Run ChICANE Analysis

This function adapts the chicane function from the ChICANE package to work with the linkSet object format. It runs the full method for detecting significant interactions in capture Hi-C experiments.

Usage

run_chicane(linkSet, ...)

# S4 method for class 'linkSet'
run_chicane(
  linkSet,
  replicate.merging.method = "sum",
  distribution = "negative-binomial",
  include.zeros = "none",
  bait.filters = c(0, 1),
  target.filters = c(0, 1),
  distance.bins = NULL,
  multiple.testing.correction = c("bait-level", "global"),
  adjustment.terms = NULL,
  remove.adjacent = FALSE,
  temp.directory = NULL,
  keep.files = FALSE,
  maxit = 100,
  epsilon = 1e-08,
  cores = 1,
  trace = FALSE,
  verbose = FALSE
)

Arguments

linkSet

A linkSet object containing interaction data

...

Additional arguments passed to methods

replicate.merging.method

Method for merging replicates (default: 'sum')

distribution

Distribution to use for modeling (default: 'negative-binomial')

include.zeros

How to handle zero counts (default: 'none')

bait.filters

Vector of length 2 for bait filtering thresholds (default: c(0,1))

target.filters

Vector of length 2 for target filtering thresholds (default: c(0,1))

distance.bins

Number of distance bins (default: NULL)

multiple.testing.correction

Method for multiple testing correction (default: 'bait-level')

adjustment.terms

Additional terms for model adjustment (default: NULL)

remove.adjacent

Whether to remove adjacent fragments (default: FALSE)

temp.directory

Directory for temporary files (default: NULL)

keep.files

Whether to keep temporary files (default: FALSE)

maxit

Maximum iterations for model fitting (default: 100)

epsilon

Convergence threshold (default: 1e-8)

cores

Number of CPU cores to use (default: 1)

trace

Whether to print trace information (default: FALSE)

verbose

Whether to print progress information (default: FALSE)

Value

A linkSet object with additional columns:

• expectedThe expected number of reads linking fragments under the fitted model

• p.valueP-value for test of observed vs expected read counts

• q.valueFDR-corrected p-value

Details

Run ChICANE Analysis on linkSet Object

Examples

# Create example data
gr1 <- GRanges(seqnames = c("chr1", "chr3", "chr3"),
               ranges = IRanges(start = c(1000, 2000, 3000), width = 100),
               strand = "+", symbol = c("BRCA1", "TP53", "NONEXISTENT"))
gr2 <- GRanges(seqnames = c("chr1", "chr2", "chr3"),
               ranges = IRanges(start = c(5000, 6000, 7000), width = 100),
               strand = "+")
ls <- linkSet(gr1, gr2, specificCol = "symbol")

# Annotate and prepare data
annotated_ls <- suppressWarnings(
  annotatePromoter(ls, genome = "hg38", upstream = 500, overwrite = TRUE)
)
annotated_ls <- countInteractibility(annotated_ls)
annotated_ls <- linkSet::pairdist(annotated_ls)

# Run analysis
result_ls <- run_chicane(
  annotated_ls, 
  replicate.merging.method = 'sum',
  bait.filters = c(0, 1),
  target.filters = c(0, 1),
  distance.bins = NULL,
  multiple.testing.correction = 'bait-level',
  verbose = TRUE
)
#> [1] "Not found column 'bait.id', adding bait name as default."
#> [1] "Not found column 'bait.to.bait' Set 'FALSE' as default."
#> FITTING MODEL
#> [1] "fitting model...."
#> 
#> Attaching package: ‘data.table’
#> The following object is masked from ‘package:SummarizedExperiment’:
#> 
#>     shift
#> The following object is masked from ‘package:GenomicRanges’:
#> 
#>     shift
#> The following object is masked from ‘package:IRanges’:
#> 
#>     shift
#> The following objects are masked from ‘package:S4Vectors’:
#> 
#>     first, second
#> *
#> Warning: no observations informative at iteration 1
#> Warning: no observations informative at iteration 1
#> Warning: glm.fit: algorithm did not converge
#> 
#> 	trans interactions