chr7.22101_chr7_91062010_91068503_+_2.R fitVsDatCorrelation=0.842882034276394 cont.fitVsDatCorrelation=0.303361339996886 fstatistic=15209.4674377184,51,669 cont.fstatistic=4841.32514001188,51,669 residuals=-0.45448180488452,-0.0737510919751482,-0.00341040986851525,0.0648037724771334,0.672154823059747 cont.residuals=-0.498798666671966,-0.131883576631873,-0.0353651966061027,0.109857729647064,1.14235918755047 predictedValues: Include Exclude Both chr7.22101_chr7_91062010_91068503_+_2.R.tl.Lung 51.0339503729628 46.9852644741367 72.0682405137079 chr7.22101_chr7_91062010_91068503_+_2.R.tl.cerebhem 60.3491041181456 54.7018233349236 56.5122075572957 chr7.22101_chr7_91062010_91068503_+_2.R.tl.cortex 55.9272957501643 46.7689522412523 70.6818670496872 chr7.22101_chr7_91062010_91068503_+_2.R.tl.heart 49.8469627204827 45.8486586019914 67.5062732791479 chr7.22101_chr7_91062010_91068503_+_2.R.tl.kidney 51.1938414353377 46.1740366128864 67.2688638410172 chr7.22101_chr7_91062010_91068503_+_2.R.tl.liver 51.2794010906726 50.5307379727766 62.4339448079546 chr7.22101_chr7_91062010_91068503_+_2.R.tl.stomach 51.4285194436365 49.1216614590322 67.5979352737463 chr7.22101_chr7_91062010_91068503_+_2.R.tl.testicle 53.1541523019141 48.8350643642341 55.059289820899 diffExp=4.04868589882608,5.64728078322204,9.15834350891201,3.99830411849132,5.01980482245131,0.748663117895987,2.30685798460433,4.31908793767999 diffExpScore=0.972411531360516 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 53.2581972407134 53.9318316563229 59.4093321554128 cerebhem 50.7938769023983 55.077118309956 55.182230141545 cortex 52.1684055535758 49.2823894950913 58.7284042634187 heart 51.9843385096574 52.8500860703941 51.0152935278152 kidney 51.9959796209824 53.6039107100155 52.1693382080999 liver 52.09706667494 52.6763748215037 48.1153422988631 stomach 50.8856298255722 54.0329909048407 53.9002354393183 testicle 50.8450784768732 51.9404803756075 60.3308278947434 cont.diffExp=-0.673634415609477,-4.28324140755766,2.88601605848447,-0.865747560736779,-1.60793108903311,-0.579308146563633,-3.14736107926852,-1.09540189873437 cont.diffExpScore=1.46032717823580 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=0.718026994539911 cont.tran.correlation=-0.195495433788888 tran.covariance=0.00261659038854916 cont.tran.covariance=-0.000110219576341620 tran.mean=50.8237141434094 cont.tran.mean=52.3389846967778 weightedLogRatios: wLogRatio Lung 0.321632112743774 cerebhem 0.398009063269973 cortex 0.703642723585382 heart 0.323338838913358 kidney 0.400836834316772 liver 0.0577987774054288 stomach 0.179773267965639 testicle 0.333127918495653 cont.weightedLogRatios: wLogRatio Lung -0.0500432335684551 cerebhem -0.321264139240423 cortex 0.223430881190978 heart -0.0653935283727918 kidney -0.120799017988258 liver -0.0437762565482826 stomach -0.237631648236391 testicle -0.083969663896099 varWeightedLogRatios=0.0350391590860129 cont.varWeightedLogRatios=0.0255411311781997 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.75293707932618 0.0644099204600145 58.2664448662996 2.74652459819021e-264 *** df.mm.trans1 0.254803160311608 0.0577853574943954 4.40947622996573 1.20710591645216e-05 *** df.mm.trans2 0.159649020241966 0.0532279634702111 2.99934489004663 0.00280608441157915 ** df.mm.exp2 0.562874786626326 0.0729349557520503 7.71748993088959 4.32800062935807e-14 *** df.mm.exp3 0.106371383039675 0.0729349557520503 1.45844172993393 0.145188127366486 df.mm.exp4 0.0173712966977972 0.0729349557520503 0.238175186625912 0.81181815232435 df.mm.exp5 0.0546277395300579 0.0729349557520503 0.748992564220788 0.454124946922459 df.mm.exp6 0.221050161916786 0.0729349557520503 3.03078489096872 0.00253333723982382 ** df.mm.exp7 0.116203859543078 0.0729349557520503 1.59325330830562 0.111575704875851 df.mm.exp8 0.348522530990033 0.0729349557520503 4.77853900638359 2.17188829563304e-06 *** df.mm.trans1:exp2 -0.39521978879886 0.0696917633401676 -5.67096841659438 2.11344560394427e-08 *** df.mm.trans2:exp2 -0.410811775116467 0.0608319582516698 -6.75322292629286 3.14265415456010e-11 *** df.mm.trans1:exp3 -0.0148099308251505 0.0696917633401676 -0.212506186030374 0.831776884043022 df.mm.trans2:exp3 -0.110985844645675 0.0608319582516698 -1.82446608387177 0.068527516273794 . df.mm.trans1:exp4 -0.0409048353577694 0.0696917633401676 -0.58693930813763 0.557442480588131 df.mm.trans2:exp4 -0.0418593853752461 0.0608319582516698 -0.688115039829366 0.491618859980123 df.mm.trans1:exp5 -0.0514996040215516 0.0696917633401676 -0.738962562479307 0.460188909483501 df.mm.trans2:exp5 -0.0720441082122181 0.0608319582516698 -1.18431348065703 0.236709582388037 df.mm.trans1:exp6 -0.216252133407767 0.0696917633401676 -3.10297979335426 0.00199653417593248 ** df.mm.trans2:exp6 -0.148302368798271 0.0608319582516698 -2.43790226487079 0.0150320723542093 * df.mm.trans1:exp7 -0.108502092803066 0.0696917633401676 -1.55688545680017 0.119970596830469 df.mm.trans2:exp7 -0.0717377825094665 0.0608319582516698 -1.17927787582767 0.238706757389979 df.mm.trans1:exp8 -0.307817409876883 0.0696917633401676 -4.41684060101073 1.16784710346475e-05 *** df.mm.trans2:exp8 -0.309907974822457 0.0608319582516698 -5.09449282464863 4.5538652328712e-07 *** df.mm.trans1:probe2 0.302673247778309 0.0348458816700838 8.6860550880583 2.86500580454736e-17 *** df.mm.trans1:probe3 -0.149819689162442 0.0348458816700838 -4.29949486085373 1.96669431554319e-05 *** df.mm.trans1:probe4 -0.128543703639971 0.0348458816700838 -3.68892097083398 0.000243504177352003 *** df.mm.trans1:probe5 0.306860089016625 0.0348458816700838 8.80620820336635 1.10245277551713e-17 *** df.mm.trans1:probe6 0.101190197122513 0.0348458816700838 2.90393562374366 0.00380651722303716 ** df.mm.trans1:probe7 -0.0787900420752353 0.0348458816700838 -2.26110054614801 0.0240734003839125 * df.mm.trans1:probe8 -0.089961127445927 0.0348458816700838 -2.58168607405796 0.0100434731581711 * df.mm.trans1:probe9 0.0281365814439458 0.0348458816700838 0.80745787150227 0.419689726021939 df.mm.trans1:probe10 -0.258304041587239 0.0348458816700838 -7.41275666469938 3.75071647150865e-13 *** df.mm.trans1:probe11 -0.0791974338463884 0.0348458816700838 -2.27279179204645 0.0233550739118188 * df.mm.trans1:probe12 -0.275201654587358 0.0348458816700838 -7.89768091371401 1.16800248669883e-14 *** df.mm.trans1:probe13 -0.207914820472947 0.0348458816700838 -5.96669708178019 3.92473600657868e-09 *** df.mm.trans1:probe14 -0.266907072639935 0.0348458816700838 -7.65964469394049 6.55627222224696e-14 *** df.mm.trans1:probe15 -0.24391325601209 0.0348458816700838 -6.99977283747412 6.24693102353338e-12 *** df.mm.trans1:probe16 -0.192294539792493 0.0348458816700838 -5.51842945496723 4.89451341171906e-08 *** df.mm.trans1:probe17 -0.104134042587829 0.0348458816700838 -2.98841750005801 0.00290700580948819 ** df.mm.trans1:probe18 -0.116609537923808 0.0348458816700838 -3.34643671891708 0.000864368620741142 *** df.mm.trans1:probe19 -0.0937293971943224 0.0348458816700838 -2.68982711018019 0.00732720032760124 ** df.mm.trans1:probe20 -0.0981928960878206 0.0348458816700838 -2.81791969040985 0.00497632180062401 ** df.mm.trans1:probe21 -0.161326095545114 0.0348458816700838 -4.62970336272527 4.39989691472359e-06 *** df.mm.trans2:probe2 -0.120495456901931 0.0348458816700838 -3.45795402862141 0.00057878514179322 *** df.mm.trans2:probe3 -0.131486824238735 0.0348458816700838 -3.77338204507593 0.000175336428038050 *** df.mm.trans2:probe4 -0.158065555488665 0.0348458816700838 -4.53613304967309 6.79071008727267e-06 *** df.mm.trans2:probe5 -0.131033487116217 0.0348458816700838 -3.76037226886164 0.000184510638885932 *** df.mm.trans2:probe6 -0.0236872958433214 0.0348458816700838 -0.679773181450526 0.496883232216846 df.mm.trans3:probe2 0.403979376873748 0.0348458816700838 11.5933177038989 1.89929907324701e-28 *** df.mm.trans3:probe3 -0.073306791320587 0.0348458816700838 -2.10374333514204 0.0357735375953532 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.9110781531904 0.114058399837103 34.2901369717281 1.69996735887413e-149 *** df.mm.trans1 0.0272309797485924 0.102327488727723 0.266115978093136 0.790231882828896 df.mm.trans2 0.098369957997658 0.0942571625091352 1.04363377147200 0.297031825005595 df.mm.exp2 0.0474479015245852 0.129154706074092 0.367372610467372 0.713457282745727 df.mm.exp3 -0.0993009210830008 0.129154706074092 -0.768852518823707 0.442252203647913 df.mm.exp4 0.107855087864654 0.129154706074092 0.83508445911976 0.403968273509364 df.mm.exp5 0.0998722125030105 0.129154706074092 0.773275829730256 0.439632290947513 df.mm.exp6 0.165253328936785 0.129154706074092 1.27949909035436 0.201164923058323 df.mm.exp7 0.0536192358397582 0.129154706074092 0.415155107154971 0.678161467992955 df.mm.exp8 -0.0993827104518013 0.129154706074092 -0.769485785479539 0.441876573618316 df.mm.trans1:exp2 -0.0948238191571562 0.123411594854247 -0.768354215575496 0.442547907135385 df.mm.trans2:exp2 -0.0264344196587166 0.107722471439012 -0.245393735453635 0.806226836924763 df.mm.trans1:exp3 0.0786262436000252 0.123411594854247 0.637105805924355 0.524273881092796 df.mm.trans2:exp3 0.00914685490287069 0.107722471439012 0.0849112982712181 0.932357312923042 df.mm.trans1:exp4 -0.132064328689291 0.123411594854247 -1.07011281107956 0.284954461113465 df.mm.trans2:exp4 -0.128116619331642 0.107722471439012 -1.18932120308971 0.234735235534194 df.mm.trans1:exp5 -0.123857543426563 0.123411594854247 -1.00361350627421 0.31592780067038 df.mm.trans2:exp5 -0.105971058440823 0.107722471439012 -0.983741433195945 0.325598293531085 df.mm.trans1:exp6 -0.187296415121954 0.123411594854247 -1.51765654874776 0.129573391196861 df.mm.trans2:exp6 -0.188807141871922 0.107722471439012 -1.75271825228051 0.0801084296034087 . df.mm.trans1:exp7 -0.0991904053764618 0.123411594854247 -0.803736516764154 0.421834644610041 df.mm.trans2:exp7 -0.0517453055035473 0.107722471439012 -0.48035757824999 0.6311301832362 df.mm.trans1:exp8 0.0530143116119021 0.123411594854247 0.429573182929153 0.667644464122318 df.mm.trans2:exp8 0.0617602929468282 0.107722471439012 0.573327850000123 0.566615395802358 df.mm.trans1:probe2 -0.0377118846314826 0.0617057974271237 -0.611156264142303 0.541303730695089 df.mm.trans1:probe3 0.0073963427375857 0.0617057974271237 0.119864632595033 0.904626367927713 df.mm.trans1:probe4 0.0196886384603752 0.0617057974271237 0.319072749746537 0.749770945863006 df.mm.trans1:probe5 0.0170983037618195 0.0617057974271237 0.277093959964022 0.781793554774489 df.mm.trans1:probe6 0.0844735038429439 0.0617057974271237 1.36897191779605 0.171467498406243 df.mm.trans1:probe7 0.0902296652968159 0.0617057974271237 1.46225588289949 0.144140686878074 df.mm.trans1:probe8 0.0742848360765294 0.0617057974271237 1.20385505372104 0.229071386478212 df.mm.trans1:probe9 -0.0162808636525471 0.0617057974271237 -0.263846580570898 0.791979391175495 df.mm.trans1:probe10 0.116395791581061 0.0617057974271237 1.88630236435284 0.0596870979150494 . df.mm.trans1:probe11 -0.00401059974775023 0.0617057974271237 -0.0649955095789316 0.948196991956688 df.mm.trans1:probe12 0.0255682441038888 0.0617057974271237 0.414357243078912 0.678745318138117 df.mm.trans1:probe13 0.159074827020927 0.0617057974271237 2.57795594018211 0.0101514367022020 * df.mm.trans1:probe14 0.0962009011881675 0.0617057974271237 1.55902532986116 0.119463293051917 df.mm.trans1:probe15 -0.0367971196095679 0.0617057974271237 -0.596331643765342 0.551155414455235 df.mm.trans1:probe16 -0.0422275687394898 0.0617057974271237 -0.684337136868894 0.493999291083229 df.mm.trans1:probe17 0.0890113950345529 0.0617057974271237 1.44251267702484 0.149625799425668 df.mm.trans1:probe18 0.103455281975062 0.0617057974271237 1.67658933663803 0.0940899915310899 . df.mm.trans1:probe19 0.0754142941078166 0.0617057974271237 1.22215897455799 0.222077941859657 df.mm.trans1:probe20 -0.00417632925157306 0.0617057974271237 -0.0676813107634728 0.946059549377701 df.mm.trans1:probe21 0.0671347067469716 0.0617057974271237 1.08798053904513 0.276995495299739 df.mm.trans2:probe2 -0.0372911324132829 0.0617057974271237 -0.604337582012854 0.545824113936133 df.mm.trans2:probe3 -0.115435312217691 0.0617057974271237 -1.87073690043505 0.0618179110683578 . df.mm.trans2:probe4 -0.0512275584624644 0.0617057974271237 -0.830190364575802 0.406727314112889 df.mm.trans2:probe5 0.000821157782826381 0.0617057974271237 0.0133076277605227 0.989386330108022 df.mm.trans2:probe6 0.00758769586523122 0.0617057974271237 0.122965688502649 0.902171199236986 df.mm.trans3:probe2 0.153012578173713 0.0617057974271237 2.47971154338334 0.0133939853260595 * df.mm.trans3:probe3 -0.0096958879174576 0.0617057974271237 -0.157130907009325 0.875189089209194