bin size的选择不是经验值，bin size的选择有两个标准：1. mean to sigma的比率大概在4-5之间, 计算mean to sigma的命令为：cnvnator -root my.root -eval2. 跟测序深度有关系：given the same data quality and read length, we observed that the optimal bin size, and thus breakpoint resolution accuracy, scales roughly inversely with the coverage, resulting in ~100-bp bins for 20–30X coverage; ~500-bp bins for 4–6X coverage, and ~30-bp bins for ~100X coverage.至于具体的bin size的选择，就要根据这两条去权衡。

非常感谢您的解答！ 我再请教您一个问题啊，就是我发现好像CNVnator不能处理case-control的sample， 那么对于这种有normal-tumor的成对样本，您觉得用什么做CNV分析比较好呢？

**How to select the bin size?1. the ratio of mean to sigam is roughly 4-5; -eval returns values of mean and sigma once histograms are calculated eg: cnvnator -root my.root -his 1000 -d dir_with_chrom_fas cnvnator -root my.root -eval 10002. Specifically, given the same data quality and read length, we observed that the optimal bin size, and thus breakpoint resolution accuracy, scales roughly inversely with the coverage, resulting in ~100-bp bins for 20–30X coverage; ~500-bp bins for 4–6X coverage, and ~30-bp bins for ~100X coverage.**How to filter the results from cnvnator? 1. q0<0.5 2. the first e-value(it is like the blast e-value) is small**批量获得genotype 和 Rd value awk '{print $2}END{print "exit"}' out.cnv | cnvnator -root out.root -genotype 100**-1 for q0 means it couldn’t be calculated. The likely reason is that no reads were mapped in that region.