这篇文章主要介绍WGCNA如何获取关系矩阵，文中介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们一定要看完！
在进行WGCNA分析的过程（学习WGCNA）中需要基于表达矩阵转换关系矩阵，结合power值构建邻接矩阵，并由此构建TOM矩阵最终构建网络。在代码实现的过程中往往：
1、计算power值
2、基于power直接利用adjacency()由表达矩阵--邻接矩阵，实现多步计算
但是如果想要关系矩阵呢？基于什么样的代码可以获得关系矩阵？并且不拘于WGCNA中？
可以基于cor 或者corAndPvalue（注意提前加载WGCNA包，否则函数无法使用）
案例数据：dat1
> dat1 A B C D E F G H I J K L CK-WT-1 3.74149 5.23528 2.821317 118.6600 1.8737693 1.7103460 30.26110 86.6405 1448.6278 173.9960 77.06166 3.19210 CK-WT-2 7.36180 2.77070 1.563395 140.1430 16.9090246 0.7802436 33.65711 116.4700 1634.0417 51.0019 98.30970 4.69276 CK-WT-3 5.81734 2.66859 1.931628 123.3830 0.9559375 2.7996091 31.46691 111.7380 1566.5626 52.3322 101.42702 3.58136 CK-tdr1-1 5.71131 3.22632 3.194809 97.2229 0.4774184 4.7297117 30.96890 82.8809 648.4734 66.9486 46.86340 3.03234 CK-tdr1-2 7.97054 1.32105 2.600854 95.2539 0.5273923 4.3637146 28.03340 85.7292 683.4113 41.1148 70.29293 2.11160 CK-tdr1-3 10.37620 1.96726 2.301278 91.8525 0.4333881 3.3732144 27.62150 79.6027 647.2750 49.7169 57.09809 3.53808 NaWT-1 6.29949 2.40259 2.044360 121.8080 39.1065780 2.2783575 35.59571 106.4650 1248.4062 192.7300 151.37454 4.79151 NaWT-2 5.55062 3.23077 2.104095 125.1350 36.5302500 2.8043996 32.99440 111.3370 1117.6042 183.2700 160.54078 4.16132 NaWT-3 5.84779 4.80378 2.630611 106.5070 19.4561309 2.9542534 32.77111 98.1677 1191.6926 111.2120 137.35694 3.40994 Natdr1-1 15.58810 2.04301 2.289544 81.6997 13.2227038 3.1700429 19.02370 69.4519 501.2779 78.8024 101.08433 6.01932 Natdr1-2 14.76360 2.29524 2.801336 84.8495 10.8897780 4.6643058 18.14860 69.7807 395.9033 96.2520 82.21420 5.59169 Natdr1-3 17.74670 1.95286 2.450605 80.3895 12.2580100 4.0243357 15.79980 68.8929 468.8953 66.7984 108.79391 8.12127
1，计算矩阵内，每个对象（需计算的对象）--基因（或者其他）两两之间的相关性：相当于列两两之间计算
cor：pearson，构成一个12X12的对称2关系矩阵，行列皆为ABC.....，譬如A行，反应了基因A和ABC...12个基因之间的pearson相关性系数，如果需要进行pvalue值计算需要借助其他的函数
> correlationDat1=cor(dat1,method = "pearson",use="p") > correlationDat1 A B C D E F G H I J K L A 1.00000000 -0.59583393 0.04210008 -0.7653767 -0.08993499 0.4431767 -0.9341117 -0.7344355 -0.7497806 -3.808723e-01 -0.1198969 8.083187e-01 B -0.59583393 1.00000000 0.26970294 0.4147493 0.07036514 -0.4175610 0.4353225 0.2769349 0.5322412 5.188070e-01 0.1793751 -3.134547e-01 C 0.04210008 0.26970294 1.00000000 -0.5854244 -0.43456495 0.6807807 -0.2927207 -0.6463363 -0.5398572 1.914220e-02 -0.4921559 -2.044431e-01 D -0.76537665 0.41474925 -0.58542443 1.0000000 0.38000901 -0.8057937 0.8364376 0.9445312 0.9418587 3.742554e-01 0.4060881 -3.653468e-01 E -0.08993499 0.07036514 -0.43456495 0.3800090 1.00000000 -0.3500579 0.2973247 0.4515257 0.2264080 6.947862e-01 0.9017631 3.043045e-01 F 0.44317672 -0.41756097 0.68078073 -0.8057937 -0.35005795 1.0000000 -0.5485788 -0.6745604 -0.8450679 -3.474236e-01 -0.3917673 5.689610e-02 G -0.93411169 0.43532253 -0.29272071 0.8364376 0.29732475 -0.5485788 1.0000000 0.8579521 0.7792997 3.536281e-01 0.2771652 -6.892251e-01 H -0.73443549 0.27693490 -0.64633635 0.9445312 0.45152567 -0.6745604 0.8579521 1.0000000 0.8767053 2.816941e-01 0.5186044 -3.793407e-01 I -0.74978056 0.53224125 -0.53985724 0.9418587 0.22640799 -0.8450679 0.7792997 0.8767053 1.0000000 3.127929e-01 0.3626986 -3.640842e-01 J -0.38087228 0.51880702 0.01914220 0.3742554 0.69478622 -0.3474236 0.3536281 0.2816941 0.3127929 1.000000e+00 0.6315628 -8.267543e-05 K -0.11989693 0.17937513 -0.49215586 0.4060881 0.90176313 -0.3917673 0.2771652 0.5186044 0.3626986 6.315628e-01 1.0000000 2.884711e-01 L 0.80831868 -0.31345469 -0.20444309 -0.3653468 0.30430452 0.0568961 -0.6892251 -0.3793407 -0.3640842 -8.267543e-05 0.2884711 1.000000e+00
corAndPvalue：pearson 计算关系矩阵，同时可以获得pvalue值，返回结果是一个列表，包括关系矩阵cor以及p值矩阵等等。。。
> correlation_pvalueDat1=corAndPvalue(dat1,method="pearson",use="p") > correlation_pvalueDat1$cor A B C D E F G H I J K L A 1.00000000 -0.59583393 0.04210008 -0.7653767 -0.08993499 0.4431767 -0.9341117 -0.7344355 -0.7497806 -3.808723e-01 -0.1198969 8.083187e-01 B -0.59583393 1.00000000 0.26970294 0.4147493 0.07036514 -0.4175610 0.4353225 0.2769349 0.5322412 5.188070e-01 0.1793751 -3.134547e-01 C 0.04210008 0.26970294 1.00000000 -0.5854244 -0.43456495 0.6807807 -0.2927207 -0.6463363 -0.5398572 1.914220e-02 -0.4921559 -2.044431e-01 D -0.76537665 0.41474925 -0.58542443 1.0000000 0.38000901 -0.8057937 0.8364376 0.9445312 0.9418587 3.742554e-01 0.4060881 -3.653468e-01 E -0.08993499 0.07036514 -0.43456495 0.3800090 1.00000000 -0.3500579 0.2973247 0.4515257 0.2264080 6.947862e-01 0.9017631 3.043045e-01 F 0.44317672 -0.41756097 0.68078073 -0.8057937 -0.35005795 1.0000000 -0.5485788 -0.6745604 -0.8450679 -3.474236e-01 -0.3917673 5.689610e-02 G -0.93411169 0.43532253 -0.29272071 0.8364376 0.29732475 -0.5485788 1.0000000 0.8579521 0.7792997 3.536281e-01 0.2771652 -6.892251e-01 H -0.73443549 0.27693490 -0.64633635 0.9445312 0.45152567 -0.6745604 0.8579521 1.0000000 0.8767053 2.816941e-01 0.5186044 -3.793407e-01 I -0.74978056 0.53224125 -0.53985724 0.9418587 0.22640799 -0.8450679 0.7792997 0.8767053 1.0000000 3.127929e-01 0.3626986 -3.640842e-01 J -0.38087228 0.51880702 0.01914220 0.3742554 0.69478622 -0.3474236 0.3536281 0.2816941 0.3127929 1.000000e+00 0.6315628 -8.267543e-05 K -0.11989693 0.17937513 -0.49215586 0.4060881 0.90176313 -0.3917673 0.2771652 0.5186044 0.3626986 6.315628e-01 1.0000000 2.884711e-01 L 0.80831868 -0.31345469 -0.20444309 -0.3653468 0.30430452 0.0568961 -0.6892251 -0.3793407 -0.3640842 -8.267543e-05 0.2884711 1.000000e+00 > correlation_pvalueDat1$p A B C D E F G H I J K L A 0.000000e+00 4.090997e-02 0.89663878 3.717762e-03 7.810452e-01 1.490336e-01 8.749707e-06 6.524667e-03 4.984374e-03 2.219086e-01 7.105239e-01 0.001462577 B 4.090997e-02 4.250614e-78 0.39657958 1.800527e-01 8.279739e-01 1.768190e-01 1.572339e-01 3.835336e-01 7.485940e-02 8.393536e-02 5.769666e-01 0.321137811 C 8.966388e-01 3.965796e-01 0.00000000 4.551601e-02 1.580397e-01 1.480954e-02 3.558405e-01 2.314921e-02 7.002489e-02 9.529153e-01 1.040909e-01 0.523883036 D 3.717762e-03 1.800527e-01 0.04551601 0.000000e+00 2.230453e-01 1.554408e-03 6.957492e-04 3.766304e-06 4.743703e-06 2.307106e-01 1.902444e-01 0.242885049 E 7.810452e-01 8.279739e-01 0.15803968 2.230453e-01 0.000000e+00 2.646438e-01 3.479692e-01 1.406229e-01 4.791912e-01 1.215050e-02 6.097575e-05 0.336215639 F 1.490336e-01 1.768190e-01 0.01480954 1.554408e-03 2.646438e-01 4.250614e-78 6.475768e-02 1.611867e-02 5.387507e-04 2.685031e-01 2.078626e-01 0.860584854 G 8.749707e-06 1.572339e-01 0.35584053 6.957492e-04 3.479692e-01 6.475768e-02 1.328317e-79 3.570559e-04 2.808413e-03 2.594651e-01 3.831218e-01 0.013159937 H 6.524667e-03 3.835336e-01 0.02314921 3.766304e-06 1.406229e-01 1.611867e-02 3.570559e-04 0.000000e+00 1.817921e-04 3.750706e-01 8.407771e-02 0.223927653 I 4.984374e-03 7.485940e-02 0.07002489 4.743703e-06 4.791912e-01 5.387507e-04 2.808413e-03 1.817921e-04 1.328317e-79 3.222157e-01 2.465757e-01 0.244640498 J 2.219086e-01 8.393536e-02 0.95291526 2.307106e-01 1.215050e-02 2.685031e-01 2.594651e-01 3.750706e-01 3.222157e-01 1.328317e-79 2.760873e-02 0.999796541 K 7.105239e-01 5.769666e-01 0.10409092 1.902444e-01 6.097575e-05 2.078626e-01 3.831218e-01 8.407771e-02 2.465757e-01 2.760873e-02 0.000000e+00 0.363188744 L 1.462577e-03 3.211378e-01 0.52388304 2.428850e-01 3.362156e-01 8.605849e-01 1.315994e-02 2.239277e-01 2.446405e-01 9.997965e-01 3.631887e-01 0.000000000
2、指定矩阵间，不同对象之间计算，譬如增加一个表达矩阵dat2，计算dat1中每一列和dat2每一列之间的关系矩阵，关系矩阵大小和两个表达矩阵的大小相关，N*n
案例数据2
> dat2 a b c d e f g h i j k l CK-WT-1 0.3664077 0.158906 261.9050 62.7705 2.0567778 20.7683 7.716667 2.93546 0.518056 34.6190 1.31144086 235.1950 CK-WT-2 2.5206383 2.839320 309.9350 81.5834 1.2001859 13.5200 13.305652 3.78978 2.938810 27.3054 2.61589225 115.6060 CK-WT-3 2.1481360 3.394500 367.1380 95.3128 1.4740055 15.9394 6.020028 4.44529 1.802080 34.2856 3.23541287 95.6566 CK-tdr1-1 1.8667110 2.059980 203.5430 74.6182 0.9724999 21.5128 8.973298 2.68723 3.896400 33.0009 6.46792884 199.5490 CK-tdr1-2 2.7575005 1.870370 155.1830 74.4062 1.3159845 24.0510 7.535809 3.52543 3.442310 26.4773 4.33091660 187.6910 CK-tdr1-3 1.4235844 0.976982 169.6500 69.3025 1.8246997 27.4637 9.426074 1.67038 3.108840 24.4855 3.11069900 233.1310 NaWT-1 6.2707832 2.722900 202.8050 83.0657 1.2524994 16.3550 6.280126 3.73328 1.925890 25.8537 0.24508389 304.0540 NaWT-2 4.4219148 3.893780 191.2740 79.8487 0.7776743 10.1857 6.321488 3.53631 1.016500 25.6810 0.07114720 322.6570 NaWT-3 2.5067114 2.505550 236.5250 84.3876 1.3424120 13.8600 7.992223 2.68571 1.086710 25.2199 2.19092550 265.5010 Natdr1-1 8.2305000 2.181010 87.1744 31.3708 1.0394537 20.3689 3.763500 3.71247 3.540770 13.9571 0.05223847 528.9090 Natdr1-2 6.5484678 2.403690 77.9025 36.0605 1.6192591 21.4447 2.804242 4.20718 3.683380 16.4149 0.29263051 495.8620 Natdr1-3 6.9019060 0.957058 82.9502 28.5191 1.7537999 25.2101 3.427119 1.88249 4.067390 14.3850 0.44852888 450.5050
cor：计算dat1和dat2关系矩阵，获得abc....与ABC....两两之间的pearson相关性系数
> correlationDat1_Dat2=cor(dat1,dat2,method = "pearson",use="p") > correlationDat1_Dat2 a b c d e f g h i j k l A 0.7459167 -0.24136960 -0.78655322 -0.8824599 0.17742322 0.5196762 -0.6056735 -0.17056393 0.7186029 -0.91460572 -0.41036887 0.7770714 B -0.4926932 -0.11679240 0.46330810 0.2952698 0.15056629 -0.4349153 0.2484750 -0.10224860 -0.7517427 0.50163720 -0.01680050 -0.2224608 C -0.1349988 -0.49651059 -0.40439381 -0.3221845 0.20345194 0.4804190 -0.2698939 -0.34944659 0.1821813 0.03611212 0.32205123 0.2443374 D -0.4470417 0.46321502 0.84006166 0.7670363 -0.21724651 -0.7597254 0.6029082 0.39922064 -0.6650228 0.66305733 0.02854352 -0.6735202 E 0.4818952 0.55257199 -0.07180092 0.1380915 -0.49039789 -0.6665837 -0.1167492 0.22591893 -0.3574666 -0.24193267 -0.62594452 0.2767556 F 0.2543805 -0.12658907 -0.64016060 -0.4038207 -0.03896878 0.5644352 -0.5238938 -0.20049261 0.6083761 -0.33756962 0.27826251 0.3851291 G -0.6119213 0.38961962 0.77990417 0.9410753 -0.30961086 -0.6075029 0.6926354 0.17115670 -0.6442575 0.79251674 0.33895247 -0.7716637 H -0.3719534 0.65346000 0.81542226 0.8592102 -0.37566899 -0.8134974 0.5466699 0.41505782 -0.6131757 0.59674642 0.07831966 -0.6806705 I -0.5109515 0.31466689 0.91777671 0.7399461 -0.01640227 -0.6843157 0.5486220 0.34573223 -0.7414820 0.69290258 0.02932018 -0.6927339 J 0.1226988 0.12057442 0.03235788 0.1237968 -0.08205620 -0.4755465 -0.1882456 0.12871298 -0.6963563 0.13514402 -0.56466856 0.2079076 K 0.4110099 0.57928237 0.08557449 0.2039083 -0.37884121 -0.7383462 -0.2207892 0.26160068 -0.5311611 -0.18339463 -0.65504786 0.2028440 L 0.7953264 -0.06627007 -0.53006300 -0.7317357 0.09382376 0.1170781 -0.5519596 -0.09422415 0.4025131 -0.78324469 -0.65678340 0.7232087
corAndPvalue： 依然类似，返回两个矩阵列之间的关系矩阵和p值矩阵等等
> correlation_pvalueDat1_Dat2=corAndPvalue(dat1,dat2,method="pearson",use = "p") > correlation_pvalueDat1_Dat2$cor a b c d e f g h i j k l A 0.7459167 -0.24136960 -0.78655322 -0.8824599 0.17742322 0.5196762 -0.6056735 -0.17056393 0.7186029 -0.91460572 -0.41036887 0.7770714 B -0.4926932 -0.11679240 0.46330810 0.2952698 0.15056629 -0.4349153 0.2484750 -0.10224860 -0.7517427 0.50163720 -0.01680050 -0.2224608 C -0.1349988 -0.49651059 -0.40439381 -0.3221845 0.20345194 0.4804190 -0.2698939 -0.34944659 0.1821813 0.03611212 0.32205123 0.2443374 D -0.4470417 0.46321502 0.84006166 0.7670363 -0.21724651 -0.7597254 0.6029082 0.39922064 -0.6650228 0.66305733 0.02854352 -0.6735202 E 0.4818952 0.55257199 -0.07180092 0.1380915 -0.49039789 -0.6665837 -0.1167492 0.22591893 -0.3574666 -0.24193267 -0.62594452 0.2767556 F 0.2543805 -0.12658907 -0.64016060 -0.4038207 -0.03896878 0.5644352 -0.5238938 -0.20049261 0.6083761 -0.33756962 0.27826251 0.3851291 G -0.6119213 0.38961962 0.77990417 0.9410753 -0.30961086 -0.6075029 0.6926354 0.17115670 -0.6442575 0.79251674 0.33895247 -0.7716637 H -0.3719534 0.65346000 0.81542226 0.8592102 -0.37566899 -0.8134974 0.5466699 0.41505782 -0.6131757 0.59674642 0.07831966 -0.6806705 I -0.5109515 0.31466689 0.91777671 0.7399461 -0.01640227 -0.6843157 0.5486220 0.34573223 -0.7414820 0.69290258 0.02932018 -0.6927339 J 0.1226988 0.12057442 0.03235788 0.1237968 -0.08205620 -0.4755465 -0.1882456 0.12871298 -0.6963563 0.13514402 -0.56466856 0.2079076 K 0.4110099 0.57928237 0.08557449 0.2039083 -0.37884121 -0.7383462 -0.2207892 0.26160068 -0.5311611 -0.18339463 -0.65504786 0.2028440 L 0.7953264 -0.06627007 -0.53006300 -0.7317357 0.09382376 0.1170781 -0.5519596 -0.09422415 0.4025131 -0.78324469 -0.65678340 0.7232087 > correlation_pvalueDat1_Dat2$p a b c d e f g h i j k l A 0.005343145 0.44979253 0.0024077537 1.445930e-04 0.5811872 0.083326632 0.03686988 0.5961141 0.008463246 3.094278e-05 0.18516364 0.002941142 B 0.103655491 0.71774480 0.1292845040 3.514709e-01 0.6404398 0.157666714 0.43613980 0.7518474 0.004809304 9.658536e-02 0.95867058 0.487090810 C 0.675718069 0.10059700 0.1922789248 3.071058e-01 0.5259410 0.113907636 0.39623229 0.2655365 0.570920348 9.112847e-01 0.30731734 0.444065385 D 0.145100960 0.12937166 0.0006260246 3.599014e-03 0.4976159 0.004144516 0.03797518 0.1985736 0.018288544 1.876101e-02 0.92983243 0.016345628 E 0.112640644 0.06244015 0.8245113950 6.686591e-01 0.1055240 0.017919584 0.71784548 0.4801668 0.253963657 4.487033e-01 0.02945712 0.383854279 F 0.424947701 0.69503317 0.0249443469 1.929701e-01 0.9042942 0.055895385 0.08041542 0.5321061 0.035812002 2.832246e-01 0.38116298 0.216354493 G 0.034457419 0.21058742 0.0027732109 5.065274e-06 0.3274258 0.036151388 0.01253384 0.5948184 0.023742645 2.112357e-03 0.28113150 0.003283103 H 0.233820856 0.02119734 0.0012264521 3.422783e-04 0.2288129 0.001287303 0.06588627 0.1796961 0.033987019 4.052267e-02 0.80882788 0.014832038 I 0.089574366 0.31916867 0.0000257490 5.935397e-03 0.9596495 0.014101469 0.06473229 0.2709979 0.005778547 1.248574e-02 0.92792752 0.012516091 J 0.704025631 0.70895097 0.9204803450 7.014839e-01 0.7998668 0.118156482 0.55794132 0.6901388 0.011876189 6.753860e-01 0.05577167 0.516716928 K 0.184410086 0.04839925 0.7914492547 5.249929e-01 0.2245885 0.006102175 0.49045398 0.4114583 0.075563069 5.683139e-01 0.02077916 0.527205048 L 0.001983196 0.83786471 0.0762830738 6.828905e-03 0.7717943 0.717079336 0.06279178 0.7708433 0.194552954 2.584682e-03 0.02032902 0.007860030
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