After years fighting, our Sclsut paper published on Nature Protocols finally. Enjoy!
Yupeng Cun, Tsun-Po Yang, Viktor Achter*, Ulrich Lang, Martin Peifer, Copy number analysis and inference of subclonal populations in cancer genomes using Sclust. Nature Protocols, 2018，DOI: 10.1038/nprot.2018.033
Sclust download link: rj.run/downloads/Sclust.tgz)
Frequent Q&A on Sclust software package uses：
Our new methods final launched on Nature Protocols, where we developed a series of methods and related C++/R combined software package, Sclust（around 1.5Gb,大文件谨慎载）. In Sclust, you can do copy number calling, cancer tissue purity estimating and clone and subclone structure inferring from normal-tumor paired whole genome/exon sequencing data.
1. 可以准确地做copy number calling， tumor purity estimating，subclonal inferring；
2. subclonal inferring的速度超级快。4000~6000 个SNVs 的 clonal inferring 过程在个人电脑上只需3到5秒。
3. sclust 给出了每个集群的倍数树变异，目前只有少数个软件提供这个功能。
联系邮件：email@example.com。 下面是clonal 推断一些背景。
A new R package, netClass, has been release. netClass integrate network information, such as protein-protein interaction network or KEGG, to mRNA classification, but also incorporate miRNA to mRNA with mi-mRNA interaction network for biomarker discovery. This methods we called stSVM and already published in PloS ONE (Cun et al 2013). Apart from stSVM, we also implement the flowing methods in netClass:
- AEP (average gene expression of pathway), Guo et al., BMC Bioinformatics 2005, 6:58.
- PAC (pathway activitive classification), Lee E, et al., PLoS Comput Biol 4(11): e1000217.
- hubc (Hub nodes classification), Taylor et al.(2009) Nat. Biotech.: doi: 10.1038/nbt.152
- frSVM (filter via top ranked genes), Cun et al. arXiv:1212.3214 ; Winter etal., PLoS Comput Biol 8(5): e1002511.
- stSVM (network smoothed t-statistic) , Cun et al., PloS One,.
NetClass can be download from souceforg ( http://sourceforge.net/projects/netclassr/) or , CRAN (http://cran.r-project.org/web/packages/netClass/ ). For more detail of netClass, you can refer these four papers:
A review collection in current approach in Translational Bioinformatics.
‘Translational Bioinformatics’ is a collection of PLOS Computational Biology Education articles which reads as a “book” to be used as a reference or tutorial for a graduate level introductory course on the science of translational bioinformatics.
Translational bioinformatics is an emerging field that addresses the current challenges of integrating increasingly voluminous amounts of molecular and clinical data. Its aim is to provide a better understanding of the molecular basis of disease, which in turn will inform clinical practice and ultimately improve human health.
The concept of a translational bioinformatics introductory book was originally conceived in 2009 by Jake Chen and Maricel Kann. Each chapter was crafted by leading experts who provide a solid introduction to the topics covered, complete with training exercises and answers. The rapid evolution of this field is expected to lead to updates and new chapters that will be incorporated into this collection.
Collection editors: Maricel Kann, Guest Editor, and Fran Lewitter, PLOS Computational Biology Education Editor.
Download the full Translational Bioinformatics collection here: PDF
Collection URL: www.ploscollections.org/translationalbioinformatics
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002796
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002826
Casey S. Greene, Olga G. Troyanskaya
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002816
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002805
Mileidy W. Gonzalez, Maricel G. Kann
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002819
Dong-Yeon Cho, Yoo-Ah Kim, Teresa M. Przytycka
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002820
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002821
Konrad J. Karczewski, Roxana Daneshjou, Russ B. Altman
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002817
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002858
Nigam H. Shah, Tyler Cole, Mark A. Musen
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002827
Xiang Zhang, Shunping Huang, Zhaojun Zhang, Wei Wang
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002828
William S. Bush, Jason H. Moore
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002822
Xochitl C. Morgan, Curtis Huttenhower
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002808
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002823
Miguel Vazquez, Victor de la Torre, Alfonso Valencia
PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002824
In our recent publication in BMC bioinformatics, we acompared a great deal of feature selection methods to finding prognostic biomakers in 6 breast cancer gene expresion data. No methods show significant performacne in prediction accuracy, feature selection stability and biogical interprety, which against previeous reseach results: current network-based appraoch did not show much benift in our analysis. Meanwhile, A group from NKI also show the simliar results in PloS One. The R codes for these algorithms in our paper is availiable as request.
Some recent paper on how disease gene network works and the metastasis of cancer. Machine Learning is a good tool for study the relation between individual gene and disease. here are the papers:
Infectious Disease Modeling of Social Contagion in Networks
Alison L. Hill1,2*, David G. Rand1,3, Martin A. Nowak1,4,5,Nicholas A. Christakis6,7,8
Information, trends, behaviors and even health states may spread between contacts in a social network, similar to disease transmission. However, a major difference is that as well as being spread infectiously, it is possible to acquire this state spontaneously. For example, you can gain knowledge of a particular piece of information either by being told about it, or by discovering it yourself. In this paper we introduce a mathematical modeling framework that allows us to compare the dynamics of these social contagions to traditional infectious diseases. We can also extract and compare the rates of spontaneous versus contagious acquisition of a behavior from longitudinal data and can use this to predict the implications for future prevalence and control strategies. As an example, we study the spread of obesity, and find that the current rate of becoming obese is about 2 per year and increases by 0.5 percentage points for each obese social contact, while the rate of recovering from obesity is 4per year. The rates of spontaneous infection and transmission have steadily increased over time since 1970, driving the increase in obesity prevalence. Our model thus provides a quantitative way to analyze the strength and implications of social contagions.