chr2.14030_chr2_127893173_127894119_-_2.R 

fitVsDatCorrelation=0.855849037538512
cont.fitVsDatCorrelation=0.272842795082392

fstatistic=11697.9276848359,54,738
cont.fstatistic=3371.44604584556,54,738

residuals=-0.577661199304706,-0.0843998000545273,-0.00415523903372555,0.0766652066509318,1.21275029311579
cont.residuals=-0.523307478434198,-0.197718482444562,-0.0348120381121604,0.153660748029917,1.24707194551617

predictedValues:
Include	Exclude	Both
chr2.14030_chr2_127893173_127894119_-_2.R.tl.Lung	57.5383349421369	66.4733791513148	98.428858878748
chr2.14030_chr2_127893173_127894119_-_2.R.tl.cerebhem	55.1424937986801	60.9382745577421	84.1287647978126
chr2.14030_chr2_127893173_127894119_-_2.R.tl.cortex	51.6914676180763	61.6205048666321	79.1123142824876
chr2.14030_chr2_127893173_127894119_-_2.R.tl.heart	58.64922171354	71.4991142175217	88.69136928576
chr2.14030_chr2_127893173_127894119_-_2.R.tl.kidney	56.9728114803192	67.9576224318667	95.5180638719129
chr2.14030_chr2_127893173_127894119_-_2.R.tl.liver	68.3567009839185	70.4834910419425	102.04149084063
chr2.14030_chr2_127893173_127894119_-_2.R.tl.stomach	61.8392435910715	59.5258439367184	86.0645824358114
chr2.14030_chr2_127893173_127894119_-_2.R.tl.testicle	56.5702427624125	60.8742533665685	78.8359738972677


diffExp=-8.93504420917785,-5.79578075906194,-9.92903724855584,-12.8498925039816,-10.9848109515475,-2.12679005802394,2.31339965435314,-4.30401060415603
diffExpScore=1.0676490629479
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=0,0,0,-1,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	64.3966677101277	69.4500607173237	57.2624271996386
cerebhem	64.2613571040733	73.9360273021098	68.8722221002413
cortex	66.7647061845747	64.2494343051281	66.9768927212255
heart	66.2096833112772	73.0623116571598	67.1230773715288
kidney	63.5737741949452	65.4056703802532	65.0534133184741
liver	63.874717257643	67.1360800115172	70.1748248782487
stomach	63.2878744679797	63.4391649815796	59.2398969004275
testicle	61.8103594129352	64.3974127204656	63.9163778601342
cont.diffExp=-5.05339300719601,-9.67467019803648,2.5152718794466,-6.85262834588261,-1.83189618530803,-3.26136275387417,-0.151290513599896,-2.58705330753035
cont.diffExpScore=1.14447935326147

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=0.455770187497756
cont.tran.correlation=0.353667890736822

tran.covariance=0.00269722936626521
cont.tran.covariance=0.000526831204114965

tran.mean=61.6333125287789
cont.tran.mean=65.9534563574433

weightedLogRatios:
wLogRatio
Lung	-0.595390384858004
cerebhem	-0.405748873160075
cortex	-0.708631233922851
heart	-0.826246599759014
kidney	-0.728289486870942
liver	-0.129910809609411
stomach	0.156531894365086
testicle	-0.298599179620821

cont.weightedLogRatios:
wLogRatio
Lung	-0.317507599047686
cerebhem	-0.593654459764686
cortex	0.160594781259976
heart	-0.417784202515253
kidney	-0.118358932615997
liver	-0.208246329467165
stomach	-0.00990613580909673
testicle	-0.169937734075219

varWeightedLogRatios=0.114535137924704
cont.varWeightedLogRatios=0.0556148165839217

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.53455522902495	0.0730128098664834	48.410069897166	3.05330866476201e-231	***
df.mm.trans1	0.482526891666705	0.0643177844324589	7.50223124015433	1.80720936776269e-13	***
df.mm.trans2	0.627503057495889	0.0580359009705553	10.8123255950529	2.14301092325553e-25	***
df.mm.exp2	0.0275146192637309	0.0772865453549196	0.356007881286151	0.72193640595356	   
df.mm.exp3	0.0354999433080083	0.0772865453549196	0.45932889282324	0.64613326682677	   
df.mm.exp4	0.196177851813226	0.0772865453549196	2.53831829217268	0.0113431149614861	*  
df.mm.exp5	0.0422241617883322	0.0772865453549196	0.546332632600255	0.585002431389515	   
df.mm.exp6	0.194819674927995	0.0772865453549196	2.52074502791311	0.0119203676925395	*  
df.mm.exp7	0.0959318376790472	0.0772865453549196	1.24124887764751	0.214908207825107	   
df.mm.exp8	0.117005070822279	0.0772865453549196	1.51391254823155	0.130476151428012	   
df.mm.trans1:exp2	-0.0700454083260461	0.0728999324840658	-0.960843253748643	0.336945679887812	   
df.mm.trans2:exp2	-0.114454713896193	0.0596453993051447	-1.91891940081824	0.0553799409912704	.  
df.mm.trans1:exp3	-0.142658632121494	0.0728999324840657	-1.95691034628429	0.0507346722597836	.  
df.mm.trans2:exp3	-0.111306810947205	0.0596453993051447	-1.86614243921415	0.0624178922662879	.  
df.mm.trans1:exp4	-0.177054967125174	0.0728999324840658	-2.42873979566269	0.0153892368449344	*  
df.mm.trans2:exp4	-0.123294344757034	0.0596453993051447	-2.06712246364991	0.0390707584100958	*  
df.mm.trans1:exp5	-0.0521014196308159	0.0728999324840657	-0.714697776190726	0.475021818414074	   
df.mm.trans2:exp5	-0.0201414044140423	0.0596453993051447	-0.33768580055939	0.735696003008365	   
df.mm.trans1:exp6	-0.0225314976033097	0.0728999324840657	-0.309074327445153	0.757352221703012	   
df.mm.trans2:exp6	-0.136242716150496	0.0596453993051447	-2.28421165316507	0.0226422359503022	*  
df.mm.trans1:exp7	-0.0238450854914161	0.0728999324840657	-0.327093382379032	0.743690009551966	   
df.mm.trans2:exp7	-0.206322821554214	0.0596453993051447	-3.45915735258424	0.000572979988753009	***
df.mm.trans1:exp8	-0.133973390420306	0.0728999324840657	-1.83777111795803	0.0664979540075502	.  
df.mm.trans2:exp8	-0.20499630852121	0.0596453993051447	-3.43691736344077	0.000621338283520522	***
df.mm.trans1:probe2	0.459226160306741	0.0425643903512186	10.7889753974496	2.66930294264473e-25	***
df.mm.trans1:probe3	0.153176594167918	0.0425643903512186	3.59870288060011	0.000341167596032519	***
df.mm.trans1:probe4	0.096424050924404	0.0425643903512186	2.26536901218986	0.023779076586987	*  
df.mm.trans1:probe5	0.35378972269475	0.0425643903512186	8.31187102118616	4.51456513972103e-16	***
df.mm.trans1:probe6	0.0181840423398455	0.0425643903512186	0.42721256406589	0.669349121654883	   
df.mm.trans1:probe7	0.0363555874520021	0.0425643903512186	0.854131520550753	0.393309281002322	   
df.mm.trans1:probe8	0.384211046157122	0.0425643903512186	9.02658402920418	1.51755192388339e-18	***
df.mm.trans1:probe9	-0.0228491075986515	0.0425643903512186	-0.536812753809296	0.591558750356661	   
df.mm.trans1:probe10	0.0344842195089192	0.0425643903512186	0.810165944452013	0.418105964144191	   
df.mm.trans1:probe11	0.0827852174046729	0.0425643903512186	1.94494075262381	0.052161593831689	.  
df.mm.trans1:probe12	-0.0865111688367891	0.0425643903512186	-2.03247757392847	0.0424628894088875	*  
df.mm.trans1:probe13	0.176708685372713	0.0425643903512186	4.1515615263042	3.68760222524515e-05	***
df.mm.trans1:probe14	-0.0784840795982367	0.0425643903512186	-1.84389060786794	0.0655997780854114	.  
df.mm.trans1:probe15	0.182992085392983	0.0425643903512186	4.29918257686836	1.94441685181025e-05	***
df.mm.trans1:probe16	0.0232566263820893	0.0425643903512186	0.546386925554155	0.58496513718937	   
df.mm.trans1:probe17	-0.204057219878672	0.0425643903512186	-4.79408299272938	1.97693888520053e-06	***
df.mm.trans1:probe18	-0.174554287200372	0.0425643903512186	-4.10094648977805	4.57209013431764e-05	***
df.mm.trans1:probe19	-0.197913622333228	0.0425643903512186	-4.64974643593272	3.93734793437021e-06	***
df.mm.trans1:probe20	-0.0506060898024622	0.0425643903512186	-1.18893021572464	0.234849538900763	   
df.mm.trans1:probe21	-0.091198898046428	0.0425643903512186	-2.14261022638650	0.0324709024525810	*  
df.mm.trans1:probe22	-0.14044847519457	0.0425643903512186	-3.29967078197677	0.00101446245638403	** 
df.mm.trans2:probe2	0.0304307772097825	0.0425643903512186	0.714935112630159	0.47487523279772	   
df.mm.trans2:probe3	0.162074769909624	0.0425643903512186	3.80775499360545	0.000151884411412367	***
df.mm.trans2:probe4	0.00609124860166837	0.0425643903512186	0.143106680288538	0.886244992186555	   
df.mm.trans2:probe5	0.159228113444719	0.0425643903512186	3.74087616739846	0.000197601663622500	***
df.mm.trans2:probe6	0.0243510331148565	0.0425643903512186	0.572098717118343	0.567429316393945	   
df.mm.trans3:probe2	-0.0923196750138255	0.0425643903512186	-2.16894155541881	0.0304057663504247	*  
df.mm.trans3:probe3	0.265322523940377	0.0425643903512186	6.23343883821846	7.67168037065733e-10	***
df.mm.trans3:probe4	-0.131383234335480	0.0425643903512186	-3.08669367166722	0.00209967236810689	** 
df.mm.trans3:probe5	-0.317875293790064	0.0425643903512186	-7.46810399883862	2.30088815783211e-13	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.47893681844983	0.135806484223579	32.9802869432663	2.66213407314763e-147	***
df.mm.trans1	-0.241221633708203	0.119633420392878	-2.0163398565052	0.0441262614123476	*  
df.mm.trans2	-0.209168213330853	0.107948888475487	-1.93765972290072	0.0530459221168555	.  
df.mm.exp2	-0.124119382093967	0.143755787808080	-0.86340441652111	0.388195657764813	   
df.mm.exp3	-0.198425309345522	0.143755787808080	-1.38029440324468	0.167913959994965	   
df.mm.exp4	-0.0804136664135221	0.143755787808080	-0.559376896329753	0.576074239865782	   
df.mm.exp5	-0.200423831896997	0.143755787808080	-1.39419660907547	0.163677830094171	   
df.mm.exp6	-0.245369543901192	0.143755787808079	-1.70684984335220	0.088270619603375	.  
df.mm.exp7	-0.141845223639506	0.143755787808079	-0.986709653936689	0.3241083750158	   
df.mm.exp8	-0.226456341203548	0.143755787808080	-1.57528503482501	0.115619221930198	   
df.mm.trans1:exp2	0.122015966374065	0.135596528183228	0.899845799954321	0.368495837716103	   
df.mm.trans2:exp2	0.186711662029115	0.110942613968096	1.68295711946005	0.092806345793635	.  
df.mm.trans1:exp3	0.234538011591476	0.135596528183228	1.72967563944227	0.0841063589198233	.  
df.mm.trans2:exp3	0.120590285033153	0.110942613968096	1.08696091357494	0.277408962180703	   
df.mm.trans1:exp4	0.108178504103792	0.135596528183228	0.797797005227253	0.425244921158313	   
df.mm.trans2:exp4	0.131118384351137	0.110942613968096	1.1818577159976	0.237642834771742	   
df.mm.trans1:exp5	0.187562973635803	0.135596528183228	1.38324318586058	0.167008614813256	   
df.mm.trans2:exp5	0.140424846204141	0.110942613968096	1.26574308267627	0.206004512010197	   
df.mm.trans1:exp6	0.237231277780436	0.135596528183228	1.74953799303675	0.0806137077128959	.  
df.mm.trans2:exp6	0.211483204997431	0.110942613968096	1.90623960832803	0.0570073068535015	.  
df.mm.trans1:exp7	0.124477089712329	0.135596528183228	0.917996141790052	0.358920795957413	   
df.mm.trans2:exp7	0.051318695086749	0.110942613968096	0.462569730883635	0.643809160172456	   
df.mm.trans1:exp8	0.185465431520204	0.135596528183228	1.36777419012963	0.171799115061598	   
df.mm.trans2:exp8	0.150921854899272	0.110942613968096	1.36035964451562	0.174131492459563	   
df.mm.trans1:probe2	-0.101120723894525	0.0791713155169584	-1.27723940462837	0.201919415104822	   
df.mm.trans1:probe3	-0.144462111360690	0.0791713155169584	-1.82467741526597	0.0684538686550533	.  
df.mm.trans1:probe4	-0.126856418531179	0.0791713155169584	-1.60230277472157	0.109516571559756	   
df.mm.trans1:probe5	-0.0267036490908584	0.0791713155169584	-0.337289445255441	0.73599461923174	   
df.mm.trans1:probe6	-0.0306634955674158	0.0791713155169584	-0.38730562157765	0.698641630268921	   
df.mm.trans1:probe7	-0.08623379142472	0.0791713155169584	-1.08920498366923	0.276419039315922	   
df.mm.trans1:probe8	-0.0433869816827654	0.0791713155169584	-0.548013903766346	0.583848068908924	   
df.mm.trans1:probe9	-0.240898267395235	0.0791713155169584	-3.04274680573717	0.00242744583353968	** 
df.mm.trans1:probe10	-0.144170854994127	0.0791713155169583	-1.82099860350617	0.0690118523862899	.  
df.mm.trans1:probe11	-0.0649427814248467	0.0791713155169584	-0.820281701785492	0.412320391515973	   
df.mm.trans1:probe12	-0.111963809898130	0.0791713155169584	-1.41419665906836	0.15772588914265	   
df.mm.trans1:probe13	0.043239827063277	0.0791713155169584	0.546155217719164	0.585124306601349	   
df.mm.trans1:probe14	-0.126729738629615	0.0791713155169584	-1.60070270150393	0.109870734755347	   
df.mm.trans1:probe15	-0.0970380454908448	0.0791713155169583	-1.22567175822738	0.220713207049527	   
df.mm.trans1:probe16	0.0284575375166579	0.0791713155169584	0.359442524490607	0.719366889553795	   
df.mm.trans1:probe17	-0.0963149952487615	0.0791713155169584	-1.21653902830667	0.224168578724814	   
df.mm.trans1:probe18	-0.145657839939658	0.0791713155169583	-1.83978046832453	0.0662019230231087	.  
df.mm.trans1:probe19	-0.191211966092715	0.0791713155169584	-2.41516722116052	0.0159700446860347	*  
df.mm.trans1:probe20	-0.141252227959528	0.0791713155169584	-1.78413390048157	0.0748126660213532	.  
df.mm.trans1:probe21	-0.0142847390544181	0.0791713155169584	-0.180428213945218	0.85686593947655	   
df.mm.trans1:probe22	-0.099443935143657	0.0791713155169584	-1.25606015883816	0.209491640835927	   
df.mm.trans2:probe2	-0.09075648858185	0.0791713155169584	-1.14633043532553	0.252029994541841	   
df.mm.trans2:probe3	-0.0574556495862057	0.0791713155169584	-0.725712958172317	0.468244822760541	   
df.mm.trans2:probe4	-0.100479537176622	0.0791713155169584	-1.26914067955710	0.204790998546269	   
df.mm.trans2:probe5	-0.0424488822370423	0.0791713155169584	-0.536164922356883	0.592006137090185	   
df.mm.trans2:probe6	-0.0296267206725900	0.0791713155169584	-0.37421028663145	0.708355361095206	   
df.mm.trans3:probe2	0.0784868778197519	0.0791713155169584	0.991354978848875	0.321837193257141	   
df.mm.trans3:probe3	0.0333255666243966	0.0791713155169584	0.420929807807201	0.673928889310664	   
df.mm.trans3:probe4	0.0520951287972641	0.0791713155169584	0.658005092590705	0.510740053012387	   
df.mm.trans3:probe5	-0.0684189909572416	0.0791713155169584	-0.864189138585002	0.387764786113655