cnvs_inheritance {CNVgears} | R Documentation

## Compute CNVs inheritance

### Description

`cnvs_inheritance` compute CNVs inheritance pattern and search good de novo CNVs candidates

### Usage

    cnvs_inheritance(
      sample_list,
      markers,
      results,
      raw_path,
      mmethod = 1,
      alfa = 0.05,
      min_NP = 10,
      adjust_pval = TRUE,
      reciprocal_overlap = 0.3
    )

### Arguments

`sample_list` | a `data.table`, the output of `read_metadt`.
`markers` | a `data.table`, the output of `read_NGS_intervals` or `read_finalreport_snps`, depending on the initial data type.
`results` | a `CNVresults` object, the output of `read_results` or `inter_res_merge`.
`raw_path` | `character`, the path where raw data processed by `read_NGS_raw` or `read_finalreport_raw`.
`mmethod` | fine-screening method.
`alfa` | p value alfa value, default in 0.05.
`min_NP` | minimum number of points to try fine screening a de novo candidate
`adjust_pval` | logical compute adjusted p-value?
`reciprocal_overlap` | minimum reciprocal overlap for inherited CNVs detection

### Details

Given a trio (mother, father, offspring) this function computes inheritance patterns of the offspring's CNVs. This is done both by comparing the actual calls in the trio (CNVs-level) and the raw data (markers-level). For this reason it is suggested to use it separately on each method and then combine the results. As an example if the focus are de novo CNVs, select only those and then combine the outputs using `inter_res_merge`. In this way it also possible to further increase the confidence in the results, e.g. by filtering out all de novo candidates called by a single method.

Internally the function is structured in several steps. [...]

### Value

a `CNVresults` object with some additional columns: inheritance and related p-values.

[Package CNVgears version 1.0.0 Index]