establish_overlap1d {idr2d} | R Documentation

## Establish m:n Mapping Between Peaks from Replicate 1 and 2

### Description

This method returns all overlapping interactions between two replicates. For each pair of overlapping interactions, the ambiguity resolution value (ARV) is calculated, which helps to reduce the m:n mapping to a 1:1 mapping. The semantics of the ARV depend on the specified `ambiguity_resolution_method`, but in general interaction pairs with lower ARVs have priority over interaction pairs with higher ARVs when the bijective mapping is established.

### Usage

    establish_overlap1d(
      rep1_df,
      rep2_df,
      ambiguity_resolution_method = c("overlap", "midpoint", "value"),
      max_gap = -1L
    )

### 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
`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
`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)

### Value

data frame with the following columns:

column 1: | `rep1_idx` | index of interaction in replicate 1 (i.e., row index in `rep1_df`)
column 2: | `rep2_idx` | index of interaction in replicate 2 (i.e., row index in `rep2_df`)
column 3: | `arv` | ambiguity resolution value used turn m:n mapping into 1:1 mapping. Interaction pairs with lower `arv` are prioritized.

### Examples

    rep1_df <- idr2d:::chipseq$rep1_df
    rep1_df$value <- preprocess(rep1_df$value, "log_additive_inverse")

    rep2_df <- idr2d:::chipseq$rep2_df
    rep2_df$value <- preprocess(rep2_df$value, "log_additive_inverse")

    # shuffle to break preexisting order
    rep1_df <- rep1_df[sample.int(nrow(rep1_df)), ]
    rep2_df <- rep2_df[sample.int(nrow(rep2_df)), ]

    # sort by value column
    rep1_df <- dplyr::arrange(rep1_df, value)
    rep2_df <- dplyr::arrange(rep2_df, value)

    pairs_df <- establish_overlap1d(rep1_df, rep2_df)

[Package idr2d version 1.6.0 Index]