Description Usage Arguments Details Value Author(s) References Examples
Clustering of tumor samples into subtypes accounting for tumor purity.
1 | InfiniumClust(tumor.data, purity, K, maxiter = 100, tol = 0.001)
|
tumor.data |
numeric matrix of beta values for tumor samlpes. The rownames of tumor.data should be probe names of Infinium 450k array, and colnames should be names of tumor samples. |
purity |
purities for tumor samples. Could be estimated by |
K |
the number of clusters. |
maxiter |
the maximum number of iterations allowed. Default is 100. |
tol |
tolerance for convergence of EM iterations. Default is 0.001. |
An EM based statistical method for subtype classification based on DNA methylation data, while adjusting for tumor purity.
InfiniumClust returns a list consisting oflikelihood tol.ll
and membership matrix Z
.
tol.ll |
total log-likelihood of converged EM algorithm. |
Z |
the membership matrix, where row corresponds to tumor samples and column corresponds to K clusters. |
Xiaoqi Zheng xqzheng@shnu.edu.cn and Hao Wu hao.wu@emory.edu
W. Zhang, H. Feng, H. Wu and X. Zheng (2016). Tumor purity improves cancer subtype classification from DNA methylation data. Submitted.
1 2 3 4 5 6 7 8 9 10 | ## load example data
data(beta.emp)
normal.data <- beta.emp[,1:21]
tumor.data <- beta.emp[,22:31]
## estimate tumor purity
purity <- getPurity(tumor.data = tumor.data,tumor.type= "LUAD")
## cluster tumor samples accounting for tumor purity
out <- InfiniumClust(tumor.data,purity,K=3, maxiter=5, tol=0.001)
|
Loading required package: matrixStats
The number of tumor samples is: 10
Calculating tumor purity ...
Iter 1 , diff= 0.1287939
Iter 2 , diff= 0.1157339
Iter 3 , diff= 0.0595212
Iter 4 , diff= 0.05458117
Iter 5 , diff= 0.03671596
# of clusters chosen: 3
Probability of each cluster = 0.3 0.4 0.3
Total log-likelihood = 1030.146
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