chr7.22248_chr7_15641617_15650542_+_2.R 

fitVsDatCorrelation=0.843067193490407
cont.fitVsDatCorrelation=0.30247338074459

fstatistic=9944.4927904131,50,646
cont.fstatistic=3157.17136802360,50,646

residuals=-0.6371105267336,-0.0827420985464969,-0.00438327324453908,0.0709962727051589,0.621695050693412
cont.residuals=-0.573769882039959,-0.161677405557730,-0.0399810875971495,0.0982801233437082,1.2793272332147

predictedValues:
Include	Exclude	Both
chr7.22248_chr7_15641617_15650542_+_2.R.tl.Lung	47.6588039426642	57.7927978808761	72.7107125832286
chr7.22248_chr7_15641617_15650542_+_2.R.tl.cerebhem	47.1694949857098	62.8178568404744	56.806569555324
chr7.22248_chr7_15641617_15650542_+_2.R.tl.cortex	47.6894232116135	53.8771753121437	63.6264284115732
chr7.22248_chr7_15641617_15650542_+_2.R.tl.heart	49.30393281664	54.3945053375253	65.2234256408377
chr7.22248_chr7_15641617_15650542_+_2.R.tl.kidney	46.7119325389652	58.0653552568997	75.2100998674272
chr7.22248_chr7_15641617_15650542_+_2.R.tl.liver	49.6744898546907	51.3403542981791	72.7376261034345
chr7.22248_chr7_15641617_15650542_+_2.R.tl.stomach	50.1878817043293	55.377270186944	70.3858135551607
chr7.22248_chr7_15641617_15650542_+_2.R.tl.testicle	45.0366110586496	60.1709358025774	64.5505645582366


diffExp=-10.1339939382119,-15.6483618547646,-6.18775210053024,-5.09057252088525,-11.3534227179346,-1.66586444348834,-5.18938848261469,-15.1343247439279
diffExpScore=0.985995119736643
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,-1
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,-1,0,0,-1,0,0,-1
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	51.305350015665	48.641669890519	55.5235990012361
cerebhem	56.406548851287	56.3453678300705	49.402737716092
cortex	56.0029425576745	52.8432349420279	53.4544679157628
heart	55.1514955767858	52.191948525761	62.1364723379328
kidney	56.8574538745752	63.2021730696159	55.4535195485956
liver	52.833306500418	53.8313030156464	48.4860290421356
stomach	59.249774137114	58.5862601741194	52.5327374668654
testicle	55.1109997735495	49.9743248641199	58.8759901443752
cont.diffExp=2.66368012514597,0.0611810212164627,3.15970761564662,2.9595470510248,-6.3447191950407,-0.997996515228444,0.663513962994593,5.13667490942964
cont.diffExpScore=2.64853155961348

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=-0.706928136558324
cont.tran.correlation=0.696891387749867

tran.covariance=-0.00165973764110331
cont.tran.covariance=0.00269791610300109

tran.mean=52.3293013143051
cont.tran.mean=54.9083845999343

weightedLogRatios:
wLogRatio
Lung	-0.763565079565604
cerebhem	-1.14510657538179
cortex	-0.478925759583355
heart	-0.387842546490566
kidney	-0.860005504142781
liver	-0.129368839383007
stomach	-0.390135847536155
testicle	-1.14504491279857

cont.weightedLogRatios:
wLogRatio
Lung	0.208520226673824
cerebhem	0.00437570408340215
cortex	0.232087156445739
heart	0.219657604336976
kidney	-0.433050895282021
liver	-0.0744135743277435
stomach	0.0459044381421868
testicle	0.387488242554816

varWeightedLogRatios=0.140361222631683
cont.varWeightedLogRatios=0.0639060147089762

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.65409440154207	0.0725949330858938	50.3353918271207	9.60088696509625e-226	***
df.mm.trans1	0.0944972188452013	0.0625957960852248	1.50964161741058	0.131623832301717	   
df.mm.trans2	0.423909244324577	0.0568554552547336	7.45591152907502	2.88253259504638e-13	***
df.mm.exp2	0.319891975853553	0.0748162863051225	4.27569973934473	2.19310545630760e-05	***
df.mm.exp3	0.0639448246883327	0.0748162863051225	0.854691242325864	0.393038949534494	   
df.mm.exp4	0.082005489035452	0.0748162863051225	1.09609141385353	0.273447218292266	   
df.mm.exp5	-0.0491595035048032	0.0748162863051225	-0.657069549059365	0.511370216113868	   
df.mm.exp6	-0.0773330210484691	0.0748162863051225	-1.03363886217344	0.30169181779504	   
df.mm.exp7	0.0415082779650606	0.0748162863051225	0.554802704263853	0.579221652305264	   
df.mm.exp8	0.102773503964623	0.0748162863051225	1.37367823291151	0.170018089855236	   
df.mm.trans1:exp2	-0.330211960608702	0.0682429528338255	-4.83877011319809	1.6355668434182e-06	***
df.mm.trans2:exp2	-0.236516761981622	0.0555860336457146	-4.25496741661934	2.40038074542505e-05	***
df.mm.trans1:exp3	-0.0633025627204777	0.0682429528338255	-0.927605856602104	0.35395849658404	   
df.mm.trans2:exp3	-0.134102063833503	0.0555860336457146	-2.41251363046015	0.0161205245914082	*  
df.mm.trans1:exp4	-0.048069013745397	0.0682429528338255	-0.704380624655078	0.481449611409529	   
df.mm.trans2:exp4	-0.142606508877288	0.0555860336457146	-2.56550970674056	0.0105269588846761	*  
df.mm.trans1:exp5	0.0290917745604427	0.0682429528338255	0.426297124499909	0.670033362651042	   
df.mm.trans2:exp5	0.0538645306685965	0.0555860336457146	0.969029936762707	0.3328929104772	   
df.mm.trans1:exp6	0.118757164040769	0.0682429528338255	1.74021139340128	0.082298102716962	.  
df.mm.trans2:exp6	-0.0410540662935054	0.0555860336457146	-0.738568010719551	0.460437592342929	   
df.mm.trans1:exp7	0.0101979435448329	0.0682429528338255	0.149435848265026	0.881256366543221	   
df.mm.trans2:exp7	-0.0842032176136446	0.0555860336457146	-1.51482687450459	0.130305366832382	   
df.mm.trans1:exp8	-0.159365141623145	0.0682429528338255	-2.33526151793587	0.0198350059514303	*  
df.mm.trans2:exp8	-0.062448225987004	0.0555860336457146	-1.12345173582678	0.261663010828344	   
df.mm.trans1:probe2	0.0983281717460977	0.0433676396639322	2.26731665610741	0.0237004718421164	*  
df.mm.trans1:probe3	0.00850194080668735	0.0433676396639322	0.196043429445809	0.844637825551799	   
df.mm.trans1:probe4	0.0521227641546997	0.0433676396639321	1.20188150793111	0.229849841009595	   
df.mm.trans1:probe5	0.269041602887482	0.0433676396639321	6.20374096843544	9.84798305198899e-10	***
df.mm.trans1:probe6	0.394755799797447	0.0433676396639322	9.10254288350759	1.08518779449967e-18	***
df.mm.trans1:probe7	0.0504709495337303	0.0433676396639322	1.16379286317733	0.244937618615081	   
df.mm.trans1:probe8	0.199232607390881	0.0433676396639322	4.59403852584068	5.22841741238033e-06	***
df.mm.trans1:probe9	0.190130487397770	0.0433676396639322	4.38415576386318	1.35901715325713e-05	***
df.mm.trans1:probe10	0.0716892080759564	0.0433676396639322	1.65305764001675	0.0988049943754348	.  
df.mm.trans1:probe11	0.460628716532676	0.0433676396639322	10.6214845931717	2.12723008747565e-24	***
df.mm.trans1:probe12	0.148520917084242	0.0433676396639322	3.4246945011343	0.000654447304788123	***
df.mm.trans1:probe13	0.155116856284608	0.0433676396639322	3.57678807255021	0.000373843069411597	***
df.mm.trans1:probe14	0.150400315898186	0.0433676396639322	3.46803093420991	0.000559101307309387	***
df.mm.trans1:probe15	0.215172576617044	0.0433676396639322	4.96159298233604	8.95195123573573e-07	***
df.mm.trans1:probe16	0.191829458764415	0.0433676396639322	4.42333178035408	1.14039335880182e-05	***
df.mm.trans2:probe2	-0.107921066161620	0.0433676396639322	-2.48851602249811	0.0130785523323372	*  
df.mm.trans2:probe3	0.0346176198119786	0.0433676396639322	0.798236198239982	0.425026775569848	   
df.mm.trans2:probe4	-0.128298483350058	0.0433676396639322	-2.95839211781592	0.00320556557962326	** 
df.mm.trans2:probe5	-0.0685231096089274	0.0433676396639322	-1.58005162697191	0.114584527894465	   
df.mm.trans2:probe6	-0.00468822785031656	0.0433676396639322	-0.108104288973228	0.91394653309043	   
df.mm.trans3:probe2	0.314731928082788	0.0433676396639322	7.25729900270645	1.13681958273166e-12	***
df.mm.trans3:probe3	-0.0861405220909882	0.0433676396639322	-1.98628569040222	0.0474237566953426	*  
df.mm.trans3:probe4	-0.129695403956363	0.0433676396639322	-2.99060324613948	0.00289023501102982	** 
df.mm.trans3:probe5	-0.339071505709222	0.0433676396639322	-7.81853723967413	2.17971632014252e-14	***
df.mm.trans3:probe6	0.644991987275629	0.0433676396639322	14.8726560235662	3.05234066705712e-43	***
df.mm.trans3:probe7	0.393098552141629	0.0433676396639322	9.06432896020763	1.48169698050799e-18	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.8114789151672	0.128660037959474	29.6244193272177	1.79126374533863e-122	***
df.mm.trans1	0.114904776712698	0.110938562212043	1.03575145036651	0.300705667407529	   
df.mm.trans2	0.0668596802472198	0.100764953149307	0.663521176337484	0.507233533059379	   
df.mm.exp2	0.358611991058454	0.132596943434257	2.70452682973237	0.00702039485440167	** 
df.mm.exp3	0.208436243089549	0.132596943434257	1.57195360383925	0.116450936699070	   
df.mm.exp4	0.0302115044649197	0.132596943434257	0.227844652240410	0.819839107197133	   
df.mm.exp5	0.365873360054261	0.132596943434257	2.75928954754274	0.00595684596545934	** 
df.mm.exp6	0.266253773079459	0.132596943434257	2.00799329293341	0.045059855855479	*  
df.mm.exp7	0.385358156357368	0.132596943434257	2.90623710001606	0.00378332353048970	** 
df.mm.exp8	0.0399578634016551	0.132596943434257	0.301348299340449	0.763245888206645	   
df.mm.trans1:exp2	-0.263821760454084	0.120947021077600	-2.18130019328723	0.0295210568063492	*  
df.mm.trans2:exp2	-0.211592526198892	0.0985151565662633	-2.14781698140601	0.0320995900700025	*  
df.mm.trans1:exp3	-0.120827043605525	0.120947021077600	-0.999008016311557	0.318164859175012	   
df.mm.trans2:exp3	-0.125587112547170	0.0985151565662633	-1.27479990820192	0.202838394487470	   
df.mm.trans1:exp4	0.0420773236695582	0.120947021077600	0.347898801431092	0.728029515288103	   
df.mm.trans2:exp4	0.0402361670261858	0.0985151565662632	0.408426159269433	0.683096176618182	   
df.mm.trans1:exp5	-0.263121069227662	0.120947021077600	-2.17550682012121	0.0299539923305653	*  
df.mm.trans2:exp5	-0.104015244178988	0.0985151565662633	-1.05582986216974	0.291440647384378	   
df.mm.trans1:exp6	-0.236907011852298	0.120947021077600	-1.95876681989792	0.0505699307300402	.  
df.mm.trans2:exp6	-0.164879203375046	0.0985151565662633	-1.67364301212012	0.0946848695851496	.  
df.mm.trans1:exp7	-0.241391223924635	0.120947021077600	-1.99584265717266	0.0463704512688322	*  
df.mm.trans2:exp7	-0.199338523984096	0.0985151565662633	-2.02343000744273	0.0434400908833157	*  
df.mm.trans1:exp8	0.0315964302035892	0.120947021077600	0.261241905109153	0.79398922754442	   
df.mm.trans2:exp8	-0.0129290612781146	0.0985151565662633	-0.131239311074111	0.895626859543952	   
df.mm.trans1:probe2	0.0861090350174	0.0768604905079595	1.12032898109703	0.262989926692966	   
df.mm.trans1:probe3	-0.0424629242840347	0.0768604905079595	-0.552467516189443	0.580819179888905	   
df.mm.trans1:probe4	0.081617457554219	0.0768604905079595	1.06189092750803	0.288682018893223	   
df.mm.trans1:probe5	0.0423799165416932	0.0768604905079595	0.55138753684254	0.581558704080557	   
df.mm.trans1:probe6	0.0299274660163103	0.0768604905079595	0.389373861896069	0.697127927223424	   
df.mm.trans1:probe7	-0.0927318386658091	0.0768604905079595	-1.20649553565113	0.228068120174432	   
df.mm.trans1:probe8	-0.0338253836168808	0.0768604905079595	-0.440088052955867	0.66002053814662	   
df.mm.trans1:probe9	0.0137966842019849	0.0768604905079595	0.179502942419501	0.857599094258063	   
df.mm.trans1:probe10	0.095687036539993	0.0768604905079595	1.24494439090372	0.213603615896644	   
df.mm.trans1:probe11	0.0906163879162537	0.0768604905079595	1.17897228234407	0.238843257784832	   
df.mm.trans1:probe12	-0.00290003928568802	0.0768604905079595	-0.0377312097089426	0.969913648579305	   
df.mm.trans1:probe13	-0.0581716273192982	0.0768604905079595	-0.756846943531723	0.449417503703486	   
df.mm.trans1:probe14	0.0277957129779362	0.0768604905079595	0.361638506262951	0.717740437755849	   
df.mm.trans1:probe15	0.0533833825361197	0.0768604905079595	0.69454907434648	0.487587646965047	   
df.mm.trans1:probe16	-0.0287603618694584	0.0768604905079595	-0.374189153352853	0.70838637997238	   
df.mm.trans2:probe2	-0.0811333907269847	0.0768604905079595	-1.05559293455957	0.291548841966773	   
df.mm.trans2:probe3	-0.0178079704255283	0.0768604905079595	-0.231692125666101	0.816850518681618	   
df.mm.trans2:probe4	0.0587359820692405	0.0768604905079595	0.764189529380611	0.445033336417237	   
df.mm.trans2:probe5	0.0193892013636659	0.0768604905079595	0.252264866325021	0.800916582721926	   
df.mm.trans2:probe6	0.100661830423440	0.0768604905079595	1.30966937314842	0.190773275932994	   
df.mm.trans3:probe2	0.106948294903618	0.0768604905079595	1.3914599581243	0.164564957319015	   
df.mm.trans3:probe3	0.0289882099643928	0.0768604905079595	0.37715359052244	0.706183330506223	   
df.mm.trans3:probe4	0.033495136760887	0.0768604905079595	0.435791347928209	0.663133702555594	   
df.mm.trans3:probe5	0.0215232592460079	0.0768604905079595	0.280030209328147	0.779543972119925	   
df.mm.trans3:probe6	-0.0303421027893126	0.0768604905079595	-0.394768529172610	0.693144024924344	   
df.mm.trans3:probe7	0.00434056027282013	0.0768604905079595	0.0564732314890787	0.954982281747776