chr1.522_chr1_76078545_76080721_+_2.R fitVsDatCorrelation=0.925670816135615 cont.fitVsDatCorrelation=0.235306788860284 fstatistic=8092.79621053789,64,968 cont.fstatistic=1213.41377985309,64,968 residuals=-0.791838067431513,-0.113806107608787,-0.00354206939708534,0.120310623081690,0.960399153698892 cont.residuals=-0.95983921057957,-0.398416305964273,-0.0590369463794813,0.328803723872041,1.88170037957685 predictedValues: Include Exclude Both chr1.522_chr1_76078545_76080721_+_2.R.tl.Lung 94.8449388265234 47.9256202713494 91.2994664249553 chr1.522_chr1_76078545_76080721_+_2.R.tl.cerebhem 90.9034352846776 57.0136602997029 92.0683308419034 chr1.522_chr1_76078545_76080721_+_2.R.tl.cortex 100.224605957399 44.943808722762 124.731683067329 chr1.522_chr1_76078545_76080721_+_2.R.tl.heart 104.012092702978 47.9900583071348 118.859128016721 chr1.522_chr1_76078545_76080721_+_2.R.tl.kidney 86.2922962190115 47.1240887019051 72.0910767189924 chr1.522_chr1_76078545_76080721_+_2.R.tl.liver 84.978644120652 49.4130032004727 79.9666409277116 chr1.522_chr1_76078545_76080721_+_2.R.tl.stomach 81.5990483000893 47.7988310659195 86.390480514433 chr1.522_chr1_76078545_76080721_+_2.R.tl.testicle 78.8792017777796 48.6905751600786 77.7443151288736 diffExp=46.9193185551741,33.8897749849748,55.2807972346367,56.0220343958427,39.1682075171064,35.5656409201793,33.8002172341697,30.188626617701 diffExpScore=0.996986450637203 diffExp1.5=1,1,1,1,1,1,1,1 diffExp1.5Score=0.888888888888889 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 81.6222466245349 94.0510132612344 87.9996698899554 cerebhem 88.6544407918028 113.102723819658 88.722328329921 cortex 87.5604543261738 87.082304709646 104.234619514867 heart 87.711942832107 83.6232502984495 90.8920268251752 kidney 88.9357157636444 87.5976697120626 86.9547235363182 liver 90.8967961658279 90.9564141325912 99.9092249058032 stomach 92.9527173923513 72.3558561569208 93.6049785239967 testicle 91.3075533063262 92.8723777238824 88.3228507613441 cont.diffExp=-12.4287666366995,-24.4482830278548,0.478149616527844,4.08869253365754,1.33804605158177,-0.0596179667632839,20.5968612354305,-1.56482441755620 cont.diffExpScore=5.00034834748856 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,-1,0,0,0,0,1,0 cont.diffExp1.2Score=2 tran.correlation=-0.157734494484783 cont.tran.correlation=-0.312391609255073 tran.covariance=-0.00108651610151225 cont.tran.covariance=-0.00166772928694943 tran.mean=69.5396193074022 cont.tran.mean=89.4552173135758 weightedLogRatios: wLogRatio Lung 2.87436323476265 cerebhem 1.99503743933643 cortex 3.37354652032394 heart 3.2934266444329 kidney 2.51375094914606 liver 2.26162567742358 stomach 2.21115000654701 testicle 1.99085332138140 cont.weightedLogRatios: wLogRatio Lung -0.633978466753903 cerebhem -1.1219198337997 cortex 0.0244744053831103 heart 0.21243636812482 kidney 0.0679192567298979 liver -0.00295711221852468 stomach 1.10389035244627 testicle -0.0768536335851097 varWeightedLogRatios=0.307775368255586 cont.varWeightedLogRatios=0.416096180443589 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.3357619125649 0.0913178172015941 47.479911866413 4.95307796724036e-255 *** df.mm.trans1 0.0427842710463833 0.0778121672290816 0.549840372912696 0.582555699546723 df.mm.trans2 -0.469644652276663 0.068358811399354 -6.87028698514061 1.14543136453290e-11 *** df.mm.exp2 0.122809034779286 0.0863603175400616 1.42205399745452 0.155332698667537 df.mm.exp3 -0.321086776536241 0.0863603175400616 -3.71798976291736 0.000212341570262664 *** df.mm.exp4 -0.170186577660512 0.0863603175400616 -1.97065715490872 0.0490477069885837 * df.mm.exp5 0.124845737082208 0.0863603175400616 1.44563777251390 0.148602490255472 df.mm.exp6 0.05325540901865 0.0863603175400616 0.616665275622052 0.537600529038196 df.mm.exp7 -0.0978073302821389 0.0863603175400616 -1.13254945174058 0.257683957428601 df.mm.exp8 -0.00777098065736453 0.0863603175400616 -0.089983233951863 0.92831915275092 df.mm.trans1:exp2 -0.165254577594108 0.0781312888712686 -2.11508833377095 0.0346774119194386 * df.mm.trans2:exp2 0.0508316273330364 0.0542778404370034 0.9365079178497 0.349245317969691 df.mm.trans1:exp3 0.376257168282321 0.0781312888712686 4.81570410162123 1.70141901186394e-06 *** df.mm.trans2:exp3 0.256849559597096 0.0542778404370034 4.73212562491693 2.55259283028837e-06 *** df.mm.trans1:exp4 0.26245041083772 0.0781312888712686 3.35909485980886 0.000812493234165936 *** df.mm.trans2:exp4 0.171530217124570 0.0542778404370034 3.16022553114754 0.00162541911910572 ** df.mm.trans1:exp5 -0.219348745622826 0.0781312888712686 -2.80743795208897 0.00509405271980539 ** df.mm.trans2:exp5 -0.141711660782075 0.0542778404370034 -2.61085665238561 0.00917121272961254 ** df.mm.trans1:exp6 -0.163098764930000 0.0781312888712686 -2.08749615277339 0.0371043256573312 * df.mm.trans2:exp6 -0.0226920281706079 0.0542778404370034 -0.41807168428053 0.675987431479732 df.mm.trans1:exp7 -0.0526184059997194 0.0781312888712686 -0.673461384803405 0.500814567301742 df.mm.trans2:exp7 0.0951582834021974 0.0542778404370034 1.75317003469659 0.0798893400470254 . df.mm.trans1:exp8 -0.176554763744317 0.0781312888712686 -2.25971907407305 0.0240603013678444 * df.mm.trans2:exp8 0.0236062320859078 0.0542778404370034 0.434914725712161 0.663721266197539 df.mm.trans1:probe2 0.174073709445156 0.0581722903308491 2.99238191336681 0.00283832569388216 ** df.mm.trans1:probe3 0.243202043251785 0.0581722903308491 4.18071975279979 3.16916816182831e-05 *** df.mm.trans1:probe4 0.0926086515441604 0.0581722903308491 1.59197189963569 0.111717499464373 df.mm.trans1:probe5 0.674274026445611 0.0581722903308491 11.5909829681923 3.49642756592731e-29 *** df.mm.trans1:probe6 -0.0168352680809738 0.0581722903308491 -0.2894035628514 0.772334540418693 df.mm.trans1:probe7 0.0400871565264181 0.0581722903308491 0.689110851548503 0.490918649673407 df.mm.trans1:probe8 0.11176224602466 0.0581722903308491 1.92122822376467 0.0549963541915024 . df.mm.trans1:probe9 0.0245163521626227 0.058172290330849 0.421443818408875 0.673524592999154 df.mm.trans1:probe10 -0.164793027839438 0.0581722903308491 -2.8328440723615 0.00470936243353365 ** df.mm.trans1:probe11 0.134163165186047 0.0581722903308491 2.30630708234122 0.0213039622598806 * df.mm.trans1:probe12 -0.252795588753575 0.0581722903308491 -4.34563582275728 1.53540159429201e-05 *** df.mm.trans1:probe13 -0.0970391578241573 0.0581722903308491 -1.66813369857464 0.0956125983465564 . df.mm.trans1:probe14 -0.107734904666473 0.0581722903308491 -1.85199695686282 0.0643305755186817 . df.mm.trans1:probe15 0.722698138658471 0.0581722903308491 12.4234087148400 5.497788110609e-33 *** df.mm.trans1:probe16 1.22588707807774 0.0581722903308491 21.0733851307148 1.94877418893381e-81 *** df.mm.trans1:probe17 0.787042814755362 0.0581722903308491 13.5295139709840 2.51370865064469e-38 *** df.mm.trans1:probe18 0.864906927212791 0.0581722903308491 14.8680225979366 3.44893155417085e-45 *** df.mm.trans1:probe19 0.990661233846127 0.0581722903308491 17.0297787522520 4.38582858493119e-57 *** df.mm.trans1:probe20 0.980109012617769 0.0581722903308491 16.8483827444905 4.73083627076473e-56 *** df.mm.trans2:probe2 -0.0366698313321994 0.0581722903308491 -0.630365954712173 0.528603958983608 df.mm.trans2:probe3 0.0122435702182884 0.0581722903308491 0.210470829816985 0.83334451008879 df.mm.trans2:probe4 0.0323546641290347 0.0581722903308491 0.556186870845566 0.578211651914599 df.mm.trans2:probe5 0.0748242451890236 0.0581722903308491 1.28625235079224 0.198662564598651 df.mm.trans2:probe6 -0.00149431285318864 0.0581722903308491 -0.0256877087817909 0.979511721999555 df.mm.trans3:probe2 0.059206152400871 0.0581722903308491 1.01777241473805 0.309040429726767 df.mm.trans3:probe3 0.930475501628147 0.0581722903308491 15.995167051807 2.82559685372164e-51 *** df.mm.trans3:probe4 0.733917668245463 0.0581722903308491 12.6162759635452 6.79183477398913e-34 *** df.mm.trans3:probe5 0.787178173685601 0.0581722903308491 13.5318408336444 2.44767826814527e-38 *** df.mm.trans3:probe6 0.738746112025035 0.0581722903308491 12.6992784334859 2.74216798539993e-34 *** df.mm.trans3:probe7 0.0229536914327182 0.0581722903308491 0.394581188090951 0.693238917470223 df.mm.trans3:probe8 1.03997459548173 0.0581722903308491 17.8774909766657 5.48338963630202e-62 *** df.mm.trans3:probe9 0.128619676021665 0.0581722903308491 2.21101275693554 0.0272680621143191 * df.mm.trans3:probe10 1.05631350679386 0.0581722903308491 18.1583620102661 1.22512549927987e-63 *** df.mm.trans3:probe11 0.933996227360476 0.0581722903308491 16.0556894364734 1.30895774766998e-51 *** df.mm.trans3:probe12 0.210224987601516 0.0581722903308491 3.61383377559801 0.000317234688905924 *** df.mm.trans3:probe13 0.987013674177823 0.0581722903308491 16.9670760522627 9.99439510662867e-57 *** df.mm.trans3:probe14 0.720876680115763 0.0581722903308491 12.3920972685766 7.70358732285709e-33 *** df.mm.trans3:probe15 0.854884912830708 0.0581722903308491 14.6957410129227 2.78023473142685e-44 *** df.mm.trans3:probe16 0.250685325106328 0.0581722903308491 4.30935972574882 1.80453255854403e-05 *** df.mm.trans3:probe17 0.83407112000638 0.0581722903308491 14.3379453561599 2.01928308963353e-42 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.47033383651204 0.234593430888705 19.0556650268389 5.40244480587619e-69 *** df.mm.trans1 -0.0593020829191735 0.199897718041789 -0.296662130514048 0.766788111288943 df.mm.trans2 0.0983907172351458 0.175612258254552 0.560272490161405 0.575423226192138 df.mm.exp2 0.258924883517401 0.221857725088148 1.16707625760845 0.243466988158211 df.mm.exp3 -0.176067272316546 0.221857725088148 -0.793604424847463 0.427620242575137 df.mm.exp4 -0.0778987413762608 0.221857725088148 -0.351120256665889 0.725574535906663 df.mm.exp5 0.0266745248939959 0.221857725088148 0.120232571948521 0.904323865875553 df.mm.exp6 -0.0527629628569786 0.221857725088148 -0.237823419653361 0.812068381879187 df.mm.exp7 -0.194002351979501 0.221857725088148 -0.874444880846136 0.382092921533346 df.mm.exp8 0.0958548022009464 0.221857725088148 0.432055283010143 0.6657974558068 df.mm.trans1:exp2 -0.176280613733504 0.200717534406280 -0.878252187856628 0.380024812458131 df.mm.trans2:exp2 -0.0744657467324041 0.139438558646625 -0.534039848483519 0.593436580634254 df.mm.trans1:exp3 0.246294878683048 0.200717534406280 1.22707206130040 0.220093856512084 df.mm.trans2:exp3 0.09908364557787 0.139438558646625 0.710590001356614 0.477509504077797 df.mm.trans1:exp4 0.149854954712383 0.200717534406280 0.746596231144689 0.455488609894267 df.mm.trans2:exp4 -0.0396169929148656 0.139438558646625 -0.284117917593122 0.776380777890779 df.mm.trans1:exp5 0.0591374340512372 0.200717534406280 0.294630133964952 0.768339611849978 df.mm.trans2:exp5 -0.0977574580991247 0.139438558646625 -0.701079092095812 0.483422150898676 df.mm.trans1:exp6 0.16038586270297 0.200717534406280 0.799062539191651 0.424450164677734 df.mm.trans2:exp6 0.01930605972408 0.139438558646625 0.138455674753543 0.889909095255086 df.mm.trans1:exp7 0.323991445694117 0.200717534406280 1.61416612979269 0.106817295977919 df.mm.trans2:exp7 -0.0682385855072973 0.139438558646625 -0.48938103039513 0.624682835466218 df.mm.trans1:exp8 0.0162768575900399 0.200717534406280 0.0810933516007287 0.935384475947078 df.mm.trans2:exp8 -0.108465863361747 0.139438558646625 -0.777875678108723 0.436832384447524 df.mm.trans1:probe2 0.0205126884031775 0.149443313359547 0.137260663873437 0.890853305381658 df.mm.trans1:probe3 0.00779280752779707 0.149443313359547 0.0521455751522871 0.958423457195614 df.mm.trans1:probe4 -0.0993134931953513 0.149443313359547 -0.664556285341531 0.506492611401185 df.mm.trans1:probe5 -0.0914861347642057 0.149443313359547 -0.612179512803615 0.540562760402956 df.mm.trans1:probe6 0.0323428971785556 0.149443313359547 0.216422511328703 0.828704019815885 df.mm.trans1:probe7 0.0901276253622446 0.149443313359547 0.60308904651629 0.54659072392944 df.mm.trans1:probe8 -0.176207755209884 0.149443313359547 -1.17909427493717 0.238650363419809 df.mm.trans1:probe9 -0.101838532070496 0.149443313359547 -0.681452584134568 0.495748153686795 df.mm.trans1:probe10 0.00885669960401823 0.149443313359547 0.0592646094690753 0.95275358127914 df.mm.trans1:probe11 -0.135278448191024 0.149443313359547 -0.905215798217455 0.365576316612566 df.mm.trans1:probe12 0.0645375645388611 0.149443313359547 0.431853142760489 0.665944323560228 df.mm.trans1:probe13 -0.00782313260417606 0.149443313359547 -0.0523484954148085 0.95826181306345 df.mm.trans1:probe14 0.0140769065735685 0.149443313359548 0.0941956268039953 0.924973252082852 df.mm.trans1:probe15 0.192965742788966 0.149443313359547 1.29123035652125 0.196932069188218 df.mm.trans1:probe16 0.000637365540447056 0.149443313359547 0.00426493180670861 0.996597965825674 df.mm.trans1:probe17 0.0747617741077974 0.149443313359547 0.50026844578805 0.616999778670587 df.mm.trans1:probe18 -0.0328560107650798 0.149443313359547 -0.219856011128655 0.826029650337912 df.mm.trans1:probe19 -0.0829398049836568 0.149443313359547 -0.554991743150869 0.579028525298021 df.mm.trans1:probe20 -0.109275007191746 0.149443313359547 -0.731213760824751 0.464825555865146 df.mm.trans2:probe2 0.029691492615994 0.149443313359547 0.198680636480261 0.842554291917855 df.mm.trans2:probe3 -0.218538255493495 0.149443313359547 -1.46234883703168 0.143970216017769 df.mm.trans2:probe4 -0.151193501261179 0.149443313359547 -1.01171138314781 0.311928885248977 df.mm.trans2:probe5 -0.0828534131228826 0.149443313359547 -0.55441365197481 0.579423847040183 df.mm.trans2:probe6 -0.149512484260654 0.149443313359547 -1.00046285711653 0.317336584769278 df.mm.trans3:probe2 0.102223912128391 0.149443313359547 0.684031354969017 0.494119082244999 df.mm.trans3:probe3 -0.0100987923112096 0.149443313359547 -0.0675760733898665 0.946137057473522 df.mm.trans3:probe4 0.0264078650375729 0.149443313359547 0.176708241030751 0.859774531199889 df.mm.trans3:probe5 -0.0314197502087254 0.149443313359548 -0.210245272955988 0.833520490917043 df.mm.trans3:probe6 0.13126402216823 0.149443313359547 0.878353264641693 0.379970002147505 df.mm.trans3:probe7 -0.115920803858323 0.149443313359547 -0.77568411227224 0.438124984199879 df.mm.trans3:probe8 -0.00178570582071897 0.149443313359547 -0.0119490513196982 0.990468725450144 df.mm.trans3:probe9 -0.241963007371743 0.149443313359547 -1.61909557498636 0.105752435752836 df.mm.trans3:probe10 -0.183189666845062 0.149443313359547 -1.22581373985147 0.220566911024472 df.mm.trans3:probe11 0.071798590758398 0.149443313359547 0.480440303044251 0.631022896780792 df.mm.trans3:probe12 -0.121608156278359 0.149443313359547 -0.813741033603696 0.415993438020348 df.mm.trans3:probe13 -0.195023337225411 0.149443313359547 -1.30499875063799 0.192203400707612 df.mm.trans3:probe14 0.125662440770952 0.149443313359548 0.840870280148427 0.400628267344944 df.mm.trans3:probe15 0.0210875896412690 0.149443313359547 0.141107615772237 0.887814286046618 df.mm.trans3:probe16 0.0181102068648967 0.149443313359547 0.121184457556325 0.903570084427431 df.mm.trans3:probe17 -0.141065336775106 0.149443313359547 -0.943938765836346 0.345436475109836