estimate_idr1d {idr2d} | R Documentation

## Estimates IDR for Genomic Peak Data

### Description

This method estimates Irreproducible Discovery Rates (IDR) for peaks in replicated ChIP-seq experiments.

### Usage

    estimate_idr1d(
      rep1_df,
      rep2_df,
      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) of replicate 1, with at least the following columns (position of columns matter, column names are irrelevant):
column 1: | `chr` | character; genomic location of peak - chromosome (e.g., `"chr3"`)
column 2: | `start` | integer; genomic location of peak - start coordinate
column 3: | `end` | integer; genomic location of peak - end coordinate
column 4: | `value` | numeric; p-value, FDR, or heuristic used to rank the interactions
`rep2_df` | data frame of observations (i.e., genomic peaks) of replicate 2, with the following columns (position of columns matter, column names are irrelevant):
column 1: | `chr` | character; genomic location of peak - chromosome (e.g., `"chr3"`)
column 2: | `start` | integer; genomic location of peak - start coordinate
column 3: | `end` | integer; genomic location of peak - end coordinate
column 4: | `value` | numeric; p-value, FDR, or heuristic used to rank the interactions
`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 in replicate 1 overlaps with multiple interactions in replicate 2 or vice versa) are resolved. Available methods:
`"value"` | interactions are prioritized by ascending or descending `value` column (see `sorting_direction`), e.g., if two interactions in replicate 1 overlap with one interaction in replicate 2, the interaction from replicate 1 is chosen which has a lower (if `sorting_direction` is `"ascending"`) or higher (if `"descending"`) value
`"overlap"` | the interaction pair is chosen which has the highest relative overlap, i.e., overlap in nucleotides of replicate 1 interaction anchor A and replicate 2 interaction anchor A, plus replicate 1 interaction anchor B and replicate 2 interaction anchor B, normalized by their lengths
`"midpoint"` | the interaction pair is chosen which has the smallest distance between their anchor midpoints, i.e., distance from midpoint of replicate 1 interaction anchor A to midpoint of replicate 2 interaction anchor A, plus distance from midpoint of replicate 1 interaction anchor B to midpoint of replicate 2 interaction anchor B
`remove_nonstandard_chromosomes` | removes peaks 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

List with three components, (`rep1_df`, `rep2_df`, and `analysis_type`) containing the interactions from input data frames `rep1_df` and `rep2_df` with the following additional columns:

column 1: | `chr` | character; genomic location of peak - chromosome (e.g., `"chr3"`)
column 2: | `start` | integer; genomic location of peak - start coordinate
column 3: | `end` | integer; genomic location of peak - end coordinate
column 4: | `value` | numeric; p-value, FDR, or heuristic used to rank the peaks
column 5: | `rep_value` | numeric; value of corresponding replicate peak. If no corresponding peak was found, `rep_value` is set to `NA`.
column 6: | `rank` | integer; rank of the peak, established by value column, ascending order
column 7: | `rep_rank` | integer; rank of corresponding replicate peak. If no corresponding peak was found, `rep_rank` is set to `NA`.
column 8: | `idx` | integer; peak index, primary key
column 9: | `rep_idx` | integer; specifies the index of the corresponding peak in the other replicate (foreign key). If no corresponding peak was found, `rep_idx` is set to `NA`.
column 10: | `idr` | IDR of the peak and the corresponding peak in the other replicate. If no corresponding peak was found, `idr` is set to `NA`.

### 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_idr1d(idr2d:::chipseq$rep1_df,
                                  idr2d:::chipseq$rep2_df,
                                  value_transformation = "log")
    summary(idr_results)

[Package idr2d version 1.6.0 Index]