CIS_grubbs_overtime: Compute CIS and Grubbs test over different time points and...

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

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

Description

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.

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

Value

A list with results and optionally missing genes info

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)
cis_overtime

calabrialab/ISAnalytics documentation built on Nov. 2, 2023, 8:57 p.m.