Grubbs test for Common Insertion Sites (CIS).

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,
  return_missing_as_df = TRUE,
  results_as_list = 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

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 ("SubjectID" column must be included in the input data frame).

return_missing_as_df

Returns those genes present in the input df but not in the refgenes as a data frame?

results_as_list

If TRUE return the group computations as a named list, otherwise return a single df with an additional column containing the group id

Value

A data frame

Details

Genomic annotation file

A data frame containing genes annotation for the specific genome. From version 1.5.4 the argument genomic_annotation_file accepts only data frames or package provided defaults. The user is responsible for importing the appropriate tabular files if customization is needed. 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

Required tags

The function will explicitly check for the presence of these tags:

• chromosome

• locus

• is_strand

• gene_symbol

• gene_strand

See also

Other Analysis functions: HSC_population_size_estimate(), compute_abundance(), cumulative_is(), gene_frequency_fisher(), is_sharing(), iss_source(), sample_statistics(), top_integrations(), top_targeted_genes()

Examples

data("integration_matrices", package = "ISAnalytics")
cis <- CIS_grubbs(integration_matrices)
#> Warning: Warning: missing genes in refgenes table
#> ℹ A total of 5 genes were found in the input data but not in the refgene table. This may be caused by a mismatch in the annotation phase of the matrix. Here is a summary: 
#>     GeneName GeneStrand chr
#> 1:    CRELD2          -  15
#> 2:      HTR4          +   6
#> 3:    UBE2D2          +  16
#> 4: LINC01133          +  19
#> 5:  PLEKHG4B          -  14
#> ℹ NOTE: missing genes will be removed from the final output! Review results carefully
#> ℹ A total of 25 IS will be removed because of missing genes ( 1.48 % of total IS in input)
cis
#> $missing_genes
#>     GeneName GeneStrand chr
#> 1:    CRELD2          -  15
#> 2:      HTR4          +   6
#> 3:    UBE2D2          +  16
#> 4: LINC01133          +  19
#> 5:  PLEKHG4B          -  14
#> 
#> $missing_is
#> $missing_is$absolute
#> [1] 25
#> 
#> $missing_is$perc
#> [1] 1.48
#> 
#> 
#> $cis
#> # A tibble: 501 × 37
#>    GeneName GeneStrand chr       n       mean    sd  median trimmed   mad    min
#>    <chr>    <chr>      <chr> <int>      <dbl> <dbl>   <dbl>   <dbl> <dbl>  <dbl>
#>  1 ABHD16A  -          6         3  31654876     0   3.17e7  3.17e7    0  3.17e7
#>  2 ABHD2    +          15        3  89642998     0   8.96e7  8.96e7    0  8.96e7
#>  3 ACAP2    -          3         2 195107772     0   1.95e8  1.95e8    0  1.95e8
#>  4 ACOX1    -          17       10  73960093. 8844.  7.40e7  7.40e7 4236. 7.40e7
#>  5 ACSM3    +          16        4  20777188     0   2.08e7  2.08e7    0  2.08e7
#>  6 ADD1     +          4         8   2865706. 6694.  2.87e6  2.87e6 8804. 2.86e6
#>  7 ADGRA3   -          4         2  22505019     0   2.25e7  2.25e7    0  2.25e7
#>  8 ADGRB3   +          6         4  69397949     0   6.94e7  6.94e7    0  6.94e7
#>  9 ADGRG3   +          16        3  57716345     0   5.77e7  5.77e7    0  5.77e7
#> 10 AHR      +          7         2  17227687     0   1.72e7  1.72e7    0  1.72e7
#> # ℹ 491 more rows
#> # ℹ 27 more variables: max <dbl>, range <dbl>, skew <dbl>, kurtosis <dbl>,
#> #   n_IS_perGene <int>, min_bp_integration_locus <dbl>,
#> #   max_bp_integration_locus <dbl>, IS_span_bp <dbl>,
#> #   avg_bp_integration_locus <dbl>, median_bp_integration_locus <dbl>,
#> #   distinct_orientations <int>, average_TxLen <dbl>,
#> #   raw_gene_integration_frequency <dbl>, …
#>