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This article mainly introduces WGCNA how to obtain the relationship matrix, the article is very detailed, has a certain reference value, interested friends must read it!

In the process of WGCNA analysis (learning WGCNA), we need to transform the relation matrix based on the expression matrix, combine the power value to build the adjacency matrix, and then construct the TOM matrix and finally build the network. In the process of code implementation, it is often:

1. Calculate the power value

2. Based on power, we directly use adjacency () to express matrix-adjacency matrix to realize multi-step calculation.

But what if you want a relational matrix? What kind of code can be used to obtain a relational matrix? And not in the WGCNA?

It can be based on cor or corAndPvalue (be careful to load the WGCNA package in advance, otherwise the function cannot be used)

Case data: dat1

> dat1 A B C D E F G H I J K LCK-WT-1 3.74149 5.23528 2.821317 118.6600 1.8737693 1.7103460 30.26110 86.6405 1448.6278 173.9960 77.06166 3.19210CK-WT-2 7.36180 2.77070 1.563395 140.1430 16.9090246 0.7802436 33.65711 116.4700 1634.0417 51.0019 98.30970 4.69276CK-WT-3 5.81734 2.66859 1.931628 123.3830 0.9559375 2.7996091 31.46691 111.7380 1566.5626 52.3322 101.42702 3.58136CK-tdr1-1 5.71131 3.22632 3.194809 97.2229 0.4774184 4.7297117 30.96890 82.8809 648.4734 66.9486 46.86340 3.03234CK-tdr1-2 7.97054 1.32105 2.600854 95.2539 0. 5273923 4.3637146 28.03340 85.7292 683.4113 41.1148 70.29293 2.11160CK-tdr1-3 10.37620 1.96726 2.301278 91.8525 0.4333881 3.3732144 27.62150 79.6027 647.2750 49.7169 57.09809 3.53808NaWT-1 6.29949 2.40259 2.044360 121.8080 39.1065780 2.27835571 106.4650 1248.4062 192.7300 151.37454 4.79151NaWT-2 5.55062 3.23077 2.104095 125.1350 36. 5302500 2.8043996 32.99440 111.3370 1117.6042 183.2700 160.54078 4.16132NaWT-3 5.84779 4.80378 2.630611 106.5070 19.4561309 2.9542534 32.77111 98.1677 1191.6926 111.2120 137.35694 3.40994Natdr1-1 15.58810 2.04301 2.289544 81.6997 13.2227038 3.1700429 19.02370 69.4519 501.2779 78.8024 101.08433 6.01932Natdr1-2 14.76360 2.29524 2.801336 84.8495 10.88977804.6643058 18.14860 69.7807 395.9033 96.2520 82.21420 5.59169Natdr1-3 17.74670 1.95286 2.450605 80.3895 12.2580100 4.0243357 15.7998068.8929 468.8953 66.7984 108.79391 8.12127

1. Calculate the correlation between each object (the object to be calculated)-genes (or other) in the matrix: it is equivalent to the calculation between two columns.

Cor:pearson, which forms a symmetric 2 relation matrix of 12X12, whose rows and columns are all ABC., such as row A, reflects the pearson correlation coefficient between gene An and ABC...12 genes. If you need to calculate the pvalue, you need to use other functions.

> correlationDat1=cor (dat1,method = "pearson" Use= "p") > correlationDat1 A B C D E F G H I J K LA 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-01B-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-01C 0.04210008 0.26970294 1.00000000-0.43456495 0.6807807-0.2927207-0.6463363-0.5398572 1.914220e-02-0.4921559-2.044431e-01D-0.76537665 0.41474925-0.58542443 1.00000 0.38000901-0.8057937 0.836430.9445312 0.9418587 3.742554e-01 0.4060881-3.653468e-01E-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-01F 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-02G-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-01H-0.73443549 0.27693490-0.64633635 0.9445312 0.45152567-0.6745604 0.8579521 1.00000 0.8767053 2.816941e-01 0.5186044-3.793407e-01I-0.74978056 0.53224125-0.53985724 0.9418587 0.22640799-0.8450679 0.7792997 0.8767053 1.00000 3.127929e-01 0.3626986-3.640842e-01J -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-05K-0.11989693 0.17937513-0.49215586 0.4060881 0.90176313-0.3917673 0.2771652 0.5186044 0.3626986 6.315628e-01 1.00000 2.884711e-01L 0.80831868-0.31345469-0.20444309-0.3653468 0.30430452 0.056892251-0.3793407-0.3640842-8.267543e-05 0.2884711 1.000000e+00

CorAndPvalue:pearson calculates the relation matrix and obtains the pvalue value at the same time. The result is a list, including the relation matrix cor and p-value matrix and so on.

> correlation_pvalueDat1=corAndPvalue (dat1,method= "pearson" Use= "p") > correlation_pvalueDat1 $cor A B C D E F G H I J K LA 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-01B-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-01C 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-01D-0.76537665 0.41474925-0.58542443 1.00000 0.38000901-0.8057937 0.8364376 0 . 9445312 0.9418587 3.742554e-01 0.4060881-3.653468e-01E-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-01F 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-02G-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-01H-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-01I-0.74978056 0.53224125-0.53985724 0.9418587 0.22640799-0.8450679 0.7792997 0.8767053 1.00000 3.127929e-01 0.3626986-3.640842e -01J-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-05K-0.11989693 0.17937513-0.49215586 0.4060881 0.90176313-0.3917673 0.2771652 0.5186044 0.3626986 6.315628e-01 1.00000 2.884711e-01L 0.80831868-0.31345469-0.20444309-0.3653468 0.30430452 0.056892251-0.3793407-0.3640842 8.267543e-05 0.2884711 1.000000e+00 > correlation_pvalueDat1 $p A B C D E F G H I J K LA 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 0.001462577B 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 0.321137811C 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 0.523883036D 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 0.242885049E 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 0.336215639F 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 0.860584854G 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 0.013159937H 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 0.223927653I 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 0.244640498J 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 0.999796541K 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 0.363188744L 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. Specify the calculation between matrices and between different objects, such as adding an expression matrix dat2, calculating the relation matrix between each column in dat1 and each column in dat2, and the correlation between the size of the relation matrix and the size of the two expression matrices.

Case data 2

> dat2 a b c d e f g h i j k lCK-WT-1 0.3664077 0.158906 261.9050 62.7705 2.0567778 20.7683 7.716667 2.93546 0.518056 34.6190 1.31144086 235.1950CK-WT-2 2.5206383 2.839320 309.9350 81.5834 1.2001859 13 .5200 13.305652 3.78978 2.938810 27.3054 2.61589225 115.6060CK-WT-3 2.1481360 367.1380 95.3128 1.4740055 15.9394 6.020028 4.44529 1.802080 34.2856 3.23541287 95.6566CK-tdr1-1 1.8667110 2.059980 203.5430 74.6182 0.9724999 21.5128 8.97298 2.68723 3.896400 33.0009 6.46792884 199.5490CK-tdr1-2 2.7575005 1.870370 155.1830 74.4062 1.3159845 24.0510 7.535809 3.52543 3.442310 26.4773 4.33091660 187.6910CK-tdr1-3 1.4235844 0.976982 169.6500 69.3025 1.8246997 27.4637 9.426074 1.67038 3.108840 24.4855 3.11069900 233.1310NaWT-1 6.2707832 2.722900 202.8050 83.0657 1.2524994 16.3550 6.280126 3.73328 1.925890 25.8537 0.24508389 304.0540NaWT-2 4.42193.893780 191.2740 79.8487 0.7776743 10. 1857 6.321488 3.53631 1.016500 25.6810 0.07114720 322.6570NaWT-3 2.5067114 2.505550 236.5250 84.3876 1.3424120 13.8600 7.992223 2.68571 1.086710 25.2199 2.19092550 265.5010Natdr1-1 8.2305000 2.181010 87.1744 31.3708 1.0394537 20.3689 3.763500 3.71247 3.540770 13.9571 0.05223847 528.9090Natdr1-2 6.5484678 2.403690 77.9025 36.0605 1.6192591 21.4447 2.804242 4 .20718 3.683380 16.4149 0.29263051 495.8620Natdr1-3 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: calculate the relation matrix of dat1 and dat2 to get abc.... And ABC.... Pearson correlation coefficient between two pairs

> correlationDat1_Dat2=cor (dat1,dat2,method = "pearson" Use= "p") > correlationDat1_Dat2 a b c d e f g h i j k lA 0.7459167-0.24136960-0.78655322-0.8824599 0.17742322 0.5196762-0.6056735-0.17056393 0.7186029-0.91460572-0.41036887 0.7770714B- 0.4926932-0.11679240 0.46330810 0.2952698 0.15056629-0.4349153 0.2484750-0.10224860-0.7517427 0.50163720-0.01680050-0.2224608C-0.1349988-0.49651059-0.40439381-0.3221845 0.20345194 0.4804190-0.2698939-0.34944659 0.1821813 0.03611212 0.32205123 0.2443374D-0.4470417 0.46321502 0.84006166 0.7670363-0.21724651-0.759254 0.6029082 0.39922064-0.6650228 0.66305733. 02854352-0.6735202E 0.4818952 0.55257199-0.07180092 0.1380915-0.49039789-0.6665837-0.1167492 0.22591893-0.3574666-0.24193267-0.62594452 0.2767556F 0.2543805-0.12658907-0.640382060-0.40386878 0.5644352-0.5238938-0.20049261 0.6083761-0.33756962 0.27826251 0.3851291G-0.611913 0.38961962 0.77990417 0.941053-0.30961086-0.6075029 0.6926354 0.17115670-0 . 6442575 0.79251674 0.33895247-0.7716637H-0.3719534 0.65346000 0.81542226 0.8592102-0.37566899-0.8134974 0.5466699 0.41505782-0.6131757 0.59674642 0.07831966-0.6806705I-0.5109515 0.31466689 0.91777671 0.7399461-0.01640227-0.6843157 0.5486220 0.34573223-0.7414820 0.692932018-0.6927339J 0.1226988 0.12057442 0.03235788 0.1237968-0.08205620-0.4755465-0.1882456 0. 12871298-0.6963563 0.13514402-0.56466856 0.2079076K 0.4110099 0.57928237 0.08557449 0.2039083-0.37884121-0.7383462-0.2207892 0.26160068-0.5311611-0.18339463-0.65504786 0.2028440L 0.7953264-0.06627007-0.53006300-0.7317357 0.09382376 0.1170781-0.5519596-0.09422415 0.4025131-0.78324469-0.656783400.7232087

CorAndPvalue: still similar, returning the relation matrix and p-value matrix between the two moment arrays, etc.

> 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 lA 0.7459167-0.24136960-0.78655322-0.8824599 0.17742322 0.5196762-0.6056735-0.17056393 0.7186029-0.91460572-0.41036887 0 .7770714B-0.4926932-0.11679240 0.46330810 0.2952698 0.15056629-0.4349153 0.2484750-0.10224860-0.7517427 0.50163720-0.01680050-0.2224608C-0.1349988-0.49651059-0.40439381-0.3221845 0.203450.4804190-0.2698939-0.34944659 0.1821813 0.03611212 0.32205123 0.2443374D-0.4470417 0.46321502 0.84006166 0.7670363-0.21724651-0.759254 0.6029082 0.39922064-0.6650228. 66305733 0.02854352-0.6735202E 0.4818952 0.55257199-0.07180092 0.1380915-0.49039789-0.6665837-0.1167492 0.22591893-0.3574666-0.24193267-0.62594452 0.2767556F 0.2543805-0.12658907-0.64016060-0.4038207-0.0383878 0.5644352-0.5238938-0.20049261 0.6083761-0.33756962 0.2782691G-0.38961962 0.77990417 0.9410753-0.30961086-0.6075029 0.6926354. 17115670-0.6442575 0.79251674 0.33895247-0.7716637H-0.3719534 0.65346000 0.81542226 0.8592102-0.37566899-0.8134974 0.5466699 0.41505782-0.6131757 0.59674642 0.07831966-0.6806705I-0.5109515 0.31466689 0.91777671 0.7399461-0.01640227-0.6843157 0.5486220 0.34573223-0.7414820 0.69290258 0.02932018-0.6927339J 0.1226988 0.12057442 0.03235788 0.1237968-0.08205620-0.4755465-0. 1882456 0.12871298-0.6963563 0.13514402-0.56466856 0.2079076K 0.4110099 0.57928237 0.08557449 0.2039083-0.37884121-0.7383462-0.2207892 0.26160068-0.5311611-0.18339463-0.65504786 0.2028440L 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 lA 0.005343145 0.44979253 0.0024077537 1.445930e-04 0.5811872 0.083326632 0.03686988 0.5961141 0.008463246 3.094278e-05 0.18516364 0.002941142B 0.103655491 0.71774480 0.1292845040 3.514709e-01 0.6404398 0.157666714 0.43613980 0. 7518474 0.004809304 9.658536e-02 0.95867058 0.487090810C 0.675718069 0.10059700 0.1922789248 3.071058e-01 0.5259410 0.113907636 0.39623229 0.2655365 0.570920348 9.112847e-01 0.30731734 0.444065385D 0.145100960 0.12937166 0.0006260246 3.599014e-03 0.4976159 0.004144516 0.03797518 0.1985736 0.018288544 1.876101e-02 0.92983243 0.016345628E 0.112640644 0.06244015 0.8245113950 6.686591e-01 0.1055240 0.017919584 0.71784548 0. 4801668 0.253963657 4.487033e-01 0.02945712 0.383854279F 0.424947701 0.69503317 0.0249443469 1.929701e-01 0.9042942 0.055895385 0.08041542 0.5321061 0.035812002 2.832246e-01 0.38116298 0.216354493G 0.034457419 0.21058742 0.0027732109 5.065274e-06 0.3274258 0.036151388 0.01253384 0.5948184 0.023742645 2.112357e-03 0.28113150 0.003283103H 0.233820856 0.02119734 0.0012264521 3.422783e-04 0.2288129 0.001287303 0.06588627 0. 1796961 0.033987019 4.052267e-02 0.80882788 0.014832038I 0.089574366 0.31916867 0.0000257490 5.935397e-03 0.9596495 0.014101469 0.06473229 0.2709979 0.005778547 1.248574e-02 0.92792752 0.012516091J 0.704025631 0.70895097 0.9204803450 7.014839e-01 0.7998668 0.118156482 0.55794132 0.6901388 0.011876189 6.753860e-01 0.05577167 0.516716928K 0.184410086 0.04839925 0.7914492547 5.249929e-01 0.2245885 0.006102175 0.49045398 0. 4114583 0.075563069 5.683139e-01 0.02077916 0.527205048L 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 and above are all the contents of the article "how to obtain the Relational Matrix by WGCNA" Thank you for reading! Hope to share the content to help you, more related knowledge, welcome to follow the industry information channel!

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