chr5.17888_chr5_110041623_110043123_-_0.R 

fitVsDatCorrelation=0.863447944182666
cont.fitVsDatCorrelation=0.258989623278168

fstatistic=9059.96883853567,49,623
cont.fstatistic=2461.88458615998,49,623

residuals=-0.765012356199107,-0.086965239187858,0.00539926570673985,0.0917714008604894,0.727503717160088
cont.residuals=-0.563834789920926,-0.228783839529720,-0.0527828633748594,0.18308999155101,0.982545672361169

predictedValues:
Include	Exclude	Both
chr5.17888_chr5_110041623_110043123_-_0.R.tl.Lung	58.8044097202139	85.0490012344797	63.5905355508753
chr5.17888_chr5_110041623_110043123_-_0.R.tl.cerebhem	57.1318177342456	84.9186503804027	69.3204957701906
chr5.17888_chr5_110041623_110043123_-_0.R.tl.cortex	59.4165484871053	85.2861797254973	68.3995198068872
chr5.17888_chr5_110041623_110043123_-_0.R.tl.heart	57.4140547334655	90.6563860328466	67.1742915654645
chr5.17888_chr5_110041623_110043123_-_0.R.tl.kidney	59.7312192694501	93.6600809952217	67.1175525265787
chr5.17888_chr5_110041623_110043123_-_0.R.tl.liver	60.5030002906836	112.021735055659	73.4090698870681
chr5.17888_chr5_110041623_110043123_-_0.R.tl.stomach	58.5829772286381	94.1597651392384	69.9868962464875
chr5.17888_chr5_110041623_110043123_-_0.R.tl.testicle	59.6170737077479	97.7650758973557	67.7110563018039


diffExp=-26.2445915142658,-27.7868326461571,-25.8696312383920,-33.2423312993811,-33.9288617257716,-51.5187347649756,-35.5767879106002,-38.1480021896079
diffExpScore=0.996341228360275
diffExp1.5=0,0,0,-1,-1,-1,-1,-1
diffExp1.5Score=0.833333333333333
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	61.9199144058583	66.2187779746217	60.2140033139665
cerebhem	71.3669890099087	67.0033054565952	57.8751747469384
cortex	71.6676626173288	64.9935863856887	65.4027909499836
heart	61.2031444193091	68.2960149215256	63.2686998124454
kidney	63.4228237523082	64.1939710382997	61.838332311599
liver	65.677301961008	63.867607032158	60.7511601189242
stomach	65.2158994784791	67.3961334605882	60.8138909568463
testicle	63.5796276115064	71.9018834938987	69.031633855976
cont.diffExp=-4.29886356876338,4.36368355331346,6.67407623164011,-7.09287050221656,-0.771147285991525,1.80969492885002,-2.18023398210912,-8.32225588239228
cont.diffExpScore=3.28277870420796

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=0.655830395580649
cont.tran.correlation=-0.26534691092108

tran.covariance=0.00120566727267172
cont.tran.covariance=-0.000615026881847335

tran.mean=75.9198734770157
cont.tran.mean=66.1202901886926

weightedLogRatios:
wLogRatio
Lung	-1.57151418428215
cerebhem	-1.68184807946008
cortex	-1.54164582292839
heart	-1.95444774032624
kidney	-1.94085502240884
liver	-2.7169866721282
stomach	-2.04422269896349
testicle	-2.14432714485967

cont.weightedLogRatios:
wLogRatio
Lung	-0.279188406383579
cerebhem	0.267282154507736
cortex	0.412818694110758
heart	-0.457145474661307
kidney	-0.0502257437149547
liver	0.116536198385595
stomach	-0.137921760186890
testicle	-0.51833657621758

varWeightedLogRatios=0.145274409719227
cont.varWeightedLogRatios=0.111484568986244

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.79075785410569	0.079748327610157	60.0734585623527	2.28442647681826e-261	***
df.mm.trans1	-0.595464270255749	0.069105619925804	-8.61672713297524	5.66393226037681e-17	***
df.mm.trans2	-0.254779331914050	0.0630238240981304	-4.04258763348523	5.94864830178858e-05	***
df.mm.exp2	-0.116665458770310	0.0835446206900903	-1.39644489144408	0.163077875565963	   
df.mm.exp3	-0.0597603765220764	0.0835446206900903	-0.71531088451234	0.474684943894453	   
df.mm.exp4	-0.0149048728029656	0.0835446206900903	-0.178406134109524	0.85846202967344	   
df.mm.exp5	0.0581015150840594	0.0835446206900903	0.6954548911005	0.487029592356085	   
df.mm.exp6	0.160358599269554	0.0835446206900903	1.9194365591102	0.0553855540341586	.  
df.mm.exp7	0.00214932837885452	0.0835446206900903	0.0257267117990454	0.979483556174748	   
df.mm.exp8	0.0902801653448929	0.0835446206900903	1.08062212263538	0.280283375149184	   
df.mm.trans1:exp2	0.08780980117444	0.0766658018662191	1.14535815235673	0.252500612582045	   
df.mm.trans2:exp2	0.115131627145360	0.0635679247604944	1.81115912748674	0.0705977158913614	.  
df.mm.trans1:exp3	0.0701163108152577	0.0766658018662191	0.91457089221619	0.360770773321328	   
df.mm.trans2:exp3	0.0625452227642306	0.0635679247604944	0.983911666141107	0.325540893626995	   
df.mm.trans1:exp4	-0.00902284519443955	0.0766658018662191	-0.117690612695662	0.906350737628087	   
df.mm.trans2:exp4	0.0787536791600571	0.0635679247604944	1.23889020220148	0.215852716342036	   
df.mm.trans1:exp5	-0.0424635428664253	0.0766658018662191	-0.553878546010953	0.579860705784826	   
df.mm.trans2:exp5	0.0383429779520479	0.0635679247604944	0.603181212797384	0.546607665220576	   
df.mm.trans1:exp6	-0.131882491215145	0.0766658018662191	-1.72022581131126	0.0858879891943759	.  
df.mm.trans2:exp6	0.115106740696575	0.0635679247604944	1.81076763368104	0.0706583985945931	.  
df.mm.trans1:exp7	-0.00592201230974839	0.0766658018662191	-0.0772445101413304	0.938453833897063	   
df.mm.trans2:exp7	0.0996160647987517	0.0635679247604944	1.56708064914933	0.117603599929306	   
df.mm.trans1:exp8	-0.0765550080265488	0.0766658018662191	-0.998554846659483	0.318398061619140	   
df.mm.trans2:exp8	0.0490596752593165	0.0635679247604944	0.771767765648465	0.440544623109725	   
df.mm.trans1:probe2	-0.300375402876855	0.0469480238233878	-6.39804145978173	3.09360300559671e-10	***
df.mm.trans1:probe3	-0.154563046648363	0.0469480238233878	-3.29221624385742	0.00105021496093515	** 
df.mm.trans1:probe4	-0.175599082441279	0.0469480238233878	-3.74028698421599	0.000200765268667159	***
df.mm.trans1:probe5	-0.266506364913025	0.0469480238233878	-5.67662583446721	2.10710200661751e-08	***
df.mm.trans1:probe6	-0.094656385717934	0.0469480238233878	-2.01619531578195	0.0442082537975137	*  
df.mm.trans1:probe7	-0.184959833442008	0.0469480238233878	-3.93967239468486	9.08199729601535e-05	***
df.mm.trans1:probe8	-0.202817589504661	0.0469480238233878	-4.3200452966377	1.81517931966358e-05	***
df.mm.trans1:probe9	0.242722211019725	0.0469480238233878	5.17001976340503	3.15785692613478e-07	***
df.mm.trans1:probe10	-0.370041615162677	0.0469480238233878	-7.88194230612825	1.44474149744932e-14	***
df.mm.trans1:probe11	-0.342762468823335	0.0469480238233878	-7.30089236796764	8.75878391785164e-13	***
df.mm.trans1:probe12	-0.390337440710153	0.0469480238233878	-8.31424645643341	5.8070010750001e-16	***
df.mm.trans1:probe13	-0.165050530436305	0.0469480238233878	-3.51560123291244	0.000470612275961833	***
df.mm.trans1:probe14	-0.185679260215313	0.0469480238233878	-3.95499629364196	8.53248167699245e-05	***
df.mm.trans1:probe15	-0.212412275368296	0.0469480238233878	-4.52441355502763	7.25388304540618e-06	***
df.mm.trans1:probe16	0.502581091443566	0.0469480238233878	10.7050531739996	1.15423521345728e-24	***
df.mm.trans2:probe2	-0.431841309376595	0.0469480238233878	-9.19828512912756	5.41054303106283e-19	***
df.mm.trans2:probe3	-0.183884118220559	0.0469480238233878	-3.91675949795685	9.96653283763958e-05	***
df.mm.trans2:probe4	-0.0792364849867442	0.0469480238233878	-1.68774910068252	0.091959910828768	.  
df.mm.trans2:probe5	-0.290093319741935	0.0469480238233878	-6.17903153566649	1.16527757079530e-09	***
df.mm.trans2:probe6	-0.127956127996227	0.0469480238233878	-2.72548485698952	0.00660113814493019	** 
df.mm.trans3:probe2	0.0199093030057036	0.0469480238233878	0.424071161772425	0.67166030251593	   
df.mm.trans3:probe3	0.687540924885575	0.0469480238233878	14.6447255686845	5.98076351946567e-42	***
df.mm.trans3:probe4	0.147337644029182	0.0469480238233878	3.13831407650823	0.00177948227293978	** 
df.mm.trans3:probe5	0.388916059361062	0.0469480238233878	8.28397081896593	7.30455815785587e-16	***
df.mm.trans3:probe6	0.0279984401789685	0.0469480238233878	0.596371005610265	0.551144031279883	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20834034330919	0.152699301487424	27.5596568046894	6.25834443640968e-110	***
df.mm.trans1	-0.076149493929642	0.132321017979337	-0.57549053878601	0.565167383497039	   
df.mm.trans2	-0.00499992113799794	0.120675808574888	-0.0414326715274928	0.966964241585116	   
df.mm.exp2	0.193387858777863	0.159968310367223	1.20891355502801	0.227154664163359	   
df.mm.exp3	0.0448623113390562	0.159968310367223	0.280444990861443	0.77922922339892	   
df.mm.exp4	-0.0302417337496686	0.159968310367223	-0.189048278876271	0.850116547291228	   
df.mm.exp5	-0.0336913325614986	0.159968310367223	-0.210612542472674	0.833258490100913	   
df.mm.exp6	0.0138785213336912	0.159968310367223	0.0867579416312623	0.930891795568357	   
df.mm.exp7	0.0595717264744063	0.159968310367223	0.372397047500556	0.709723896929934	   
df.mm.exp8	-0.0278702887722534	0.159968310367223	-0.174223811630406	0.861746171309074	   
df.mm.trans1:exp2	-0.0513942820868442	0.146796989275721	-0.350104469719833	0.7263786257063	   
df.mm.trans2:exp2	-0.181609983356970	0.121717633445348	-1.49205976337450	0.136189665932906	   
df.mm.trans1:exp3	0.101335478063731	0.146796989275721	0.690310329685294	0.490256134579086	   
df.mm.trans2:exp3	-0.0635377952273224	0.121717633445348	-0.522009781399927	0.601849063354016	   
df.mm.trans1:exp4	0.0185984544023608	0.146796989275721	0.126695067072720	0.899222628659236	   
df.mm.trans2:exp4	0.06112907401461	0.121717633445348	0.502220362689334	0.615689976781514	   
df.mm.trans1:exp5	0.0576732783686351	0.146796989275721	0.392877801194619	0.694544129557576	   
df.mm.trans2:exp5	0.00263655186073377	0.121717633445348	0.0216612152742651	0.982725137919683	   
df.mm.trans1:exp6	0.0450330174731677	0.146796989275721	0.306770715771185	0.759120402716102	   
df.mm.trans2:exp6	-0.0500302986140679	0.121717633445348	-0.411035748871439	0.681187666542343	   
df.mm.trans1:exp7	-0.00771027718439078	0.146796989275721	-0.0525234013478913	0.958128486102511	   
df.mm.trans2:exp7	-0.0419481551814804	0.121717633445348	-0.344634988325792	0.730485105706523	   
df.mm.trans1:exp8	0.0543215402847509	0.146796989275721	0.370045329626766	0.711474477844358	   
df.mm.trans2:exp8	0.110208671250792	0.121717633445348	0.905445399579483	0.365579498527036	   
df.mm.trans1:probe2	-0.0664138847078221	0.0898944298755825	-0.738798664163526	0.460307500924137	   
df.mm.trans1:probe3	0.0577853317201819	0.0898944298755825	0.642813262180527	0.520581776808821	   
df.mm.trans1:probe4	0.00924277608032122	0.0898944298755825	0.102818117797884	0.91814041162254	   
df.mm.trans1:probe5	0.0176340642499812	0.0898944298755825	0.196164148038844	0.844545656789804	   
df.mm.trans1:probe6	0.112395162497265	0.0898944298755825	1.25030174453328	0.211658754891509	   
df.mm.trans1:probe7	-0.0209096776997163	0.0898944298755825	-0.232602595385011	0.816146404249599	   
df.mm.trans1:probe8	-0.0131743725680028	0.0898944298755825	-0.146553825261884	0.883531611673097	   
df.mm.trans1:probe9	-0.0273934176401301	0.0898944298755825	-0.304728754362686	0.760674515866946	   
df.mm.trans1:probe10	0.0384483081755430	0.0898944298755825	0.427705122873097	0.669013618680121	   
df.mm.trans1:probe11	-0.079976846476494	0.0898944298755825	-0.889675217776954	0.373983711506771	   
df.mm.trans1:probe12	0.0982924730406909	0.0898944298755825	1.09342117388955	0.274631520292872	   
df.mm.trans1:probe13	-0.0158330784784124	0.0898944298755826	-0.176129694579808	0.860249288361589	   
df.mm.trans1:probe14	-0.0767447131360127	0.0898944298755825	-0.853720450112765	0.393588027543948	   
df.mm.trans1:probe15	-0.092093501636081	0.0898944298755825	-1.02446282560045	0.306014335860016	   
df.mm.trans1:probe16	-0.0618896696551161	0.0898944298755825	-0.688470573101958	0.491412775762695	   
df.mm.trans2:probe2	-0.0339313811424161	0.0898944298755825	-0.377458104905704	0.705961742951021	   
df.mm.trans2:probe3	-0.0498308493227544	0.0898944298755825	-0.554326329136547	0.579554458441637	   
df.mm.trans2:probe4	-0.122698104998186	0.0898944298755825	-1.36491332297235	0.172773013143428	   
df.mm.trans2:probe5	0.0762610455198512	0.0898944298755825	0.848340054276995	0.396574437966111	   
df.mm.trans2:probe6	0.00568315900355909	0.0898944298755825	0.0632203687305744	0.949611303358572	   
df.mm.trans3:probe2	-0.114393257146144	0.0898944298755825	-1.27252886863478	0.203659987015992	   
df.mm.trans3:probe3	-0.0529855902040543	0.0898944298755825	-0.589420170720127	0.555793127202625	   
df.mm.trans3:probe4	0.0265244117331802	0.0898944298755825	0.295061793816269	0.768044976761826	   
df.mm.trans3:probe5	-0.0603308243401982	0.0898944298755825	-0.671129728768496	0.502386635830545	   
df.mm.trans3:probe6	0.0254693212279271	0.0898944298755825	0.283324798468355	0.777021990270835