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 = system.file("extdata", "hg19.refGene.oracle.tsv.xz", package = "ISAnalytics"), grubbs_flanking_gene_bp = 1e+05, threshold_alpha = 0.05, add_standard_padjust = TRUE )
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 |
add_standard_padjust |
Compute the standard padjust? |
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
A data frame
Other Analysis functions:
comparison_matrix()
,
compute_abundance()
,
cumulative_count_union()
,
sample_statistics()
,
separate_quant_matrices()
,
threshold_filter()
,
top_integrations()
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)