Description Details Author(s) References See Also Examples
Clomial fits binomial distributions to counts obtained from Next Gen Sequencing data of multiple samples of the same tumor. The trained parameters can be interpreted to infer the clonal structure of the tumor.
Package: | Clomial |
Type: | Package |
Version: | 0.99.0 |
Date: | 2014-02-11 |
License: | GPL (>= 2) |
The main function is Clomial() which requires 2 matrices Dt and Dc among its inputs. They contain the counts of the alternative allele, and the total number of processed reads, accordingly. Their rows correspond to the genomic loci, and their columns correspond to the samples. Several models should be trained using different initial values to escape from local optima, and the best one in terms of the likelihood can be chosen by choose.best() function.
Habil Zare and Alex Hu
Maintainer: Habil Zare <zare@u.washington.edu>
Inferring clonal composition from multiple sections of a breast cancer, Zare et al., PLoS Computational Biology 10.7 (2014): e1003703.
Clomial
, choose.best
,
Clomial.iterate
, Clomial.likelihood
,
compute.bic
, breastCancer
1 2 3 4 5 6 7 8 9 10 11 | set.seed(1)
data(breastCancer)
Dc <- breastCancer$Dc
Dt <- breastCancer$Dt
ClomialResult <-Clomial(Dc=Dc,Dt=Dt,maxIt=20,C=4,doParal=FALSE,binomTryNum=2)
chosen <- choose.best(models=ClomialResult$models)
M1 <- chosen$bestModel
print("Genotypes:")
print(round(M1$Mu))
print("Clone frequencies:")
print(M1$P)
|
Loading required package: matrixStats
There were 50 or more warnings (use warnings() to see the first 50)
[1] "Genotypes:"
C1 C2 C3 C4
chr12:111884756 0 0 1 1
chr16:7743333 0 1 0 1
chr9:139352016 0 0 1 1
chr17:10404056 0 0 0 1
chr20:33764653 0 0 0 1
chr9:136081779 0 0 0 1
chr4:187250053 0 0 0 1
chr3:132219738 0 0 0 1
chr1:117620562 0 0 0 1
chr4:39606599 0 0 1 1
chr5:140215480 0 0 0 1
chr6:51907756 0 0 1 0
chr17:34913028 0 0 1 0
chr20:2639398 0 0 1 0
chr3:190165603 0 0 1 0
chr3:193201749 0 0 1 0
chr17:10369879 0 0 1 0
[1] "Clone frequencies:"
A5-2 A1-1 A1-4 A1-3 A1-2 A2-1
C1 0.991577225 0.11624705 0.08356962 0.3792096621 0.6257125753 0.60261619
C2 0.003984806 0.40250835 0.38943283 0.2127421740 0.1403864050 0.18715486
C3 0.001256652 0.02979094 0.02928620 0.0007160315 0.0003952281 0.01749114
C4 0.003181317 0.45145366 0.49771135 0.4073321324 0.2335057916 0.19273781
A2-3 A3-1 A3-2 A3-3 A3-4 B1-1
C1 0.28325271 0.419965845 0.267547575 0.1525604 0.31201884 0.8171230521
C2 0.25692616 0.213801538 0.316470882 0.3247213 0.35203785 0.0002490596
C3 0.01418404 0.003358363 0.003344565 0.0157617 0.01912434 0.1821438657
C4 0.44563709 0.362874254 0.412636978 0.5069566 0.31681897 0.0004840226
B1-2
C1 0.866834468
C2 0.002329632
C3 0.129641698
C4 0.001194202
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