chr5.18952_chr5_37005533_37007548_+_2.R 

fitVsDatCorrelation=0.880313415457695
cont.fitVsDatCorrelation=0.230367712285199

fstatistic=11299.6391211045,58,830
cont.fstatistic=2674.57168293979,58,830

residuals=-0.551057178394051,-0.0845026015848733,-0.00575573074463915,0.0835823155795551,0.89112057761143
cont.residuals=-0.611943984423933,-0.222442241102312,-0.060151640583925,0.164464614189251,1.00709091531997

predictedValues:
Include	Exclude	Both
chr5.18952_chr5_37005533_37007548_+_2.R.tl.Lung	59.9028243155253	53.622282969302	65.5151715022208
chr5.18952_chr5_37005533_37007548_+_2.R.tl.cerebhem	62.0154342322745	68.9044834100566	64.68487619375
chr5.18952_chr5_37005533_37007548_+_2.R.tl.cortex	68.6006810584373	53.5132618320125	78.5297157847929
chr5.18952_chr5_37005533_37007548_+_2.R.tl.heart	63.3926437300701	54.3486506687906	70.5968853854128
chr5.18952_chr5_37005533_37007548_+_2.R.tl.kidney	62.0651137412005	53.2438967791584	72.0438835341486
chr5.18952_chr5_37005533_37007548_+_2.R.tl.liver	63.020930672933	54.8354331041776	69.2147810603961
chr5.18952_chr5_37005533_37007548_+_2.R.tl.stomach	62.6914523540363	55.0506600312634	70.5937203114859
chr5.18952_chr5_37005533_37007548_+_2.R.tl.testicle	66.8743938894617	52.6171944866597	68.3281348630619


diffExp=6.28054134622335,-6.88904917778213,15.0874192264249,9.04399306127956,8.82121696204206,8.18549756875543,7.6407923227729,14.2571994028020
diffExpScore=1.20145955699767
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=0,0,1,0,0,0,0,1
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	64.6090544042728	62.6287534855253	65.1670601713049
cerebhem	71.3527864709643	73.1183803294737	66.0389072191438
cortex	65.0696878570588	71.3684921911748	64.1456130160626
heart	69.4484000288164	58.9639710331798	61.493044167485
kidney	68.6744732290955	65.7915248521766	62.8100291818262
liver	66.4716822302267	64.2929034662292	65.1110527029618
stomach	66.1105251081902	62.7980963329004	63.7936952233392
testicle	63.4110616755337	61.569108757208	56.8328914139557
cont.diffExp=1.98030091874748,-1.76559385850948,-6.29880433411603,10.4844289956365,2.88294837691897,2.17877876399749,3.31242877528987,1.84195291832577
cont.diffExpScore=1.96877366713101

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=-0.275101723663629
cont.tran.correlation=0.325261545471199

tran.covariance=-0.00107315162569643
cont.tran.covariance=0.000904872110191171

tran.mean=59.66870857971
cont.tran.mean=65.9799313407516

weightedLogRatios:
wLogRatio
Lung	0.447171985364159
cerebhem	-0.440318153219898
cortex	1.01935141391483
heart	0.626854051907869
kidney	0.62110363696026
liver	0.566803291832473
stomach	0.529404065738691
testicle	0.978977018736986

cont.weightedLogRatios:
wLogRatio
Lung	0.129276622541022
cerebhem	-0.104614194695742
cortex	-0.390071329039361
heart	0.680611224458922
kidney	0.18046371327438
liver	0.139309638784214
stomach	0.214126543409568
testicle	0.121888797270036

varWeightedLogRatios=0.200994825898200
cont.varWeightedLogRatios=0.0911246134400092

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.24972597552199	0.0716206160925144	59.3366296937811	7.90421231815415e-301	***
df.mm.trans1	-0.207211559357285	0.0617596388894943	-3.35512906297988	0.000829286918468307	***
df.mm.trans2	-0.275700375422989	0.0547011490938866	-5.04012036291577	5.71284893726111e-07	***
df.mm.exp2	0.298170497643434	0.0704301628341963	4.2335625198734	2.55704132610502e-05	***
df.mm.exp3	-0.0476517490819579	0.0704301628341963	-0.676581554896268	0.498859984851697	   
df.mm.exp4	-0.00462503683392792	0.0704301628341962	-0.0656684103487876	0.947657636392975	   
df.mm.exp5	-0.066614809191556	0.0704301628341963	-0.94582784578218	0.344511662077694	   
df.mm.exp6	0.0181824182254598	0.0704301628341962	0.258162376654788	0.796345592230958	   
df.mm.exp7	-0.00286884953124956	0.0704301628341963	-0.0407332514338110	0.9675183486931	   
df.mm.exp8	0.0491308657091608	0.0704301628341962	0.69758273631743	0.485633538803535	   
df.mm.trans1:exp2	-0.263510858867796	0.0648755030182727	-4.06179292041213	5.33128588978141e-05	***
df.mm.trans2:exp2	-0.0474139592027713	0.0482689188757134	-0.982287573601067	0.326244432071832	   
df.mm.trans1:exp3	0.183230557210617	0.0648755030182727	2.82434121796341	0.00485134102154348	** 
df.mm.trans2:exp3	0.0456165482506652	0.0482689188757134	0.945050133982123	0.344908305748376	   
df.mm.trans1:exp4	0.0612492075315456	0.0648755030182727	0.944103778498554	0.345391353401835	   
df.mm.trans2:exp4	0.0180801146663442	0.0482689188757134	0.374570532911630	0.708075570302161	   
df.mm.trans1:exp5	0.102075210252533	0.0648755030182727	1.57340144590143	0.116006977630973	   
df.mm.trans2:exp5	0.0595332840110228	0.0482689188757134	1.23336684138946	0.217788100310578	   
df.mm.trans1:exp6	0.0325608313731832	0.0648755030182727	0.501897170092265	0.615872964557922	   
df.mm.trans2:exp6	0.00418944754645117	0.0482689188757134	0.0867938964458369	0.93085627308566	   
df.mm.trans1:exp7	0.0483703072654954	0.0648755030182727	0.74558662384277	0.4561282545473	   
df.mm.trans2:exp7	0.0291579935713612	0.0482689188757134	0.604073889585956	0.545959587487133	   
df.mm.trans1:exp8	0.0609616216113276	0.0648755030182727	0.93967088924393	0.347659778492775	   
df.mm.trans2:exp8	-0.0680526167269094	0.0482689188757134	-1.40986411778015	0.158954409026599	   
df.mm.trans1:probe2	0.07571883772525	0.0444172205407531	1.70471805312035	0.0886212463476836	.  
df.mm.trans1:probe3	-0.0177286320129875	0.0444172205407531	-0.399138707851416	0.689893675609428	   
df.mm.trans1:probe4	0.173251958046321	0.0444172205407531	3.9005582955683	0.000103732553748968	***
df.mm.trans1:probe5	0.257170865169691	0.0444172205407531	5.78989099360089	9.9867607266784e-09	***
df.mm.trans1:probe6	0.0416869603065067	0.0444172205407531	0.938531492943342	0.348244367392535	   
df.mm.trans1:probe7	0.0538029997724992	0.0444172205407531	1.21130946775777	0.226121583449981	   
df.mm.trans1:probe8	-0.00958193312766733	0.0444172205407531	-0.21572563548581	0.82925461860367	   
df.mm.trans1:probe9	0.0651870655170717	0.0444172205407531	1.46760793952116	0.142589572908379	   
df.mm.trans1:probe10	0.273185258817742	0.0444172205407531	6.15043569795396	1.20023857610125e-09	***
df.mm.trans1:probe11	0.360221070176161	0.0444172205407531	8.10994172509413	1.81392335566518e-15	***
df.mm.trans1:probe12	0.436103196098543	0.0444172205407531	9.81833601448374	1.33448381059238e-21	***
df.mm.trans1:probe13	0.427088914636706	0.0444172205407531	9.61539036970693	7.98402062221888e-21	***
df.mm.trans1:probe14	0.452490491228118	0.0444172205407531	10.1872761446871	4.79792301879673e-23	***
df.mm.trans1:probe15	0.289880028637482	0.0444172205407531	6.52629824893062	1.17220548136569e-10	***
df.mm.trans1:probe16	-0.184025979744595	0.0444172205407531	-4.14312236344797	3.77725415215887e-05	***
df.mm.trans1:probe17	-0.272979367504081	0.0444172205407531	-6.14580030404246	1.23425022743388e-09	***
df.mm.trans1:probe18	-0.207791213022890	0.0444172205407531	-4.67816784781119	3.37833847310045e-06	***
df.mm.trans1:probe19	-0.214780983824537	0.0444172205407531	-4.83553408362134	1.58234276170919e-06	***
df.mm.trans1:probe20	-0.209592322616597	0.0444172205407531	-4.71871765195876	2.78441605653193e-06	***
df.mm.trans1:probe21	-0.232820825281304	0.0444172205407531	-5.24167929570671	2.01986197154605e-07	***
df.mm.trans2:probe2	-0.0130983037584319	0.0444172205407531	-0.294892467357659	0.768149770334455	   
df.mm.trans2:probe3	0.0717973455077674	0.0444172205407531	1.61643039869847	0.106381281447595	   
df.mm.trans2:probe4	-0.0409984120035601	0.0444172205407531	-0.923029660668294	0.356259989522787	   
df.mm.trans2:probe5	-0.0007655019994064	0.0444172205407531	-0.0172343516790756	0.986253799442121	   
df.mm.trans2:probe6	0.110090610276545	0.0444172205407531	2.47855694112009	0.0133892553807499	*  
df.mm.trans3:probe2	0.327029265472043	0.0444172205407531	7.36266838605967	4.34502845586744e-13	***
df.mm.trans3:probe3	1.14183495770526	0.0444172205407531	25.7070330787046	1.14924912843909e-107	***
df.mm.trans3:probe4	-0.00671388331342763	0.0444172205407531	-0.151154962685421	0.879890205439872	   
df.mm.trans3:probe5	0.420079035631634	0.0444172205407531	9.45757142201657	3.14505840821753e-20	***
df.mm.trans3:probe6	0.377033108367424	0.0444172205407531	8.4884444316252	9.57652287021398e-17	***
df.mm.trans3:probe7	0.227047461533123	0.0444172205407531	5.11169899351999	3.96529030075434e-07	***
df.mm.trans3:probe8	0.128310386264209	0.0444172205407531	2.88875316154651	0.00396837914190117	** 
df.mm.trans3:probe9	0.539817077411131	0.0444172205407531	12.1533286153249	2.14409995435422e-31	***
df.mm.trans3:probe10	1.00023960159302	0.0444172205407531	22.5191848885568	5.12272381052253e-88	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15721286250589	0.146912684879795	28.2971675720673	7.50007984008794e-124	***
df.mm.trans1	0.0443110996454128	0.126685232011158	0.349773205147623	0.726597640524927	   
df.mm.trans2	-0.0118502323916237	0.112206416501810	-0.105611004798755	0.915916510648885	   
df.mm.exp2	0.240847174744267	0.144470752738671	1.66709988131596	0.0958717941903634	.  
df.mm.exp3	0.153534673839169	0.144470752738671	1.06273879611393	0.288209525240586	   
df.mm.exp4	0.0699616363110367	0.144470752738671	0.484261589178456	0.628327985908837	   
df.mm.exp5	0.147128902431980	0.144470752738671	1.01839922366929	0.308785024880926	   
df.mm.exp6	0.0555060434560291	0.144470752738671	0.38420263204714	0.700926675564074	   
df.mm.exp7	0.0469734332319886	0.144470752738671	0.325141472177122	0.745155941470116	   
df.mm.exp8	0.101058425469756	0.144470752738671	0.699507848848528	0.484430697095041	   
df.mm.trans1:exp2	-0.141565340055351	0.133076687291132	-1.06378767714324	0.287734282206782	   
df.mm.trans2:exp2	-0.0859918922043807	0.099012223786459	-0.868497736096107	0.385372962317023	   
df.mm.trans1:exp3	-0.146430419400609	0.133076687291132	-1.10034614162181	0.271500357947483	   
df.mm.trans2:exp3	-0.0229026812323236	0.099012223786459	-0.231311653818806	0.817129703528583	   
df.mm.trans1:exp4	0.00226783285815089	0.133076687291132	0.0170415487814899	0.986407564958352	   
df.mm.trans2:exp4	-0.130259532894490	0.099012223786459	-1.31559041816314	0.188674829303345	   
df.mm.trans1:exp5	-0.0861059029938824	0.133076687291132	-0.647039723836142	0.517785132891848	   
df.mm.trans2:exp5	-0.0978623675405629	0.099012223786459	-0.98838672436672	0.323251326604217	   
df.mm.trans1:exp6	-0.0270845796342195	0.133076687291132	-0.203526103523803	0.83877373190019	   
df.mm.trans2:exp6	-0.0292812778083329	0.099012223786459	-0.295733967873343	0.767507224050656	   
df.mm.trans1:exp7	-0.0240000309953575	0.133076687291132	-0.18034737326195	0.856923890152743	   
df.mm.trans2:exp7	-0.0442731668932215	0.099012223786459	-0.447148495409073	0.654884505483615	   
df.mm.trans1:exp8	-0.119774666891722	0.133076687291132	-0.900042444171244	0.368358720337534	   
df.mm.trans2:exp8	-0.118122655433753	0.099012223786459	-1.19301083155661	0.233206047520283	   
df.mm.trans1:probe2	0.033099497747266	0.0911113793842673	0.363286100714896	0.71648367842167	   
df.mm.trans1:probe3	-0.0617413926423251	0.0911113793842673	-0.677647436133388	0.498184108762684	   
df.mm.trans1:probe4	-0.129883847309483	0.0911113793842673	-1.42555022421174	0.154374166871975	   
df.mm.trans1:probe5	-0.0855450731644944	0.0911113793842673	-0.93890657503607	0.348051855331145	   
df.mm.trans1:probe6	0.0602629745731433	0.0911113793842673	0.661420944127966	0.508525962521658	   
df.mm.trans1:probe7	-0.0146875732338526	0.0911113793842673	-0.161204597418145	0.871971489649243	   
df.mm.trans1:probe8	-0.0141150164595090	0.0911113793842673	-0.154920456203151	0.876921691161969	   
df.mm.trans1:probe9	-0.0592816042973884	0.0911113793842673	-0.650649838670151	0.51545261866481	   
df.mm.trans1:probe10	-0.00545142321328155	0.0911113793842673	-0.0598325176297669	0.952303429479764	   
df.mm.trans1:probe11	0.0524911884882066	0.0911113793842673	0.576121104113924	0.564689509564169	   
df.mm.trans1:probe12	-0.0688542690795766	0.0911113793842673	-0.755715362284	0.450034282083201	   
df.mm.trans1:probe13	-0.107425185964607	0.0911113793842673	-1.17905344744629	0.238714765886924	   
df.mm.trans1:probe14	-0.139839518992314	0.0911113793842672	-1.53481946972324	0.125209260091766	   
df.mm.trans1:probe15	-0.0851122473731411	0.0911113793842672	-0.934156062045505	0.350495078028117	   
df.mm.trans1:probe16	-0.106614077878127	0.0911113793842673	-1.17015106783178	0.242276061304649	   
df.mm.trans1:probe17	-0.0605931061735871	0.0911113793842673	-0.665044329073675	0.506206882945935	   
df.mm.trans1:probe18	-0.0073622817086008	0.0911113793842673	-0.0808052930199857	0.935616278422958	   
df.mm.trans1:probe19	-0.168683977973367	0.0911113793842673	-1.85140406295391	0.0644664678238108	.  
df.mm.trans1:probe20	-0.0102082683774822	0.0911113793842673	-0.112041640094464	0.910817480285488	   
df.mm.trans1:probe21	-0.0487062009210338	0.0911113793842673	-0.53457867996502	0.593084395396278	   
df.mm.trans2:probe2	-0.0621922109903122	0.0911113793842673	-0.682595427822612	0.495052985102599	   
df.mm.trans2:probe3	-0.0378946161787844	0.0911113793842673	-0.415915294388879	0.677579500373427	   
df.mm.trans2:probe4	0.000751456173785884	0.0911113793842673	0.00824766542735102	0.993421371601574	   
df.mm.trans2:probe5	-0.00314825891152236	0.0911113793842673	-0.0345539594812235	0.972443722504087	   
df.mm.trans2:probe6	-0.0277265558324806	0.0911113793842673	-0.304314960654281	0.760964228196795	   
df.mm.trans3:probe2	0.00345011862937707	0.0911113793842673	0.0378670441902323	0.969802794723997	   
df.mm.trans3:probe3	-0.133544224260210	0.0911113793842673	-1.46572497489012	0.143101945608788	   
df.mm.trans3:probe4	-0.0521704317556817	0.0911113793842673	-0.572600613757037	0.567070213619711	   
df.mm.trans3:probe5	-0.00448923209908889	0.0911113793842673	-0.0492719145448924	0.960714455182292	   
df.mm.trans3:probe6	0.0336233123464895	0.0911113793842673	0.369035268412317	0.712195555134175	   
df.mm.trans3:probe7	-0.000492278642237323	0.0911113793842673	-0.00540304235940838	0.995690315215858	   
df.mm.trans3:probe8	-0.00427148263036137	0.0911113793842673	-0.0468819883885871	0.962618561141512	   
df.mm.trans3:probe9	0.0256278434992448	0.0911113793842673	0.281280380918809	0.778565463751179	   
df.mm.trans3:probe10	0.00507036904995353	0.0911113793842673	0.0556502281517325	0.95563384306396