chr6.19717_chr6_114516570_114527644_+_1.R fitVsDatCorrelation=0.944357610441775 cont.fitVsDatCorrelation=0.389717904644521 fstatistic=6676.74012039363,37,347 cont.fstatistic=843.522780719834,37,347 residuals=-0.65123651022023,-0.0796638686515616,-0.00725329167367047,0.0803934899244418,0.785601984778526 cont.residuals=-0.763416907622789,-0.271136292496216,-0.116228888576632,0.134829624652859,1.58112779726826 predictedValues: Include Exclude Both chr6.19717_chr6_114516570_114527644_+_1.R.tl.Lung 44.7719209256752 47.9971732618752 102.326042659285 chr6.19717_chr6_114516570_114527644_+_1.R.tl.cerebhem 52.2595800433417 72.0013117034212 99.4154165612107 chr6.19717_chr6_114516570_114527644_+_1.R.tl.cortex 46.7669283085411 56.0998254472015 206.629309554130 chr6.19717_chr6_114516570_114527644_+_1.R.tl.heart 48.8077815317553 47.4106379788271 113.577520019229 chr6.19717_chr6_114516570_114527644_+_1.R.tl.kidney 44.5714549101165 50.1964296434321 116.003364248918 chr6.19717_chr6_114516570_114527644_+_1.R.tl.liver 50.4064146997157 49.4815806141453 104.491068997982 chr6.19717_chr6_114516570_114527644_+_1.R.tl.stomach 47.6324250804777 50.1178395383404 95.6709969291692 chr6.19717_chr6_114516570_114527644_+_1.R.tl.testicle 51.4200320023045 57.3885849278415 145.743828895767 diffExp=-3.22525233619995,-19.7417316600794,-9.33289713866038,1.39714355292821,-5.6249747333156,0.924834085570438,-2.48541445786269,-5.96855292553693 diffExpScore=1.08087462021383 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,-1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,-1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 64.1333918061088 62.849799067087 66.4858010420405 cerebhem 74.3375672751827 67.5900696855285 49.5174957988985 cortex 68.7112238113429 56.1403948959643 54.8464313572046 heart 64.6229374400408 78.5655095809413 82.2365807228122 kidney 68.7372419132272 70.866998469358 62.4112957902124 liver 87.0073374953964 83.4357049621213 62.3714126101287 stomach 52.1610829055521 55.8012524697987 51.1985941280524 testicle 46.9553613183869 66.2529351080408 71.0569944374757 cont.diffExp=1.28359273902181,6.7474975896542,12.5708289153786,-13.9425721409005,-2.12975655613089,3.57163253327512,-3.64016956424662,-19.2975737896539 cont.diffExpScore=3.98974160589951 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,-1 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,0,0,0,0,0,0,-1 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,1,-1,0,0,0,-1 cont.diffExp1.2Score=1.5 tran.correlation=0.62236163395258 cont.tran.correlation=0.57573450243559 tran.covariance=0.00512954752789299 cont.tran.covariance=0.0146174665257459 tran.mean=51.0831200385633 cont.tran.mean=66.7605505127548 weightedLogRatios: wLogRatio Lung -0.266860925992234 cerebhem -1.31916335931784 cortex -0.716208533688159 heart 0.112494714009528 kidney -0.458348303654056 liver 0.072421104944937 stomach -0.19780457520625 testicle -0.438714313697457 cont.weightedLogRatios: wLogRatio Lung 0.083919621870625 cerebhem 0.405460702101289 cortex 0.834269745697817 heart -0.833469224003906 kidney -0.129547429663769 liver 0.186318592083095 stomach -0.269033515017663 testicle -1.38447600152076 varWeightedLogRatios=0.213453631259378 cont.varWeightedLogRatios=0.492815590738634 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 2.68288297201881 0.0895994006385101 29.9430906110960 3.33250009179251e-98 *** df.mm.trans1 1.11063743209129 0.0746485966214594 14.8782091339681 4.5095525303351e-39 *** df.mm.trans2 1.12028994153224 0.0746485966214593 15.0075151072591 1.38449931248028e-39 *** df.mm.exp2 0.589041278621093 0.102850084538579 5.7271832226851 2.21440751595943e-08 *** df.mm.exp3 -0.50317650335311 0.102850084538579 -4.89232950668475 1.52804452561179e-06 *** df.mm.exp4 -0.0303082929882778 0.102850084538579 -0.294684181585764 0.7684113652014 df.mm.exp5 -0.08514074627464 0.102850084538579 -0.827814062152802 0.408344764222567 df.mm.exp6 0.128058246011212 0.102850084538579 1.24509616677250 0.213936573679625 df.mm.exp7 0.172416477023969 0.102850084538579 1.67638634229120 0.094563195396958 . df.mm.exp8 -0.0365363297492067 0.102850084538579 -0.355238694388258 0.722626606795606 df.mm.trans1:exp2 -0.434399232340669 0.0869241865469675 -4.99744949705052 9.23116078009942e-07 *** df.mm.trans2:exp2 -0.183499060464098 0.0869241865469675 -2.11102418962470 0.0354850738608102 * df.mm.trans1:exp3 0.546771618531161 0.0869241865469675 6.29021265831145 9.5458485904933e-10 *** df.mm.trans2:exp3 0.659167085622652 0.0869241865469675 7.5832413486721 3.11619640870554e-13 *** df.mm.trans1:exp4 0.116616872883579 0.0869241865469675 1.34159291580564 0.180605332102766 df.mm.trans2:exp4 0.0180128076455557 0.0869241865469675 0.207224345272680 0.835956207930986 df.mm.trans1:exp5 0.0806531980910133 0.0869241865469675 0.927856805970041 0.354126787935052 df.mm.trans2:exp5 0.129942529006009 0.0869241865469675 1.49489496730347 0.135850685437537 df.mm.trans1:exp6 -0.00952098111447732 0.0869241865469675 -0.109532012811335 0.912843847089679 df.mm.trans2:exp6 -0.097599873287559 0.0869241865469675 -1.12281606724986 0.262291887554738 df.mm.trans1:exp7 -0.110483926397460 0.0869241865469675 -1.27103779496127 0.204566590825389 df.mm.trans2:exp7 -0.129181572500689 0.0869241865469675 -1.48614071218128 0.138149882870478 df.mm.trans1:exp8 0.174982976087795 0.0869241865469675 2.01305278817015 0.0448809842953363 * df.mm.trans2:exp8 0.215239625648574 0.0869241865469675 2.47617647284251 0.0137561586518349 * df.mm.trans1:probe2 0.0151088291688942 0.0476103377645612 0.317343456868741 0.751173865802719 df.mm.trans1:probe3 0.059773885728356 0.0476103377645612 1.25548123653197 0.210148881650075 df.mm.trans1:probe4 0.100551557011033 0.0476103377645612 2.11196899102612 0.0354033725305562 * df.mm.trans1:probe5 -0.0421964830689772 0.0476103377645612 -0.88628825272452 0.376076022195241 df.mm.trans1:probe6 -0.0526300512430795 0.0476103377645612 -1.10543326752567 0.269737743587253 df.mm.trans2:probe2 0.0906358670477275 0.0476103377645612 1.90370140820955 0.0577761613453513 . df.mm.trans2:probe3 0.0196734194162287 0.0476103377645612 0.413217388070551 0.67970258142689 df.mm.trans2:probe4 0.312216404832812 0.0476103377645612 6.55774395839742 1.9833635908223e-10 *** df.mm.trans2:probe5 -0.030383153961446 0.0476103377645612 -0.638162957626857 0.523788525685172 df.mm.trans2:probe6 0.287549515116059 0.0476103377645612 6.03964451035877 3.97672938203809e-09 *** df.mm.trans3:probe2 -0.312428865589237 0.0476103377645612 -6.56220645050314 1.93126132425270e-10 *** df.mm.trans3:probe3 0.098377253215243 0.0476103377645612 2.06630025818616 0.0395417698607221 * df.mm.trans3:probe4 -0.92853292215827 0.0476103377645612 -19.5027585552948 1.01158518367267e-57 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.11428082312906 0.250866567102508 16.4002755355117 3.70252134000550e-45 *** df.mm.trans1 0.0289675546175881 0.209006277274097 0.138596577076004 0.889849344599445 df.mm.trans2 0.0960772395573964 0.209006277274097 0.45968590422477 0.646029557348412 df.mm.exp2 0.515026963925743 0.287966743644783 1.78849459283759 0.0745685231363064 . df.mm.exp3 0.148506549818268 0.287966743644782 0.515707292927742 0.606387275791228 df.mm.exp4 0.0181774935868953 0.287966743644782 0.0631235862753579 0.949704437779608 df.mm.exp5 0.252625232571806 0.287966743644782 0.877272248087885 0.380945741678575 df.mm.exp6 0.652237257163233 0.287966743644782 2.26497424288616 0.0241304469597501 * df.mm.exp7 -0.0643035723822068 0.287966743644782 -0.223302078456419 0.823431806813848 df.mm.exp8 -0.325529627562567 0.287966743644782 -1.13044174282889 0.259070875127412 df.mm.trans1:exp2 -0.367375685197613 0.243376318611704 -1.50949643454729 0.132081958460213 df.mm.trans2:exp2 -0.442313627818351 0.243376318611704 -1.8174061894824 0.0700172945432515 . df.mm.trans1:exp3 -0.0795591509794729 0.243376318611704 -0.326897667913228 0.743942382213835 df.mm.trans2:exp3 -0.261398682788811 0.243376318611704 -1.07405142899651 0.283545931729812 df.mm.trans1:exp4 -0.0105732383897851 0.243376318611704 -0.0434439901552388 0.965372599747325 df.mm.trans2:exp4 0.205007562132596 0.243376318611704 0.842348028362104 0.400173367438854 df.mm.trans1:exp5 -0.183299246717755 0.243376318611704 -0.753151529957198 0.451869534452029 df.mm.trans2:exp5 -0.132568111280933 0.243376318611704 -0.54470423431969 0.586306833793423 df.mm.trans1:exp6 -0.347209964338183 0.243376318611704 -1.42663824614810 0.154583310741488 df.mm.trans2:exp6 -0.368908660370543 0.243376318611704 -1.51579521982630 0.130481535945038 df.mm.trans1:exp7 -0.142324910289574 0.243376318611704 -0.584793586744351 0.559066556836368 df.mm.trans2:exp7 -0.0546478507949783 0.243376318611704 -0.224540543248855 0.822468882188553 df.mm.trans1:exp8 0.0137618574815943 0.243376318611704 0.0565455898096263 0.95493972316506 df.mm.trans2:exp8 0.378261656957255 0.243376318611704 1.55422540333826 0.121042209104971 df.mm.trans1:probe2 0.0648805794217875 0.133302699666195 0.486716169921959 0.626766758707238 df.mm.trans1:probe3 -0.00742104420340179 0.133302699666195 -0.0556706219902894 0.955636243281088 df.mm.trans1:probe4 -0.147080870517939 0.133302699666195 -1.10336002861342 0.270635433314331 df.mm.trans1:probe5 0.172301358171114 0.133302699666195 1.29255715452558 0.197024283303896 df.mm.trans1:probe6 0.094487812669371 0.133302699666195 0.708821448522644 0.478911212930296 df.mm.trans2:probe2 -0.192906619838349 0.133302699666195 -1.44713213101767 0.148762691149799 df.mm.trans2:probe3 -0.299860600425435 0.133302699666195 -2.24947132485929 0.0251089444268692 * df.mm.trans2:probe4 -0.105146449433070 0.133302699666195 -0.788779594834683 0.43077940449369 df.mm.trans2:probe5 -0.0526843237539163 0.133302699666195 -0.395223231681308 0.692920994242076 df.mm.trans2:probe6 -0.045505253335139 0.133302699666195 -0.341367830127143 0.733033307755365 df.mm.trans3:probe2 -0.135285078697653 0.133302699666195 -1.01487125944502 0.310874298349835 df.mm.trans3:probe3 -0.00101053721427775 0.133302699666195 -0.00758077080815503 0.993955834262936 df.mm.trans3:probe4 -0.0330532265092343 0.133302699666195 -0.247956167369479 0.804314985132702