从邻接矩阵构建 MST 网络并在 R 中添加社区

问题描述

我想从表示 46 个基因组之间成对距离的最小哈希值的邻接矩阵构建一个图。我对网络相当陌生，所以不确定我采取的方法是否不违反任何假设等。

# build the graph object
network <- graph_from_adjacency_matrix(dismat,mode = "undirected",weighted = TRUE) # building network

mstNetwork = mst(network,weights = E(network)$weights,algorithm = "prim") # Performing Prims MST on network

# plot it

mstLayout = layout.fruchterman.reingold(mstNetwork) # setting layout

mstNetwork.com = fastgreedy.community(mstNetwork) # generating communities

V(mstNetwork)$color = mstNetwork.com$membership # setting community to colour

plot(mstNetwork,layout = mstLayout,vertex.label=NA,vertex.size = 5) # Ploting

这个方法靠谱吗？

我主要关心的是社区聚类，因为我知道 fastgreedy.community 方法通常是一种探索性的方法，而不是最终的方法，但我不确定在此之后使用哪个方法。

其次是否允许在 MST 上执行 fastgreedy.community 或其他此类社区算法？

分裂

structure(c(0,0.215016,0.185827,0.171263,0.204284,0.179364,0.175137,0.209345,0.200976,0.107331,0.177395,0.175878,0.197886,0.202933,0.184014,0.211692,0.195553,0.213325,0.204974,0.178964,0.165549,0.0261931,0.181002,0.0250965,0.176252,0.184461,0.206387,0.0580669,0.0252653,0.208588,0.200342,0.205675,0.193331,0.207109,0.192258,0.180176,0.179768,0.177011,0.115365,0.0454514,0.207842,0.0528548,0.227796,0.177782,0.239237,0.212502,0.159933,0.231311,0.15863,0.178567,0.166759,0.210114,0.218593,0.210897,0.242205,0.16615,0.172635,0.236445,0.174771,0.203604,0.22668,0.22559,0.228939,0.175506,0.223483,0.0194097,0.216771,0.194429,0.0142772,0.0346079,0.181421,0.160465,0.0340804,0.0348582,0.00472448,0.222464,0.233808,0.215885,0.033903,0.201619,0.18918,0.11676,0.190183,0.0345062,0.217673,0.191209,0.185367,0.224525,0.154223,0.168002,0.219531,0.220489,0.172984,0.0148814,0.0329036,0.0347669,0.183134,0.192791,0.202271,0.161822,0.194988,0.171602,0.196705,0.169279,0.171944,0.197292,0.188197,0.196126,0.159669,0.163504,0.221466,0.245371,0.16437,0.17663,0.23011,0.17026,0.154692,0.240698,0.247037,0.235108,0.0321452,0.156619,0.0316915,0.0325746,0.0148291,0.031508,0.183572,0.113166,0.0320311,0.168318,0.169931,0.0132913,0.0313257,0.032218,0.182699,0.190693,0.174048,0.158119,0.164955,0.0174938,0.00557568,0.0343261,0.0180468,0.178173,0.0177716,0.214163,0.186761,0.153298,0.169604,0.168956,0.0328716,0.0174058,0.00592222,0.187235,0.156373,0.193877,0.23782,0.0557338,0.172288,0.243761,0.136788,0.174408,0.199716,0.158887,0.161003,0.157613,0.199098,0.14791,0.0666486,0.151504,0.156129,0.167376,0.198488,0.191731,0.167067,0.110368,0.173691,0.10807,0.112152,0.108153,0.189679,0.0204722,0.130424,0.111519,0.0172773,0.0335952,0.0178133,0.114491,0.00580821,0.154928,0.170592,0.0327758,0.00742049,0.0174468,0.186292,0.0346987,0.0179867,0.113918,0.0174409,0.170926,0.0333339,0.0173824,0.00589198,0.173336,0.184912,0.146509,0.15399,0.16322,0.161274,0.139745,0.166453,0.10725,0.136,0.106365,0.159407,0.232543,0.13759,0.250557,0.0248096,0.130286,0.108822,0.248764,0.0341695,0.116659,0.0340027,0.152843,0.0148553,0.0325851,0.0345175,0.0435568,0.132399,0.13342,0.133864,0.145921,0.168636,0.165848,0.14291,0.135072,0.136157,0.145341,0.0713864,0.0290769,0.164079,0.11566,0.181843,0.0178909,0.0329999,0.0179687,0.0181313,0.187713,0.0253542,0.0613777,0.182269,0.117681,0.0469534,0.0550181,0.114684,0.146116,0.114204,0.0600164,0.0248969,0.116157,0.0458408,0.0543589,0.0332581,0.00710613,0.0175821,0.180587,0.117065,0.124977,0.151946,0.081352,0.162376,0.157113,0.0598929,0.117992,0.0797333,0.0358812,0.138572,0.115858,0.0455321,0.0553849,0.0241909,0.147104,0.150633,0.151725,0.165251,0.128007,0.153758,0.0319277,0.0330427,0.0172541,0.13663,0.0729317,0),.Dim = c(46L,46L),.Dimnames = list(c("IC0024","D27","T153","D52","GCA_003313675","GCA_001263195","IC0027","GCA_001972325","IC0019","IC0035-1","IC0001","IC0032","D44","PseudaA","T112","IC0020","D8","D25","IC0026","IC0038","D28","ANC-D","D12","ANC-H","IC0010","D13","D45","GCA_003313785","D10","D59","ANC-I","GCA_003313075","D46","D36","D43","D58","IC0035-2","GCA_003316525","GCA_003313055","D11","IC0034","IC0033","D42","Micro","D9","D53"),c("IC0024","IC0035.1","ANC.D","ANC.H","ANC.I","IC0035.2","D53")))

解决方法

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