chr11.4033_chr11_102024741_102025366_+_1.R 

fitVsDatCorrelation=0.924928337397973
cont.fitVsDatCorrelation=0.220153666185321

fstatistic=6630.91157325446,45,531
cont.fstatistic=997.017092540246,45,531

residuals=-0.735942347071476,-0.088248817900859,-0.0076572030033917,0.0905251773289413,1.83812468511615
cont.residuals=-0.591700024845221,-0.256217812720407,-0.117509847649780,0.085016572896614,2.10547927385357

predictedValues:
Include	Exclude	Both
chr11.4033_chr11_102024741_102025366_+_1.R.tl.Lung	51.7451449966264	155.371375540288	257.683561954942
chr11.4033_chr11_102024741_102025366_+_1.R.tl.cerebhem	53.2510045280264	58.68618676525	61.4234746046583
chr11.4033_chr11_102024741_102025366_+_1.R.tl.cortex	47.7200900330589	57.3985570325238	66.7922319174709
chr11.4033_chr11_102024741_102025366_+_1.R.tl.heart	48.9618564462105	64.9782519713713	82.4579018887507
chr11.4033_chr11_102024741_102025366_+_1.R.tl.kidney	47.69596652995	52.7801310710432	60.6868869172432
chr11.4033_chr11_102024741_102025366_+_1.R.tl.liver	55.5380521929492	57.1097212787601	57.2441097440223
chr11.4033_chr11_102024741_102025366_+_1.R.tl.stomach	48.6005707119104	57.4517921281053	61.7106559947649
chr11.4033_chr11_102024741_102025366_+_1.R.tl.testicle	50.3469274030262	50.3613057185556	58.3517891143426


diffExp=-103.626230543662,-5.43518223722361,-9.67846699946492,-16.0163955251608,-5.08416454109321,-1.57166908581099,-8.85122141619489,-0.0143783155294059
diffExpScore=0.993389640755201
diffExp1.5=-1,0,0,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=-1,0,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=-1,0,0,-1,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,0,-1,-1,0,0,0,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	67.4332164977568	62.2689225233669	67.3423591844752
cerebhem	68.9199888197594	59.3810324409958	64.7535668344308
cortex	60.7156041173184	62.2047745923314	72.8692198830328
heart	64.7660869548805	65.5065220649572	67.5654672416607
kidney	64.3680081267077	64.9811964183258	68.1706968727201
liver	61.6653705445352	67.0827098293695	68.237594221174
stomach	62.8826667846789	56.6416121354373	59.3975499639647
testicle	69.3000036635021	73.9046485507531	67.4140094471521
cont.diffExp=5.16429397438986,9.53895637876364,-1.48917047501296,-0.740435110076646,-0.613188291618002,-5.41733928483424,6.24105464924163,-4.60464488725096
cont.diffExpScore=3.72366129028056

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=0.184301079928032
cont.tran.correlation=0.270181366022411

tran.covariance=0.00397621418001004
cont.tran.covariance=0.00101064497032857

tran.mean=59.8748083967285
cont.tran.mean=64.5013977540423

weightedLogRatios:
wLogRatio
Lung	-4.94337812001287
cerebhem	-0.391045497598911
cortex	-0.730852702724664
heart	-1.14125548247954
kidney	-0.396592153301086
liver	-0.112489446133374
stomach	-0.663771080815563
testicle	-0.00111906986554351

cont.weightedLogRatios:
wLogRatio
Lung	0.332349408958217
cerebhem	0.619491662282727
cortex	-0.0997910207457117
heart	-0.0474763700915735
kidney	-0.0395305137945764
liver	-0.35060990214551
stomach	0.427410212122929
testicle	-0.274731652549492

varWeightedLogRatios=2.60827730064177
cont.varWeightedLogRatios=0.121269037219114

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.56312768109296	0.0918638065041745	38.7870676895045	4.66176010715501e-157	***
df.mm.trans1	0.394173320452457	0.0731343691524918	5.38971382429743	1.06297116070317e-07	***
df.mm.trans2	1.46507302258277	0.0731343691524918	20.0326199509284	6.54627734314747e-67	***
df.mm.exp2	0.489012458591707	0.0975124922033224	5.01486986479713	7.24627768595008e-07	***
df.mm.exp3	0.273368357466522	0.0975124922033224	2.80341883680426	0.0052413322093158	** 
df.mm.exp4	0.212389795443147	0.0975124922033224	2.17807780976713	0.0298395083554383	*  
df.mm.exp5	0.28483750036807	0.0975124922033224	2.92103600197355	0.00363707467865156	** 
df.mm.exp6	0.574301507040859	0.0975124922033224	5.88951727172954	6.87770848616389e-09	***
df.mm.exp7	0.371708344161424	0.0975124922033224	3.81190487252011	0.000154062225422307	***
df.mm.exp8	0.331254203495293	0.0975124922033224	3.39704376342467	0.000732240908908084	***
df.mm.trans1:exp2	-0.460326401058144	0.0755328516701297	-6.09438662621268	2.11305102410346e-09	***
df.mm.trans2:exp2	-1.46262630099500	0.0755328516701296	-19.3641080490731	1.34476377113959e-63	***
df.mm.trans1:exp3	-0.354346484798266	0.0755328516701296	-4.69128964368754	3.45782904323034e-06	***
df.mm.trans2:exp3	-1.26916741564765	0.0755328516701296	-16.8028531636859	4.01190259475354e-51	***
df.mm.trans1:exp4	-0.267678851601547	0.0755328516701296	-3.54387323770808	0.000429067775797557	***
df.mm.trans2:exp4	-1.08415538897387	0.0755328516701296	-14.3534285413801	1.02471476321648e-39	***
df.mm.trans1:exp5	-0.366321276423768	0.0755328516701297	-4.84982717220292	1.62575506004420e-06	***
df.mm.trans2:exp5	-1.36452090833365	0.0755328516701296	-18.0652640296549	3.20780438632721e-57	***
df.mm.trans1:exp6	-0.503563707377921	0.0755328516701296	-6.66681710333282	6.56628009290002e-11	***
df.mm.trans2:exp6	-1.57514537718840	0.0755328516701296	-20.853778751363	5.33283501930708e-71	***
df.mm.trans1:exp7	-0.434403681534626	0.0755328516701296	-5.75118868054622	1.49727258157865e-08	***
df.mm.trans2:exp7	-1.36658036816664	0.0755328516701296	-18.0925297794240	2.36207322243798e-57	***
df.mm.trans1:exp8	-0.358647222213047	0.0755328516701296	-4.74822827793324	2.64333562046839e-06	***
df.mm.trans2:exp8	-1.45784928934799	0.0755328516701296	-19.3008638905198	2.76132574552142e-63	***
df.mm.trans1:probe2	-0.0486758763749103	0.0534097916183063	-0.911366154033571	0.362516021140371	   
df.mm.trans1:probe3	0.0647544904505453	0.0534097916183063	1.21240859566190	0.225895159755077	   
df.mm.trans1:probe4	-0.0703684718291326	0.0534097916183063	-1.31752005946815	0.188232542585844	   
df.mm.trans1:probe5	-0.161179140041337	0.0534097916183063	-3.01778260423118	0.00266849910695891	** 
df.mm.trans1:probe6	0.0180019725515027	0.0534097916183063	0.33705378744321	0.736209532116869	   
df.mm.trans2:probe2	0.0889741428879389	0.0534097916183063	1.66587698981853	0.0963276920898071	.  
df.mm.trans2:probe3	-0.0300211617109199	0.0534097916183063	-0.562090972484343	0.574291224687825	   
df.mm.trans2:probe4	0.053873407379952	0.0534097916183063	1.00868035144115	0.313587367132064	   
df.mm.trans2:probe5	-0.0141483661933151	0.0534097916183063	-0.264902104363682	0.791187673864055	   
df.mm.trans2:probe6	0.218437314393425	0.0534097916183063	4.08983648456241	4.98644392220737e-05	***
df.mm.trans3:probe2	0.124402472935624	0.0534097916183063	2.32920723272369	0.0202222801311125	*  
df.mm.trans3:probe3	0.331787277796535	0.0534097916183063	6.21210582822822	1.05657261420625e-09	***
df.mm.trans3:probe4	0.057639679912609	0.0534097916183063	1.07919686945292	0.280989933378563	   
df.mm.trans3:probe5	0.184763409710221	0.0534097916183063	3.45935462603253	0.000584985593864521	***
df.mm.trans3:probe6	-0.110381389642476	0.0534097916183063	-2.06668826628866	0.0392477269779403	*  
df.mm.trans3:probe7	0.267322975317532	0.0534097916183063	5.00513046798531	7.60499883741844e-07	***
df.mm.trans3:probe8	0.154729815600428	0.0534097916183063	2.89703087977214	0.00392254953678972	** 
df.mm.trans3:probe9	0.292898582390671	0.0534097916183063	5.48398661586014	6.44123155277846e-08	***
df.mm.trans3:probe10	-0.132438537725926	0.0534097916183063	-2.47966774842335	0.0134595969566358	*  
df.mm.trans3:probe11	0.127099362558286	0.0534097916183063	2.37970152489271	0.0176788301961654	*  
df.mm.trans3:probe12	0.254475998800193	0.0534097916183063	4.76459448894331	2.44569669140194e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20395695198866	0.235727862922579	17.8339416472349	4.28491337757833e-56	***
df.mm.trans1	0.00841452667009294	0.187667038875909	0.0448375309830348	0.96425366598715	   
df.mm.trans2	-0.0839502795049017	0.187667038875909	-0.447336303741716	0.65481467593109	   
df.mm.exp2	0.0135215790468206	0.250222718501212	0.0540381749819217	0.956925073731792	   
df.mm.exp3	-0.184844574069899	0.250222718501211	-0.738720189665768	0.460403310007543	   
df.mm.exp4	0.00702407390669553	0.250222718501212	0.0280712876463354	0.977615840878213	   
df.mm.exp5	-0.0161108655445522	0.250222718501211	-0.0643861022734201	0.94868702684698	   
df.mm.exp6	-0.0281575380629607	0.250222718501211	-0.112529902287127	0.91044578876926	   
df.mm.exp7	-0.0390492506610717	0.250222718501211	-0.156057974651421	0.876046633935412	   
df.mm.exp8	0.197557095391492	0.250222718501211	0.789525014254594	0.430157614459721	   
df.mm.trans1:exp2	0.00828694749690175	0.193821684319588	0.0427555230777874	0.96591249103951	   
df.mm.trans2:exp2	-0.061009188278975	0.193821684319588	-0.314769673440555	0.753060185786242	   
df.mm.trans1:exp3	0.0799075856812783	0.193821684319588	0.412273714170808	0.680305199895645	   
df.mm.trans2:exp3	0.183813867371199	0.193821684319588	0.948365855020191	0.343374634211577	   
df.mm.trans1:exp4	-0.0473796799007566	0.193821684319588	-0.244449840930251	0.80697687225399	   
df.mm.trans2:exp4	0.0436631718558192	0.193821684319588	0.225274958315934	0.821852049829014	   
df.mm.trans1:exp5	-0.030410115889615	0.193821684319588	-0.156897387391766	0.875385355534567	   
df.mm.trans2:exp5	0.0587463422846724	0.193821684319588	0.303094787824705	0.761936332800394	   
df.mm.trans1:exp6	-0.0612576668398762	0.193821684319588	-0.316051669114948	0.752087481530076	   
df.mm.trans2:exp6	0.102621405748928	0.193821684319588	0.529462975771678	0.596705600623708	   
df.mm.trans1:exp7	-0.0308179143149052	0.193821684319588	-0.159001375016896	0.873728245951809	   
df.mm.trans2:exp7	-0.0556693029477254	0.193821684319588	-0.287219168191386	0.774056557776912	   
df.mm.trans1:exp8	-0.170249859226687	0.193821684319588	-0.878383963199734	0.380132665379739	   
df.mm.trans2:exp8	-0.0262438315902158	0.193821684319588	-0.135401937519762	0.892345403931509	   
df.mm.trans1:probe2	0.0690734316693908	0.137052627323379	0.503992028597967	0.61447601661956	   
df.mm.trans1:probe3	-0.0240691420920107	0.137052627323379	-0.175619705817233	0.860659669714647	   
df.mm.trans1:probe4	-0.0809947573307668	0.137052627323379	-0.590975590272033	0.554788237350228	   
df.mm.trans1:probe5	0.126612580059821	0.137052627323379	0.923824537570347	0.355997280376499	   
df.mm.trans1:probe6	-0.112829716046138	0.137052627323379	-0.823258322366297	0.410730682897186	   
df.mm.trans2:probe2	0.00869418890182014	0.137052627323379	0.0634368641566134	0.949442505332149	   
df.mm.trans2:probe3	-0.0566619489165969	0.137052627323379	-0.413432051783303	0.67945700672298	   
df.mm.trans2:probe4	0.116396047370088	0.137052627323379	0.849279941897424	0.396108260996226	   
df.mm.trans2:probe5	0.131659325979153	0.137052627323379	0.960647953639736	0.337166430524235	   
df.mm.trans2:probe6	0.00611665974054225	0.137052627323379	0.0446300071731559	0.964419002270022	   
df.mm.trans3:probe2	0.000541002461120886	0.137052627323379	0.00394740671292917	0.996851915819035	   
df.mm.trans3:probe3	0.222661846002515	0.137052627323379	1.62464485614814	0.104831637835383	   
df.mm.trans3:probe4	0.141586691445026	0.137052627323379	1.03308265014832	0.302035499546612	   
df.mm.trans3:probe5	-0.0133838020804853	0.137052627323379	-0.0976544728975237	0.92224351705656	   
df.mm.trans3:probe6	-0.0114835646961782	0.137052627323379	-0.0837894531498651	0.93325541178383	   
df.mm.trans3:probe7	0.152262859043001	0.137052627323379	1.11098095685341	0.267079343309095	   
df.mm.trans3:probe8	0.146505348870929	0.137052627323379	1.06897147272665	0.285568221705227	   
df.mm.trans3:probe9	0.168105026642205	0.137052627323379	1.22657281312497	0.220526796965525	   
df.mm.trans3:probe10	-0.0179727115735386	0.137052627323379	-0.131137300499403	0.89571636265672	   
df.mm.trans3:probe11	0.000155362999646927	0.137052627323379	0.00113360103108673	0.99909594317281	   
df.mm.trans3:probe12	0.187440915564441	0.137052627323379	1.36765649243754	0.171998176975301