peaks {FunChIP} | R Documentation

## Coverage of a ChIP-Seq experiment

### Description

It contains a data-set used in all the examples of the FunChIP, together with all the metadata columns generated in the intermediate steps.

### Usage

    data("peaks")

### Format

Several data-set are included

- peaks.data: a GRanges object with 10 ranges and 1 metadata column:

  - `counts`. A vector for each range, with length equal to the width of the range, containing the coverage of the range, i.e. the base-level read counts. It can be computed with the pileup_peak method.

- peaks.data.smooth: a GRanges object with 10 ranges and 6 metadata columns:

  - `counts`. As in `peaks.data`.

  - `spline`. A vector for each range, containing the evaluation of the spline approximation of the peak for each genomic base. It can be computed with the smooth_peak method.

  - `spline_der`. A vector for each range, containing the evaluation of the derivatives of the spline approximation of the peak for each genomic base. It can be computed with the smooth_peak method.

  - `width_spline`. Integer. The number of evaluated points of the spline approximation, i.e. the number of non-zero points, for each range. It can be computed with the smooth_peak method.

  - `start_spline`. Integer. The starting point of the spline approximation. It could be smaller than `start(object)` since the approximation can increase the values at the border to make the curve smooth. It can be computed with the smooth_peak method.

  - `end_spline`. Integer. The end point of the spline approximation. It could be larger than `end(object)` since the approximation can increase the values at the border to make the curve smooth. It can be computed with the smooth_peak method.

- peaks.data.smooth.scaled: a GRanges object with 10 ranges and, beside the 6 metadata columns of `peaks.data.smooth`, 2 more coumns

  - `spline_rescaled`. A vector for each range, containing the evaluation of the scaled spline approximation on the common grid of all the peaks.

  - `spline_der_rescaled`. A vector for each range, containing the evaluation of the derivatives of the scaled spline approximation on the common grid.

- peaks.data.summit: a GRanges object with 10 ranges and 7 metadata columns:

  - `counts`. As `peaks.data`.

  - `spline`. As `peaks.data.smooth`.

  - `spline_der`. As `peaks.data.smooth`.

  - `width_spline`. As `peaks.data.smooth`.

  - `start_spline`. As `peaks.data.smooth`.

  - `end_spline`. As `peaks.data.smooth`.

  - `summit_spline`. The distance from the starting point of the spline of the maximum point (integer) of the spline (or the summit of the peak), for each range. It can be computed with the summit_peak method

- peaks.data.summit.scaled: a GRanges object with 10 ranges and, beside the 7 metadata columns of `peaks.data.summit`, 3 more coumns

  - `spline_rescaled`. As `peaks.data.smooth.scaled`.

  - `spline_der_rescaled`. As `peaks.data.smooth.scaled`.

  - `summit_spline_rescaled`. The distance from the starting point of the scaled spline of the maximum point (integer) of the scaled spline (or the summit of the peak), for each range. It can be computed with the summit_peak method setting to `TRUE` the `rescale` argument.

- peaks.data.cluster: a GRanges object with 10 ranges and 12 metadata columns:

  - `counts`. As `peaks.data`.

  - `spline`. As `peaks.data.smooth`.

  - `spline_der`. As `peaks.data.smooth`.

  - `width_spline`. As `peaks.data.smooth`.

  - `start_spline`. As `peaks.data.smooth`.

  - `end_spline`. As `peaks.data.smooth`.

  - `summit_spline`. As `peaks.data.summit`.

  - `cluster_NOshift`. A vector of length 5 for each range, containing the label of the cluster assigned to the peak in case of clustering without alignment. For example, the second element of the vector is the label of the corresponding peak when the k-mean alignment algorithm is run with 2 clusters. It can be computed with the cluster_peak method with `n.clust = 1:5` and `shift.peak=FALSE`.

  - `dist_NOshift`. A vector of length 5 for each range, containing the distance from the center of the cluster assigned to the peak in case of clustering without alignment. For example, the second element of the vector is the distance of the corresponding peak from center of the corresponding cluster when the k-mean alignment algorithm is run with 2 clusters. It can be computed with the cluster_peak method with `n.clust = 1:5` and `shift.peak=FALSE`.

  - `cluster_shift`. A vector of length 5 for each range, containing the label of the cluster assigned to the peak in case of clustering with alignment. For example, the second element of the vector is the label of the corresponding peak when the k-mean alignment algorithm is run with 2 clusters. It can be computed with the cluster_peak method with `n.clust = 1:5` and `shift.peak=TRUE`.

  - `coef_shift`. A vector of length 5 for each range, containing the optimal shift coefficient of the peak. For example, the second element of the vector is the shift coefficient of the corresponding peak when the k-mean alignment algorithm is run with 2 clusters. It can be computed with the cluster_peak method with `n.clust = 1:5` and `shift.peak=TRUE`.

  - `dist_shift`. A vector of length 5 for each range, containing the distance from the center of the cluster assigned to the peak in case of clustering with alignment. For example, the second element of the vector is the distance of the corresponding peak from the center of the corresponding cluster when the k-mean alignment algorithm is run with 2 clusters. It can be computed with the cluster_peak method with `n.clust = 1:5` and `shift.peak=TRUE`.

- peaks.data.cluster.scaled: a GRanges object with 10 ranges and, beside the 12 metadata columns of `peaks.data.cluster`, 3 more coumns

  - `spline_rescaled`. As `peaks.data.smooth.scaled`.

  - `spline_der_rescaled`. As `peaks.data.smooth.scaled`.

  - `summit_spline_rescaled`. As `peaks.data.summit.scaled`.

  It is computed from `peaks.data.summit.scaled` with the cluster_peak method setting `rescale = TRUE`.

- peaks.data.classified: a GRanges object with 10 ranges and 13 metadata columns:

  - `counts`. As `peaks.data`.

  - `spline`. As `peaks.data.smooth`.

  - `spline_der`. As `peaks.data.smooth`.

  - `width_spline`. As `peaks.data.smooth`.

  - `start_spline`. As `peaks.data.smooth`.

  - `end_spline`. As `peaks.data.smooth`.

  - `summit_spline`. As `peaks.data.summit`.

  - `cluster_NOshift`. As `peaks.data.cluster`.

  - `dist_NOshift`. As `peaks.data.cluster`.

  - `cluster_shift`. As `peaks.data.cluster`.

  - `coef_shift`. As `peaks.data.cluster`..

  - `dist_shift`. As `peaks.data.cluster`.

  - `cluster`. Integer. The index of the final label assigned, for each range. It can be computed with the choose_k with `k = 3` and `shift.peak = TRUE`.

- peaks.data.classified.scaled: a GRanges object with 10 ranges and, beside the 13 metadata columns of `peaks.data.classified`, 3 more coumns

  - `spline_rescaled`. As `peaks.data.smooth.scaled`.

  - `spline_der_rescaled`. As `peaks.data.smooth.scaled`.

  - `summit_spline_rescaled`. As `peaks.data.summit.scaled`.

  It is computed from `peaks.data.cluster.scaled` with the choose_k method setting `rescale = TRUE`.

### Examples

    data(peaks)

[Package FunChIP version 1.6.0 Index]