Sclust paper published on NP

After years fighting, our Sclsut paper published on Nature Protocols finally. Enjoy!

Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust

  • Nature Protocols volume13pages1488–1501 (2018)
  • doi:10.1038/nprot.2018.033
Published: 24 May 2018

Abstract

The genomes of cancer cells constantly change during pathogenesis. This evolutionary process can lead to the emergence of drug-resistant mutations in subclonal populations, which can hinder therapeutic intervention in patients. Data derived from massively parallel sequencing can be used to infer these subclonal populations using tumor-specific point mutations. The accurate determination of copy-number changes and tumor impurity is necessary to reliably infer subclonal populations by mutational clustering. This protocol describes how to use Sclust, a copy-number analysis method with a recently developed mutational clustering approach. In a series of simulations and comparisons with alternative methods, we have previously shown that Sclust accurately determines copy-number states and subclonal populations. Performance tests show that the method is computationally efficient, with copy-number analysis and mutational clustering taking <10 min. Sclust is designed such that even non-experts in computational biology or bioinformatics with basic knowledge of the Linux/Unix command-line syntax should be able to carry out analyses of subclonal populations.

IMG_20171205_112001

A new fast method for copy number calling, tissue purity estimating and subclone inferring in cancer genome

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.

先总结一下,我们方法的有如下点:

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,目前还有少数个软件提供这个功能。

欢迎使用软件,欢迎咨询,欢迎交流。

联系邮件:yp.cun@outlook.com。 下面clonal 推断一些背景。

Continue reading “A new fast method for copy number calling, tissue purity estimating and subclone inferring in cancer genome”

【c】Frontiers in Single Cell Genomics, Suzhou

Frontiers in Single Cell Genomics

http://www.csh-asia.org/2016meetings/cell.html

 

We are pleased to announce the Cold Spring Harbor Asia conference on Frontiers in Single Cell Genomics which will be held in Suzhou, China, located approximately 60 miles west of Shanghai. The conference will begin at 7:00pm on the evening of Monday November 7, and will conclude after lunch on November 11, 2016.

Continue reading “【c】Frontiers in Single Cell Genomics, Suzhou”

A brief introduction to “apply” in R

a good, practical guidline for “apply” in R.

What You're Doing Is Rather Desperate

At any R Q&A site, you’ll frequently see an exchange like this one:

Q: How can I use a loop to […insert task here…] ?
A: Don’t. Use one of the apply functions.

So, what are these wondrous apply functions and how do they work? I think the best way to figure out anything in R is to learn by experimentation, using embarrassingly trivial data and functions.

If you fire up your R console, type “??apply” and scroll down to the functions in the base package, you’ll see something like this:

Let’s examine each of those.

1. apply
Description: “Returns a vector or array or list of values obtained by applying a function to margins of an array or matrix.”

OK – we know about vectors/arrays and functions, but what are these “margins”? Simple: either the rows (1), the columns (2) or both (1:2). By “both”, we mean “apply the…

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Inferring tumour evolution 2 – Comparison to classical phylogenetics

Scientific B-sides

Quick recap: Last time we talked about tumor evolution and I presented a toy example to introduce key concepts. I also introduced the intra-tumor phylogeny problem: Given a sample of the genomes of clones in a tumour, reconstruct its `life history’. This problem consists of two sub-problems: (1)identification of clones, and (2) inferring evolutionary relationships between clones.

This problem falls into the general area of reconstructing phylogenetic trees — so how does inferring clonal trees compare to classical phylogenetic methods?

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