 2.277877, 2.213459, 2.149603, 
    2.224343, 2.338195, 2.190145, 2.162302, 2.110749, 2.168522, 
    2.279969, 2.175953, 1.417074, 2.224395, 2.272887, 1.755112, 
    2.101585, 2.101585, 2.099968, 2.099968, 2.128569, 2.128569, 
    2.694193, 2.564887, 2.314764, 2.261425, 1.946358, 2.534585, 
    2.1971, 2.25031, 2.428223, 2.420678, 2.322512, 2.172753, 
    2.449615, 2.231486, 2.225631, 2.344465, 2.416885, 2.125062, 
    2.198904, 2.111299, 2.073994, 2.148302, 2.344976, 2.107045, 
    2.134083, 2.248754, 2.18011, 2.03877, 2.070728, 2.325273, 
    2.351624, 2.260225, 2.204828, 2.328498, 2.235699, 2.279503, 
    2.291735, 2.429524, 2.347288, 2.025209, 2.123207, 2.065328, 
    2.072388, 2.167139, 2.19004, 2.143521, 2.161281, 2.159734, 
    2.201138, 2.186752, 2.34815, 2.210854, 2.175663, 2.378525, 
    2.402285, 2.310025, 2.245944, 2.213895, 2.405134, 2.251619, 
    2.378685, 2.361806, 2.287945, 2.259435, 2.08419, 2.191935, 
    2.103453, 2.144895, 2.190272, 2.244015, 2.15636, 2.233805, 
    2.153179, 2.098052, 2.191568, 2.130575, 2.230142, 2.154311, 
    2.092892, 2.188252, 2.170973, 2.199386, 2.229897, 2.19169, 
    2.209387, 2.041156, 2.080759, 2.129413, 2.171794, 2.327398, 
    2.088617, 2.224659, 2.157428, 2.095628, 2.207119, 2.169195, 
    2.185353, 2.244007, 2.253546, 2.135968, 2.148702, 2.252366, 
    2.128714, 2.09676, 2.254963, 2.174496, 2.074059, 2.246549, 
    2.323532, 2.275209, 2.201546, 2.206006, 2.203116, 1.922022, 
    2.15399, 2.16023, 2.21066, 2.220847, 2.1042, 2.166522, 2.419542, 
    2.105471, 2.162086, 2.110638, 2.10253, 2.258193, 2.117779, 
    2.24563, 2.228901, 2.168267, 2.279787, 2.21575, 2.104758, 
    2.153866, 2.088131, 2.150313, 2.177547, 2.218625, 2.156767, 
    2.228171, 2.133743, 2.117583, 2.188858, 2.04043, 2.188775, 
    2.141219, 2.177059, 2.336666, 2.332583, 2.035018, 2.158863, 
    2.136201, 2.316251, 2.15058, 2.108461, 2.125801, 2.173043, 
    2.147717, 2.133278, 2.125712, 2.288505, 2.357054, 2.051841, 
    2.042184, 2.163136, 3.252666, 2.137751, 3.075602, 1.954388, 
    2.011636, 2.214401, 2.145071, 2.164361, 2.048259, 2.17592, 
    2.89887, 3.14396, 2.712584, 3.463721, 2.146412, 2.13156, 
    3.265061, 3.348222, 2.09418, 2.178885, 1.987424, 2.163439, 
    2.069097, 2.142727, 2.173843, 2.12378, 1.959276, 2.061579, 
    2.283583, 2.236381, 2.378646, 2.258915, 2.162754, 2.205929, 
    2.337513, 2.226166, 2.302526, 2.29592, 2.415604, 2.316184, 
    2.3491, 2.271443, 2.272601, 2.188199, 2.035553, 2.218111, 
    2.135625, 2.168354, 2.212543, 3.201331, 2.236726, 2.041484
    ), `val5` = c(2.205883, 2.138808, 2.077683, 2.145272, 2.140336, 
    1.922036, 2.124524, 2.21395, 2.233357, 2.238282, 2.174845, 
    2.18728, 2.291566, 2.200844, 2.074897, 2.120694, 2.13741, 
    2.220063, 2.240819, 2.278081, 2.344973, 2.22191, 2.33297, 
    2.235565, 2.163177, 2.116454, 2.135605, 2.172574, 2.122485, 
    2.12934, 1.821374, 2.096029, 2.160734, 1.67997, 2.189523, 
    2.068242, 1.978585, 2.045954, 2.273401, 2.206602, 2.094542, 
    1.997064, 1.931612, 2.012061, 2.140568, 2.084762, 1.927587, 
    1.538069, 2.202355, 2.023682, 2.305089, 2.131391, 1.836854, 
    1.606727, 1.998197, 1.868502, 1.636886, 1.832838, 2.117169, 
    2.310318, 2.417686, 2.388326, 2.118177, 2.090245, 2.295759, 
    2.005638, 2.080542, 1.712372, 1.858493, 2.321916, 1.89028, 
    2.063164, 2.037167, 2.073301, 2.279731, 2.27351, 2.077682, 
    1.97822, 1.91448, 2.08932, 2.250001, 2.040835, 2.275865, 
    2.306706, 2.291881, 2.127415, 2.033734, 2.126216, 1.887513, 
    2.169935, 2.072144, 2.076124, 1.991255, 2.33843, 2.290549, 
    2.360705, 2.255152, 2.12727, 2.180296, 1.939317, 1.96596, 
    2.073531, 2.177269, 2.178081, 2.019706, 1.831482, 1.705669, 
    1.937907, 2.237815, 2.27279, 1.86944, 1.551953, 2.021657, 
    1.877711, 2.072241, 1.880281, 2.006795, 1.902573, 1.816364, 
    2.295615, 2.257875, 1.772723, 1.967391, 2.070067, 2.141384, 
    1.829313, 2.274327, 2.320299, 2.333113, 2.211248, 2.223768, 
    2.192608, 2.339105, 1.940122, 1.949549, 2.207718, 2.196507, 
    1.909539, 1.82168, 1.893651, 1.852073, 1.978035, 2.365995, 
    2.369893, 1.911889, 2.106383, 2.148052, 2.196957, 2.119374, 
    2.271152, 2.11809, 2.233256, 2.115329, 2.239077, 2.271444, 
    2.305705, 1.873262, 1.913524, 2.087152, 2.198402, 2.231835, 
    2.093793, 2.147368, 2.21914, 2.1441, 2.174357, 2.224865, 
    2.083651, 2.158534, 2.06352, 2.174625, 2.168381, 2.347363, 
    1.496778, 2.17655, 2.205306, 1.851269, 2.205678, 2.205678, 
    2.101734, 2.101734, 2.123564, 2.123564, 1.93621, 2.289793, 
    2.287898, 2.318628, 2.055624, 1.998863, 2.296364, 2.089131, 
    2.245203, 2.071056, 2.219998, 2.027608, 2.297961, 2.157261, 
    2.024074, 2.220135, 2.202394, 2.135486, 2.061067, 2.171635, 
    2.035892, 1.958031, 2.136896, 2.094457, 2.094218, 2.166129, 
    2.126551, 2.005676, 2.042307, 2.143985, 2.191011, 2.127468, 
    2.147525, 2.436999, 2.338819, 2.288538, 2.174344, 2.074859, 
    2.134957, 1.963659, 2.088555, 2.101662, 2.146974, 2.106074, 
    2.091452, 2.200799, 2.150823, 2.133833, 2.195167, 2.126187, 
    2.175083, 1.983333, 2.107533, 2.072242, 2.120645, 2.249016, 
    2.007933, 2.371245, 2.069811, 2.147182, 2.221025, 2.194299, 
    2.191193, 2.197913, 2.290911, 2.110344, 2.083548, 2.165094, 
    2.282859, 2.193421, 2.041276, 2.160484, 2.108207, 2.133532, 
    2.210509, 1.965951, 2.135821, 2.163696, 2.16769, 2.124543, 
    2.130831, 2.10493, 2.167604, 2.030589, 2.184318, 2.034473, 
    2.119289, 2.081737, 2.079179, 2.225343, 2.12243, 2.188467, 
    2.179326, 2.093398, 2.1698, 2.056133, 2.227862, 2.194625, 
    2.127432, 2.119466, 2.071805, 2.129211, 2.174826, 2.129491, 
    2.089535, 2.14714, 2.254218, 2.090754, 2.255141, 2.204579, 
    2.09242, 2.131324, 2.028279, 1.898828, 2.034569, 2.115417, 
    2.104312, 2.107143, 2.076757, 2.247945, 2.199677, 2.02578, 
    1.997606, 2.080415, 1.978941, 2.120822, 2.03952, 2.15905, 
    2.176023, 2.139808, 2.068431, 2.138224, 2.218875, 2.040483, 
    2.0435, 2.136538, 2.072061, 2.159958, 2.13571, 2.14123, 2.213969, 
    2.033575, 2.076335, 2.125759, 2.29935, 2.190341, 2.209615, 
    2.212204, 2.323666, 2.22632, 2.131567, 2.193508, 2.242983, 
    2.091444, 2.11958, 2.008892, 2.131797, 2.072565, 1.982571, 
    2.106211, 2.212871, 2.094693, 1.999156, 2.046164, 2.085549, 
    2.160318, 2.099178, 2.033786, 2.013754, 2.005703, 2.085651, 
    2.169668, 2.137398, 2.153037, 2.134659, 2.210465, 2.114096, 
    2.154082, 2.305219, 2.064591, 2.069177, 2.042963, 2.256995, 
    2.208851, 2.147816, 1.896875, 2.087929, 1.982611, 2.107752, 
    2.073508, 2.001149, 2.027708, 2.005028, 2.254312, 2.219452, 
    2.185899, 2.085647, 2.206456, 2.157227, 2.100942, 2.118613, 
    2.171959, 2.246866, 2.15426, 2.235049, 2.083087, 2.106987, 
    2.088798, 2.05145, 1.987925, 2.185864, 2.201295, 2.1024, 
    2.141197, 2.037241, 2.164477, 2.097156)), row.names = c(NA, 
400L), class = "data.frame")

Hi,

I'm not sure what your data represent and what exactly you are trying to achieve, but I tried the following approach to identify samples that stand out. I assumed all data came from a Gaussian process, and I'm looking for samples standing out, that is, with all 5 values being large or small. For this, I standardised the data (calculated a Z score in each column) and then carried out a one-sided t-test against zero to identify statistically significant samples, with the null hypothesis being Z = 0. I applied Benjamini-Hochberg multiple test correction.

library(tidyverse)
library(broom)

res <- DF |> 
  as_tibble() |> 
  pivot_longer(-samples) |>
  group_by(name) |>
  mutate(Z = (value - mean(value)) / sd(value)) |>
  ungroup() |>
  group_by(samples) |>
  nest(data = c(name, value, Z)) |>
  mutate(
    fits = map(data, ~t.test(.x$Z, mu = 0)),
    tidied = map(fits, tidy)
  ) |> 
  unnest(tidied) |>
  ungroup() |> 
  mutate(p_adj = p.adjust(p.value, method = "BH")) |> 
  arrange(p.value) |> 
  select(samples, estimate, p_val = p.value, p_adj)

There are 101 samples with FDR < 0.05. The top of this result looks like this:

# A tibble: 400 × 4
   samples    estimate     p_val  p_adj
   <chr>         <dbl>     <dbl>  <dbl>
 1 sample_21     1.64  0.0000821 0.0185
 2 sample_305   -0.589 0.000106  0.0185
 3 sample_162   -0.200 0.000221  0.0185
 4 sample_369   -1.28  0.000249  0.0185
 5 sample_223   -1.07  0.000274  0.0185
 6 sample_174   -4.76  0.000293  0.0185
 7 sample_325   -0.593 0.000349  0.0185
 8 sample_185    1.50  0.000381  0.0185
 9 sample_24     0.872 0.000415  0.0185
10 sample_119   -1.84  0.000469  0.0188
# ℹ 390 more rows

I made a volcano plot indicating significant samples in black:

res |> 
  ggplot(aes(x = estimate, y = -log10(p_val), colour = p_adj < 0.05)) +
  theme_bw() +
  geom_point() +
  scale_colour_manual(values = c("grey80", "black"))

Is this any way useful to you? The result identifies samples where all 5 values are consistently different from the mean.

why not it's an idea, but that's not what I'm trying to show..
I am trying to isolate certain values that vary at the same time and in the same way. For example, I made a PCA plot (see attachment) and I see that the variables val1, val2, val3 and val5 seem to follow the same trend because their values appear to be correlated whereas val4 not.

You can show the same by plotting a dendrogram:

DF |>
  select(-samples) |>
  t() |>
  dist() |>
  hclust() |>
  plot()

Alternatively, you can create a heatmap with dendrograms and see at which samples values are similars. You'd need to make it much longer to read sample names, or make it interactive (package ComplexHeatmap allows for that):

library(ComplexHeatmap)

DF |>
  select(-samples) |>
  as.matrix() |> 
  Heatmap()

Even in this low-quality image you can see there is a group of samples where val4 is much higher than other values (top-left corner). You can use the sample dendrogram at cut it at the highest level to see which samples belong to this group:

x <- DF |>
  select(-samples) |>
  dist() |>
  hclust() |> 
  cutree(k = 2)
x[x == 2]
34  48  54  57 107 112 174 
  2   2   2   2   2   2   2 

My answer would be that val4 is different and the source of this difference is in samples 34 , 48, 54, 57, 107, 112 and 174. Is this what you are looking for?

Yes, these are alternative solutions that allow me to answer my question. Thank you.
This correlation between values is sample-dependent, as you can see visually, but is there a statistical test that can confirm it ?

this package: https://www.youtube.com/watch?v=c_NvVOQK8kk&t=22s
can do the trick but it failed on my computer:

library(ggbiplot)
pc <- prcomp(df[-1],center=TRUE,scale=TRUE)
pc$scale
       val1       val2      val3       val4      val5 
0.2887456 0.1486474 0.1578612 0.3309003 0.1432616 
print(pc)
Standard deviations (1, .., p=5):
[1] 1.7356084 0.9188768 0.8415759 0.5397724 0.3791100

Rotation (n x k) = (5 x 5):
          PC1          PC2        PC3         PC4         PC5
1p  0.2955794  0.818059770 -0.4921433  0.01989496 -0.02846376
17p 0.5268733 -0.069691548  0.2569475  0.36145746 -0.72171613
19p 0.5262744 -0.029743169  0.2441691  0.43156064  0.69013586
20q 0.3280745 -0.570099768 -0.7525007 -0.03122286  0.01100975
22q 0.5004396 -0.004785319  0.2567043 -0.82567139  0.04366840
summary(pc)
Importance of components:
                          PC1    PC2    PC3     PC4     PC5
Standard deviation     1.7356 0.9189 0.8416 0.53977 0.37911
Proportion of Variance 0.6025 0.1689 0.1416 0.05827 0.02874
Cumulative Proportion  0.6025 0.7713 0.9130 0.97126 1.00000

g <- ggbiplot(pc,
+               obs.scale=1,
+               var.scale=1,
+               groups=df$samples,
+               ellipse=TRUE,
+               circle=TRUE,
+               ellipse.prob=0.68)
Error in names(ell) <- `*vtmp*` : 
  attribut 'names' [2] must be same lenght of vector [0]

This topic was automatically closed 90 days after the last reply. New replies are no longer allowed.

If you have a query related to it or one of the replies, start a new topic and refer back with a link.