CIS_grubbs {ISAnalytics} | R Documentation

## Grubbs test for Common Insertion Sites (CIS).

### Description

Statistical approach for the validation of common insertion sites significance based on the comparison of the integration frequency at the CIS gene with respect to other genes contained in the surrounding genomic regions. For more details please refer to this paper: https://ashpublications.org/blood/article/117/20/5332/21206/Lentiviral-vector-common-integration-sites-in

### Usage

    CIS_grubbs(
      x,
      genomic_annotation_file = "hg19",
      grubbs_flanking_gene_bp = 1e+05,
      threshold_alpha = 0.05,
      by = NULL
    )

### Arguments

`x` | An integration matrix, must include the `mandatory_IS_vars()` columns and the `annotation_IS_vars()` columns
`genomic_annotation_file` | Database file for gene annotation, see details.
`grubbs_flanking_gene_bp` | Number of base pairs flanking a gene
`threshold_alpha` | Significance threshold
`by` | Either `NULL` or a character vector of column names. If not NULL, the function will perform calculations for each group and return a list of data frames with the results. E.g. for `by = "SubjectID"`, CIS will be computed for each distinct SubjectID found in the table (of course, "SubjectID" column must be included in the input data frame).

### Details

#### Genomic annotation file

This file is a data base, or more simply a .tsv file to import, with genes annotation for the specific genome. The annotations for the human genome (hg19) and murine genome (mm9) are already included in this package: to use one of them just set the argument `genomic_annotation_file` to either `"hg19"` or `"mm9"`. If for any reason the user is performing an analysis on another genome, this file needs to be changed respecting the USCS Genome Browser format, meaning the input file headers should include:

name2 chrom strand min_txStart max_txEnd minmax_TxLen average_TxLen name min_cdsStart max_cdsEnd minmax_CdsLen average_CdsLen

### Value

A data frame

### See Also

Other Analysis functions: `comparison_matrix()`, `compute_abundance()`, `cumulative_count_union()`, `cumulative_is()`, `is_sharing()`, `iss_source()`, `purity_filter()`, `sample_statistics()`, `separate_quant_matrices()`, `threshold_filter()`, `top_integrations()`

### Examples

    data("integration_matrices", package = "ISAnalytics")
    cis <- CIS_grubbs(integration_matrices)
    head(cis)

[Package ISAnalytics version 1.4.3 Index]