ConsensusClusterPlus， 一步到位的一致性聚类！
ConsensusClusterPlus， 一步到位的一致性聚类！
生信修炼手册
发布于 2022-06-09 17:51:57
发布于 2022-06-09 17:51:57
文章被收录于专栏：生信修炼手册
在之前的文章中分享了一致性聚类的原理，本文介绍下如何用R语言进行分析。ConsensusClusterPlus这个R包，就是专门用于一致性聚类分析的，为了简化调用，甚至将所有的步骤都封装到了一个函数里面，所以其使用方法非常的简单，一共三步
1. 加载R包
2. 把表达量数据读进去
3. 运行一致性聚类的函数
是不是和把大象装进冰箱一样简单，但是我们必须注意，这样简单的背后，实际是一个黑盒子，如果不了解原理，你只能得到结果，但是结果说明了什么信息，你一无所知。
下面是具体步骤
1. 准备输入数据
行为基因，列为样本的表达量数据，为了获得最佳的聚类效果，可以对基因进行筛选， 对矩阵进行归一化操作，代码如下
> library(ALL)
> data(ALL)
> d=exprs(ALL)
# 表达量数据
> d[1:5,1:5]
01005 01010 03002 04006 04007
1000_at 7.597323 7.479445 7.567593 7.384684 7.905312
1001_at 5.046194 4.932537 4.799294 4.922627 4.844565
1002_f_at 3.900466 4.208155 3.886169 4.206798 3.416923
1003_s_at 5.903856 6.169024 5.860459 6.116890 5.687997
1004_at 5.925260 5.912780 5.893209 6.170245 5.615210
> mad(d[1, ])
[1] 0.2701619
> mads=apply(d,1,mad)
> d=d[rev(order(mads))[1:5000],]
> dim(d)
[1] 5000 128
# 归一化操作
> d = sweep(d,1, apply(d,1,median,na.rm=T))
> dim(d)
[1] 5000 128
> d[1:5,1:5]
01005 01010 03002 04006 04007
36638_at 1.5561207 0.9521271 -0.05018082 4.780378 3.93006775
39318_at 1.1913532 2.5013225 -2.38793537 -1.199521 1.93626914
38514_at 1.0207162 3.2785671 1.55949145 -3.345919 -0.01548269
266_s_at 1.8292604 0.3624327 1.54913247 -1.286294 1.75669694
38585_at -0.9240204 0.1895020 3.44968363 -2.216822 5.187027262. 运行ConsensusClusterPlus
ConsensusClusterPlus就是核心函数了，包括了以下几个参数
1. pItem, 选择80%的样本进行重复抽样
2. pfeature, 选择80%的基因进行重复抽样
3. maxK, 最大的K值，形成一系列梯度
4. reps, 重复抽样的数目
5. clusterAlg, 层次聚类的算法
6. distanc, 距离矩阵的算法
7. title, 输出结果的文件夹名字，包含了输出的图片
8. seed, 随机种子，用于重复结果
注意，在实际运行中，推荐reps设置的更大，比如1000， maxK设置的更大，比如20，具体代码如下
> library(ConsensusClusterPlus)
> title=tempdir()
> results = ConsensusClusterPlus(d,maxK=6,reps=50,pItem=0.8,pFeature=1, title=title,clusterAlg="hc",distance="pearson",seed=1262118388.71279,plot="png", writeTable = TRUE)
end fraction
clustered
clustered
clustered
clustered
clustered函数的返回值是一个列表，每个列表子项对应给具体的K， K最小值为2
> str(results[[2]])
List of 5
$ consensusMatrix: num [1:128, 1:128] 1 1 0.895 1 1 ...
$ consensusTree :List of 7
..$ merge : int [1:127, 1:2] -1 -4 -5 -6 -7 -9 -11 -12 -14 -15 ...
..$ height : num [1:127] 0 0 0 0 0 0 0 0 0 0 ...
..$ order : int [1:128] 101 128 127 126 125 124 123 122 121 120 ...
..$ labels : NULL
..$ method : chr "average"
..$ call : language hclust(d = as.dist(1 - fm), method = finalLinkage)
..$ dist.method: NULL
..- attr(*, "class")= chr "hclust"
$ consensusClass : Named int [1:128] 1 1 1 1 1 1 1 1 1 1 ...
..- attr(*, "names")= chr [1:128] "01005" "01010" "03002" "04006" ...
$ ml : num [1:128, 1:128] 1 1 0.895 1 1 ...
$ clrs :List of 3
..$ : chr [1:128] "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" ...
..$ : num 2
..$ : chr [1:2] "#A6CEE3" "#1F78B4"
# 一致性矩阵，样本的邻接矩阵
> dim(d)
[1] 5000 128
> dim(results[[2]][["consensusMatrix"]])
[1] 128 128
> results[[2]][["consensusMatrix"]][1:5,1:5]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.0000000 1.0000000 0.8947368 1.0000000 1.000000
[2,] 1.0000000 1.0000000 0.9142857 1.0000000 1.000000
[3,] 0.8947368 0.9142857 1.0000000 0.8857143 0.969697
[4,] 1.0000000 1.0000000 0.8857143 1.0000000 1.000000
[5,] 1.0000000 1.0000000 0.9696970 1.0000000 1.000000
> results[[2]][["consensusTree"]]
Call:
hclust(d = as.dist(1 - fm), method = finalLinkage)
Cluster method : average
Number of objects: 128
# 样本的聚类树
> results[[2]][["consensusTree"]]
Call:
hclust(d = as.dist(1 - fm), method = finalLinkage)
Cluster method : average
Number of objects: 128
# consensusClass， 样本的聚类结果
> length(results[[2]][["consensusClass"]])
[1] 128
> results[[2]][["consensusClass"]][1:5]
01005 01010 03002 04006 04007
1 1 1 1 1
# ml, 就是consensusMatrix
> results[[2]][["ml"]][1:5,1:5]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.0000000 1.0000000 0.8947368 1.0000000 1.000000
[2,] 1.0000000 1.0000000 0.9142857 1.0000000 1.000000
[3,] 0.8947368 0.9142857 1.0000000 0.8857143 0.969697
[4,] 1.0000000 1.0000000 0.8857143 1.0000000 1.000000
[5,] 1.0000000 1.0000000 0.9696970 1.0000000 1.000000
> results[[2]][["consensusMatrix"]][1:5,1:5]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.0000000 1.0000000 0.8947368 1.0000000 1.000000
[2,] 1.0000000 1.0000000 0.9142857 1.0000000 1.000000
[3,] 0.8947368 0.9142857 1.0000000 0.8857143 0.969697
[4,] 1.0000000 1.0000000 0.8857143 1.0000000 1.000000
[5,] 1.0000000 1.0000000 0.9696970 1.0000000 1.000000
# clrs, 颜色
> results[[2]][["clrs"]]
[[1]]
[1] "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3"
[13] "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3"
[25] "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3"
[37] "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3"
[49] "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3"
[61] "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3"
[73] "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3"
[85] "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#A6CEE3" "#1F78B4"
[97] "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4" "#A6CEE3" "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4"
[109] "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4" "#A6CEE3" "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4"
[121] "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4" "#1F78B4"
[[2]]
[1] 2
[[3]]
[1] "#A6CEE3" "#1F78B4"3. 收集cluster-consensus和item-consensus 矩阵
代码如下
> icl = calcICL(results,title=title,plot="png")
> icl[["clusterConsensus"]]
k cluster clusterConsensus
[1,] 2 1 0.7681668
[2,] 2 2 0.9788274
[3,] 3 1 0.6176820
[4,] 3 2 0.9190744
[5,] 3 3 1.0000000
[6,] 4 1 0.8446083
[7,] 4 2 0.9067267
[8,] 4 3 0.6612850
[9,] 4 4 1.0000000
[10,] 5 1 0.8175802
[11,] 5 2 0.9066489
[12,] 5 3 0.6062040
[13,] 5 4 0.8154580
[14,] 5 5 1.0000000
[15,] 6 1 0.7511726
[16,] 6 2 0.8802040
[17,] 6 3 0.7410730
[18,] 6 4 0.8154580
[19,] 6 5 0.7390864
[20,] 6 6 1.0000000
> dim(icl[["itemConsensus"]])
[1] 2560 4
> 128 * (2 + 3 + 4 + 5 + 6)
[1] 2560
> icl[["itemConsensus"]][1:5,]
k cluster item itemConsensus
1 2 1 28031 0.6173782
2 2 1 28023 0.5797202
3 2 1 43012 0.5961974
4 2 1 28042 0.5644619
5 2 1 28047 0.62593504. 结果解读
在输出文件夹中，包含了多种输出可视化结果，每种结果的含义如下
1）consensus matrix 热图
consensus matrix 为样本方阵，数值代表两个同属一个cluster的可能性，取值范围从0到1， 颜色从白色到深蓝色
2）consensus 累计分布图 CDF
对于每个K对应的consensus matrix, 采用100个bin的柱状图来计算累计分布，
CDF图可以用来帮助决定最佳的K值
3）delta area plot
对于每个K, 计算K和K-1相比，CDF 曲线下面积的相对变化，对于K=2, 因为没有K=1, 所以是totla CDF curve area，选取增加不明显的点作为最佳的K值
4）tracling plot
行为样本，列为每个K, 用热图展示样本在每个K下的cluster, 用于定性评估不稳定的聚类和不稳定的样本
·end·
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原始发表：2022-04-27，如有侵权请联系 cloudcommunity@tencent.com 删除
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