chr10.2589_chr10_82814188_82822394_-_2.R fitVsDatCorrelation=0.754405951917875 cont.fitVsDatCorrelation=0.271160745509205 fstatistic=11500.5326404583,45,531 cont.fstatistic=5342.20818862444,45,531 residuals=-0.496361538563958,-0.074102076338109,-0.00317469161286491,0.0711040413449395,0.677549349685559 cont.residuals=-0.499723124510652,-0.120289902100294,-0.0264029568254034,0.0883534016026765,0.861948303127787 predictedValues: Include Exclude Both chr10.2589_chr10_82814188_82822394_-_2.R.tl.Lung 52.5423468570022 47.9329458049252 53.672628546825 chr10.2589_chr10_82814188_82822394_-_2.R.tl.cerebhem 55.6613664334201 48.2943105072941 49.4972252752476 chr10.2589_chr10_82814188_82822394_-_2.R.tl.cortex 61.3123760485756 49.5207897620339 57.6276114126854 chr10.2589_chr10_82814188_82822394_-_2.R.tl.heart 52.6362545777324 49.2303492124841 50.5229641949631 chr10.2589_chr10_82814188_82822394_-_2.R.tl.kidney 49.6737764655946 46.741352118435 54.7421254271143 chr10.2589_chr10_82814188_82822394_-_2.R.tl.liver 52.0036683490217 49.1862806273469 56.6824670176489 chr10.2589_chr10_82814188_82822394_-_2.R.tl.stomach 52.9215641002298 49.5589643573943 49.3655885014442 chr10.2589_chr10_82814188_82822394_-_2.R.tl.testicle 52.0570334176341 46.2457200741874 54.3185433658599 diffExp=4.60940105207698,7.36705592612607,11.7915862865418,3.40590536524832,2.93242434715962,2.81738772167474,3.3625997428355,5.81131334344669 diffExpScore=0.97679689152166 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=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 52.7554979444188 50.6461732336132 50.0951883276353 cerebhem 51.7409458441213 56.1448384657564 55.9125522387895 cortex 53.166046722234 53.7554595068117 54.9199898306038 heart 51.2838540240912 50.829327413275 47.9699167846494 kidney 50.7808685202917 53.3800932715302 54.6951804565905 liver 52.7604354867334 54.3199906018901 51.8720714576442 stomach 51.2947290709256 50.0161407976932 53.4661022520622 testicle 56.4328508171558 52.1696066628346 51.2061906084179 cont.diffExp=2.10932471080563,-4.40389262163511,-0.589412784577711,0.454526610816202,-2.59922475123842,-1.55955511515671,1.27858827323239,4.26324415432124 cont.diffExpScore=8.43322721575989 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=0.50439975735915 cont.tran.correlation=0.00939243897854023 tran.covariance=0.000864468657197892 cont.tran.covariance=2.68959620497124e-05 tran.mean=50.969943669582 cont.tran.mean=52.592303648961 weightedLogRatios: wLogRatio Lung 0.359526430875910 cerebhem 0.560550210321905 cortex 0.856318831018185 heart 0.262893914276712 kidney 0.235788850619044 liver 0.218535111630464 stomach 0.258388633127847 testicle 0.460836555427498 cont.weightedLogRatios: wLogRatio Lung 0.160983884274464 cerebhem -0.325686797292681 cortex -0.0438687703662109 heart 0.0350127325487814 kidney -0.197300672229018 liver -0.115949822865792 stomach 0.0990749186243353 testicle 0.313716568739748 varWeightedLogRatios=0.0482955005152696 cont.varWeightedLogRatios=0.0421512958277047 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.44353683633064 0.0712510563059934 48.3296250590601 1.44203525578709e-196 *** df.mm.trans1 0.465069732597046 0.0633082248630076 7.34611866946843 7.73638403600014e-13 *** df.mm.trans2 0.417607434101617 0.05914246345511 7.06104226481175 5.20025943931543e-12 *** df.mm.exp2 0.146164089293569 0.0812835412615504 1.79820031245008 0.0727131369163165 . df.mm.exp3 0.115853112603661 0.0812835412615504 1.42529607846285 0.154659157448146 df.mm.exp4 0.0889680885941108 0.0812835412615504 1.09454001650634 0.274214441254158 df.mm.exp5 -0.101046478024345 0.0812835412615504 -1.24313577455001 0.214366412826598 df.mm.exp6 -0.039055304431071 0.0812835412615504 -0.480482319359102 0.631082359136934 df.mm.exp7 0.124201100090361 0.0812835412615504 1.52799814283081 0.127108434401834 df.mm.exp8 -0.0570761943537023 0.0812835412615504 -0.702186364765349 0.482870623724865 df.mm.trans1:exp2 -0.0884972351141167 0.0763525920051074 -1.15905999770377 0.246952687437214 df.mm.trans2:exp2 -0.138653402302294 0.0682918343926908 -2.03030718877766 0.0428232133953054 * df.mm.trans1:exp3 0.0385091518208652 0.0763525920051073 0.504359456693877 0.614218016512149 df.mm.trans2:exp3 -0.0832636078585569 0.0682918343926908 -1.21923226398892 0.223297364232935 df.mm.trans1:exp4 -0.0871824070124468 0.0763525920051073 -1.14183952008617 0.254035368024392 df.mm.trans2:exp4 -0.0622608732085907 0.0682918343926908 -0.911688399678636 0.362346470116875 df.mm.trans1:exp5 0.0449041841054734 0.0763525920051074 0.588116040677043 0.556704447380999 df.mm.trans2:exp5 0.075872663375167 0.0682918343926908 1.11100637506506 0.267068412278776 df.mm.trans1:exp6 0.0287501144326198 0.0763525920051073 0.376544052763745 0.706662883784496 df.mm.trans2:exp6 0.0648669681571977 0.0682918343926908 0.949849549862733 0.342620809400892 df.mm.trans1:exp7 -0.117009656607725 0.0763525920051073 -1.53249095459520 0.125996871048825 df.mm.trans2:exp7 -0.0908410121400646 0.0682918343926908 -1.33018849102386 0.184027197268666 df.mm.trans1:exp8 0.0477966571210814 0.0763525920051073 0.625999142476843 0.531584484675614 df.mm.trans2:exp8 0.0212420432448069 0.0682918343926908 0.311048069417219 0.755886146622628 df.mm.trans1:probe2 0.0624199481918614 0.0381762960025537 1.63504464099100 0.102632314863995 df.mm.trans1:probe3 -0.087216305425098 0.0381762960025537 -2.28456698416379 0.022731878748995 * df.mm.trans1:probe4 -0.0626727488868081 0.0381762960025537 -1.64166656929252 0.101251211630422 df.mm.trans1:probe5 -0.105090407335044 0.0381762960025537 -2.75276593957712 0.00611164488513409 ** df.mm.trans1:probe6 -0.00482096130671047 0.0381762960025537 -0.126281536228344 0.899556871434349 df.mm.trans1:probe7 0.096256455966246 0.0381762960025537 2.52136707971374 0.0119815624550134 * df.mm.trans1:probe8 0.0162913742544109 0.0381762960025537 0.426740568370519 0.669741257542642 df.mm.trans1:probe9 -0.014289058122295 0.0381762960025537 -0.374291369737367 0.708336969285527 df.mm.trans1:probe10 0.0220791725320966 0.0381762960025537 0.578347688068526 0.56327460356033 df.mm.trans1:probe11 0.116245045815246 0.0381762960025537 3.04495349175494 0.0024426172211215 ** df.mm.trans1:probe12 0.1880652183833 0.0381762960025537 4.92623009761659 1.12157018562656e-06 *** df.mm.trans1:probe13 0.222515317724586 0.0381762960025537 5.82862511621613 9.70482416915486e-09 *** df.mm.trans1:probe14 0.172137982213962 0.0381762960025537 4.50902785860754 8.02022142304565e-06 *** df.mm.trans1:probe15 0.332310848069173 0.0381762960025537 8.70463829301156 4.02692923792828e-17 *** df.mm.trans2:probe2 0.0353136328202915 0.0381762960025537 0.925014643063573 0.355378470115539 df.mm.trans2:probe3 0.0410006871106398 0.0381762960025537 1.07398284809760 0.283318162492944 df.mm.trans2:probe4 -0.0205763152768873 0.0381762960025537 -0.538981447427769 0.590125550087459 df.mm.trans2:probe5 0.0110845520773864 0.0381762960025537 0.290351690395657 0.771660554138616 df.mm.trans2:probe6 0.0111066560179361 0.0381762960025537 0.290930686863733 0.771217929596083 df.mm.trans3:probe2 -0.537585904593031 0.0381762960025537 -14.0816674450835 1.70559470807443e-38 *** df.mm.trans3:probe3 -0.492346621252348 0.0381762960025537 -12.8966576856858 2.68291482685738e-33 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.95862362765081 0.104480070390916 37.8887917364477 4.37051021666568e-153 *** df.mm.trans1 0.0328392203348529 0.0928329786663749 0.353745197090694 0.723670256452882 df.mm.trans2 -0.0600822807577621 0.0867244510501704 -0.69279516941542 0.488740980063846 df.mm.exp2 -0.0262113987685262 0.119191357334520 -0.219910229690243 0.826025584252678 df.mm.exp3 -0.0246188945198631 0.119191357334520 -0.206549325978125 0.83644101288817 df.mm.exp4 0.0186686788296251 0.119191357334520 0.156627789523614 0.875597731727458 df.mm.exp5 -0.0734247318331693 0.119191357334520 -0.61602395907865 0.538142676653484 df.mm.exp6 0.0352666630819144 0.119191357334520 0.295882720614848 0.767435320756666 df.mm.exp7 -0.105720787244056 0.119191357334520 -0.886983667342105 0.375489241380296 df.mm.exp8 0.0750843867084526 0.119191357334520 0.629948247822382 0.528999413575281 df.mm.trans1:exp2 0.00679286271345241 0.111960784875430 0.0606718032658514 0.951643415940305 df.mm.trans2:exp2 0.129282476994979 0.100140770318277 1.29100741470314 0.197262869499999 df.mm.trans1:exp3 0.0323708742523577 0.111960784875430 0.289126896425156 0.772597115975013 df.mm.trans2:exp3 0.0842004538858239 0.100140770318277 0.840820912583456 0.400826729220647 df.mm.trans1:exp4 -0.0469607059247388 0.111960784875430 -0.419438877433632 0.675065050810624 df.mm.trans2:exp4 -0.0150588542437542 0.100140770318277 -0.150376856458091 0.880524403097093 df.mm.trans1:exp5 0.0352764181938112 0.111960784875430 0.315078339554876 0.752825951771745 df.mm.trans2:exp5 0.125998948416858 0.100140770318277 1.25821828628236 0.208865788636005 df.mm.trans1:exp6 -0.0351730745184132 0.111960784875430 -0.314155304980645 0.75352647256005 df.mm.trans2:exp6 0.0347619724512517 0.100140770318277 0.347131067004657 0.728630389445403 df.mm.trans1:exp7 0.0776407936270248 0.111960784875430 0.693464177778045 0.488321516593712 df.mm.trans2:exp7 0.0932028818287528 0.100140770318277 0.930718642691949 0.352422060414735 df.mm.trans1:exp8 -0.007700929831549 0.111960784875430 -0.068782385190647 0.945188732463078 df.mm.trans2:exp8 -0.0454479839338309 0.100140770318277 -0.453840965966048 0.650128725225288 df.mm.trans1:probe2 -0.0608762028230581 0.0559803924377151 -1.08745580679503 0.27732877374471 df.mm.trans1:probe3 -0.080388261711693 0.0559803924377151 -1.43600746995717 0.151589051364332 df.mm.trans1:probe4 -0.0545258159517403 0.0559803924377151 -0.974016322097185 0.33049192959762 df.mm.trans1:probe5 -0.0215423243880821 0.0559803924377151 -0.384819102725128 0.700525524474201 df.mm.trans1:probe6 -0.0198630235708858 0.0559803924377151 -0.354821084775098 0.722864509194861 df.mm.trans1:probe7 0.0750097886233571 0.0559803924377151 1.33992966746016 0.180841317988663 df.mm.trans1:probe8 -0.0271141666844868 0.0559803924377151 -0.484351136242116 0.62833640238958 df.mm.trans1:probe9 -0.0103572680303236 0.0559803924377151 -0.185015995410309 0.853287133463269 df.mm.trans1:probe10 -0.106690606018556 0.0559803924377151 -1.90585670040206 0.0572084976035261 . df.mm.trans1:probe11 -0.0111039381614028 0.0559803924377151 -0.198354060732199 0.842843974164639 df.mm.trans1:probe12 -0.0205088484369356 0.0559803924377151 -0.366357711045955 0.714244095642471 df.mm.trans1:probe13 -0.00268115734240293 0.0559803924377151 -0.0478945792562288 0.961818267592804 df.mm.trans1:probe14 -0.0778922354845045 0.0559803924377151 -1.39141996139396 0.164680754782453 df.mm.trans1:probe15 -0.0457733232908965 0.0559803924377151 -0.81766706694357 0.413914056291728 df.mm.trans2:probe2 0.0275797855299243 0.0559803924377151 0.492668670742353 0.62245036676237 df.mm.trans2:probe3 0.0482926775712961 0.0559803924377151 0.862671293793223 0.388707662125913 df.mm.trans2:probe4 0.0951399246110507 0.0559803924377151 1.69952228750281 0.089806479230912 . df.mm.trans2:probe5 0.0440534641422209 0.0559803924377151 0.78694453939807 0.431665413567109 df.mm.trans2:probe6 0.0218350984392098 0.0559803924377151 0.390049041965969 0.696656690228852 df.mm.trans3:probe2 -0.0586038746569202 0.0559803924377151 -1.04686430560708 0.295638448566859 df.mm.trans3:probe3 0.00623705931519165 0.0559803924377151 0.111415069519763 0.911329311327705