differentialNbinomWaldTestUMI4C {UMI4Cats} | R Documentation

## Differential UMI4C contacts using DESeq2 Wald Test

### Description

Using a `UMI4C` object, infers the differences between conditions specified in `design` of the smooth monotone fitted values using a Wald Test from `DESeq2` package.

### Usage

    differentialNbinomWaldTestUMI4C(
      umi4c,
      design = ~condition,
      normalized = TRUE,
      padj_method = "fdr",
      query_regions = NULL,
      padj_threshold = 0.05,
      penalty = 0.1,
      alpha = 20
    )

### Arguments

`umi4c` | UMI4C object as generated by `makeUMI4C` or the `UMI4C` constructor.
`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.
`query_regions` | `GRanges` object or `data.frame` containing the coordinates of the genomic regions you want to use to perform the analysis in specific genomic intervals. Default: NULL.
`padj_threshold` | Numeric indicating the adjusted p-value threshold to use to define significant differential contacts. Default: 0.05.
`penalty` | Amount of smoothing to be applied to the estimated functional parameter. Default: 0.1.
`alpha` | Approximate number of fragments desired for every basis function of the B-spline basis. `floor((max(number of fragments)) / alpha)` is passed to `create.bspline.basis` as nbasis argument. 4 is the minimum allowed value. Default: 20.

### Details

This function fits the signal trend of a variance stabilized count values using a symmetric monotone fit for the distance dependency. Then scales the raw counts across the samples to obtain normalized factors. Finally, it detects differences between conditions applying the DESeq2 Wald Test.

### Value

`UMI4C` object with the DESeq2 Wald Test results.

### Examples

     files <- list.files(system.file("extdata", "CIITA", "count", package="UMI4Cats"),
                         pattern = "*_counts.tsv.gz",
                         full.names = TRUE
     )
    # Create colData including all relevant information
    colData <- data.frame(
      sampleID = gsub("_counts.tsv.gz", "", basename(files)),
      file = files,
      stringsAsFactors = FALSE
    )

    library(tidyr)
    colData <- colData %>%
      separate(sampleID,
               into = c("condition", "replicate", "viewpoint"),
               remove = FALSE
      )

    # Make UMI-4C object including grouping by condition
    umi <- makeUMI4C(
      colData = colData,
      viewpoint_name = "CIITA",
      grouping = NULL,
      bait_expansion = 2e6
    )

    umi_wald <- differentialNbinomWaldTestUMI4C(umi4c=umi,
                                                design=~condition,
                                                alpha = 100)

[Package UMI4Cats version 1.0.1 Index]