hic_compare: Detect differences between two jointly normalized Hi-C...

In dozmorovlab/HiCcompare: HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasets

Detect differences between two jointly normalized Hi-C datasets.

Description

Detect differences between two jointly normalized Hi-C datasets.

Usage

hic_compare(
  hic.table,
  A.min = NA,
  adjust.dist = TRUE,
  p.method = "fdr",
  Plot = FALSE,
  Plot.smooth = TRUE,
  parallel = FALSE,
  BP_param = bpparam()
)

Arguments

hic.table	A hic.table or list of hic.tables output from the hic_loess function. hic.table must be jointly normalized before being entered.
A.min	The required value of A in order for a differences to be considered. All Z-scores where the corresponding A value is < A.min will be set to 0. Defaults to NA. If NA, then the 10th percentile of A will automatically be calculated and set as the A.min value. To better determine how to set A.min see the help for ?filter_params().
adjust.dist	Logical, should the p-value adjustment be performed on a per distance basis. i.e. The p-values at distance 1 will be grouped and the p-value adjustment will be applied. This process is repeated for each distance. The highest 15 if you matrix has a maximum distance of 100, then distances 85-100 will be pooled together for p-value adjustment.
p.method	The method for p-value adjustment. See ?p.adjust() help for options and more information. Defaults to "fdr". Can be set to "none" for no p-value adjustments.
Plot	Logical, should the MD plot showing before/after loess normalization be output?
Plot.smooth	Logical, defaults to TRUE indicating the MD plot will be a smooth scatter plot. Set to FALSE for a scatter plot with discrete points.
parallel	Logical, set to TRUE to utilize the parallel package's parallelized computing. Only works on unix operating systems. Only useful if entering a list of hic.tables.
BP_param	Parameters for BiocParallel. Defaults to bpparam(), see help for BiocParallel for more information http://bioconductor.org/packages/release/bioc/vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.pdf

Details

The function takes in a hic.table or a list of hic.table objects created with the hic_loess function. If you wish to perform difference detection on Hi-C data for multiple chromosomes use a list of hic.tables. The process can be parallelized using the parallel setting. The adjusted IF and adjusted M calculated from hic_loess are used for difference detection. Difference detection is performed by converting adjusted M values to Z-scores. Any M value with a corresponding average expression level (A; mean of IF1 and IF2) less than the specified A.quantile is not considered for Z-score calculation. This throws out the untrustworthy interactions that tend to produce false positives. The Z-scores are assumed to follow a roughly standard normal distribution and p-values are obtained. P-value adjustment for multiple testing is then performed on a per distance basis (or on all p-values, optionally). i.e. at each distance the vector of p-values corresponding to the interactions occuring at that distance have the selected multiple testing correction applied to them. See methods of Stansfield & Dozmorov 2017 for more details.

Value

A hic.table with additional columns containing a p-value for the significance of the difference and the raw fold change between the IFs of the two datasets.

Examples

# Create hic.table object using included Hi-C data in sparse upper triangular
# matrix format
data('HMEC.chr22')
data('NHEK.chr22')
hic.table <- create.hic.table(HMEC.chr22, NHEK.chr22, chr = 'chr22')
# Plug hic.table into hic_loess()
result <- hic_loess(hic.table, Plot = TRUE)
# perform difference detection
diff.result <- hic_compare(result, Plot = TRUE)

dozmorovlab/HiCcompare documentation built on June 30, 2023, 3:09 a.m.