CIS_grubbs: Grubbs test for Common Insertion Sites (CIS).

In ISAnalytics: Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies

Description

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experimental 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 = system.file("extdata", "hg19.refGene.oracle.tsv.xz",
    package = "ISAnalytics"),
  grubbs_flanking_gene_bp = 1e+05,
  threshold_alpha = 0.05,
  add_standard_padjust = TRUE
)

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
add_standard_padjust	Compute the standard padjust?

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) is already included in this package. 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 be:

## 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(), sample_statistics(), separate_quant_matrices(), threshold_filter(), top_integrations()

Examples

op <- options(ISAnalytics.widgets = FALSE)

path_AF <- system.file("extdata", "ex_association_file.tsv",
    package = "ISAnalytics"
)
root_correct <- system.file("extdata", "fs.zip",
    package = "ISAnalytics"
)
root_correct <- unzip_file_system(root_correct, "fs")

matrices <- import_parallel_Vispa2Matrices_auto(
    association_file = path_AF, root = root_correct,
    quantification_type = c("seqCount", "fragmentEstimate"),
    matrix_type = "annotated", workers = 2, patterns = NULL,
    matching_opt = "ANY",
    dates_format = "dmy"
)

cis <- CIS_grubbs(matrices)

options(op)

ISAnalytics documentation built on April 9, 2021, 6:01 p.m.