Biases in CNV detection:
- GC content
- exon capture and amplification efficiency
- latent systemic articacts
Steps:
- 起始文件為排序并索引好的bam文件，通過可比對性（mappability），外顯子大小， 以及最小測序深度閾值來對bam進行過濾。過濾完成之后進行各位點測序深度的計算；
We start with mapped reads from BAM fles (35) that are assembled, sorted and indexed by the same pipeline,
and compute depth of coverage after a series of quality filtering based on mappability, exon size and a cutoff on minimum coverage. - 接著，使用
log-linear模型對測序深度進行一個歸一化。在歸一化（normalization）過程中會對每一個樣本的每一個外顯子生成一個"control coverage"（它表示沒有cnv時候的從測序深度），這些coverage將會用來與實際觀察到的coverage進行比較；
Then, we fit a normalization model based on a log-linear decomposition of the depth of coverage matrix into effects due to GC content, exon capture and amplifcation and other latent systemic factors. - 將每個樣本實際檢測到的coverage與normalization生成的"control coverage"通過"Poisson likelihood-based segmentation algorithm"進行比較，生成同源cnv變異（即與參考基因組序列相同的拷貝數(shù)變異）；
Next, the observed coverage for each exon and each sample is compared to the corresponding estimated control coverage in a Poisson likelihood-based segmentation algorithm, which returns a segmentation of the genome into regions of homogeneous copy number. - 最后，通過比較后得到的倍數(shù)就可以得到cnv了。
A direct estimate of the relative copy number, in terms of fold change from the expected control value, can be used for genotyping.
Sample selection and target fltering
- 推薦使用的數(shù)據(jù)都來自相同的捕獲測序平臺（reducing artifacts）；
- 對于外顯子，采取4個步驟進行過濾：（1）coverage，對于所有樣本的平均深度低于20的外顯子過濾掉；（2）短外顯子（<20bp）;（3）難以進行比對的（mappappability < 0.9）;（4）極端GC值（<20%或>80%）;
Read depth normalization
Due to the extremely high level of systemic bias in WES data, normalization is crucial in WES CNV calling.
CODEX’s multi-sample normalization model takes as input the WES depth of coverage, exon-wise GC content and sample-wise total number of reads
Poisson latent factors and choice of K
有些影響cnv檢測的原因可以直接檢測到（如GC含量，mappability，外顯子大?。?，然而也有些因素是難以直接檢測的，如捕獲建庫測序或樣本導致的bias，稱之為潛在因素（latent factors）。
潛在因素的個數(shù)K是一個非常關(guān)鍵的因素，太大容易抑制屏蔽掉那些產(chǎn)生真實cnv的信號，太小又無法屏蔽那些干擾信號（artifacts），對結(jié)果造成干擾。
CODEX分別使用兩個統(tǒng)計參數(shù)來評估K值：Akaike informa?tion criterion (AIC) and Bayes information criterion (BIC)：
最后使用BIC值來確定K值。
Both CoNIFER and XHMM(28) use latent factor models to remove systemic bias, but their models assume continuous measurements with Gaussian noise structure, while CODEX is based on a Poisson log-linear model, which is more suitable for modeling the discrete counts in WES data, especially when there is high variance in depth of coverage between exons.
CNV detection and copy number estimation
Proper normalization sets the stage for accurate segmentation and CNV calling. For germline CNV detection in normal samples, many CNVs are short and extend over only one or two exons. In this case, simple gene- or exon-level thresholding is suffcient.
For longer CNVs and for copy number estimation in tumors where the events are expected to be large and exhibit nested structure, we propose a Poisson likelihood-based recursive segmentation algorithm.
Discuss
The distinguishing features of CODEX compared to existing methods are:
- (i) CODEX does not require matched normal samples as controls for normalization;
- (ii) The Poisson log-linear model fts better with the WES count data than SVD approaches;
- (iii) Dependence on GC content is modeled by a ?exible non-parametric function in CODEX allowing it to capture non-linear biases;
- (iv) CODEX implements the BIC criterion for choosing the number of latent variables, which gives a conservative normalization on simulated and real data sets;
- (v) Compared to HMM-based segmentation procedures, the segmentation procedure in CODEX is completely off-the-shelf and does not require large relevant training set;
- (vi) CODEX estimates relative copy number, which can be converted to genotypes by thresholding, rather than broad categorizations (deletion, duplication and copy number neutral states)
文獻：CODEX: a normalization and copy number variation detection method for whole exome sequencing.
