chr10.1944_chr10_18418339_18422017_-_1.R 

fitVsDatCorrelation=0.855546490621058
cont.fitVsDatCorrelation=0.302242368073283

fstatistic=6810.37115938154,50,646
cont.fstatistic=1999.86517268207,50,646

residuals=-0.527576996087129,-0.097843695931308,-0.00595236937620411,0.0804849196849847,1.32529937727431
cont.residuals=-0.575481489598304,-0.217076549747025,-0.0684740148366531,0.143034652288934,1.74825149265244

predictedValues:
Include	Exclude	Both
chr10.1944_chr10_18418339_18422017_-_1.R.tl.Lung	59.2557010132628	46.3560675417777	50.4117791214277
chr10.1944_chr10_18418339_18422017_-_1.R.tl.cerebhem	81.1729383890999	63.1135211377437	50.8088888652415
chr10.1944_chr10_18418339_18422017_-_1.R.tl.cortex	59.8258062110241	45.0926267451743	49.4761780182594
chr10.1944_chr10_18418339_18422017_-_1.R.tl.heart	60.2715437467866	45.4172077056	50.6440217803013
chr10.1944_chr10_18418339_18422017_-_1.R.tl.kidney	58.585148501655	45.6066255285772	51.0767659064825
chr10.1944_chr10_18418339_18422017_-_1.R.tl.liver	60.3592112240713	52.7586181208923	50.3134983581954
chr10.1944_chr10_18418339_18422017_-_1.R.tl.stomach	75.699013687233	48.5232935013952	60.1045416709189
chr10.1944_chr10_18418339_18422017_-_1.R.tl.testicle	112.722190107280	52.9453624053521	77.8986775557402


diffExp=12.8996334714852,18.0594172513561,14.7331794658499,14.8543360411866,12.9785229730778,7.60059310317902,27.1757201858378,59.7768277019282
diffExpScore=0.994085578025904
diffExp1.5=0,0,0,0,0,0,1,1
diffExp1.5Score=0.666666666666667
diffExp1.4=0,0,0,0,0,0,1,1
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,1,1,0,0,1,1
diffExp1.3Score=0.8
diffExp1.2=1,1,1,1,1,0,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	64.5839749796996	58.0884935425862	55.0061271187558
cerebhem	59.306841064398	51.2116427720949	68.3857972222463
cortex	68.3517964844385	56.8634729947463	56.7955489744746
heart	61.3765239718249	55.2453942076271	62.9043516276094
kidney	59.1460087440048	52.856667623559	67.8369016966444
liver	64.6008378899339	57.8805472309624	56.6903773278627
stomach	63.614298875883	58.0479058113964	64.6998437590633
testicle	56.9496730892113	55.7352377759606	54.4961569920014
cont.diffExp=6.49548143711345,8.09519829230308,11.4883234896923,6.13112976419784,6.28934112044585,6.72029065897142,5.56639306448651,1.21443531325072
cont.diffExpScore=0.981132286626495

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=0,0,1,0,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.513471172006563
cont.tran.correlation=0.648534920891297

tran.covariance=0.0157719796343914
cont.tran.covariance=0.00180044074359725

tran.mean=60.4815547229328
cont.tran.mean=58.9912073161454

weightedLogRatios:
wLogRatio
Lung	0.97199960843653
cerebhem	1.07472310013783
cortex	1.11675956760691
heart	1.11981470018966
kidney	0.988006233789564
liver	0.542789812123707
stomach	1.82531559230249
testicle	3.28494986204062

cont.weightedLogRatios:
wLogRatio
Lung	0.436181214521994
cerebhem	0.588402601896262
cortex	0.760470129199374
heart	0.427748033833287
kidney	0.452376308683222
liver	0.451830133862481
stomach	0.376081398892942
testicle	0.0868980988939364

varWeightedLogRatios=0.725242652202327
cont.varWeightedLogRatios=0.0362141394510219

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.17819253707713	0.0897923481021154	46.5317215262655	1.84572710947395e-208	***
df.mm.trans1	0.00378922471128402	0.0774244602603774	0.0489409251099835	0.960981501450658	   
df.mm.trans2	-0.368974557937822	0.0703242583569416	-5.24676074172066	2.10161905367136e-07	***
df.mm.exp2	0.615456306291403	0.092539929965477	6.65071074206568	6.21859965734804e-11	***
df.mm.exp3	0.000675229603623146	0.092539929965477	0.0072966297237857	0.994180436126192	   
df.mm.exp4	-0.00805938013539556	0.092539929965477	-0.0870908389319314	0.930626295146126	   
df.mm.exp5	-0.0407848113257974	0.0925399299654769	-0.440726628397197	0.659558361762415	   
df.mm.exp6	0.149777978524245	0.0925399299654769	1.61852271316957	0.106038094105798	   
df.mm.exp7	0.114734359883491	0.0925399299654769	1.23983625151104	0.215486148328619	   
df.mm.exp8	0.340788479254045	0.092539929965477	3.68261008389762	0.000250171486529325	***
df.mm.trans1:exp2	-0.300736380760671	0.0844094031896257	-3.56283031743592	0.000393892505668926	***
df.mm.trans2:exp2	-0.306873469088367	0.0687541164453772	-4.46334685039793	9.5203491069636e-06	***
df.mm.trans1:exp3	0.00889988523631145	0.0844094031896257	0.105437130224909	0.916061684415429	   
df.mm.trans2:exp3	-0.0283086737581499	0.0687541164453772	-0.411737874351715	0.680668135893468	   
df.mm.trans1:exp4	0.0250574661335436	0.0844094031896257	0.296856335747951	0.766671614743215	   
df.mm.trans2:exp4	-0.0124017527709675	0.0687541164453772	-0.180378330958849	0.856912134659892	   
df.mm.trans1:exp5	0.0294040422299233	0.0844094031896257	0.348350315472165	0.727690601876686	   
df.mm.trans2:exp5	0.0244856234764188	0.0687541164453772	0.356133199615354	0.721857089845417	   
df.mm.trans1:exp6	-0.131326407587867	0.0844094031896257	-1.55582675182340	0.120239043701722	   
df.mm.trans2:exp6	-0.0204030333077131	0.0687541164453772	-0.296753625274534	0.766749999067979	   
df.mm.trans1:exp7	0.130168776224734	0.0844094031896257	1.54211226837263	0.123536002622602	   
df.mm.trans2:exp7	-0.0690425893520212	0.0687541164453772	-1.00419571833016	0.31566027545243	   
df.mm.trans1:exp8	0.302275822752631	0.0844094031896257	3.58106811955024	0.000367889239638117	***
df.mm.trans2:exp8	-0.207880185949484	0.0687541164453772	-3.02353076000385	0.00259745681394054	** 
df.mm.trans1:probe2	0.247766215771698	0.0536412395678284	4.61895022873963	4.65613106716353e-06	***
df.mm.trans1:probe3	0.261114956903253	0.0536412395678285	4.8678024409387	1.42005591960154e-06	***
df.mm.trans1:probe4	-0.0713855673526302	0.0536412395678284	-1.33079637845364	0.183725539119971	   
df.mm.trans1:probe5	0.125733300541726	0.0536412395678285	2.3439670961134	0.0193819712264864	*  
df.mm.trans1:probe6	-0.204883427517898	0.0536412395678285	-3.81951329179905	0.000146610148368545	***
df.mm.trans1:probe7	-0.446527568031608	0.0536412395678284	-8.32433350961215	5.05812878052162e-16	***
df.mm.trans1:probe8	-0.495771250452328	0.0536412395678284	-9.2423526086759	3.44379479845398e-19	***
df.mm.trans1:probe9	-0.0382598623208109	0.0536412395678284	-0.713254627019421	0.475945784528818	   
df.mm.trans1:probe10	-0.0672704424119689	0.0536412395678285	-1.25408068407716	0.210266281421907	   
df.mm.trans1:probe11	-0.00905795244793366	0.0536412395678284	-0.168861728791335	0.86595826092212	   
df.mm.trans1:probe12	-0.108840028470775	0.0536412395678284	-2.02903641578134	0.0428641478045608	*  
df.mm.trans1:probe13	-0.540930965120213	0.0536412395678284	-10.0842368572824	2.62064009841749e-22	***
df.mm.trans1:probe14	-0.359226378433558	0.0536412395678285	-6.69683216360655	4.63394557863179e-11	***
df.mm.trans1:probe15	0.0113330352304594	0.0536412395678284	0.21127467078998	0.832739578258988	   
df.mm.trans1:probe16	-0.406309168033468	0.0536412395678285	-7.57456709253888	1.25186742214748e-13	***
df.mm.trans2:probe2	0.08448318508302	0.0536412395678284	1.57496705452141	0.115753628088774	   
df.mm.trans2:probe3	-0.0455100611478303	0.0536412395678284	-0.848415538389704	0.396520847985416	   
df.mm.trans2:probe4	0.0216830590401456	0.0536412395678284	0.404223675941116	0.686182033767043	   
df.mm.trans2:probe5	0.093492662225762	0.0536412395678284	1.74292508858864	0.0818225081193271	.  
df.mm.trans2:probe6	0.198595902416411	0.0536412395678285	3.70229890316553	0.000231902322827182	***
df.mm.trans3:probe2	-0.0492436824207206	0.0536412395678284	-0.918019099063749	0.358951482430437	   
df.mm.trans3:probe3	0.329705432415970	0.0536412395678285	6.14649167454573	1.38655269604183e-09	***
df.mm.trans3:probe4	0.286816699780346	0.0536412395678284	5.34694392022151	1.24255345924031e-07	***
df.mm.trans3:probe5	0.114460554646474	0.0536412395678284	2.13381636160254	0.0332338268974401	*  
df.mm.trans3:probe6	0.09939738524003	0.0536412395678284	1.85300313789997	0.0643377341208042	.  
df.mm.trans3:probe7	-0.0306127931337636	0.0536412395678285	-0.570695110336782	0.568404787898829	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.13371507973069	0.165324633351283	25.0036246621962	4.96769488757936e-97	***
df.mm.trans1	0.0669215728067244	0.142553021226386	0.469450399795087	0.638906138295484	   
df.mm.trans2	-0.0897769660573568	0.129480211558120	-0.69336437573751	0.488330125857868	   
df.mm.exp2	-0.428962925036365	0.170383449316829	-2.51763259140684	0.0120553983746781	*  
df.mm.exp3	0.00337373900515031	0.170383449316829	0.0198008610500473	0.984208345099987	   
df.mm.exp4	-0.235292286116709	0.170383449316829	-1.38095740554695	0.167769591775825	   
df.mm.exp5	-0.39200267477046	0.170383449316829	-2.3007086447788	0.0217257481345038	*  
df.mm.exp6	-0.0334850788713457	0.170383449316829	-0.196527767254435	0.844258910897562	   
df.mm.exp7	-0.178141216594021	0.170383449316829	-1.04553122564602	0.296168571393136	   
df.mm.exp8	-0.157838985694622	0.170383449316829	-0.926375104668288	0.354597025784965	   
df.mm.trans1:exp2	0.343721273349842	0.155413617403739	2.21165480278933	0.0273397991442671	*  
df.mm.trans2:exp2	0.302962230617724	0.126589284420947	2.39326916179006	0.0169834307753182	*  
df.mm.trans1:exp3	0.0533277927394024	0.155413617403739	0.343134621214469	0.731608819833383	   
df.mm.trans2:exp3	-0.0246881534003518	0.126589284420947	-0.195025617794443	0.84543421304645	   
df.mm.trans1:exp4	0.184353387735752	0.155413617403739	1.18621129097608	0.235974957341892	   
df.mm.trans2:exp4	0.185109661776224	0.126589284420947	1.46228539503137	0.144149318336161	   
df.mm.trans1:exp5	0.304045471377284	0.155413617403739	1.95636313250096	0.0508528763118421	.  
df.mm.trans2:exp5	0.297618941901758	0.126589284420947	2.35105951710799	0.0190196095740513	*  
df.mm.trans1:exp6	0.0337461453226630	0.155413617403739	0.217137634953803	0.828169644308616	   
df.mm.trans2:exp6	0.0298988366621248	0.126589284420947	0.236187737365685	0.813361859573201	   
df.mm.trans1:exp7	0.163013172077686	0.155413617403739	1.04889889831343	0.294616903023108	   
df.mm.trans2:exp7	0.177442249981177	0.126589284420947	1.40171619416955	0.161480287774838	   
df.mm.trans1:exp8	0.0320406205734454	0.155413617403739	0.206163533856941	0.836728127895327	   
df.mm.trans2:exp8	0.116483969209015	0.126589284420947	0.920172428036408	0.357826136330443	   
df.mm.trans1:probe2	-0.165373369180772	0.098763630214618	-1.67443591149301	0.0945289749883355	.  
df.mm.trans1:probe3	0.00630386963402815	0.098763630214618	0.0638278445246449	0.949127051607242	   
df.mm.trans1:probe4	0.117465475082745	0.098763630214618	1.18935963398152	0.234735145848172	   
df.mm.trans1:probe5	-0.144400298637493	0.098763630214618	-1.46207969799920	0.144205655257817	   
df.mm.trans1:probe6	-0.184896907753209	0.098763630214618	-1.87211534601775	0.0616422284406124	.  
df.mm.trans1:probe7	-0.00237238145816977	0.098763630214618	-0.0240208005013027	0.980843434907508	   
df.mm.trans1:probe8	-0.084289044609062	0.098763630214618	-0.85344214693099	0.393730490959973	   
df.mm.trans1:probe9	-0.020287658537149	0.098763630214618	-0.20541629031925	0.837311605432443	   
df.mm.trans1:probe10	0.010319219235484	0.098763630214618	0.104484000973434	0.916817694936982	   
df.mm.trans1:probe11	0.0474818503936802	0.098763630214618	0.480762506304192	0.630848076866895	   
df.mm.trans1:probe12	-0.0875644814134766	0.098763630214618	-0.886606549629605	0.375620702837645	   
df.mm.trans1:probe13	-0.117336669277993	0.098763630214618	-1.18805545141481	0.235248168344823	   
df.mm.trans1:probe14	-0.0134399233688503	0.098763630214618	-0.136081706794745	0.891799083367065	   
df.mm.trans1:probe15	-0.0881027330415664	0.098763630214618	-0.892056446792357	0.372694829606958	   
df.mm.trans1:probe16	0.0404159580424712	0.098763630214618	0.409219041003712	0.682514561333555	   
df.mm.trans2:probe2	0.102621550229205	0.098763630214618	1.03906215280061	0.299164575678356	   
df.mm.trans2:probe3	-0.052511529125495	0.098763630214618	-0.531688932569459	0.595124240993088	   
df.mm.trans2:probe4	0.156430907289583	0.098763630214618	1.58389183295158	0.113707734933154	   
df.mm.trans2:probe5	0.0405547620561639	0.098763630214618	0.410624457282874	0.68148408816003	   
df.mm.trans2:probe6	-0.0127123876299445	0.098763630214618	-0.128715273044540	0.897623011664125	   
df.mm.trans3:probe2	-0.164945952006869	0.098763630214618	-1.67010823365275	0.0953823682263703	.  
df.mm.trans3:probe3	-0.113564001480965	0.098763630214618	-1.14985649306516	0.250628482823116	   
df.mm.trans3:probe4	-0.113484556682170	0.098763630214618	-1.14905209980195	0.250959755030981	   
df.mm.trans3:probe5	-0.210715218713084	0.098763630214618	-2.13353051376494	0.0332573379862323	*  
df.mm.trans3:probe6	-0.114131530470406	0.098763630214618	-1.15560282891984	0.248270876960793	   
df.mm.trans3:probe7	-0.00706447769853805	0.098763630214618	-0.0715291416808658	0.942998770909333