chr14.7721_chr14_58177593_58208691_-_2.R 

fitVsDatCorrelation=0.808287666039201
cont.fitVsDatCorrelation=0.247354697857192

fstatistic=11845.3792462424,58,830
cont.fstatistic=4365.04892065491,58,830

residuals=-0.600846877667528,-0.087654572592561,-0.00359845741194957,0.0847124694821609,0.546100259705596
cont.residuals=-0.469564996177865,-0.162139351215201,-0.0433331306666206,0.118152578219760,1.06223291158377

predictedValues:
Include	Exclude	Both
chr14.7721_chr14_58177593_58208691_-_2.R.tl.Lung	51.2303008326681	59.2752845252276	60.2359354998372
chr14.7721_chr14_58177593_58208691_-_2.R.tl.cerebhem	59.4792615285124	54.6943002078805	59.448482667848
chr14.7721_chr14_58177593_58208691_-_2.R.tl.cortex	52.5622257972908	76.4226033151813	71.0303025255408
chr14.7721_chr14_58177593_58208691_-_2.R.tl.heart	53.1400277290372	65.2619636165619	63.172604309291
chr14.7721_chr14_58177593_58208691_-_2.R.tl.kidney	52.0025153427007	54.95693333102	55.6950066705395
chr14.7721_chr14_58177593_58208691_-_2.R.tl.liver	55.0502981812394	56.8366063918309	51.2326833773014
chr14.7721_chr14_58177593_58208691_-_2.R.tl.stomach	53.9518273922794	83.2167917801671	58.8830614000716
chr14.7721_chr14_58177593_58208691_-_2.R.tl.testicle	56.0112985694126	60.656099593937	56.4564458709065


diffExp=-8.04498369255943,4.78496132063196,-23.8603775178906,-12.1219358875246,-2.95441798831927,-1.78630821059149,-29.2649643878877,-4.64480102452447
diffExpScore=1.10862739902886
diffExp1.5=0,0,0,0,0,0,-1,0
diffExp1.5Score=0.5
diffExp1.4=0,0,-1,0,0,0,-1,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,-1,0,0,0,-1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,0,-1,-1,0,0,-1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	54.9341233008425	53.6672749562256	62.2069492455916
cerebhem	54.6959957801094	55.3798657007337	54.5897706746868
cortex	56.4679769383794	59.422689394185	54.9031553616809
heart	55.9800571151414	51.8789324688738	55.2657727177615
kidney	57.3207343751705	57.4842110933304	59.2788160464066
liver	57.2541634141858	63.0286015426923	55.1090551667666
stomach	56.9217073138512	56.1058453584359	59.5915209564203
testicle	55.113673125318	52.1143140244695	54.1524745376481
cont.diffExp=1.26684834461687,-0.683869920624254,-2.95471245580563,4.10112464626755,-0.163476718159956,-5.77443812850648,0.815861955415386,2.99935910084857
cont.diffExpScore=13.4641846757303

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=-0.274606114656416
cont.tran.correlation=0.677401986669784

tran.covariance=-0.00210915317570102
cont.tran.covariance=0.000857527021739404

tran.mean=59.0467711334342
cont.tran.mean=56.1106353688715

weightedLogRatios:
wLogRatio
Lung	-0.584795985580766
cerebhem	0.339137316137928
cortex	-1.55294267586367
heart	-0.837464244263865
kidney	-0.219866255067030
liver	-0.128506500020430
stomach	-1.82216914293033
testicle	-0.323876237082212

cont.weightedLogRatios:
wLogRatio
Lung	0.0931961493497693
cerebhem	-0.0498018026149873
cortex	-0.207027793108499
heart	0.303338323944719
kidney	-0.0115342640063100
liver	-0.393533714795389
stomach	0.0582445288438853
testicle	0.222792963046869

varWeightedLogRatios=0.538542309401132
cont.varWeightedLogRatios=0.0505772329036069

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.17970577305372	0.0680676388545409	61.405182306759	0	***
df.mm.trans1	-0.226992303812954	0.0589294439834291	-3.85193357461143	0.000126214457096275	***
df.mm.trans2	-0.114123328438325	0.0522080008770225	-2.18593561372223	0.0290990380718968	*  
df.mm.exp2	0.082022616393057	0.0674771614327605	1.21556115656689	0.224497769817131	   
df.mm.exp3	0.114915153497575	0.0674771614327605	1.70302293483529	0.0889381554756942	.  
df.mm.exp4	0.0852146182017781	0.0674771614327605	1.26286607783128	0.206991975476519	   
df.mm.exp5	0.0176969487530554	0.0674771614327605	0.262265755957889	0.793181638092799	   
df.mm.exp6	0.191795708248535	0.0674771614327605	2.84237961668935	0.00458765251898305	** 
df.mm.exp7	0.413732755153106	0.0674771614327605	6.13144872084434	1.34563088683860e-09	***
df.mm.exp8	0.177049667207549	0.0674771614327605	2.62384580868855	0.00885398690289549	** 
df.mm.trans1:exp2	0.0672739195318785	0.0625530028163935	1.07547066492302	0.282476580962792	   
df.mm.trans2:exp2	-0.162455545375382	0.0469621168412815	-3.45928923784324	0.00056920283473992	***
df.mm.trans1:exp3	-0.0892486026075857	0.0625530028163935	-1.42676767843663	0.154022923152869	   
df.mm.trans2:exp3	0.139170921801077	0.0469621168412815	2.96347207412806	0.00312883699172323	** 
df.mm.trans1:exp4	-0.0486153261904125	0.0625530028163935	-0.77718613018641	0.437270409985001	   
df.mm.trans2:exp4	0.0110023296116378	0.0469621168412815	0.234280955622645	0.814824647194743	   
df.mm.trans1:exp5	-0.00273602949767880	0.0625530028163935	-0.0437393789984733	0.965122669528485	   
df.mm.trans2:exp5	-0.0933395326222045	0.0469621168412815	-1.98754951651062	0.0471897656141563	*  
df.mm.trans1:exp6	-0.119879598672752	0.0625530028163935	-1.91644834420859	0.0556512097756229	.  
df.mm.trans2:exp6	-0.233807543407384	0.0469621168412815	-4.97864149091887	7.78920928991012e-07	***
df.mm.trans1:exp7	-0.361972362193089	0.0625530028163935	-5.78665045474405	1.01736241676296e-08	***
df.mm.trans2:exp7	-0.0744760354370096	0.0469621168412815	-1.58587475280804	0.113148610551904	   
df.mm.trans1:exp8	-0.087827406786341	0.0625530028163935	-1.40404781276661	0.160678670456460	   
df.mm.trans2:exp8	-0.154021898516061	0.0469621168412815	-3.27970519379719	0.00108245423499171	** 
df.mm.trans1:probe2	-0.0308570615265593	0.0419618299482575	-0.735360244408041	0.462327861824767	   
df.mm.trans1:probe3	-0.0040798309389024	0.0419618299482575	-0.0972271929973783	0.922569445840257	   
df.mm.trans1:probe4	-0.112944404573315	0.0419618299482575	-2.69159864363841	0.0072541839447385	** 
df.mm.trans1:probe5	-0.176921989416108	0.0419618299482575	-4.21626010196095	2.75676200356128e-05	***
df.mm.trans1:probe6	-0.180679432628612	0.0419618299482575	-4.30580441442627	1.86257114266765e-05	***
df.mm.trans1:probe7	-0.0950701774558476	0.0419618299482575	-2.26563468688275	0.0237306306807223	*  
df.mm.trans1:probe8	-0.0755190660266131	0.0419618299482575	-1.79970859516219	0.072269725071912	.  
df.mm.trans1:probe9	-0.164641747102934	0.0419618299482575	-3.92360741430847	9.44516485041476e-05	***
df.mm.trans1:probe10	-0.0222024162382522	0.0419618299482575	-0.529109818747888	0.59687074174603	   
df.mm.trans1:probe11	0.663872872922286	0.0419618299482575	15.8208751558475	1.79711131767836e-49	***
df.mm.trans1:probe12	-0.176844729369311	0.0419618299482575	-4.21441890373645	2.77886876551309e-05	***
df.mm.trans1:probe13	-0.123664427412075	0.0419618299482575	-2.94706945727970	0.00329784689513028	** 
df.mm.trans1:probe14	-0.119441545190995	0.0419618299482575	-2.84643318316375	0.0045302160366756	** 
df.mm.trans1:probe15	-0.170987850575506	0.0419618299482575	-4.07484256016356	5.04652358617399e-05	***
df.mm.trans1:probe16	-0.142195975649469	0.0419618299482575	-3.38869815317418	0.000735349761298467	***
df.mm.trans1:probe17	0.123698901413991	0.0419618299482575	2.94789101348826	0.00328918738733124	** 
df.mm.trans1:probe18	-0.00632553482904271	0.0419618299482575	-0.15074497077088	0.880213524086327	   
df.mm.trans1:probe19	0.233068696102501	0.0419618299482575	5.55430247894085	3.75396463671467e-08	***
df.mm.trans1:probe20	-0.0279230438996752	0.0419618299482575	-0.66543913680854	0.50595453080353	   
df.mm.trans1:probe21	0.0417551762845058	0.0419618299482575	0.995075198960424	0.319989696510627	   
df.mm.trans1:probe22	0.060052313712823	0.0419618299482575	1.43111760823760	0.152772909287854	   
df.mm.trans2:probe2	0.0111145040931033	0.0419618299482575	0.264871768147587	0.79117401055405	   
df.mm.trans2:probe3	0.121157855775054	0.0419618299482575	2.88733489279310	0.00398613147192183	** 
df.mm.trans2:probe4	-0.080454861848603	0.0419618299482575	-1.91733444294042	0.0555384402906955	.  
df.mm.trans2:probe5	0.0753665075549923	0.0419618299482575	1.79607294648316	0.0728464938277591	.  
df.mm.trans2:probe6	0.121965805616657	0.0419618299482575	2.90658929238908	0.00375118392168698	** 
df.mm.trans3:probe2	0.35848711925732	0.0419618299482575	8.54317172771933	6.20261533081064e-17	***
df.mm.trans3:probe3	0.284195183201356	0.0419618299482575	6.77270709003379	2.39215972524377e-11	***
df.mm.trans3:probe4	0.190772268040534	0.0419618299482575	4.54632861044841	6.27093615945827e-06	***
df.mm.trans3:probe5	0.0912636965340164	0.0419618299482575	2.17492174784923	0.0299176649357521	*  
df.mm.trans3:probe6	0.424280072892917	0.0419618299482575	10.1110955698570	9.6073824070408e-23	***
df.mm.trans3:probe7	0.436339506037089	0.0419618299482575	10.3984861140501	6.85569565240434e-24	***
df.mm.trans3:probe8	0.341639077466149	0.0419618299482575	8.1416629800802	1.42364405766933e-15	***
df.mm.trans3:probe9	0.210133794246251	0.0419618299482575	5.00773666223242	6.72905520703834e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.79169262859781	0.112013936482996	33.8501863933102	1.74210717617386e-158	***
df.mm.trans1	0.198768797316734	0.096975877324673	2.04967258662957	0.0407101747377994	*  
df.mm.trans2	0.170449037628348	0.0859148898442048	1.98392895500925	0.0475927960262203	*  
df.mm.exp2	0.157688678921114	0.111042230962854	1.42007844721585	0.155960343034689	   
df.mm.exp3	0.254307603501412	0.111042230962854	2.29018816801765	0.0222604500974031	*  
df.mm.exp4	0.103283105649575	0.111042230962854	0.930124554901327	0.352577036353973	   
df.mm.exp5	0.159449324269888	0.111042230962854	1.43593408460270	0.151397864396310	   
df.mm.exp6	0.323303533729635	0.111042230962854	2.9115367272996	0.00369288584052494	** 
df.mm.exp7	0.122932069753712	0.111042230962854	1.10707492714942	0.268582273785508	   
df.mm.exp8	0.112562416300430	0.111042230962854	1.01369015485725	0.311026027770755	   
df.mm.trans1:exp2	-0.162032884641712	0.102938903158806	-1.57406849761883	0.115852691590142	   
df.mm.trans2:exp2	-0.126275997071363	0.0772821220405152	-1.63396130614999	0.102646193527382	   
df.mm.trans1:exp3	-0.226768615061491	0.102938903158806	-2.20294376666954	0.0278728275788171	*  
df.mm.trans2:exp3	-0.152434884574292	0.0772821220405151	-1.97244693273792	0.0488901649498993	*  
df.mm.trans1:exp4	-0.0844223104655299	0.102938903158806	-0.820120555736734	0.41238283902275	   
df.mm.trans2:exp4	-0.13717373418317	0.0772821220405152	-1.77497370104895	0.076268664908746	.  
df.mm.trans1:exp5	-0.116921618723601	0.102938903158806	-1.13583509378591	0.256353447792808	   
df.mm.trans2:exp5	-0.0907424146946148	0.0772821220405152	-1.17417084700447	0.240663385184757	   
df.mm.trans1:exp6	-0.281937879727977	0.102938903158806	-2.73888560181203	0.0062969319533848	** 
df.mm.trans2:exp6	-0.162518328460653	0.0772821220405152	-2.10292269634434	0.0357730734979708	*  
df.mm.trans1:exp7	-0.0873900111894746	0.102938903158806	-0.848950285147843	0.396153736569746	   
df.mm.trans2:exp7	-0.0784954781556528	0.0772821220405152	-1.01570034676974	0.310068084196119	   
df.mm.trans1:exp8	-0.109299289011616	0.102938903158806	-1.06178796992812	0.288640798047142	   
df.mm.trans2:exp8	-0.141926174763195	0.0772821220405152	-1.83646839677604	0.0666458902533676	.  
df.mm.trans1:probe2	-0.0418684182083719	0.0690535154977074	-0.606318417050932	0.544469118336893	   
df.mm.trans1:probe3	-0.0507897844397429	0.0690535154977074	-0.735513377902232	0.462234680604629	   
df.mm.trans1:probe4	0.0665395449870283	0.0690535154977074	0.963593880882682	0.335530227159285	   
df.mm.trans1:probe5	-0.0429410544663228	0.0690535154977074	-0.621851822558526	0.534210075736427	   
df.mm.trans1:probe6	0.0603621222355367	0.0690535154977074	0.874135397748731	0.382297372817254	   
df.mm.trans1:probe7	0.0926857061748146	0.0690535154977074	1.34223008787861	0.179888423822727	   
df.mm.trans1:probe8	-0.00123649258680486	0.0690535154977074	-0.0179062945295798	0.985717910912238	   
df.mm.trans1:probe9	-0.0129716460353462	0.0690535154977074	-0.187849176712471	0.851040798185102	   
df.mm.trans1:probe10	-0.0431313147284013	0.0690535154977074	-0.624607080718914	0.532400626669013	   
df.mm.trans1:probe11	0.00996308338982211	0.0690535154977074	0.144280610740997	0.885313912913486	   
df.mm.trans1:probe12	0.0289001739459017	0.0690535154977075	0.418518503187012	0.675676329547116	   
df.mm.trans1:probe13	0.0182076462325729	0.0690535154977075	0.263674428468126	0.792096249260933	   
df.mm.trans1:probe14	-0.00685366249209997	0.0690535154977075	-0.0992514637770688	0.920962580107962	   
df.mm.trans1:probe15	0.118914235336238	0.0690535154977075	1.72205910849226	0.0854313683639486	.  
df.mm.trans1:probe16	0.0321936072009138	0.0690535154977074	0.466212429141028	0.641185781667048	   
df.mm.trans1:probe17	0.0980523639151721	0.0690535154977074	1.41994745971228	0.155998465374290	   
df.mm.trans1:probe18	-0.00481351582712051	0.0690535154977075	-0.0697070350789066	0.944443632693753	   
df.mm.trans1:probe19	-0.00620041957937157	0.0690535154977075	-0.089791512201539	0.928474555703274	   
df.mm.trans1:probe20	0.0956966831353322	0.0690535154977074	1.38583361680564	0.166169942255026	   
df.mm.trans1:probe21	0.0262175847978827	0.0690535154977074	0.379670529572866	0.704287120643331	   
df.mm.trans1:probe22	0.0489453382526627	0.0690535154977074	0.708802989969246	0.478645771933448	   
df.mm.trans2:probe2	0.150956655384312	0.0690535154977074	2.18608211756176	0.0290882803577447	*  
df.mm.trans2:probe3	0.0359830766978971	0.0690535154977074	0.521089714818238	0.60244326979773	   
df.mm.trans2:probe4	0.0424097921688067	0.0690535154977075	0.614158335938953	0.539279000700205	   
df.mm.trans2:probe5	0.0854457366108578	0.0690535154977074	1.23738430976327	0.216294386508728	   
df.mm.trans2:probe6	-0.00486909165174012	0.0690535154977075	-0.0705118576026991	0.943803248319686	   
df.mm.trans3:probe2	-0.0423899969726459	0.0690535154977074	-0.613871671371362	0.5394683377466	   
df.mm.trans3:probe3	-0.0125978011840795	0.0690535154977074	-0.182435334295149	0.85528564708382	   
df.mm.trans3:probe4	-0.0218761298912373	0.0690535154977075	-0.316799655072791	0.751475289219152	   
df.mm.trans3:probe5	0.00100433312746106	0.0690535154977074	0.0145442722245532	0.98839925408306	   
df.mm.trans3:probe6	-0.0566457089962184	0.0690535154977074	-0.820316077870054	0.412271460654658	   
df.mm.trans3:probe7	-0.0294551149573679	0.0690535154977074	-0.426554893622264	0.66981416629567	   
df.mm.trans3:probe8	-0.0426217828156013	0.0690535154977074	-0.617228283142462	0.537253446384903	   
df.mm.trans3:probe9	-0.0694115108054647	0.0690535154977074	-1.00518431690519	0.315101055332926