CGH-TRIMMER on a) cell line BT-474, chromosome 5 b) cell line HS578T, chromosome 11
About this Web Site
This web site contains the code, links to the datasets and a brief summary of our contributions contained in our submission to the Journal of Experimental Algorithmics.
Contributions
Let P=(P1,..,Pn) the noisy input measurements. Our contributions are can be summarized in the following bullets:
- We propose a new formulation of the aCGH denoising problem which we solve using a vanilla dynamic programming algorithm in
O(n^2) time.
- We provide a technique which approximates the optimal value of our objective function within additive \epsilon error and runs in
Õ(n^{4/3+\delta}log(\frac{U}{\epsilon}) time, where \delta is an arbitrarily small positive constant and
U=max(sqrt{C},(|P_i|)_{i=1,...,n}).
-
We provide another technique for approximate dynamic programming
which solves the corresponding recurrence within a multiplicative factor
of (1+$\epsilon$) and runs in $O(n \log{n} / \epsilon )$.
- We validate our proposed model on both synthetic and real data. Specifically,
our segmentations result in superior precision and recall compared to leading competitors on benchmarks of synthetic
data and real data from the Coriell cell lines. In addition, we are able to find
several novel markers not recorded in the benchmarks but supported in the oncology literature.
Related Publications
Source Code
CGHTRIMMER is implemented in MATLAB. You can download the source code from this
link (zip)
or from this
link (tar.gz). A C implementation (not used in the paper) with a simple demo is also available from this
link (.c).
Datasets
Figures
You can download the figures contained in our paper from this
link (zip) .