chr9.24995_chr9_96253742_96259527_-_0.R 

fitVsDatCorrelation=0.807697222755167
cont.fitVsDatCorrelation=0.257496289025675

fstatistic=7379.57272706533,43,485
cont.fstatistic=2740.41659942185,43,485

residuals=-0.384089701471844,-0.0902018476172058,-0.00469437687051311,0.0765785919584677,1.08696217413123
cont.residuals=-0.518136792512991,-0.159816601915408,-0.0530195111158998,0.0983030252546966,1.38917973433312

predictedValues:
Include	Exclude	Both
chr9.24995_chr9_96253742_96259527_-_0.R.tl.Lung	48.0384058720519	47.3533268582766	55.0366035287865
chr9.24995_chr9_96253742_96259527_-_0.R.tl.cerebhem	67.436693957075	52.4093582854229	65.322011635704
chr9.24995_chr9_96253742_96259527_-_0.R.tl.cortex	52.7077957425335	43.8382509880708	71.4976451925083
chr9.24995_chr9_96253742_96259527_-_0.R.tl.heart	48.2599031693651	43.1728252674393	55.9703256774174
chr9.24995_chr9_96253742_96259527_-_0.R.tl.kidney	48.7151114186995	45.9376547116511	62.8583676251416
chr9.24995_chr9_96253742_96259527_-_0.R.tl.liver	48.021320072707	44.6868979352845	54.259772735847
chr9.24995_chr9_96253742_96259527_-_0.R.tl.stomach	45.6712322241908	48.4684530143298	54.3805927182181
chr9.24995_chr9_96253742_96259527_-_0.R.tl.testicle	52.6079806950897	47.1786982689429	53.4028321666837


diffExp=0.68507901377533,15.0273356716521,8.8695447544627,5.08707790192587,2.77745670704839,3.33442213742251,-2.79722079013901,5.42928242614678
diffExpScore=1.11657179523439
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=0,1,1,0,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	57.7390298524567	58.6837995811344	52.0930255088506
cerebhem	51.1893630954441	57.4909554334244	54.4426233924546
cortex	53.3567986419264	54.5442201569309	52.5942891037866
heart	54.9551860957846	58.9838190411583	55.3917770377598
kidney	59.7425980389816	59.9056126466881	53.5445969900103
liver	53.5733212142721	54.6939777675843	56.4688738127095
stomach	50.4703325555473	55.4205202870743	53.542411087223
testicle	53.6762370101617	56.6671640009067	57.1244559075898
cont.diffExp=-0.944769728677699,-6.30159233798032,-1.18742151500452,-4.02863294537377,-0.163014607706486,-1.12065655331214,-4.95018773152704,-2.99092699074502
cont.diffExpScore=0.95592228685081

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=0.670378495797985
cont.tran.correlation=0.70703106529326

tran.covariance=0.00475774326132292
cont.tran.covariance=0.00141344195357721

tran.mean=49.0314942800707
cont.tran.mean=55.6933084637172

weightedLogRatios:
wLogRatio
Lung	0.0555132357498242
cerebhem	1.02987993405314
cortex	0.713558665477705
heart	0.425609976709884
kidney	0.226400544519653
liver	0.276032442668253
stomach	-0.228932212436117
testicle	0.42572439823945

cont.weightedLogRatios:
wLogRatio
Lung	-0.0659608487195848
cerebhem	-0.463638150252345
cortex	-0.0877774100722737
heart	-0.285943946491747
kidney	-0.0111486772490149
liver	-0.0826316175108834
stomach	-0.371278597105435
testicle	-0.217444867527868

varWeightedLogRatios=0.149511466382835
cont.varWeightedLogRatios=0.0266439372929716

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.51486595350565	0.081733519077325	43.0039718488123	1.32339021952853e-167	***
df.mm.trans1	0.33514864668037	0.0654319563202309	5.12209424153723	4.36772636531811e-07	***
df.mm.trans2	0.323578902475026	0.0654319563202309	4.94527323761186	1.04976343260480e-06	***
df.mm.exp2	0.269305985962963	0.0876178471841595	3.07364303755286	0.00223372424936478	** 
df.mm.exp3	-0.246033805776872	0.0876178471841595	-2.80803299423399	0.00518547842718821	** 
df.mm.exp4	-0.104648754229429	0.0876178471841595	-1.19437714566843	0.232914263300601	   
df.mm.exp5	-0.149249048643111	0.0876178471841595	-1.70340921900777	0.089132124285065	.  
df.mm.exp6	-0.0440970996297883	0.0876178471841595	-0.503289010709232	0.614989582102485	   
df.mm.exp7	-0.0152650067376038	0.0876178471841595	-0.174222572548708	0.861763244606609	   
df.mm.exp8	0.117307125047904	0.0876178471841596	1.33884966154603	0.181246805546370	   
df.mm.trans1:exp2	0.0698824912251418	0.0687330942355415	1.01672261379157	0.309792320970553	   
df.mm.trans2:exp2	-0.167857895510926	0.0687330942355416	-2.44216992378801	0.0149553664830747	*  
df.mm.trans1:exp3	0.338796363872316	0.0687330942355416	4.92915920111634	1.13564329911844e-06	***
df.mm.trans2:exp3	0.168903474113342	0.0687330942355416	2.45738208052335	0.0143443315733658	*  
df.mm.trans1:exp4	0.109248994672534	0.0687330942355415	1.58946713933973	0.112606585495233	   
df.mm.trans2:exp4	0.0122229286502938	0.0687330942355415	0.177831782291178	0.85892933494017	   
df.mm.trans1:exp5	0.163237513356571	0.0687330942355416	2.37494783513124	0.0179393843201094	*  
df.mm.trans2:exp5	0.118897115320824	0.0687330942355416	1.72983795714733	0.0842954244747564	.  
df.mm.trans1:exp6	0.0437413667905148	0.0687330942355415	0.636394553119018	0.524819508677708	   
df.mm.trans2:exp6	-0.0138596309459431	0.0687330942355415	-0.201644216662901	0.840279420353502	   
df.mm.trans1:exp7	-0.0352671987024847	0.0687330942355416	-0.513103608890754	0.608112478741273	   
df.mm.trans2:exp7	0.0385410615693716	0.0687330942355416	0.560735145100483	0.57523705009402	   
df.mm.trans1:exp8	-0.0264401058870227	0.0687330942355416	-0.384677951445269	0.700644652875131	   
df.mm.trans2:exp8	-0.121001720350165	0.0687330942355416	-1.76045792344958	0.0789604418823354	.  
df.mm.trans1:probe2	0.136676582211215	0.047058332699918	2.90440766532838	0.00384742038900083	** 
df.mm.trans1:probe3	0.210396512903737	0.047058332699918	4.47097253201457	9.7055423885569e-06	***
df.mm.trans1:probe4	-0.0216402746574707	0.047058332699918	-0.459860632875938	0.645822332801982	   
df.mm.trans1:probe5	0.00171096326677580	0.047058332699918	0.0363583486411702	0.97101158466462	   
df.mm.trans1:probe6	0.0246356263406499	0.047058332699918	0.523512520040745	0.60085675345731	   
df.mm.trans2:probe2	-0.0434917981225167	0.047058332699918	-0.924210349734565	0.355836393617681	   
df.mm.trans2:probe3	0.0616335146836842	0.047058332699918	1.30972584763488	0.190908643816495	   
df.mm.trans2:probe4	0.238298297435595	0.047058332699918	5.06389163753798	5.84579133840647e-07	***
df.mm.trans2:probe5	0.0416634929338935	0.047058332699918	0.885358459246179	0.376402062872362	   
df.mm.trans2:probe6	0.00897204797614857	0.047058332699918	0.190658008080346	0.84887331933517	   
df.mm.trans3:probe2	-0.144401597503342	0.047058332699918	-3.06856595247781	0.00227128618065724	** 
df.mm.trans3:probe3	0.216711302539527	0.047058332699918	4.60516321140092	5.27256015309435e-06	***
df.mm.trans3:probe4	-0.28618559122957	0.047058332699918	-6.0815072445197	2.41557332897299e-09	***
df.mm.trans3:probe5	-0.456257284676785	0.047058332699918	-9.6955684253892	1.95528941902432e-20	***
df.mm.trans3:probe6	0.000930714456582008	0.047058332699918	0.0197778884882513	0.984228690615059	   
df.mm.trans3:probe7	0.160857450386200	0.047058332699918	3.418256473555	0.00068332065879883	***
df.mm.trans3:probe8	-0.462812660647995	0.047058332699918	-9.83487161772736	6.20083016344713e-21	***
df.mm.trans3:probe9	-0.330584815413985	0.047058332699918	-7.02500059919379	7.28176817755039e-12	***
df.mm.trans3:probe10	-0.354207718737926	0.047058332699918	-7.526992530667	2.54825538278182e-13	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.18749212628012	0.133951396238485	31.2612801648203	2.62582976254375e-118	***
df.mm.trans1	-0.131672874475287	0.107235097750025	-1.2278897230292	0.220083946561519	   
df.mm.trans2	-0.0763168356369244	0.107235097750025	-0.711677773771663	0.477006496803434	   
df.mm.exp2	-0.185053970011305	0.143595101473911	-1.28872063261103	0.198109603173256	   
df.mm.exp3	-0.161660387367766	0.143595101473911	-1.12580711812886	0.260803944263842	   
df.mm.exp4	-0.105715947954837	0.143595101473911	-0.736208595347133	0.461959683000256	   
df.mm.exp5	0.0272346049602487	0.143595101473911	0.189662493223675	0.849652962338437	   
df.mm.exp6	-0.225950768780421	0.143595101473911	-1.57352699682080	0.116248920079163	   
df.mm.exp7	-0.219204455227826	0.143595101473911	-1.52654549478244	0.127525914628306	   
df.mm.exp8	-0.200133011682414	0.143595101473911	-1.39373146874912	0.164036986049383	   
df.mm.trans1:exp2	0.0646523563386784	0.112645265303355	0.573946505115582	0.566270067965339	   
df.mm.trans2:exp2	0.164517906582214	0.112645265303355	1.46049553116294	0.144801412208168	   
df.mm.trans1:exp3	0.0827284196166511	0.112645265303355	0.734415418116884	0.463050507429171	   
df.mm.trans2:exp3	0.0885084370232578	0.112645265303355	0.785727094564544	0.432411069872809	   
df.mm.trans1:exp4	0.0563006307077837	0.112645265303355	0.499804679372588	0.617439287293577	   
df.mm.trans2:exp4	0.110815398656544	0.112645265303355	0.983755494366467	0.325726236373830	   
df.mm.trans1:exp5	0.0068773238920255	0.112645265303355	0.0610529335032836	0.951342202849084	   
df.mm.trans2:exp5	-0.0066281059997864	0.112645265303355	-0.0588405201224997	0.953103369438245	   
df.mm.trans1:exp6	0.151068602311996	0.112645265303355	1.34110032858608	0.180515582265309	   
df.mm.trans2:exp6	0.155540674440846	0.112645265303355	1.38080081769946	0.167976157895893	   
df.mm.trans1:exp7	0.0846567726384751	0.112645265303355	0.751534229250498	0.452695729339601	   
df.mm.trans2:exp7	0.161990680642886	0.112645265303355	1.43806027005789	0.151061872995653	   
df.mm.trans1:exp8	0.127170029389332	0.112645265303355	1.12894251744058	0.259480171926848	   
df.mm.trans2:exp8	0.165164234462698	0.112645265303355	1.46623325905361	0.143232795090909	   
df.mm.trans1:probe2	0.00840885374732146	0.0771229410035022	0.109031808666887	0.913222356159699	   
df.mm.trans1:probe3	0.0436252709647673	0.0771229410035022	0.565658809131595	0.57188733600125	   
df.mm.trans1:probe4	0.0327837769707276	0.0771229410035022	0.425084631682276	0.670963512284298	   
df.mm.trans1:probe5	-0.0284320816482671	0.0771229410035022	-0.368659198914315	0.712542632323444	   
df.mm.trans1:probe6	-0.0545598954313622	0.0771229410035022	-0.707440545205409	0.479632536586196	   
df.mm.trans2:probe2	-0.170158368842923	0.0771229410035022	-2.20632624519851	0.0278291246284451	*  
df.mm.trans2:probe3	-0.103746363503613	0.0771229410035022	-1.3452075628042	0.179186844495368	   
df.mm.trans2:probe4	-0.143007378867771	0.0771229410035023	-1.85427807870133	0.0643062132899339	.  
df.mm.trans2:probe5	-0.0990825272155724	0.0771229410035022	-1.28473481335564	0.199498220069805	   
df.mm.trans2:probe6	-0.108190779062226	0.0771229410035022	-1.40283523494407	0.161305788605180	   
df.mm.trans3:probe2	-0.024265237185425	0.0771229410035022	-0.314630599789019	0.753177448935468	   
df.mm.trans3:probe3	0.0439348184237972	0.0771229410035023	0.569672497600967	0.569163605029375	   
df.mm.trans3:probe4	-0.0313349754844568	0.0771229410035023	-0.406299021753253	0.684702115046916	   
df.mm.trans3:probe5	-0.159574239872588	0.0771229410035022	-2.06908914255929	0.0390668845333577	*  
df.mm.trans3:probe6	-0.0118278611961678	0.0771229410035022	-0.153363720862650	0.878175240508568	   
df.mm.trans3:probe7	-0.119136436505605	0.0771229410035022	-1.54476002802065	0.123056537102465	   
df.mm.trans3:probe8	0.0593771968368875	0.0771229410035022	0.769903171018739	0.441732154871809	   
df.mm.trans3:probe9	-0.057606987753695	0.0771229410035023	-0.746950090389824	0.455455636627724	   
df.mm.trans3:probe10	0.0357202851159289	0.0771229410035023	0.463160308089221	0.643457222265463