chr15.8093_chr15_90268702_90269048_-_0.R fitVsDatCorrelation=0.788694090957176 cont.fitVsDatCorrelation=0.268233463710544 fstatistic=12471.4844273969,61,899 cont.fstatistic=5070.45034220578,61,899 residuals=-0.501695259276109,-0.0873302255835666,-0.00633176993432429,0.0808963912771981,0.800497556618617 cont.residuals=-0.641101071314528,-0.154256896564980,-0.0345782337597517,0.130823106627367,0.92915942801753 predictedValues: Include Exclude Both chr15.8093_chr15_90268702_90269048_-_0.R.tl.Lung 56.6900647185408 79.1527898484375 55.2392688468267 chr15.8093_chr15_90268702_90269048_-_0.R.tl.cerebhem 65.284498650292 74.9878436007 65.3555155528685 chr15.8093_chr15_90268702_90269048_-_0.R.tl.cortex 57.3132360970251 68.0822209296459 54.5739700201603 chr15.8093_chr15_90268702_90269048_-_0.R.tl.heart 57.590181917058 72.0307380968034 54.508135458993 chr15.8093_chr15_90268702_90269048_-_0.R.tl.kidney 56.2915011702942 75.1267594004738 55.9087119657022 chr15.8093_chr15_90268702_90269048_-_0.R.tl.liver 57.2283004263001 76.0717666191933 57.7168529513264 chr15.8093_chr15_90268702_90269048_-_0.R.tl.stomach 61.4483632000149 79.9624680588439 53.7920302419617 chr15.8093_chr15_90268702_90269048_-_0.R.tl.testicle 55.3845190217812 78.9175425515755 54.1282780504774 diffExp=-22.4627251298967,-9.70334495040804,-10.7689848326208,-14.4405561797454,-18.8352582301795,-18.8434661928932,-18.5141048588290,-23.5330235297943 diffExpScore=0.992758947141266 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,-1 diffExp1.4Score=0.5 diffExp1.3=-1,0,0,0,-1,-1,-1,-1 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,0,0,-1,-1,-1,-1,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 58.654696745164 58.612314189464 56.2128347868953 cerebhem 59.5995220121152 59.2791451756435 59.485898009766 cortex 55.6559873620366 56.2715750595092 57.5441905501524 heart 58.3054874642562 60.6302329303666 62.1717062348453 kidney 61.5767007266743 65.0616060750894 58.8026784365479 liver 59.5043563855368 54.1662744283024 56.2052371611299 stomach 57.2296133927918 61.3960284427318 57.5035866170593 testicle 58.362079572817 63.4035128134044 63.0796825514622 cont.diffExp=0.0423825556999944,0.320376836471709,-0.615587697472577,-2.32474546611042,-3.48490534841506,5.33808195723444,-4.16641504993996,-5.04143324058737 cont.diffExpScore=1.95146808982819 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,0,0,0 cont.diffExp1.4Score=0 cont.diffExp1.3=0,0,0,0,0,0,0,0 cont.diffExp1.3Score=0 cont.diffExp1.2=0,0,0,0,0,0,0,0 cont.diffExp1.2Score=0 tran.correlation=0.0486387754352918 cont.tran.correlation=0.389913645930229 tran.covariance=0.000147671692813491 cont.tran.covariance=0.00067131683359677 tran.mean=66.9726746441862 cont.tran.mean=59.231820798494 weightedLogRatios: wLogRatio Lung -1.40337897674161 cerebhem -0.588656867163584 cortex -0.711918192282735 heart -0.931930409352311 kidney -1.20500344234188 liver -1.19241271190132 stomach -1.11924843018951 testicle -1.48417204844056 cont.weightedLogRatios: wLogRatio Lung 0.00294289705778596 cerebhem 0.0220178670718956 cortex -0.0442710325711834 heart -0.159722653078145 kidney -0.228341215350256 liver 0.379634945297231 stomach -0.286871754491867 testicle -0.340367446102896 varWeightedLogRatios=0.0995082108746278 cont.varWeightedLogRatios=0.0526782735398946 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.63272209870025 0.0661053924277517 70.0808501176884 0 *** df.mm.trans1 -0.531531003672518 0.0555745307410717 -9.56429135045663 1.04901569311766e-20 *** df.mm.trans2 -0.265595733484547 0.0497690048881062 -5.33656909720571 1.19966301581907e-07 *** df.mm.exp2 -0.0810661109595866 0.0632254708943492 -1.28217488636897 0.200111835060842 df.mm.exp3 -0.127614240688551 0.0632254708943492 -2.0183992129026 0.0438462381059456 * df.mm.exp4 -0.0652098294962054 0.0632254708943492 -1.03138543017216 0.302637496718946 df.mm.exp5 -0.0713047480449026 0.0632254708943492 -1.12778516373653 0.259711557533714 df.mm.exp6 -0.0741283894373907 0.0632254708943492 -1.17244503502806 0.241329013640239 df.mm.exp7 0.117324344506153 0.0632254708943492 1.85564999115949 0.063830569835615 . df.mm.exp8 -0.00595800283257361 0.0632254708943492 -0.094234218397986 0.924944101427892 df.mm.trans1:exp2 0.222221762431513 0.0553672912470894 4.01359281673717 6.47827106318413e-05 *** df.mm.trans2:exp2 0.027012092717473 0.040836896250594 0.661462921954558 0.508484976362415 df.mm.trans1:exp3 0.138546864233975 0.0553672912470894 2.5023233232718 0.0125147165777746 * df.mm.trans2:exp3 -0.0230496865447543 0.0408368962505940 -0.56443286980751 0.572600367342892 df.mm.trans1:exp4 0.0809629598830854 0.0553672912470895 1.46228861949864 0.144011612228127 df.mm.trans2:exp4 -0.0290772578973249 0.040836896250594 -0.712033983163008 0.476628466786226 df.mm.trans1:exp5 0.0642493457154876 0.0553672912470895 1.16042060697461 0.246185660996631 df.mm.trans2:exp5 0.0191015269491111 0.040836896250594 0.467751682985291 0.640075542680524 df.mm.trans1:exp6 0.0835779582086127 0.0553672912470894 1.50951864044832 0.131517659916860 df.mm.trans2:exp6 0.0344255484589884 0.040836896250594 0.84300109997923 0.399452055620621 df.mm.trans1:exp7 -0.0367261150956633 0.0553672912470894 -0.663317895249101 0.507297025566235 df.mm.trans2:exp7 -0.107147002498850 0.040836896250594 -2.62377928629411 0.00884340164803272 ** df.mm.trans1:exp8 -0.0173408524622587 0.0553672912470895 -0.313196691975974 0.754203877340055 df.mm.trans2:exp8 0.00298151171028289 0.040836896250594 0.073010242795803 0.941814207023089 df.mm.trans1:probe2 -0.134405413304423 0.0418013882044941 -3.21533372640417 0.00134942270471401 ** df.mm.trans1:probe3 -0.117316680542061 0.0418013882044941 -2.80652594521844 0.00511612868830627 ** df.mm.trans1:probe4 -0.343172500332791 0.0418013882044941 -8.2095957831347 7.66382506753441e-16 *** df.mm.trans1:probe5 -0.206350740516101 0.0418013882044941 -4.93645664365558 9.48048297474537e-07 *** df.mm.trans1:probe6 -0.280175007988639 0.0418013882044941 -6.70252879205856 3.61257100715588e-11 *** df.mm.trans1:probe7 0.00571320624958532 0.0418013882044941 0.136675036284347 0.891318263081966 df.mm.trans1:probe8 -0.230389132344936 0.0418013882044941 -5.51151868971104 4.64793612730819e-08 *** df.mm.trans1:probe9 -0.131004329177118 0.0418013882044941 -3.13397077954062 0.00178051231531670 ** df.mm.trans1:probe10 0.074044017838558 0.0418013882044941 1.77132915960426 0.0768447242449251 . df.mm.trans1:probe11 -0.0891162580365212 0.0418013882044941 -2.13189709395681 0.0332858640777779 * df.mm.trans1:probe12 0.0302770722194305 0.0418013882044941 0.724307816556567 0.469065227234908 df.mm.trans1:probe13 -0.136697740897846 0.0418013882044941 -3.27017227822949 0.00111567986131540 ** df.mm.trans1:probe14 -0.0271595551662564 0.0418013882044941 -0.649728545697831 0.516033556260607 df.mm.trans1:probe15 -0.0676421898300404 0.0418013882044941 -1.61818046566138 0.105974477807543 df.mm.trans2:probe2 0.00939411376966837 0.0418013882044941 0.224732100372169 0.82223875937692 df.mm.trans2:probe3 -0.165192286089216 0.0418013882044941 -3.95183732370534 8.36335289870613e-05 *** df.mm.trans2:probe4 0.0936802794217152 0.0418013882044941 2.24108058238228 0.0252642582449015 * df.mm.trans2:probe5 0.186308483095519 0.0418013882044941 4.45699272435858 9.36094080606907e-06 *** df.mm.trans2:probe6 -0.0178488876606908 0.0418013882044941 -0.426992701136462 0.669486882706841 df.mm.trans3:probe2 0.461213218267037 0.0418013882044941 11.0334426218279 1.23431418537738e-26 *** df.mm.trans3:probe3 0.0513446938265515 0.0418013882044941 1.22830116491278 0.219655398396748 df.mm.trans3:probe4 0.321796546788373 0.0418013882044941 7.69822631760772 3.63016741903353e-14 *** df.mm.trans3:probe5 0.470651577059864 0.0418013882044941 11.2592331804250 1.32907149558576e-27 *** df.mm.trans3:probe6 0.0637237818709657 0.0418013882044941 1.52444176158042 0.127750083898877 df.mm.trans3:probe7 0.0964967339165715 0.0418013882044941 2.30845763888284 0.0211998053879190 * df.mm.trans3:probe8 0.0912547293889951 0.0418013882044941 2.18305499670426 0.029289782615664 * df.mm.trans3:probe9 0.413649744038551 0.0418013882044941 9.89559825178435 5.50962159534058e-22 *** df.mm.trans3:probe10 0.156465434456363 0.0418013882044941 3.74306790221721 0.000193334271075996 *** df.mm.trans3:probe11 0.275064891872681 0.0418013882044941 6.58028127025478 7.96562336808625e-11 *** df.mm.trans3:probe12 0.140659478568258 0.0418013882044941 3.36494754385062 0.000797986757068307 *** df.mm.trans3:probe13 0.157748211330621 0.0418013882044941 3.77375532503635 0.000171349965648443 *** df.mm.trans3:probe14 0.0687141513565807 0.0418013882044941 1.6438246265992 0.100562070767787 df.mm.trans3:probe15 -0.00511011611595705 0.0418013882044941 -0.122247521803777 0.902730312693267 df.mm.trans3:probe16 0.280778098122267 0.0418013882044941 6.71695630653913 3.28796877296755e-11 *** df.mm.trans3:probe17 0.044675759527746 0.0418013882044941 1.06876258054375 0.285463550710694 df.mm.trans3:probe18 0.144060562695563 0.0418013882044941 3.44631049071416 0.000594624575048134 *** df.mm.trans3:probe19 0.112308391517339 0.0418013882044941 2.68671439732868 0.00734904120733399 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.09343843564289 0.103585485855070 39.5174903303555 9.18115646763023e-199 *** df.mm.trans1 -0.00584799347450001 0.0870838906867253 -0.0671535622534083 0.946474385085232 df.mm.trans2 -0.0252651583501917 0.0779867778183485 -0.32396720388988 0.746038218395218 df.mm.exp2 -0.0293015205850706 0.0990727213088486 -0.295757704017499 0.767483425144028 df.mm.exp3 -0.116641357475508 0.0990727213088486 -1.17733071156783 0.239375127766398 df.mm.exp4 -0.0728774963735926 0.0990727213088486 -0.735595988591095 0.462168470150299 df.mm.exp5 0.107963406084123 0.0990727213088486 1.08973897817502 0.276120153218424 df.mm.exp6 -0.064369301533221 0.0990727213088486 -0.64971770920228 0.516040554054602 df.mm.exp7 -0.000898050256715765 0.0990727213088486 -0.00906455626585838 0.992769640605773 df.mm.exp8 -0.0416804652390398 0.0990727213088486 -0.420705767323232 0.674070496260084 df.mm.trans1:exp2 0.0452814218936194 0.0867591516165205 0.52192098527848 0.601853911142268 df.mm.trans2:exp2 0.0406142670963071 0.063990388432442 0.634693242082532 0.525789986544401 df.mm.trans1:exp3 0.064163366224387 0.0867591516165205 0.73955732656299 0.459761708257201 df.mm.trans2:exp3 0.0758860673758517 0.063990388432442 1.18589790177581 0.235975958664706 df.mm.trans1:exp4 0.0669060571022607 0.0867591516165205 0.77117002478296 0.440808793507013 df.mm.trans2:exp4 0.106726344024729 0.063990388432442 1.66784960427933 0.0956938340349067 . df.mm.trans1:exp5 -0.0593474948497603 0.0867591516165205 -0.68404881495475 0.494120625400573 df.mm.trans2:exp5 -0.00357361354186478 0.063990388432442 -0.0558460986002239 0.955476819590552 df.mm.trans1:exp6 0.0787511751686048 0.0867591516165205 0.90769876953948 0.364280638915262 df.mm.trans2:exp6 -0.0145170409736125 0.063990388432442 -0.226862835641933 0.820581976630356 df.mm.trans1:exp7 -0.0236981214690066 0.0867591516165205 -0.273148377173551 0.784801894733365 df.mm.trans2:exp7 0.0472983857159761 0.063990388432442 0.739148282650472 0.460009902311317 df.mm.trans1:exp8 0.0366791689892288 0.0867591516165205 0.422770028357958 0.672564158800985 df.mm.trans2:exp8 0.120254918040141 0.063990388432442 1.87926532384000 0.0605315585841902 . df.mm.trans1:probe2 -0.0691562253987069 0.0655017230449282 -1.05579246138719 0.291346684013488 df.mm.trans1:probe3 -0.0176633586219528 0.0655017230449282 -0.269662503531966 0.787481793113708 df.mm.trans1:probe4 -0.0352736723547913 0.0655017230449282 -0.538515182731862 0.590354734558523 df.mm.trans1:probe5 -0.0458944849834603 0.0655017230449282 -0.70066072845108 0.483696066213782 df.mm.trans1:probe6 -0.0191679362819205 0.0655017230449282 -0.292632550578448 0.769870555920468 df.mm.trans1:probe7 -0.0102401456839328 0.0655017230449282 -0.156333989518245 0.875804864707895 df.mm.trans1:probe8 -0.00372161970565759 0.0655017230449282 -0.0568171268274102 0.954703495926697 df.mm.trans1:probe9 0.00761679268289169 0.0655017230449282 0.116283852222746 0.907453553069907 df.mm.trans1:probe10 -0.0436956511826704 0.0655017230449282 -0.667091629829327 0.504884787327777 df.mm.trans1:probe11 -0.0702292410703699 0.0655017230449282 -1.07217394910664 0.283929639290047 df.mm.trans1:probe12 -0.0474269842076874 0.0655017230449282 -0.724057047707841 0.469219084745025 df.mm.trans1:probe13 -0.0450882128857231 0.0655017230449282 -0.688351554581193 0.491409004519681 df.mm.trans1:probe14 -0.0209053715187242 0.0655017230449282 -0.319157581616365 0.749681163787227 df.mm.trans1:probe15 0.00685358762527064 0.0655017230449282 0.104632173119625 0.916691023036335 df.mm.trans2:probe2 0.0216046847030572 0.0655017230449282 0.329833837931841 0.741602355772787 df.mm.trans2:probe3 -0.0164667274786614 0.0655017230449282 -0.251393806348677 0.801567107468333 df.mm.trans2:probe4 0.0495022129835977 0.0655017230449282 0.755739096353904 0.450003603586705 df.mm.trans2:probe5 0.0141102790400398 0.0655017230449282 0.215418440677682 0.829489955071631 df.mm.trans2:probe6 0.000537974900736612 0.0655017230449282 0.00821314120801998 0.993448757254665 df.mm.trans3:probe2 -0.102314727672517 0.0655017230449282 -1.56201582059663 0.118636192267558 df.mm.trans3:probe3 -0.0850913344285027 0.0655017230449282 -1.29907016904178 0.194252918171149 df.mm.trans3:probe4 -0.00826300053367363 0.0655017230449282 -0.126149361414599 0.899641901032592 df.mm.trans3:probe5 0.00677890993018104 0.0655017230449282 0.103492085628516 0.917595512212903 df.mm.trans3:probe6 0.121305401755484 0.0655017230449282 1.85194214925124 0.064361815984397 . df.mm.trans3:probe7 0.0200519048452150 0.0655017230449282 0.306127898825824 0.759578144924957 df.mm.trans3:probe8 0.00685556830755661 0.0655017230449282 0.104662411748380 0.916667034665144 df.mm.trans3:probe9 -0.0638940305077869 0.0655017230449282 -0.975455721431349 0.329596654302124 df.mm.trans3:probe10 0.0140052585349803 0.0655017230449282 0.213815116365320 0.830739718679437 df.mm.trans3:probe11 -0.0173208200599320 0.0655017230449282 -0.264433044731534 0.791506861053577 df.mm.trans3:probe12 -0.00121036317854570 0.0655017230449282 -0.0184783410615855 0.985261356068234 df.mm.trans3:probe13 -0.0875591632047659 0.0655017230449282 -1.33674595315162 0.181643755578285 df.mm.trans3:probe14 -0.0316443989506587 0.0655017230449282 -0.483107886016273 0.629136763239009 df.mm.trans3:probe15 -0.0537893070181966 0.0655017230449282 -0.821189191943882 0.411756219089898 df.mm.trans3:probe16 -0.0218962242712045 0.0655017230449282 -0.334284706620399 0.73824269673297 df.mm.trans3:probe17 -0.120642328026594 0.0655017230449282 -1.84181915251060 0.0658308430010057 . df.mm.trans3:probe18 -0.0574068280914166 0.0655017230449282 -0.876417068479874 0.381037462364514 df.mm.trans3:probe19 -0.148387861459781 0.0655017230449282 -2.26540393995438 0.0237250892890122 *