hic_compare  R Documentation 
Detect differences between two jointly normalized HiC datasets.
hic_compare(
hic.table,
A.min = NA,
adjust.dist = TRUE,
p.method = "fdr",
Plot = FALSE,
Plot.smooth = TRUE,
parallel = FALSE,
BP_param = bpparam()
)
hic.table 
A hic.table or list of hic.tables output from the

A.min 
The required value of A in order for a differences to be considered. All Zscores 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 pvalue adjustment be performed on a per distance basis. i.e. The pvalues at distance 1 will be grouped and the pvalue 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 85100 will be pooled together for pvalue adjustment. 
p.method 
The method for pvalue adjustment. See ?p.adjust() help for options and more information. Defaults to "fdr". Can be set to "none" for no pvalue 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 
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 
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 HiC 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 Zscores. 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 Zscore calculation.
This throws out the
untrustworthy interactions that tend to produce false positives.
The Zscores are assumed to follow a roughly standard normal
distribution and pvalues are obtained. Pvalue adjustment for multiple testing
is then performed on a per distance basis (or on all pvalues, optionally).
i.e. at each distance the vector of pvalues 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.
A hic.table with additional columns containing a pvalue for the significance of the difference and the raw fold change between the IFs of the two datasets.
# Create hic.table object using included HiC 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)
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