chrX.25546_chrX_128889280_128890098_+_0.R 

fitVsDatCorrelation=0.873534703517792
cont.fitVsDatCorrelation=0.246733316669942

fstatistic=9130.4666947555,49,623
cont.fstatistic=2294.07592745987,49,623

residuals=-0.585542326042826,-0.0922708181077361,0.000321969204711024,0.0976641963717608,0.585254455869847
cont.residuals=-0.604958318259353,-0.233764033472616,-0.0333717321655512,0.167367241524108,1.24122864381554

predictedValues:
Include	Exclude	Both
chrX.25546_chrX_128889280_128890098_+_0.R.tl.Lung	68.8077959583038	45.6066044849249	83.2319272766487
chrX.25546_chrX_128889280_128890098_+_0.R.tl.cerebhem	64.3262140645099	52.4275782614783	74.2234732466321
chrX.25546_chrX_128889280_128890098_+_0.R.tl.cortex	88.223192499193	46.7801135987101	106.020600434196
chrX.25546_chrX_128889280_128890098_+_0.R.tl.heart	75.6578478919768	47.4544520609556	85.890529075369
chrX.25546_chrX_128889280_128890098_+_0.R.tl.kidney	60.8940992184984	46.1750849312216	70.5663187402517
chrX.25546_chrX_128889280_128890098_+_0.R.tl.liver	59.4477804800179	50.8597890514407	73.2022283633722
chrX.25546_chrX_128889280_128890098_+_0.R.tl.stomach	65.6680113685026	51.0184131502111	75.4462903463298
chrX.25546_chrX_128889280_128890098_+_0.R.tl.testicle	74.2878516074278	48.6406747315933	87.1593988412394


diffExp=23.2011914733789,11.8986358030316,41.443078900483,28.2033958310212,14.7190142872768,8.58799142857723,14.6495982182916,25.6471768758345
diffExpScore=0.994095072270645
diffExp1.5=1,0,1,1,0,0,0,1
diffExp1.5Score=0.8
diffExp1.4=1,0,1,1,0,0,0,1
diffExp1.4Score=0.8
diffExp1.3=1,0,1,1,1,0,0,1
diffExp1.3Score=0.833333333333333
diffExp1.2=1,1,1,1,1,0,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	65.2883084787632	69.2567334062714	69.0945687400712
cerebhem	69.7617907294335	64.2422888192767	68.7137011836026
cortex	65.8613616739157	63.0615272670666	67.6293502845638
heart	68.341011275592	60.8195209630788	63.1596898332929
kidney	57.9722415992297	68.849736617555	59.1083382656605
liver	68.0147557119568	64.6698201606012	70.0449140855076
stomach	68.595969998207	69.4061847972557	68.7660362169454
testicle	74.193369664654	59.0703134294373	62.5624799279041
cont.diffExp=-3.96842492750820,5.51950191015675,2.79983440684907,7.52149031251317,-10.8774950183253,3.34493555135559,-0.810214799048666,15.1230562352167
cont.diffExpScore=2.54239848342806

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

tran.correlation=-0.414853963292472
cont.tran.correlation=-0.650643985182274

tran.covariance=-0.00277468427483126
cont.tran.covariance=-0.00278462327454821

tran.mean=59.1422189599354
cont.tran.mean=66.0878084120184

weightedLogRatios:
wLogRatio
Lung	1.65562128783834
cerebhem	0.830757681210266
cortex	2.64084273596183
heart	1.90918125878464
kidney	1.09870089447609
liver	0.625208215593937
stomach	1.02444268341248
testicle	1.73469037476439

cont.weightedLogRatios:
wLogRatio
Lung	-0.248321811064363
cerebhem	0.346503273486859
cortex	0.180968211168684
heart	0.485776962483497
kidney	-0.712945456020258
liver	0.211529301575712
stomach	-0.0497176853426017
testicle	0.955710830361232

varWeightedLogRatios=0.44567486658726
cont.varWeightedLogRatios=0.249768651102604

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.73532768980396	0.0799596651543274	46.7151492267323	9.91009188494804e-206	***
df.mm.trans1	0.487230037224639	0.0692887536972718	7.031877631302	5.37533902609289e-12	***
df.mm.trans2	0.0771636653760313	0.0631908407693043	1.22112104280648	0.222502111169192	   
df.mm.exp2	0.186580958731619	0.0837660186239957	2.22740631340178	0.0262765819821238	*  
df.mm.exp3	0.0319560749446747	0.0837660186239957	0.381492107057366	0.702968199339865	   
df.mm.exp4	0.103179376106284	0.0837660186239957	1.23175695587765	0.218504606027563	   
df.mm.exp5	0.0552851059055485	0.0837660186239957	0.659994432273417	0.509501323218749	   
df.mm.exp6	0.0912064603641267	0.0837660186239957	1.08882410627070	0.276652465500725	   
df.mm.exp7	0.163638932747425	0.0837660186239957	1.95352405946328	0.0512047137015197	.  
df.mm.exp8	0.0949303528164367	0.0837660186239957	1.13327999081053	0.257532618635298	   
df.mm.trans1:exp2	-0.253930778695313	0.0768689705441565	-3.30342369486327	0.00100982228352973	** 
df.mm.trans2:exp2	-0.047200744381803	0.0637363833289621	-0.740562013664725	0.459238055660913	   
df.mm.trans1:exp3	0.216596755140182	0.0768689705441564	2.81773976686420	0.00498969939073129	** 
df.mm.trans2:exp3	-0.00655042665942187	0.0637363833289621	-0.102773742677133	0.918175616843443	   
df.mm.trans1:exp4	-0.00827525363559143	0.0768689705441565	-0.107654019261749	0.914304804238603	   
df.mm.trans2:exp4	-0.0634615703165641	0.0637363833289621	-0.995688286688946	0.319788186090907	   
df.mm.trans1:exp5	-0.17746588071529	0.0768689705441565	-2.30868033562836	0.0212877595921725	*  
df.mm.trans2:exp5	-0.0428972817169525	0.0637363833289621	-0.673042295097716	0.501169959415229	   
df.mm.trans1:exp6	-0.237425224021685	0.0768689705441564	-3.08870045144287	0.0020998072231363	** 
df.mm.trans2:exp6	0.0178136107608709	0.0637363833289621	0.279488885789607	0.779962427392314	   
df.mm.trans1:exp7	-0.210344067061542	0.0768689705441565	-2.73639760715558	0.00638868962149028	** 
df.mm.trans2:exp7	-0.0515048642505927	0.0637363833289621	-0.80809204351557	0.419346045002435	   
df.mm.trans1:exp8	-0.0182999709389857	0.0768689705441565	-0.238067074522267	0.811907348528399	   
df.mm.trans2:exp8	-0.0305227845343209	0.0637363833289621	-0.478891065669414	0.632184159916603	   
df.mm.trans1:probe2	-0.177748025622085	0.0470724387215534	-3.77605304610441	0.000174586095779552	***
df.mm.trans1:probe3	-0.246051324327160	0.0470724387215534	-5.2270783288417	2.35319953284075e-07	***
df.mm.trans1:probe4	0.0281735642897719	0.0470724387215534	0.598515077079954	0.549713838216128	   
df.mm.trans1:probe5	-0.143746878912836	0.0470724387215534	-3.05373766086645	0.0023565064671245	** 
df.mm.trans1:probe6	-0.133197744012346	0.0470724387215534	-2.82963338271568	0.00481024193427409	** 
df.mm.trans1:probe7	-0.325549534211211	0.0470724387215534	-6.9159266664922	1.15501140852019e-11	***
df.mm.trans1:probe8	0.167130358652332	0.0470724387215534	3.55049288270266	0.000413504251516127	***
df.mm.trans1:probe9	0.365698338887885	0.0470724387215534	7.7688419979915	3.27558182498544e-14	***
df.mm.trans1:probe10	0.0524046542791226	0.0470724387215534	1.11327680703162	0.266018912926039	   
df.mm.trans1:probe11	0.238971216459418	0.0470724387215534	5.0766695533452	5.07907515639756e-07	***
df.mm.trans1:probe12	-0.055878534415403	0.0470724387215534	-1.18707540830719	0.235650309765431	   
df.mm.trans1:probe13	0.0491231577386833	0.0470724387215534	1.04356517471424	0.297091372070679	   
df.mm.trans1:probe14	-0.08615194472006	0.0470724387215534	-1.83019930685285	0.0676977535708186	.  
df.mm.trans1:probe15	0.369759752502089	0.0470724387215534	7.85512207449717	1.75573555660521e-14	***
df.mm.trans1:probe16	0.0722494401851905	0.0470724387215534	1.53485653489436	0.125326736657151	   
df.mm.trans2:probe2	0.0548620965502847	0.0470724387215534	1.16548235103792	0.244269822794382	   
df.mm.trans2:probe3	-0.0254434145758129	0.0470724387215534	-0.540516176064678	0.589034288754276	   
df.mm.trans2:probe4	0.00251629115140801	0.0470724387215534	0.0534557210067781	0.957385946008594	   
df.mm.trans2:probe5	0.0705253496291489	0.0470724387215534	1.49823020741131	0.134579945518037	   
df.mm.trans2:probe6	-0.0117260901733761	0.0470724387215534	-0.249107343741826	0.80335985199247	   
df.mm.trans3:probe2	0.0231262824465072	0.0470724387215534	0.491291360180117	0.623393415997321	   
df.mm.trans3:probe3	-0.0400012123387447	0.0470724387215534	-0.849779901469796	0.395773904502393	   
df.mm.trans3:probe4	-0.0550581914332208	0.0470724387215534	-1.16964816203608	0.242589869400751	   
df.mm.trans3:probe5	0.0977836597161234	0.0470724387215534	2.07730175813794	0.0381828342039187	*  
df.mm.trans3:probe6	0.70560720032062	0.0470724387215534	14.9898161107497	1.34087931563419e-43	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.16811520452125	0.159189923783626	26.1832853829783	1.76041522396102e-102	***
df.mm.trans1	0.0119223470096170	0.137945442853498	0.086427987492699	0.931153968902194	   
df.mm.trans2	0.056749157946277	0.125805243262007	0.451087382964547	0.652083535812383	   
df.mm.exp2	-0.00335769001058072	0.166767908478291	-0.0201339097025241	0.983942996043983	   
df.mm.exp3	-0.0635364625295145	0.166767908478291	-0.380987344083561	0.703342521303222	   
df.mm.exp4	0.00559734862849252	0.166767908478291	0.0335637034700903	0.973235816642548	   
df.mm.exp5	0.0313614777094866	0.166767908478291	0.188054632306965	0.850895059795149	   
df.mm.exp6	-0.0412745771162288	0.166767908478291	-0.247497120356353	0.804605063283262	   
df.mm.exp7	0.0563425818639523	0.166767908478291	0.337850263747157	0.735589838434737	   
df.mm.exp8	0.0680803166852365	0.166767908478291	0.408233918062832	0.683242215728995	   
df.mm.trans1:exp2	0.0696311620938386	0.153036728438423	0.454996410367305	0.649270331445889	   
df.mm.trans2:exp2	-0.071800985438043	0.126891351843440	-0.565846169931515	0.57170210035839	   
df.mm.trans1:exp3	0.0722754374956211	0.153036728438423	0.472275108290114	0.63689581791973	   
df.mm.trans2:exp3	-0.0301730380225872	0.126891351843440	-0.237786402179834	0.812124945935134	   
df.mm.trans1:exp4	0.0400997186124954	0.153036728438423	0.262026763259188	0.793387420256956	   
df.mm.trans2:exp4	-0.135506916360287	0.126891351843440	-1.06789717653474	0.285980515119825	   
df.mm.trans1:exp5	-0.150210151890034	0.153036728438423	-0.981530077274708	0.326712416445467	   
df.mm.trans2:exp5	-0.0372554516393807	0.126891351843440	-0.293601187930811	0.769160447605909	   
df.mm.trans1:exp6	0.082186277468232	0.153036728438423	0.537036293880922	0.591434283181548	   
df.mm.trans2:exp6	-0.0272511619515782	0.126891351843440	-0.214759804791117	0.830024871156724	   
df.mm.trans1:exp7	-0.0069217722875688	0.153036728438423	-0.045229484178067	0.963938886691186	   
df.mm.trans2:exp7	-0.0541869738500554	0.126891351843440	-0.42703441221835	0.669501801290902	   
df.mm.trans1:exp8	0.0597814948282517	0.153036728438423	0.390634950434828	0.696200625787947	   
df.mm.trans2:exp8	-0.227172202956315	0.126891351843440	-1.79028909106905	0.0738929188263813	.  
df.mm.trans1:probe2	-0.0375133925873554	0.0937154741447531	-0.400290271480803	0.689079952896509	   
df.mm.trans1:probe3	0.0631283721809521	0.0937154741447531	0.673617380235881	0.500804426025262	   
df.mm.trans1:probe4	0.00815894918076844	0.0937154741447531	0.0870608536661311	0.93065111583383	   
df.mm.trans1:probe5	0.0346204909579307	0.0937154741447531	0.369421285800206	0.711939262590796	   
df.mm.trans1:probe6	-0.0232935178731361	0.0937154741447531	-0.248555727703590	0.803786369260667	   
df.mm.trans1:probe7	0.0637511753376965	0.0937154741447531	0.68026306135128	0.496590620730781	   
df.mm.trans1:probe8	-0.0364759655748722	0.0937154741447531	-0.389220306547575	0.697246183678212	   
df.mm.trans1:probe9	-0.0333473764951080	0.0937154741447531	-0.35583639521046	0.722083547089324	   
df.mm.trans1:probe10	-0.071858109627855	0.0937154741447531	-0.766768885113496	0.443509394072375	   
df.mm.trans1:probe11	0.0503760777326972	0.0937154741447531	0.537542793145198	0.591084682357938	   
df.mm.trans1:probe12	0.0092339846839645	0.0937154741447531	0.0985321236245539	0.921541458567002	   
df.mm.trans1:probe13	-0.00369444461445388	0.0937154741447531	-0.0394219273622564	0.968566627292818	   
df.mm.trans1:probe14	0.0479370364380559	0.0937154741447531	0.51151676791404	0.609170494699238	   
df.mm.trans1:probe15	-0.0859263336840542	0.0937154741447531	-0.916885225926854	0.359557575628101	   
df.mm.trans1:probe16	-0.00958843571260115	0.0937154741447531	-0.102314327490792	0.918540105586326	   
df.mm.trans2:probe2	-0.085019058001182	0.0937154741447531	-0.907204053301393	0.364649660264778	   
df.mm.trans2:probe3	0.113420521432297	0.0937154741447531	1.21026460643103	0.226636351983618	   
df.mm.trans2:probe4	0.0719386605010093	0.0937154741447531	0.767628410969705	0.442998808552167	   
df.mm.trans2:probe5	0.0429935370341926	0.0937154741447531	0.458766681026281	0.646561727661194	   
df.mm.trans2:probe6	0.0121384731532251	0.0937154741447531	0.129524747796463	0.896984250015902	   
df.mm.trans3:probe2	-0.0221458037678077	0.0937154741447531	-0.236308933715699	0.813270625281161	   
df.mm.trans3:probe3	-0.0473298737640681	0.0937154741447531	-0.505037980077466	0.613710762653249	   
df.mm.trans3:probe4	0.0295852536774416	0.0937154741447531	0.315692301057392	0.752341796398447	   
df.mm.trans3:probe5	-0.0411355529301204	0.0937154741447531	-0.438940882554597	0.660856559829611	   
df.mm.trans3:probe6	0.0731624860347743	0.0937154741447531	0.780687359291032	0.435282923575813