Compute CIS and Grubbs test over different time points and groups.

Computes common insertion sites and Grubbs test for each separate group and separating different time points among the same group. The logic applied is the same as the function CIS_grubbs().

Usage

CIS_grubbs_overtime(
  x,
  genomic_annotation_file = "hg19",
  grubbs_flanking_gene_bp = 1e+05,
  threshold_alpha = 0.05,
  group = "SubjectID",
  timepoint_col = "TimePoint",
  as_df = TRUE,
  return_missing_as_df = TRUE,
  max_workers = 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
group: A character vector of column names that identifies a group. Each group must contain one or more time points.
timepoint_col: What is the name of the column containing time points?
as_df: Choose the result format: if TRUE the results are returned as a single data frame containing a column for the group id and a column for the time point, if FALSE results are returned in the form of nested lists (one table for each time point and for each group), if "group" results are returned as a list separated for each group but containing a single table with all time points.
return_missing_as_df: Returns those genes present in the input df but not in the refgenes as a data frame?
max_workers: Maximum number of parallel workers. If NULL the maximum number of workers is calculated automatically.

Value

A list with results and optionally missing genes info

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

Examples

data("integration_matrices", package = "ISAnalytics")
data("association_file", package = "ISAnalytics")
aggreg <- aggregate_values_by_key(
    x = integration_matrices,
    association_file = association_file,
    value_cols = c("seqCount", "fragmentEstimate")
)
cis_overtime <- CIS_grubbs_overtime(aggreg)
#> 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: 
#> # A tibble: 5 × 3
#>   GeneName  GeneStrand chr  
#>   <chr>     <chr>      <chr>
#> 1 PLEKHG4B  -          14   
#> 2 CRELD2    -          15   
#> 3 UBE2D2    +          16   
#> 4 LINC01133 +          19   
#> 5 HTR4      +          6    
#> 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 ( 2.33 % of total IS in input)
cis_overtime
#> $missing_genes
#> # A tibble: 5 × 3
#>   GeneName  GeneStrand chr  
#>   <chr>     <chr>      <chr>
#> 1 PLEKHG4B  -          14   
#> 2 CRELD2    -          15   
#> 3 UBE2D2    +          16   
#> 4 LINC01133 +          19   
#> 5 HTR4      +          6    
#> 
#> $missing_is
#> $missing_is$absolute
#> [1] 25
#> 
#> $missing_is$perc
#> [1] 2.33
#> 
#> 
#> $cis
#> # A tibble: 932 × 39
#>    GeneName GeneStrand chr       n      mean    sd   median trimmed   mad    min
#>    <chr>    <chr>      <chr> <int>     <dbl> <dbl>    <dbl>   <dbl> <dbl>  <dbl>
#>  1 ABHD2    +          15        1  89642998    NA   8.96e7  8.96e7     0 8.96e7
#>  2 ACAP2    -          3         1 195107772    NA   1.95e8  1.95e8     0 1.95e8
#>  3 ACOX1    -          17        1  73953431    NA   7.40e7  7.40e7     0 7.40e7
#>  4 ADD1     +          4         1   2859237    NA   2.86e6  2.86e6     0 2.86e6
#>  5 ANKEF1   +          20        1  10028583    NA   1.00e7  1.00e7     0 1.00e7
#>  6 ANKRD52  -          12        1  56647382    NA   5.66e7  5.66e7     0 5.66e7
#>  7 ASAP1    -          8         1 131310762    NA   1.31e8  1.31e8     0 1.31e8
#>  8 ATF1     +          12        1  51189341    NA   5.12e7  5.12e7     0 5.12e7
#>  9 ATF7     -          12        1  53987984    NA   5.40e7  5.40e7     0 5.40e7
#> 10 C6orf106 -          6         1  34643226    NA   3.46e7  3.46e7     0 3.46e7
#> # ℹ 922 more rows
#> # ℹ 29 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>, …
#>