我正在执行聚类分析并在R中使用pheatmap函数。我想提取聚类的每个成员。
我用来生成带有kmeans聚类的pheatmap的命令是：
pheatmap(t, kmeans_k=65, cluster_cols=F, mypalette3,display_numbers = T)
现在，我如何获得每个群集的成员？
数据：
geneid S1 S2 S3 S4 M3 M4 M6
ENSRNOG00000000012 0.8032270364 1.5058909297 1.0496307677 1.4168397419 0.2750070475 0.9708536543 1.1570437101
ENSRNOG00000000021 3.0250287945 3.7782085764 3.4449320489 2.7004397181 3.2464080872 3.1795110503 2.9429835982
ENSRNOG00000000024 2.0669502439 2.5210507369 2.2555007331 1.7949356628 1.4382928516 1.9373443922 1.5210507369
ENSRNOG00000000033 2.7004397181 2.4724877715 2.1391420191 2.1309308698 1.8032270364 1.8757800631 1.7527485914
ENSRNOG00000000034 1.4541758932 1.3617683594 0.9963887464 0.7136958148 0.8718436485 0.6690267655 0.516015147
ENSRNOG00000000040 4.9420452599 5.0565835284 5.3527938294 4.8639384504 4.0891591319 4.2742616613 3.1731274335
ENSRNOG00000000041 2.6194130106 3.2637856139 3.4489009511 3.2032011563 3.7015490569 3.5410191531 3.0976107966
ENSRNOG00000000042 4.1263947376 4.6284819944 3.9731520379 3.014355293 3.0018022426 2.8972404256 2.5285713189
ENSRNOG00000000043 5.1051751923 5.7436226761 6.3211163506 6.5046203924 6.6071823374 6.2467880938 5.8371863852
ENSRNOG00000000044 3.2854022189 4.0465783666 4.1513717763 3.9250499647 4.5316933609 4.2727697324 3.7980505148
ENSRNOG00000000047 2.5248159284 1.8933622108 1.5210507369 1.0908534305 1.6229303509 1.9523335664 2.0976107966
ENSRNOG00000000048 3.5722833667 3.8569856898 3.8841094514 3.7202784652 4.2311251579 3.8399595875 3.6028844087
ENSRNOG00000000054 2.0823619696 2.6241008946 2.5058909297 1.3729520979 0.748461233 0.9927684308 0.8073549221
ENSRNOG00000000062 3.846994687 4.0609120496 4.1647058402 3.6644828404 3.6496154591 3.2957230245 3.1602748314
ENSRNOG00000000064 4.971543554 4.9993235782 5.1185258489 4.194559886 3.8639384504 4.2883585622 4.0531113365
ENSRNOG00000000066 3.2809563138 4.0413306068 4.0759604132 3.5422580498 3.7495342677 2.9411063109 2.6040713237
ENSRNOG00000000068 3.2986583156 3.5204222485 3.7436226761 3.3132458518 3.6427015718 3.4019034716 3.166715445
ENSRNOG00000000070 1.5235619561 2.266036894 2.2433644257 1.6229303509 2.1009776477 2.2630344058 1.9107326619
ENSRNOG00000000073 2.6780719051 2.9269482479 1.8559896973 1.3950627995 2.0426443374 2.266036894 1.9297909977
ENSRNOG00000000075 2.8559896973 2.9392265777 2.7235585615 2.2448870591 1.5109619193 1.8718436485 1.7092906357
ENSRNOG00000000081 4.8609627979 5.1501534552 5.7869883453 5.7993463875 5.6383635059 4.5478199566 4.2764966656
ENSRNOG00000000082 4.0018022426 4.1787146412 4.2067213574 3.5285713189 3.8063240574 4.0626398283 3.2913088598
ENSRNOG00000000091 0.7697717392 1.0036022367 0.867896464 0.5459683691 1.4541758932 1.8032270364 1.7311832416
ENSRNOG00000000095 3.5410191531 3.5348086612 3.9527994779 3.408711861 3.6028844087 3.0992952043 2.8011586561
ENSRNOG00000000096 1.4568061492 1.5655971759 1.6135316529 1.7527485914 1.4594316186 1.8559896973 1.673556424
ENSRNOG00000000098 2.414135533 3.5122268865 3.5147534984 3.3015876466 4.0755326312 3.8747969659 3.187451054
ENSRNOG00000000104 2.7125957804 2.5969351424 2.5459683691 1.3219280949 1.5849625007 1.6088092427 1.3161457423
ENSRNOG00000000105 1.6016965165 1.3015876466 1.1890338244 1.516015147 0.7570232465 0.6870606883 0.6040713237
ENSRNOG00000000108 3.2854022189 3.6976626335 3.8865501473 2.6369145804 2.6040713237 2.3923174228 1.8953026213
ENSRNOG00000000111 1.6229303509 2.09592442 2.0772429989 1.7782085764 1.673556424 0.9927684308 1.2570106182
ENSRNOG00000000112 2.2078928516 2.1826922975 2.4249220882 2.0250287945 2.1110313124 2.0635029423 1.8953026213
ENSRNOG00000000121 1.9202933002 2.5273206079 2.5741015081 2.2265085298 2.582556003 2.5753123307 2.1984941536
ENSRNOG00000000122 4.1255684518 4.4299506574 4.5071603491 4.2637856139 4.34269696 3.5849625007 3.9040023163
ENSRNOG00000000123 1.7070829918 1.9616233283 2.1127001327 1.4222330007 1.9221978484 1.9708536543 1.5801454844
ENSRNOG00000000127 2.3881895372 3.0347439493 2.9981955032 3.2295879227 4.0435194937 3.7729413378 3.2957230245
ENSRNOG00000000129 2.3074285252 2.979110755 3.1992797213 2.2203299549 3.6322682155 3.8982083525 3.5801454844
ENSRNOG00000000130 4.1622906135 4.7150696794 4.8733210629 3.9772799235 4.5849625007 4.9236246114 4.7739963251
ENSRNOG00000000133 3.2000648615 3.1168637577 3.1787146412 2.9579145986 2.7928553524 2.6780719051 2.2078928516
ENSRNOG00000000138 0.516015147 0.5993177937 1.0356239097 1.5849625007 2.2326607568 1.9745293125 2.0285691522
ENSRNOG00000000142 2.9278964537 2.3291235963 0.9671686075 1.4168397419 0.7048719645 1.9927684308 1.7224660245
ENSRNOG00000000145 3.2164548651 3.5490530293 3.4195388915 2.8797057663 2.3362833879 2.5849625007 2.6937657122
ENSRNOG00000000150 2.6380738372 2.9708536543 3.014355293 2.6870606883 2.6158870739 2.3161457423 2.4329594073
ENSRNOG00000000151 2.7125957804 3.5484366247 3.8354188405 4.5447326559 5.6959938131 5.3077927961 5.1941658685
ENSRNOG00000000155 3.0565835284 3.9354597478 3.6803243568 3.5134907456 3.8032270364 3.8865501473 3.2494453411
ENSRNOG00000000156 3.34269696 3.2772408983 1.7761039881 1.1505596766 0.5360529002 0.2750070475 0.3334237337
ENSRNOG00000000157 1.9164766444 2.1424134379 2.054848477 1.9145645235 2.2448870591 2.3305584 1.6599245584
ENSRNOG00000000161 1.7202784652 2.0772429989 1.9945797242 1.4541758932 1.7655347464 2.1602748314 1.8757800631
ENSRNOG00000000164 3.6616356023 4.2596491206 4.0635029423 3.2494453411 3.2418401836 3.1618876824 2.2295879227
ENSRNOG00000000165 1.3504972471 1.6158870739 0.9373443922 0.4541758932 0.7311832416 4.6392321632 4.5403993056
ENSRNOG00000000166 3.3441183345 3.3603642765 3.2494453411 1.9597701552 2.2357270598 3.1456774552 2.8698714062
命令：
d=read.table("FPKM.1.SelectedSamples.txt", header=T, sep="\t", row.names=1)
dm=data.matrix(d)
normalization<-function(x){
dimm=dim(x)
for(i in 1:dimm[1]){
x[i,]=(x[i,]-mean(x[i,]))/sd(x[i,])
}
return(x)
}
t=normalization(dm)
pheatmap(t, kmeans_k=2, cluster_cols=F, mypalette3,display_numbers = T)
谢谢
答案 0 :(得分：2)
obj <- pheatmap(t, kmeans_k=2, cluster_cols=F, display_numbers = T)
obj$kmeans
返回
K-means clustering with 2 clusters of sizes 2, 48
Cluster means:
geneid S1 S2 S3 S4 M3 M4 M6
1 -1.007304 -0.3010378 1.3287680 0.4170368 0.1981381 -0.7853443 0.06406497 0.08567815
2 2.416899 -0.3287130 -0.2537424 -0.2699460 -0.3668002 -0.3736923 -0.36333425 -0.46067116
Clustering vector:
[1] 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Within cluster sum of squares by cluster:
[1] 6.575403 14.153827
(between_SS / total_SS = 59.2 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss" "betweenss" "size" "iter" "ifault"
如果您只想要成员索引，只需输入obj$kmeans$cluster。