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顺手策个文章套路：

用R实现一致性聚类

1. 关于ConsensusClusterPlus

#载入R包library(ConsensusClusterPlus)ls("package:ConsensusClusterPlus")# [1] "calcICL" "ConsensusClusterPlus"

ConsensusClusterPlus function for determing cluster number and class membership by stability evidence.
calcICL function for calculating cluster-consensus and item-consensus.

2.操作

使用 ConsensusClusterPlus 的主要三个步骤：

• 准备输入数据

• 跑程序

• 计算聚类一致性 (cluster-consensus) 和样品一致性 (item-consensus)

3. 准备输入数据

首先收集用于聚类分析的数据，比如 mRNA 表达微阵列或免疫组织化学染色强度的实验结果数据。输入数据的格式应为矩阵。下面以 ALL 基因表达数据为例进行操作。

library(ALL)data(ALL)#这是个表达为列阵数据示例dataset <- exprs(ALL)#取前五行、前五列看看长什么样子dataset[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

取矩阵中 MAD 值（绝对中位差） top 5000 的数据：
在统计学中，绝对中位数MAD是对单变量数值型数据的样本偏差的一种鲁棒性测量。

#取绝对中位差mads <- apply(dataset, 1, mad) #按绝对中位差排序，取前5000数据dataset <- dataset[rev(order(mads))[1:5000],]dim(dataset)# [1] 5000 128

4. 运行 ConsensusClusterPlus

先设定几个参数：

• pItem (item resampling, proportion of items to sample) : 80%

• pFeature (gene resampling, proportion of features to sample) : 80%

• maxK (a maximum evalulated k, maximum cluster number to evaluate) : 6 设置最多想尝试的分组数

• reps (resamplings, number of subsamples) : 50

• clusterAlg (agglomerative heirarchical clustering algorithm) : 'hc' (hclust)

• distance : 'pearson' (1 - Pearson correlation)

title <- “YOUR PATH” #所有的图片以及数据都会输出到这里的results <- ConsensusClusterPlus(dataset, maxK = 6, reps = 50, pItem = 0.8, pFeature = 0.8,  clusterAlg = "hc",  distance = "pearson", title = title, plot = "png") ## 作者这里是pFeature = 1，和前文不符，于是我依然是按0.8输入计算的

这时工作路径的文件夹会出现9张图。

查看一下结果：

# [,1] [,2] [,3] [,4] [,5]# [1,] 1.00000 0.9375000 1.0000000 0.90625 1.0000000# [2,] 0.93750 1.0000000 0.9677419 1.00000 0.9393939# [3,] 1.00000 0.9677419 1.0000000 0.93750 1.0000000# [4,] 0.90625 1.0000000 0.9375000 1.00000 0.9062500# [5,] 1.00000 0.9393939 1.0000000 0.90625 1.0000000results[[2]][["consensusTree"]] # Call:# hclust(d = as.dist(1 - fm), method = finalLinkage)# # Cluster method : average # Number of objects: 128 results[[2]][["consensusClass"]][1:5] # 01005 01010 03002 04006 04007 # 1 1 1 1 1

4.1 一致性矩阵

分别为图例、k = 2, 3, 4, 5 时的矩阵热图。
这个图叫做CM plots，其目的是展示分类情况，找到最“干净”的一张图（也就是白的方块中尽量不掺杂蓝色），就是分类效果最好的一类。

CM plots

 4.2 一致性累积分布函数图

cdf plot

不同聚类数k时的cdf分布。
Empirical cumulative distribution function (CDF) plots display consensus distributions for each k . The purpose of the CDF plot is to find the k at which the distribution reaches an approximate maximum, which indicates a maximum stability and after which divisions are equivalent to random picks rather than true cluster structure.

4.3 Delta Area Plot

一般用elbow method，取拐点处的k值，为最佳分类数。
The delta area score (y-axis) indicates the relative increase in cluster stability.

4.4 Tracking Plot

这个图从行（k）开始看，展示了不同聚类数(k)下，每个sample(列)都被分为了哪一类。比如，k=2时，大部分sample都被分为了淡蓝色那一类，只有中间一小撮被分为深蓝色那一类。
The item tracking plot shows the consensus cluster of items (in columns) at each k (in rows). This allows a user to track an item's cluster assignments across different k, to identify promiscuous items that are suggestive of weak class membership, and to visualize the distribution of cluster sizes across k.

5. 计算聚类一致性 (cluster-consensus) 和样品一致性 (item-consensus)

icl <- calcICL(results, title = title, plot = "png")## 返回了具有两个元素的list，然后分别查看一下dim(icl[["clusterConsensus"]])# [1] 20 3icl[["clusterConsensus"]] # k cluster clusterConsensus# [1,] 2 1 0.9402982# [2,] 2 2 0.9062500# [3,] 3 1 0.8504193# [4,] 3 2 0.9062500# [5,] 3 3 0.9869781# [6,] 4 1 0.9652282# [7,] 4 2 0.9045058# [8,] 4 3 0.9062500# [9,] 4 4 0.9728043# [10,] 5 1 0.9216686# [11,] 5 2 0.9145987# [12,] 5 3 0.9062500# [13,] 5 4 0.9874950# [14,] 5 5 NaN# [15,] 6 1 0.9307379# [16,] 6 2 0.8897721# [17,] 6 3 0.7474747# [18,] 6 4 0.8750000# [19,] 6 5 0.9885269# [20,] 6 6 0.6333333dim(icl[["itemConsensus"]])# [1] 2560 4icl[["itemConsensus"]][1:5,] # k cluster item itemConsensus# 1 2 1 28032 0.9523526# 2 2 1 28024 0.9366226# 3 2 1 03002 0.9686272# 4 2 1 01005 0.9573623# 5 2 1 04007 0.9549235

5.1 item-Consensus Plot

IC plot

Item-consensus (IC) is the average consensus value between an item and members of a consensus cluster, so that there are multiple IC values for an item at a k corresponding to the k clusters. IC plots display items as vertical bars of coloured rectangles whose height corresponds to IC values.

（这张图不重要）

5.2 Cluster-Consensus Plot

cluster-consensus plot

（这张图也不重要）

References

• ConsensusClusterPlus Tutorial https://bioconductor.org/packages/release/bioc/vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf

• Nowicka, Malgorzata, et al. "CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets." F1000Research 6 (2017).