estimate_idr {idr2d} | R Documentation

## Estimates IDR for Genomic Peaks or Genomic Interactions

### Description

Estimates IDR for Genomic Peaks or Genomic Interactions

### Usage

    estimate_idr(
      rep1_df,
      rep2_df,
      analysis_type = "IDR2D",
      value_transformation = c("identity", "additive_inverse", "multiplicative_inverse",
        "log", "log_additive_inverse"),
      ambiguity_resolution_method = c("overlap", "midpoint", "value"),
      remove_nonstandard_chromosomes = TRUE,
      max_factor = 1.5,
      jitter_factor = 1e-04,
      max_gap = -1L,
      mu = 0.1,
      sigma = 1,
      rho = 0.2,
      p = 0.5,
      eps = 0.001,
      max_iteration = 30,
      local_idr = TRUE
    )

### Arguments

`rep1_df` | data frame of observations (i.e., genomic peaks or genomic interactions) of replicate 1. If `analysis_type` is IDR1D, the columns of `rep1_df` are described in `establish_bijection1d`, otherwise in `establish_bijection2d`
`rep2_df` | data frame of observations (i.e., genomic peaks or genomic interactions) of replicate 2. Same columns as `rep1_df`.
`analysis_type` | "IDR2D" for genomic interaction data sets, "IDR1D" for genomic peak data sets
`value_transformation` | the values in `x` have to be transformed in a way such that when ordered in descending order, more significant interactions end up on top of the list. If the values in `x` are p-values, `"log_additive_inverse"` is recommended. The following transformations are supported:
`"identity"` | no transformation is performed on `x`
`"additive_inverse"` | `x. = -x`
`"multiplicative_inverse"` | `x. = 1 / x`
`"log"` | `x. = log(x)`. Note: zeros are replaced by `.Machine$double.xmin`
`"log_additive_inverse"` | `x. = -log(x)`, recommended if `x` are p-values. Note: zeros are replaced by `.Machine$double.xmin`
either `"ascending"` (more significant interactions have lower value in `value` column) or `"descending"` (more significant interactions have higher value in `value` column)
`ambiguity_resolution_method` | defines how ambiguous assignments (when one interaction or peak in replicate 1 overlaps with multiple interactions or peaks in replicate 2 or vice versa) are resolved. For available methods, see `establish_overlap1d` or `establish_overlap2d`, respectively.
`remove_nonstandard_chromosomes` | removes peaks and interactions containing genomic locations on non-standard chromosomes using `keepStandardChromosomes` (default is TRUE)
`max_factor` | numeric; controls the replacement values for `Inf` and `-Inf`. `Inf` are replaced by `max(x) * max_factor` and `-Inf` are replaced by `min(x) / max_factor`.
`jitter_factor` | numeric; controls the magnitude of the noise that is added to `x`. This is done to break ties in `x`. Set `jitter_factor = NULL` for no jitter.
`max_gap` | integer; maximum gap in nucleotides allowed between two anchors for them to be considered as overlapping (defaults to -1, i.e., overlapping anchors)
`mu` | a starting value for the mean of the reproducible component.
`sigma` | a starting value for the standard deviation of the reproducible component.
`rho` | a starting value for the correlation coefficient of the reproducible component.
`p` | a starting value for the proportion of reproducible component.
`eps` | Stopping criterion. Iterations stop when the increment of log-likelihood is < eps*log-likelihood, Default=0.001.
`max_iteration` | integer; maximum number of iterations for IDR estimation (defaults to 30)
`local_idr` | see `est.IDR`

### Value

See `estimate_idr1d` or `estimate_idr2d`, respectively.

### References

Q. Li, J. B. Brown, H. Huang and P. J. Bickel. (2011) Measuring reproducibility of high-throughput experiments. Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779.

### Examples

    idr_results <- estimate_idr(idr2d:::chiapet$rep1_df,
                                idr2d:::chiapet$rep2_df,
                                analysis_type = "IDR2D",
                                value_transformation = "log_additive_inverse")
    summary(idr_results)

[Package idr2d version 1.1.4 Index]