chr4.17056_chr4_45019922_45021258_-_1.R 

fitVsDatCorrelation=0.782364331564797
cont.fitVsDatCorrelation=0.329329599664069

fstatistic=7211.9684658624,43,485
cont.fstatistic=3131.52451404532,43,485

residuals=-0.724674725408874,-0.091501425692348,-0.00696219761189894,0.0788859992480042,0.983041697982336
cont.residuals=-0.516389596646737,-0.155720439177184,-0.028677067205152,0.086220795930015,1.03391799875337

predictedValues:
Include	Exclude	Both
chr4.17056_chr4_45019922_45021258_-_1.R.tl.Lung	60.7397087191815	60.8629393324622	64.6305906293451
chr4.17056_chr4_45019922_45021258_-_1.R.tl.cerebhem	82.3619001713426	62.4233649043795	57.3939830176111
chr4.17056_chr4_45019922_45021258_-_1.R.tl.cortex	54.575259688059	55.9846394640013	69.6723016659253
chr4.17056_chr4_45019922_45021258_-_1.R.tl.heart	54.4101092350476	56.6369772914992	68.1215874736617
chr4.17056_chr4_45019922_45021258_-_1.R.tl.kidney	59.8012214106777	60.1846434217567	70.1960392124899
chr4.17056_chr4_45019922_45021258_-_1.R.tl.liver	57.6640263457554	59.6452559424998	70.1217205824268
chr4.17056_chr4_45019922_45021258_-_1.R.tl.stomach	56.9404183087506	55.9570663931747	69.4225425351957
chr4.17056_chr4_45019922_45021258_-_1.R.tl.testicle	64.2650522684625	53.5424093826927	63.1133624473136


diffExp=-0.123230613280711,19.9385352669631,-1.40937977594231,-2.22686805645165,-0.383422011079013,-1.98122959674443,0.983351915575895,10.7226428857699
diffExpScore=1.42413614050748
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=0,1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,1,0,0,0,0,0,1
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	59.4535486401802	62.3015844389594	68.4797086479131
cerebhem	58.4291583833729	65.1111712170079	62.1553384636511
cortex	64.883935408576	60.2806448203303	61.234630393362
heart	67.7457356736627	64.7911371847869	72.0185972808734
kidney	73.6153071236791	67.7216059017684	61.2087088015072
liver	72.5367039095879	60.8118877362861	63.2833109015618
stomach	55.8501248235096	66.8312379379343	61.6250684107034
testicle	59.0221343229499	66.2951045772194	64.4849877893839
cont.diffExp=-2.84803579877916,-6.68201283363497,4.60329058824573,2.95459848887582,5.89370122191075,11.7248161733018,-10.9811131144247,-7.27297025426954
cont.diffExpScore=14.6797582163729

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=0.51957570401477
cont.tran.correlation=-0.184049312563021

tran.covariance=0.00350591697785242
cont.tran.covariance=-0.000969970397008166

tran.mean=59.7496870174839
cont.tran.mean=64.1050638812382

weightedLogRatios:
wLogRatio
Lung	-0.00832520690038326
cerebhem	1.18427436970649
cortex	-0.102301137129589
heart	-0.161114553964432
kidney	-0.0261667496744561
liver	-0.137540541085882
stomach	0.0702628363056062
testicle	0.743264209183175

cont.weightedLogRatios:
wLogRatio
Lung	-0.192247326086356
cerebhem	-0.446329814766028
cortex	0.304349770736401
heart	0.186997907099695
kidney	0.355247678887134
liver	0.739775105935999
stomach	-0.73817485059632
testicle	-0.480619442309999

varWeightedLogRatios=0.244408673356968
cont.varWeightedLogRatios=0.257707346635411

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.78953635894968	0.0860512601699505	44.0381274076101	1.36398414771541e-171	***
df.mm.trans1	0.308474419192338	0.0688885338634963	4.47787754931155	9.4090222831537e-06	***
df.mm.trans2	0.286620326097598	0.0688885338634962	4.16063907914692	3.75442431096153e-05	***
df.mm.exp2	0.448588857794702	0.0922464400002417	4.86293951065783	1.5653178359806e-06	***
df.mm.exp3	-0.265679092701755	0.0922464400002417	-2.88010131015418	0.00415138301205186	** 
df.mm.exp4	-0.234616376680140	0.0922464400002417	-2.54336510633391	0.0112884514766409	*  
df.mm.exp5	-0.109382841316399	0.0922464400002417	-1.18576761678947	0.236294689924478	   
df.mm.exp6	-0.153718730882479	0.0922464400002417	-1.66639201341620	0.0962810033931059	.  
df.mm.exp7	-0.220155747851953	0.0922464400002417	-2.38660427276517	0.0173871851413453	*  
df.mm.exp8	-0.0479767679542442	0.0922464400002418	-0.520093436170745	0.603235762127581	   
df.mm.trans1:exp2	-0.144063568440389	0.0723640611724151	-1.99081652005603	0.0470617443930388	*  
df.mm.trans2:exp2	-0.423273654858747	0.0723640611724152	-5.84922471183938	9.09016217436687e-09	***
df.mm.trans1:exp3	0.158662089426008	0.0723640611724152	2.19255369109230	0.028813895312226	*  
df.mm.trans2:exp3	0.182132010600022	0.0723640611724152	2.5168848686653	0.0121611750367929	*  
df.mm.trans1:exp4	0.124568680733376	0.0723640611724151	1.72141638701811	0.085812967106134	.  
df.mm.trans2:exp4	0.162654017687561	0.0723640611724151	2.24771820503578	0.0250427222884752	*  
df.mm.trans1:exp5	0.093811262921387	0.0723640611724152	1.29637918880577	0.195461372514266	   
df.mm.trans2:exp5	0.0981756285023003	0.0723640611724152	1.35669041941118	0.175510731283169	   
df.mm.trans1:exp6	0.101754585681453	0.0723640611724152	1.40614808003950	0.160320505806743	   
df.mm.trans2:exp6	0.133508904779146	0.0723640611724151	1.84496147142774	0.0656526262008045	.  
df.mm.trans1:exp7	0.155563512087277	0.0723640611724152	2.14973440637377	0.0320701341943827	*  
df.mm.trans2:exp7	0.136116033068360	0.0723640611724152	1.88098941467711	0.0605721938861455	.  
df.mm.trans1:exp8	0.104395076772321	0.0723640611724152	1.4426370643238	0.149768221510769	   
df.mm.trans2:exp8	-0.0801736333469448	0.0723640611724152	-1.10792058997245	0.268445248332977	   
df.mm.trans1:probe2	0.161686488999062	0.0495442858210194	3.26347400754065	0.00117822997122313	** 
df.mm.trans1:probe3	-0.0586729420787213	0.0495442858210194	-1.18425245427252	0.236893183458036	   
df.mm.trans1:probe4	-0.117929320293332	0.0495442858210194	-2.38028096154935	0.0176848544992822	*  
df.mm.trans1:probe5	0.00205516798992318	0.0495442858210194	0.0414814333452611	0.966929164912	   
df.mm.trans1:probe6	0.150250780242441	0.0495442858210194	3.03265609247509	0.00255397736752399	** 
df.mm.trans2:probe2	0.123679161294804	0.0495442858210194	2.49633553587996	0.0128792399983409	*  
df.mm.trans2:probe3	0.0680814717094894	0.0495442858210194	1.37415386217164	0.170028600397425	   
df.mm.trans2:probe4	0.0186076022309717	0.0495442858210194	0.375575142977989	0.707396978198859	   
df.mm.trans2:probe5	0.121202607925346	0.0495442858210194	2.44634887589651	0.0147852577252657	*  
df.mm.trans2:probe6	0.187913234888633	0.0495442858210194	3.79283365931394	0.000167741988097703	***
df.mm.trans3:probe2	-0.458014291614866	0.0495442858210194	-9.24454322077544	7.4751650834792e-19	***
df.mm.trans3:probe3	-0.225995668738817	0.0495442858210194	-4.56148807059677	6.44158034446487e-06	***
df.mm.trans3:probe4	-0.248345392849026	0.0495442858210194	-5.01259406071939	7.54071103603284e-07	***
df.mm.trans3:probe5	-0.331018539102818	0.0495442858210194	-6.68126573261415	6.51545216560339e-11	***
df.mm.trans3:probe6	0.401925987126887	0.0495442858210194	8.11245899434013	4.10113342818590e-15	***
df.mm.trans3:probe7	-0.355810750875370	0.0495442858210194	-7.18167080177016	2.60697229631044e-12	***
df.mm.trans3:probe8	-0.551744103531103	0.0495442858210194	-11.1363822161914	8.32505628852694e-26	***
df.mm.trans3:probe9	-0.317725861942393	0.0495442858210194	-6.41296683718865	3.39202263880750e-10	***
df.mm.trans3:probe10	-0.0727040422864695	0.0495442858210194	-1.46745565268850	0.142900305290698	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.95625223927012	0.130456242018494	30.3262778235576	4.51818834484129e-114	***
df.mm.trans1	0.136561023192480	0.104437044015932	1.30759180786128	0.191631288219052	   
df.mm.trans2	0.180134553973713	0.104437044015931	1.72481475008269	0.0851979477520338	.  
df.mm.exp2	0.123629784143002	0.139848316901446	0.884027687155693	0.377119278129097	   
df.mm.exp3	0.166253556763276	0.139848316901446	1.18881342619546	0.235094833621588	   
df.mm.exp4	0.119361263488534	0.139848316901446	0.853505184282273	0.393800543976208	   
df.mm.exp5	0.409324078955848	0.139848316901446	2.92691458878484	0.00358419268148149	** 
df.mm.exp6	0.253611649733009	0.139848316901446	1.81347659630208	0.0703762804069198	.  
df.mm.exp7	0.113128929915436	0.139848316901446	0.808940231974045	0.418946171926777	   
df.mm.exp8	0.114951446187856	0.139848316901446	0.821972324978817	0.411496544457806	   
df.mm.trans1:exp2	-0.141010044052198	0.109706045665166	-1.28534433264119	0.19928540930332	   
df.mm.trans2:exp2	-0.0795205066515909	0.109706045665166	-0.72485072421893	0.468893162063028	   
df.mm.trans1:exp3	-0.0788488045941113	0.109706045665166	-0.718727979994518	0.47265465488611	   
df.mm.trans2:exp3	-0.199229343853485	0.109706045665166	-1.81602884914431	0.0699832277332387	.  
df.mm.trans1:exp4	0.0112049396767553	0.109706045665166	0.102136027315704	0.9186909178489	   
df.mm.trans2:exp4	-0.0801792992011972	0.109706045665166	-0.730855794820207	0.465220156741557	   
df.mm.trans1:exp5	-0.195666410148806	0.109706045665166	-1.78355175380215	0.0751217210561964	.  
df.mm.trans2:exp5	-0.325905666001413	0.109706045665166	-2.97071746616507	0.00311845705979665	** 
df.mm.trans1:exp6	-0.0547142676846777	0.109706045665166	-0.498735209649895	0.618192050891496	   
df.mm.trans2:exp6	-0.277813215771492	0.109706045665166	-2.53234189681219	0.0116447352199679	*  
df.mm.trans1:exp7	-0.175652481952358	0.109706045665166	-1.60111943591939	0.110001579465362	   
df.mm.trans2:exp7	-0.0429451827550964	0.109706045665166	-0.391456847202107	0.695631504769675	   
df.mm.trans1:exp8	-0.122234227147635	0.109706045665166	-1.11419773091362	0.265746211134789	   
df.mm.trans2:exp8	-0.0528222470468908	0.109706045665166	-0.481488934603563	0.630386211325987	   
df.mm.trans1:probe2	-0.00182556106997455	0.0751105948818794	-0.0243049741896662	0.980619342008192	   
df.mm.trans1:probe3	-0.0660782497618072	0.0751105948818794	-0.879746058005842	0.379432577213816	   
df.mm.trans1:probe4	-0.0254862651015937	0.0751105948818794	-0.339316512426429	0.734518179268469	   
df.mm.trans1:probe5	-0.00748093367811277	0.0751105948818794	-0.0995989139731547	0.920703910134723	   
df.mm.trans1:probe6	-0.0210161893513039	0.0751105948818794	-0.279803260570023	0.779747740859575	   
df.mm.trans2:probe2	-0.046033495823819	0.0751105948818794	-0.6128761980412	0.54024547201412	   
df.mm.trans2:probe3	0.0866660910868469	0.0751105948818794	1.15384642104273	0.249131408422552	   
df.mm.trans2:probe4	-0.080753982465848	0.0751105948818794	-1.07513437475557	0.282849129908637	   
df.mm.trans2:probe5	0.0731816184483142	0.0751105948818794	0.974318184583697	0.330384269171374	   
df.mm.trans2:probe6	-0.103459197113800	0.0751105948818794	-1.37742481305736	0.169016254227595	   
df.mm.trans3:probe2	-0.0487283807157171	0.0751105948818794	-0.648755089642793	0.516803749879986	   
df.mm.trans3:probe3	-0.0582058550669629	0.0751105948818793	-0.77493534911418	0.438755504748429	   
df.mm.trans3:probe4	-0.103966921518646	0.0751105948818794	-1.38418450395908	0.166938539685267	   
df.mm.trans3:probe5	-0.00352771934527091	0.0751105948818794	-0.0469670004720197	0.962558861185865	   
df.mm.trans3:probe6	-0.0839044655309413	0.0751105948818794	-1.11707896419795	0.26451363930724	   
df.mm.trans3:probe7	-0.144213735466977	0.0751105948818794	-1.92001854989660	0.0554417602815126	.  
df.mm.trans3:probe8	-0.0360269850291855	0.0751105948818793	-0.479652505559867	0.631690726381067	   
df.mm.trans3:probe9	-0.0509370300029306	0.0751105948818794	-0.678160385802234	0.497993423041071	   
df.mm.trans3:probe10	0.0654077009844468	0.0751105948818794	0.870818572097697	0.384284042168984