DESeq2 Wald test for differential contacts

Using a UMI4C object, infers the differences between conditions specified in design using a Wald Test from DESeq2 package.

waldUMI4C(
  umi4c,
  query_regions = NULL,
  subset = "sum",
  design = ~condition,
  normalized = TRUE,
  padj_method = "fdr",
  padj_threshold = 0.05
)

Arguments

umi4c	UMI4C object as generated by `makeUMI4C` or the `UMI4C` constructor.
query_regions	`GRanges` object containing the coordinates of the genomic regions you want to use to perform the analysis in specific genomic intervals. Default: NULL.
subset	If `query_regions` are provided, how to subset the UMI4C object: "sum" for summing raw UMIs in fragments overlapping `query_regions` (default) or "overlap" for selecting overlapping fragments.
design	A `formula` or `matrix`. The formula expresses how the counts for each fragment end depend on the variables in `colData`. See `DESeqDataSet`.
normalized	Logical indicating if the function should return normalized or raw UMI counts. Default: TRUE.
padj_method	The method to use for adjusting p-values, see `p.adjust`. Default: fdr.
padj_threshold	Numeric indicating the adjusted p-value threshold to use to define significant differential contacts. Default: 0.05.

Value

UMI4C object with the DESeq2 Wald Test results, which can be accessed using resultsUMI4C.

Examples

data("ex_ciita_umi4c")

umi_dif <- waldUMI4C(ex_ciita_umi4c)
#> Warning: some variables in design formula are characters, converting to factors
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.
#> final dispersion estimates
#> fitting model and testing