CIS_grubbs {ISAnalytics} | R Documentation |
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
CIS_grubbs( x, genomic_annotation_file = "hg19", grubbs_flanking_gene_bp = 1e+05, threshold_alpha = 0.05, by = NULL )
x |
An integration matrix, must include the |
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 |
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
A data frame
Other Analysis functions:
comparison_matrix()
,
compute_abundance()
,
cumulative_count_union()
,
cumulative_is()
,
is_sharing()
,
purity_filter()
,
sample_statistics()
,
separate_quant_matrices()
,
threshold_filter()
,
top_integrations()
data("integration_matrices", package = "ISAnalytics") cis <- CIS_grubbs(integration_matrices) head(cis)