chr17.10015_chr17_23012356_23013848_+_1.R 

fitVsDatCorrelation=0.824714907721262
cont.fitVsDatCorrelation=0.268852627420507

fstatistic=7057.55489087519,49,623
cont.fstatistic=2424.87113239533,49,623

residuals=-0.951913292043798,-0.102352223501226,-0.00458590398184854,0.102320031889533,0.768074041349445
cont.residuals=-0.740211768148995,-0.249126239075358,-0.0247704253256804,0.205144343395683,1.13279483764102

predictedValues:
Include	Exclude	Both
chr17.10015_chr17_23012356_23013848_+_1.R.tl.Lung	128.058951697092	80.5009776331273	141.797117004664
chr17.10015_chr17_23012356_23013848_+_1.R.tl.cerebhem	120.883185343806	77.5328855976463	124.149839274473
chr17.10015_chr17_23012356_23013848_+_1.R.tl.cortex	111.188172910986	84.4739879818081	143.896239516476
chr17.10015_chr17_23012356_23013848_+_1.R.tl.heart	113.951835898668	81.809518238899	104.059546983030
chr17.10015_chr17_23012356_23013848_+_1.R.tl.kidney	118.812535352000	80.9684884342986	104.521715654521
chr17.10015_chr17_23012356_23013848_+_1.R.tl.liver	121.552866623656	77.6408670798745	89.6446641079332
chr17.10015_chr17_23012356_23013848_+_1.R.tl.stomach	125.141784720835	78.3062252451876	95.4948515704678
chr17.10015_chr17_23012356_23013848_+_1.R.tl.testicle	110.705617547250	74.8011923131731	107.243416249439


diffExp=47.5579740639648,43.3502997461593,26.7141849291781,32.1423176597689,37.8440469177012,43.9119995437818,46.8355594756479,35.9044252340773
diffExpScore=0.996828023097108
diffExp1.5=1,1,0,0,0,1,1,0
diffExp1.5Score=0.8
diffExp1.4=1,1,0,0,1,1,1,1
diffExp1.4Score=0.857142857142857
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	123.108464841092	113.889519283612	102.594612557749
cerebhem	96.2397148206318	127.614131791278	104.760826470784
cortex	93.7494795845161	130.313172119827	99.7270886136697
heart	96.7890391374582	115.043185645091	99.5444353330287
kidney	112.605634424436	117.180202469807	107.286673338429
liver	114.568219712527	134.173320993149	105.196485081762
stomach	89.8518898395248	127.815537529482	105.229676211669
testicle	94.0746722080039	127.699042626495	109.089350363714
cont.diffExp=9.21894555747983,-31.3744169706463,-36.563692535311,-18.2541465076331,-4.57456804537115,-19.6051012806221,-37.9636476899573,-33.6243704184915
cont.diffExpScore=1.10036716334474

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,0,0,0,0,-1,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,-1,-1,0,0,0,-1,-1
cont.diffExp1.3Score=0.8
cont.diffExp1.2=0,-1,-1,0,0,0,-1,-1
cont.diffExp1.2Score=0.8

tran.correlation=-0.137131099860368
cont.tran.correlation=-0.395699027533333

tran.covariance=-0.000248908799142944
cont.tran.covariance=-0.00293986958726302

tran.mean=99.1455682886443
cont.tran.mean=113.419701689183

weightedLogRatios:
wLogRatio
Lung	2.14487923295935
cerebhem	2.03086708782721
cortex	1.25679973065261
heart	1.51444505379216
kidney	1.75859542619744
liver	2.05131585487086
stomach	2.15424658680184
testicle	1.76843870715959

cont.weightedLogRatios:
wLogRatio
Lung	0.371605044310291
cerebhem	-1.32843097732686
cortex	-1.54951782399656
heart	-0.804939565111262
kidney	-0.188903846177391
liver	-0.761400535694278
stomach	-1.64736891735259
testicle	-1.43530819439069

varWeightedLogRatios=0.104348436532832
cont.varWeightedLogRatios=0.5141176502829

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.22322742184938	0.100781077482048	41.9049639809781	2.01784917179375e-183	***
df.mm.trans1	0.636211414880568	0.0796456689436543	7.98802274271385	6.64780305664838e-15	***
df.mm.trans2	0.167493061194812	0.0796456689436544	2.10297764355907	0.0358682304420984	*  
df.mm.exp2	0.0376748637912266	0.105578601373754	0.356841853377613	0.721331031497392	   
df.mm.exp3	-0.107787575469995	0.105578601373754	-1.02092255502061	0.307687405678041	   
df.mm.exp4	0.208843396880814	0.105578601373754	1.9780845186753	0.0483593255554521	*  
df.mm.exp5	0.235849345556882	0.105578601373754	2.23387450191694	0.0258454163890854	*  
df.mm.exp6	0.37022676703568	0.105578601373754	3.50664587538016	0.000486416125292975	***
df.mm.exp7	0.344639376950818	0.105578601373754	3.26429193479061	0.00115746491793699	** 
df.mm.exp8	0.0602444755901055	0.105578601373754	0.570612556012527	0.568468049287921	   
df.mm.trans1:exp2	-0.0953409129128878	0.0803332702094739	-1.18681727588433	0.235752060408476	   
df.mm.trans2:exp2	-0.0752420160040255	0.080333270209474	-0.93662334183368	0.349315268871354	   
df.mm.trans1:exp3	-0.0334791251120481	0.0803332702094739	-0.416752922229473	0.677002691270171	   
df.mm.trans2:exp3	0.155961899045859	0.080333270209474	1.94143595348701	0.0526559579993609	.  
df.mm.trans1:exp4	-0.325558247906091	0.0803332702094739	-4.0525954820111	5.70601763393998e-05	***
df.mm.trans2:exp4	-0.192719129014331	0.0803332702094739	-2.39899519230082	0.0167329874878065	*  
df.mm.trans1:exp5	-0.310793145791713	0.0803332702094739	-3.86879738595605	0.000120888129799764	***
df.mm.trans2:exp5	-0.230058627113896	0.080333270209474	-2.86380258781951	0.00432677204443943	** 
df.mm.trans1:exp6	-0.422368200674498	0.0803332702094739	-5.25769957544548	2.00731495066098e-07	***
df.mm.trans2:exp6	-0.406402169705858	0.080333270209474	-5.05895214580633	5.55384437460122e-07	***
df.mm.trans1:exp7	-0.367682722716708	0.0803332702094738	-4.57696694978249	5.69626694718681e-06	***
df.mm.trans2:exp7	-0.372281600928997	0.0803332702094739	-4.63421444139208	4.36568327450347e-06	***
df.mm.trans1:exp8	-0.205860609506631	0.0803332702094739	-2.56258221493831	0.0106235657786721	*  
df.mm.trans2:exp8	-0.133679979582703	0.080333270209474	-1.66406744346551	0.0966018075546347	.  
df.mm.trans1:probe2	0.0588193468373963	0.0593300520319785	0.991392133040655	0.321879025048445	   
df.mm.trans1:probe3	-0.0043923978144466	0.0593300520319785	-0.0740332708975061	0.941007675294416	   
df.mm.trans1:probe4	-0.0863860797487307	0.0593300520319785	-1.45602568664813	0.145889261994346	   
df.mm.trans1:probe5	-0.0640863548607331	0.0593300520319785	-1.08016684067951	0.280485868751548	   
df.mm.trans1:probe6	-0.0568131258590067	0.0593300520319785	-0.957577549879525	0.338647269199776	   
df.mm.trans2:probe2	-0.136342114390823	0.0593300520319785	-2.29802789178974	0.0218905946173778	*  
df.mm.trans2:probe3	-0.0345709134856681	0.0593300520319785	-0.582688069564385	0.56031413737216	   
df.mm.trans2:probe4	0.0918076348628484	0.0593300520319785	1.54740526459280	0.122273357273870	   
df.mm.trans2:probe5	-0.0561876956402029	0.0593300520319785	-0.947036008158532	0.343987607780709	   
df.mm.trans2:probe6	0.0813676966383313	0.0593300520319785	1.37144151827938	0.170731098438133	   
df.mm.trans3:probe2	-0.0104542097317304	0.0593300520319785	-0.176204290636652	0.860190710719691	   
df.mm.trans3:probe3	-0.3862504574496	0.0593300520319785	-6.51019920295052	1.54540361483504e-10	***
df.mm.trans3:probe4	-0.583180434795623	0.0593300520319785	-9.82942732767691	2.70388906866181e-21	***
df.mm.trans3:probe5	0.194974175142978	0.0593300520319785	3.28626334320232	0.0010722742156775	** 
df.mm.trans3:probe6	0.288101157746169	0.0593300520319785	4.8559060354588	1.51722922348449e-06	***
df.mm.trans3:probe7	-0.450177630384941	0.0593300520319785	-7.5876830538139	1.19177987741152e-13	***
df.mm.trans3:probe8	-0.334018173816448	0.0593300520319785	-5.6298311290274	2.73018476167224e-08	***
df.mm.trans3:probe9	0.107843637843336	0.0593300520319785	1.81768992525423	0.0695917269719344	.  
df.mm.trans3:probe10	0.255444185547598	0.0593300520319785	4.3054771873437	1.93514135905328e-05	***
df.mm.trans3:probe11	0.162838163759587	0.0593300520319785	2.74461521914423	0.00623280198401254	** 
df.mm.trans3:probe12	0.186909852660456	0.0593300520319785	3.15034027881373	0.00170893745273165	** 
df.mm.trans3:probe13	-0.105994754778286	0.0593300520319785	-1.78652725133556	0.0745000839904576	.  
df.mm.trans3:probe14	-0.447151928248895	0.0593300520319785	-7.53668525367014	1.70683084325934e-13	***
df.mm.trans3:probe15	0.0478009433698988	0.0593300520319785	0.805678433319667	0.420735687819168	   
df.mm.trans3:probe16	-0.0872383741170679	0.0593300520319785	-1.47039099291615	0.141960738186267	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.8634479572868	0.171639424547596	28.3352613777738	4.11349477439133e-114	***
df.mm.trans1	-0.0546070450759161	0.135643884018130	-0.402576536872221	0.687397868818091	   
df.mm.trans2	-0.0798233554646698	0.135643884018131	-0.588477365142393	0.556425201572128	   
df.mm.exp2	-0.153335906832639	0.179810048047552	-0.852766063396457	0.394116765447531	   
df.mm.exp3	-0.109379879820382	0.179810048047552	-0.608307939450947	0.543204961217734	   
df.mm.exp4	-0.200272011683582	0.179810048047552	-1.11379766513726	0.265795521741041	   
df.mm.exp5	-0.105408968180185	0.179810048047552	-0.58622401431264	0.557937311714463	   
df.mm.exp6	0.06696374538552	0.179810048047552	0.372413811756565	0.709711423386395	   
df.mm.exp7	-0.224903818029043	0.179810048047552	-1.25078559552782	0.211482244303562	   
df.mm.exp8	-0.215911378466508	0.179810048047552	-1.20077482215793	0.230294903368795	   
df.mm.trans1:exp2	-0.0928877794826279	0.136814932081240	-0.678930128967732	0.497434266899924	   
df.mm.trans2:exp2	0.267118172932119	0.136814932081240	1.95240511301431	0.0513376243545195	.  
df.mm.trans1:exp3	-0.163059801209907	0.136814932081240	-1.19182752006252	0.233782685278642	   
df.mm.trans2:exp3	0.244091600160123	0.136814932081240	1.78410058351805	0.0748939145067842	.  
df.mm.trans1:exp4	-0.040260027263078	0.136814932081240	-0.294266324959119	0.768652420117376	   
df.mm.trans2:exp4	0.210350747508557	0.136814932081240	1.53748384265287	0.124682573560970	   
df.mm.trans1:exp5	0.0162349271603353	0.136814932081240	0.118663415705934	0.905580272113488	   
df.mm.trans2:exp5	0.133893060754123	0.136814932081240	0.978643622573431	0.328135962228549	   
df.mm.trans1:exp6	-0.138859089230091	0.136814932081240	-1.01494103836296	0.310527929454043	   
df.mm.trans2:exp6	0.0969398096453924	0.136814932081240	0.70854699973706	0.478870664313749	   
df.mm.trans1:exp7	-0.0899993304570963	0.136814932081240	-0.65781803994651	0.510898040148398	   
df.mm.trans2:exp7	0.340263080101462	0.136814932081240	2.48703175103295	0.0131420262034003	*  
df.mm.trans1:exp8	-0.053065564195782	0.136814932081240	-0.387863834660034	0.698249288443634	   
df.mm.trans2:exp8	0.330358795031032	0.136814932081240	2.41463990812688	0.0160378471146913	*  
df.mm.trans1:probe2	0.0608128979323245	0.101044523868696	0.60184258982059	0.547497873408126	   
df.mm.trans1:probe3	-0.124974811717833	0.101044523868696	-1.23682914157954	0.216616548099547	   
df.mm.trans1:probe4	0.0490178147703539	0.101044523868696	0.485111047027654	0.627768109354959	   
df.mm.trans1:probe5	0.0973644424063488	0.101044523868696	0.96357960509439	0.335630595137863	   
df.mm.trans1:probe6	0.0107270728412637	0.101044523868696	0.106161842626951	0.91548811871711	   
df.mm.trans2:probe2	-0.25753340953581	0.101044523868696	-2.54871218820790	0.0110508523817688	*  
df.mm.trans2:probe3	-0.184482037321329	0.101044523868696	-1.82574997890096	0.0683664712576838	.  
df.mm.trans2:probe4	-0.187908225364324	0.101044523868696	-1.85965768524482	0.0634051236455333	.  
df.mm.trans2:probe5	-0.203838230851493	0.101044523868696	-2.01731101347337	0.0440914200400397	*  
df.mm.trans2:probe6	-0.230944650342728	0.101044523868696	-2.28557314637687	0.0226142588262870	*  
df.mm.trans3:probe2	-0.118466716497854	0.101044523868696	-1.17242094833163	0.241476207756242	   
df.mm.trans3:probe3	-0.163112132820280	0.101044523868696	-1.61425999722894	0.106977376532210	   
df.mm.trans3:probe4	-0.05318284548138	0.101044523868696	-0.526330803938364	0.598845703996517	   
df.mm.trans3:probe5	0.00468489078271201	0.101044523868696	0.0463646183221158	0.963034494546074	   
df.mm.trans3:probe6	-0.051445475225564	0.101044523868696	-0.509136697921558	0.610836683120722	   
df.mm.trans3:probe7	-0.0676299897295603	0.101044523868696	-0.669308807050675	0.503546466148329	   
df.mm.trans3:probe8	-0.223213938332495	0.101044523868696	-2.20906517034564	0.0275332390902311	*  
df.mm.trans3:probe9	-0.0916549912907444	0.101044523868696	-0.907075294944702	0.364717687293757	   
df.mm.trans3:probe10	-0.0858108800504426	0.101044523868696	-0.849238303719962	0.396074909854772	   
df.mm.trans3:probe11	-0.090355242416139	0.101044523868696	-0.894212164664684	0.371553701466858	   
df.mm.trans3:probe12	-0.168961114694235	0.101044523868696	-1.67214519129997	0.0949977994878793	.  
df.mm.trans3:probe13	-0.181073511832468	0.101044523868696	-1.79201707227368	0.0736153830084266	.  
df.mm.trans3:probe14	-0.0695440981181047	0.101044523868696	-0.688252024508275	0.491550273164024	   
df.mm.trans3:probe15	-0.109664369898794	0.101044523868696	-1.08530740410336	0.278205308282158	   
df.mm.trans3:probe16	-0.102283737545673	0.101044523868696	-1.01226403598663	0.311804796176719