Package {shaman}

shaman: Sampling Hi-C Contact Matrices for Aparametric Normalization

Description

Generate expected Hi-C (genome-wide chromosome conformation capture) data maintaining marginal coverage, and the decay in contact probability with increasing genomic distance. Expected Hi-C data is used for normalization and graphical presentation of statistical significance of observed Hi-C contact enrichments. The method is described in Mendelson Cohen et al. (2017) doi:10.1101/187203.

Author(s)

Maintainer: Aviezer Lifshitz aviezer.lifshitz@weizmann.ac.il

Authors:

• Amos Tanay

• Netta Mendelson Cohen netta.mendelsoncohen@weizmann.ac.il

Other contributors:

• Weizmann Institute of Science [copyright holder]

See Also

Useful links:

• https://tanaylab.github.io/shaman/

• https://github.com/tanaylab/shaman

• Report bugs at https://github.com/tanaylab/shaman/issues

Dump config file templates.

Description

Dump templates of config files required by the package.

Usage

.shaman_dump_config(config_dir)

Arguments

Load all the configuration files required for the package.

Description

Loads the configuration files which were filled by the user. Accepts either directory path from config_dir, or individual files supplied by the other parameters.

Usage

.shaman_load_config(
  config_dir,
  shaman_config = file.path(config_dir, "shaman.conf"),
  reset = FALSE
)

Arguments

Human CTCF motifs found on forward strand on hg19 genome

Description

A dataset containing the chromosomal coordinates of all CTCF forward strand motifs including a chip-seq peak and not overlapping with a TSS and a reverse strand CTCF motif.

Usage

ctcf_forward

Format

An object of class data.frame with 8791 rows and 3 columns.

Human CTCF motifs found on reverse strand on hg19 genome

Description

A dataset containing the chromosomal coordinates of all CTCF reverse strand motifs including a chip-seq peak and not overlapping with a TSS and a forward strand CTCF motif.

Usage

ctcf_reverse

Format

An object of class data.frame with 8842 rows and 3 columns.

Get params from saved var

Description

Get params from saved var

Usage

get_param(nm, params)

Arguments

Value

The parameter, or NA if it is missing.

Get params from saved var

Description

Get params from saved var

Usage

get_param_list(nm, params)

Arguments

Value

The comma-separated parameter split into a list, or NA if it is missing.

Read params from files

Description

Read params from files

Usage

init_params(fn, prev_params = NULL)

Arguments

Value

A named list of the parameters.

Calculate spatial enrichment of contacts between two features

Description

shaman_generate_feature_grid

Usage

shaman_generate_feature_grid(
  feature1,
  feature2,
  obs_track_nm,
  exp_track_nm = paste0(obs_track_nm, "_shuffle"),
  score_track_nm = paste0(obs_track_nm, "_score"),
  score_filter = 30,
  range = 25000,
  resolution = 500,
  min_dist = 200000,
  max_dist = 1000000
)

Arguments

Details

Build a grid of all combinations of intervals from feature 1 and feature 2 that fall within a band defined by min_dist and max_dist. For each point on the grid, look at the surrounding window, defined by range parameter. Discard all windows that do not contain a point with a score (defined in score_track_nm) above the score_filter parameter. This allows for focusing on potentially enriched pairs. Dissect the window into small bins, size in base pairs defined by the resolution parameter, and count the number of observed contacts, and the number of expected contacts in each bin. All windows are then summed together, generating a single matrix of observed and expected contacts, which is returned by function. Note that grid contains only points in which feature 1 position is smaller than feature 2 position.

Value

list containing obs - observed matrix, sum of the number of observed contacts in each bin in the window, exp - expected matrix, sum of the number of expected contacts in each bin in the window, grid_size - number of windows included in statistics

Examples

# Set misha db to test
library(misha)
gsetroot(shaman_get_test_track_db())
# the test db has chr2 only, with contacts in chr2:176.5e06-177e06
grid <- shaman_generate_feature_grid(
    ctcf_forward[ctcf_forward$chrom == "chr2", ], ctcf_reverse[ctcf_reverse$chrom == "chr2", ],
    "hic_obs",
    exp_track_nm = "hic_exp", min_dist = 1e05, max_dist = 5e05
)
plot <- shaman_plot_feature_grid(list(grid), 25000, 500, 1000)

Returns the example misha database

Description

shaman_get_test_track_db Returns the path of an example misha database with Hi-C contacts from the ELA K562 dataset.

Usage

shaman_get_test_track_db(full = FALSE)

Arguments

Details

By default this is a small database that is built in the session's temporary directory on first use; later calls in the session reuse it. It has one chromosome (hg19 chr2) and the observed (hic_obs), expected (hic_exp) and score (hic_score) tracks of chr2:176.5e06-177e06, around the hoxd locus (46,735 observed contacts, each stored in both orientations).

With full = TRUE it returns the full example database of earlier shaman versions: 4.6 million contacts from the ELA K562 dataset covering the hoxd locus (chr2:175e06-178e06) and convergent CTCF regions, with scores for chr2:175e06-178e06. It is downloaded (about 100MB, from the lab's public S3 bucket) and extracted into the user cache directory (tools::R_user_dir("shaman", "cache"), about 480MB) on first use; later calls reuse it. Processing the complete matrix from this study requires downloading the full contact list and regenerating the reshuffled matrix.

Value

The path of the database.

Examples

library(misha)
gsetroot(shaman_get_test_track_db())
gtrack.ls()

Plot an observed or expected hic map

Description

shaman_gplot_map

Usage

shaman_gplot_map(
  points,
  interval_range = NA,
  rotate = TRUE,
  point_size = 0.1,
  add_axis = TRUE
)

Arguments

Details

Plots hic contact matrix.

Value

A ggplot object with the map.

Examples

# Set misha db to test
library(misha)
gsetroot(shaman_get_test_track_db())
points <- gextract("hic_obs", gintervals.2d(2, 176.5e06, 177e06, 2, 176.5e06, 177e06))
shaman_gplot_map(points)

Plot a normalized hic map

Description

shaman_gplot_map_score

Usage

shaman_gplot_map_score(
  points_score,
  interval_range = NA,
  rotate = TRUE,
  point_size = 0.1,
  add_axis = TRUE
)

Arguments

Details

Plots observed hic contact matrix color-coded by normalized scores. Data can be either extracted directly from score track or computed via the functions: shaman_score_hic_mat(), shaman_shuffle_and_score_hic_mat().

Value

A ggplot object with the map.

Examples

# Set misha db to test
library(misha)
gsetroot(shaman_get_test_track_db())
points <- gextract("hic_score", gintervals.2d(2, 176.5e06, 177e06, 2, 176.5e06, 177e06),
    colnames = "score"
)
shaman_gplot_map_score(points)

Compute a score matrix for observed data based on the expected for a given set of points

Description

shaman_kk_norm

Usage

shaman_kk_norm(obs, exp, points, k = 100, k_exp = 100)

Arguments

Details

This function receives observed and expected data and computes the score on a given set of points. The score for a point is the KS D-statistic of the distances to its k nearest neighbors in the observed data compared to the expected data.

Value

NULL if insufficient observed data, otherwise returns a list containing 3 elements: 1) points - start1, start2 and score for all observed points. 2) obs - the observed points. 3) exp - the expected points.

Examples

# Set misha db to test
library(misha)
gsetroot(shaman_get_test_track_db())
focus <- gintervals.2d(2, 176.6e06, 176.9e06, 2, 176.6e06, 176.9e06)
regional <- gintervals.2d(2, 176.5e06, 177e06, 2, 176.5e06, 177e06)
points <- gextract("hic_obs", focus, band = c(-5e05, -1024))
obs <- gextract("hic_obs", regional, band = c(-5e05, -1024))
exp <- gextract("hic_exp", regional, band = c(-5e05, -1024))
mat_score <- shaman_kk_norm(obs, exp, points, k = 100, k_exp = 200)
shaman_gplot_map_score(mat_score$points)

Plot spatial enrichment of contacts between two features

Description

shaman_plot_feature_grid

Usage

shaman_plot_feature_grid(
  grids,
  range,
  grid_resolution,
  plot_resolution,
  type = "enrichment",
  fig_fn = "",
  fig_width = 400,
  fig_height = 400,
  pal = colorRampPalette(c("blue", "#87FFFF", "white", "#FF413D", "black")),
  zlim = c(-1, 1)
)

Arguments

Details

After contact feature grids are generated via shaman_generate_feature_grid, use this function to visualize them.

Value

A list with the observed (obs) and expected (exp) matrices summed over the grids, at plot_resolution.

Examples

# Set misha db to test
library(misha)
gsetroot(shaman_get_test_track_db())
# the test db has chr2 only, with contacts in chr2:176.5e06-177e06
grid <- shaman_generate_feature_grid(
    ctcf_forward[ctcf_forward$chrom == "chr2", ], ctcf_reverse[ctcf_reverse$chrom == "chr2", ],
    "hic_obs",
    exp_track_nm = "hic_exp", score_track_nm = "hic_score", min_dist = 1e05, max_dist = 5e05
)
shaman_plot_feature_grid(list(grid), 25000, 500, 500)
shaman_plot_feature_grid(list(grid), 25000, 500, 1000)
shaman_plot_feature_grid(list(grid), 25000, 500, 2000)

Plot a normalized hic map with annotations

Description

shaman_plot_map_score_with_annotations

Usage

shaman_plot_map_score_with_annotations(
  genome,
  points_score,
  interval_range,
  point_size = 0.1,
  misha_tracks = list(),
  mt_colors = getOption("shaman.track_colors"),
  mt_ylims = NULL,
  annotations = list(),
  a_colors = getOption("shaman.annotation_colors"),
  add_genes = TRUE,
  add_ideogram = TRUE,
  add_axis = TRUE,
  gene_stacking = "squish",
  gene_size = 0.7,
  track_size = 0.8,
  annotation_size = 0.7,
  fig_fn = "",
  fig_width = 900,
  fig_height = 5/6 * 900
)

Arguments

Details

Plots observed hic contact matrix color-coded by normalized scores, aligned with linear data such as gene annotations, rna, chromatin, transcription factor binding, etc.).

Value

No return value, called for the plot. Needs the Bioconductor package Gviz.

Examples

library(misha)
gsetroot(shaman_get_test_track_db())
points <- gextract("hic_score", gintervals.2d(2, 176.5e06, 177e06, 2, 176.5e06, 177e06),
    colnames = "score"
)

# loading Gviz takes a few seconds
if (requireNamespace("Gviz", quietly = TRUE)) {
    shaman_plot_map_score_with_annotations("hg19", points, gintervals(2, 176.5e06, 177e06),
        add_genes = FALSE, add_ideogram = FALSE
    )
}

# gene annotations and the ideogram are downloaded from UCSC
if (interactive() && requireNamespace("Gviz", quietly = TRUE)) {
    shaman_plot_map_score_with_annotations("hg19", points, gintervals(2, 176.5e06, 177e06))
}

Plot misha 1D tracks and annotations

Description

shaman_plot_tracks_and_annotations

Usage

shaman_plot_tracks_and_annotations(
  genome,
  interval_range,
  misha_tracks = list(),
  mt_colors = getOption("shaman.track_colors"),
  mt_ylims = NULL,
  annotations = list(),
  a_colors = getOption("shaman.annotation_colors"),
  add_genes = TRUE,
  add_ideogram = TRUE,
  add_axis = TRUE,
  gene_stacking = "squish",
  gene_size = 0.7,
  track_size = 0.8,
  annotation_size = 0.7
)

Arguments

Details

Plots 1D tracks in UCSC format. Along with gene annotations and chromosomal axis, this function plots one dimensional misha tracks (e.g. rna-seq, chip-seq data) and marks annotated intervals in the plotting region.

Value

Called for the plot. Returns what Gviz::plotTracks() returns, invisibly. Needs the Bioconductor package Gviz.

Examples

library(misha)
gsetroot(shaman_get_test_track_db())
# the test db has chr2 only
ctcf <- list(
    ctcf_forward[ctcf_forward$chrom == "chr2", ],
    ctcf_reverse[ctcf_reverse$chrom == "chr2", ]
)

# loading Gviz takes a few seconds
if (requireNamespace("Gviz", quietly = TRUE)) {
    shaman_plot_tracks_and_annotations("hg19", gintervals(2, 176.5e06, 177e06),
        annotations = ctcf, add_genes = FALSE, add_ideogram = FALSE
    )
}

# gene annotations and the ideogram are downloaded from UCSC
if (interactive() && requireNamespace("Gviz", quietly = TRUE)) {
    shaman_plot_tracks_and_annotations("hg19", gintervals(2, 176.5e06, 177e06), annotations = ctcf)
}

Generate a score matrix for observed data based on the expected for a given 2D focus interval

Description

shaman_score_hic_mat

Usage

shaman_score_hic_mat(
  obs_track_nms,
  exp_track_nms,
  focus_interval,
  regional_interval,
  points_track_nms = obs_track_nms,
  min_dist = 1024,
  k = 100,
  k_exp = 2 * k
)

Arguments

Details

This function extracts observed data and expected data in an expanded matrix and computes the score for each observed point. The score for a point is the KS D-statistic of the distances to its k nearest neighbors in the observed data compared to the expected data.

Value

NULL if insufficient observed data, otherwise a list with one element: points - the points in the focus interval (chrom1, start1, end1, chrom2, start2, end2) and their score.

Examples

# Set misha db to test
library(misha)
gsetroot(shaman_get_test_track_db())
mat_score <- shaman_score_hic_mat(
    obs_track_nms = "hic_obs", exp_track_nms = "hic_exp",
    focus_interval = gintervals.2d(2, 176.7e06, 176.8e06, 2, 176.7e06, 176.8e06),
    regional_interval = gintervals.2d(2, 176.5e06, 177e06, 2, 176.5e06, 177e06)
)
shaman_gplot_map_score(mat_score$points)

Generate a score matrix for observed data based on the expected

Description

shaman_score_hic_mat_for_track

Usage

shaman_score_hic_mat_for_track(
  track_db,
  work_dir,
  obs_track_nms,
  exp_track_nms,
  points_track_nms,
  chrom,
  start1,
  end1,
  start2,
  end2,
  expand = 2000000,
  k = 100,
  min_dist = 1024
)

Arguments

Details

This function extracts observed data and expected data in an expanded matrix and computes the score for each observed point. The score for a point is the KS D-statistic of the distances to its k nearest neighbors in the observed data compared to the expected data.

chrom, start1, end1, start2 and end2 can be vectors, to score several matrices in one call. Matrices with the same chrom, start1 and end1 then share one extraction of each track over the union of their expanded intervals, instead of one extraction per matrix (each extraction of a 2D track reads the whole chromosome pair). The output files are the same as scoring the matrices one by one.

options(shaman.score.threads = N) computes the kNN distances and scores on N threads (default 1).

Value

0, 1 or -1 per matrix.

Examples

# The example below runs on the test misha db provided with shaman.
# Called by shaman_score_hic_track() for each matrix of its grid; here it scores the
# observed contacts of chr2:176.7e06-176.8e06.
library(misha)
track_db <- shaman_get_test_track_db()
gsetroot(track_db)
work_dir <- tempfile("shaman_score_")
dir.create(work_dir)
shaman_score_hic_mat_for_track(track_db, work_dir,
    obs_track_nms = "hic_obs", exp_track_nms = "hic_exp", points_track_nms = "hic_obs",
    chrom = "chr2", start1 = 176.7e06, end1 = 176.8e06, start2 = 176.7e06, end2 = 176.8e06
)
# the score of each observed contact (start1 <= start2) in the matrix
scores <- read.delim(list.files(work_dir, full.names = TRUE))
head(scores)
unlink(work_dir, recursive = TRUE)

Generate a score matrix for observed data based on the expected for a given set of points

Description

shaman_score_hic_points

Usage

shaman_score_hic_points(
  obs_track_nms,
  exp_track_nms,
  points,
  regional_interval,
  min_dist = 1024,
  k = 100,
  k_exp = 2 * k
)

Arguments

Details

This function extracts observed data and expected data in an expanded matrix and computes the score for each observed point. The score for a point is the KS D-statistic of the distances to its k nearest neighbors in the observed data compared to the expected data.

Value

NULL if insufficient observed data, otherwise a list with one element: points - the given points with their score (column score).

Examples

# Set misha db to test
library(misha)
gsetroot(shaman_get_test_track_db())
focus <- gintervals.2d(2, 176.7e06, 176.8e06, 2, 176.7e06, 176.8e06)
points <- gextract("hic_obs", focus, band = c(-5e05, -1024))
mat_score <- shaman_score_hic_points(
    obs_track_nms = "hic_obs", exp_track_nms = "hic_exp",
    points = points, regional_interval = gintervals.2d(2, 176.5e06, 177e06, 2, 176.5e06, 177e06)
)
shaman_gplot_map_score(mat_score$points)

Generate a score hic track based on observed and expected (shuffled) hic data

Description

shaman_score_hic_track

Usage

shaman_score_hic_track(
  track_db,
  work_dir,
  score_track_nm,
  obs_track_nms,
  exp_track_nms = paste0(obs_track_nms, "_shuffle"),
  points_track_nms = obs_track_nms,
  near_cis = 5000000,
  expand = 2000000,
  k = 100,
  max_jobs = 100
)

Arguments

Details

This function generates a 2D score track based on observed and expected hic data. The score is computed by generating a grid of small matrices spanning all chromosomes and computing the score of each matrix independently. The model for computing the score relies on the KS D statistic computed for each observed point, over the distances of the k-nearest neighbors in the observed compared to the expected. High scores represent contact enrichment while low scores depict insulation. Note that this function requires either sge (qsub) or multicore to compute in a timely manner. Parameters can be set via shaman.sge_support or shaman.mc_support in shaman.conf file. Sun Grid Engine mode, options(shaman.sge_support = 1), is preferred; work_dir must then be accessible by all jobs. Multi-core mode is options(shaman.mc_support = 1). options(shaman.score.threads = N) computes the kNN distances and scores of each matrix on N threads (default 1); in multi-core mode each of the max_jobs processes uses N threads, and in SGE mode each job does, so shaman.sge_flags should ask for N slots. Each extraction of a 2D track can keep about the whole chromosome's track file in memory; misha's options(gtrack.num.chunks = 1000) bounds that (2-3x lower peak memory on dense matrices in our runs) without changing the scores.

Each step creates temporary files of the matrix scores which are then joined to a track. Temporary files are deleted upon track creation. Matrices that still have no score after 3 rounds of jobs stop the run with an error that names them. The finished score files stay in work_dir, so a rerun with the same work_dir computes only the missing ones.

Value

No return value, called for side effects: creates the 2D score track score_track_nm in track_db.

Examples

# The example below runs on the test misha db provided with shaman.
# Note that this is a toy db sampled from K562 ela data -
# scoring based on the observed and expected tracks will not produce the score track,
# as most of the genome is missing.
library(misha)
track_db <- shaman_get_test_track_db()
gsetroot(track_db)
old_opts <- options(shaman.mc_support = 1) # configuring multi-core mode
if (gtrack.exists("hic_score_new")) {
    gtrack.rm("hic_score_new", force = TRUE)
    gdb.reload()
}
ret <- shaman_score_hic_track(track_db,
    # work_dir can be tempdir() only in multi-core mode.
    # For sge mode, work_dir must be accessible by all jobs.
    work_dir = tempdir(),
    score_track_nm = "hic_score_new",
    obs_track_nms = "hic_obs",
    exp_track_nms = "hic_exp",
    near_cis = 1e09, # this test db contains very little data, can increase the size of each job
    k = 20, # default is set to 100
    max_jobs = 2
) # increase number of jobs for optimal runtime when running in sge mode
gdb.reload()
gtrack.ls("hic_score_new") # new score track that was created
options(old_opts)

Returns the color palette used to display normalized scores

Description

shaman_score_pal

Usage

shaman_score_pal()

Value

A vector of 201 colors, for scores -100 to 100.

Examples

pal <- shaman_score_pal()
# the colors of the scores -100, 0, 50 and 100
pal[c(-100, 0, 50, 100) + 101]

Inline function for generating an expected matrix and computing the score for a given interval

Description

shaman_shuffle_and_score_hic_mat

Usage

shaman_shuffle_and_score_hic_mat(
  obs_track_nms,
  interval,
  work_dir,
  expand = 1000000,
  min_dist = 1024,
  k = 100,
  dist_resolution = NA,
  decay_smooth = NA,
  hic_mcmc_max_resolution = 400,
  shuffle = 80,
  grid_small = 500000,
  grid_high = 1000000,
  grid_increase = 500000,
  grid_step_iter = 40,
  seed = NULL
)

Arguments

Details

This function generates an expected 2D hic matrix based on observed hic data, and computes its score.

Value

NULL if insufficient observed data, otherwise returns a list containing: 1) points - start1, start2 and score for all observed points. 2) obs - the observed points. 3) exp - the expected points. 4) exp_fn - the name of the expected (shuffled) data file 5) seed - the seed the shuffler used

Examples

# Set misha db to test
library(misha)
gsetroot(shaman_get_test_track_db())
mat_score <- shaman_shuffle_and_score_hic_mat(
    obs_track_nms = "hic_obs",
    interval = gintervals.2d(2, 176.6e06, 176.9e06, 2, 176.6e06, 176.9e06),
    expand = 1e05,
    work_dir = tempdir(),
    shuffle = 2, # default is set to 80
    grid_step_iter = 1, # default is set to 40
    seed = 1
)
shaman_gplot_map_score(mat_score$points)

Generate an expected matrix from observed data as a process for generating an expected track

Description

shaman_shuffle_hic_mat_for_track

Usage

shaman_shuffle_hic_mat_for_track(
  track_db,
  track,
  work_dir,
  chrom,
  start1,
  end1,
  start2,
  end2,
  min_dist = 1024,
  max_dist = max(gintervals.all()$end),
  dist_resolution = NA,
  decay_smooth = NA,
  proposal_iterations = 10000000,
  shuffle = 80,
  hic_mcmc_max_resolution = 400,
  raw_ext = "raw",
  shuffled_ext = "shuffled",
  grid_small = 500000,
  grid_high = 1000000,
  grid_increase = 500000,
  grid_step_iter = 40,
  sort_uniq = FALSE,
  seed = NULL
)

Arguments

Details

This function generates an expected 2D hic matrix from observed hic data. Should not be called externally, but rather as jobs when creating a 2D shuffled track. The observed data is a combination of observed contacts in scope plus already shuffled near-cis contacts (stored in work_dir) which we sample from to maintain the decay probability curve.

Each step creates temporary files of the shuffled matrices which are then joined to a track. Temporary files are deleted upon track creation.

Value

The seed the shuffler used, or NA if the matrix was not shuffled here (no or too few contacts, or the shuffled file already existed).

Examples

# The example below runs on the test misha db provided with shaman.
# Called by shaman_shuffle_hic_track() for each chromosome; here it shuffles the contacts
# of chr2:176.5e06-177e06.
library(misha)
track_db <- shaman_get_test_track_db()
work_dir <- tempfile("shaman_shuffle_")
dir.create(work_dir)
seed <- shaman_shuffle_hic_mat_for_track(track_db, "hic_obs", work_dir,
    chrom = "chr2", start1 = 176.5e06, end1 = 177e06, start2 = 176.5e06, end2 = 177e06,
    shuffle = 1, # default is set to 80
    grid_step_iter = 1, # default is set to 40
    seed = 1, # the same seed gives the same shuffle
    sort_uniq = TRUE
)
seed
# the shuffled contacts (in both orientations) and their counts
shuffled <- read.delim(list.files(work_dir, pattern = "uniq$", full.names = TRUE))
head(shuffled)
unlink(work_dir, recursive = TRUE)

Generate an expected hic track based on observed hic data

Description

shaman_shuffle_hic_track

Usage

shaman_shuffle_hic_track(
  track_db,
  obs_track_nm,
  work_dir,
  exp_track_nm = paste0(obs_track_nm, "_shuffle"),
  max_jobs = 25,
  shuffle = 80,
  grid_small = 500000,
  grid_high = 1000000,
  grid_step_iter = 40,
  dist_resolution = NA,
  smooth = NA,
  seed = NULL
)

Arguments

Details

This function generates an expected 2D hic track based on observed hic data. Each chromosome is shuffled separately, to generate an expected shuffled contact matrix. Note that this function requires sge (qsub) or multicore to be enabled. Parameter can be set via shaman.sge_support or shaman.mc_support in shaman.conf file. Sun Grid Engine mode, options(shaman.sge_support = 1), is preferred; work_dir must then be accessible by all jobs. Multi-core mode is options(shaman.mc_support = 1).

Each step creates temporary files of the shuffled matrices which are then joined to a track. Temporary files are deleted upon track creation. If the shuffle of a chromosome fails (an error, or its process or job is killed), no track is imported and work_dir keeps the shuffled chromosomes, so a rerun with the same work_dir shuffles only the others.

Value

No return value, called for side effects: creates the 2D track exp_track_nm in track_db, with the seeds in its attribute seed.

Examples

# The example below runs on the test misha db provided with shaman.
# Note that this is a toy db sampled from K562 ela data - shuffling the observed track
# will not produce the expected track.
library(misha)
track_db <- shaman_get_test_track_db()
gsetroot(track_db)
old_opts <- options(shaman.mc_support = 1) # configuring multi-core mode
if (gtrack.exists("hic_obs_shuffle")) {
    gtrack.rm("hic_obs_shuffle", force = TRUE)
    gdb.reload()
}
ret <- shaman_shuffle_hic_track(track_db,
    obs_track_nm = "hic_obs",
    # work_dir can be tempdir() only in multi-core mode.
    # For sge mode, work_dir must be accessible by all jobs.
    work_dir = tempdir(),
    shuffle = 1, # default is set to 80
    grid_step_iter = 1, # default is set to 40
    max_jobs = 2, # optimally set to number of chromosomes
    seed = 1 # the same seed gives the same track
)
gdb.reload()
gtrack.ls("hic_obs_shuffle") # new shuffled track that was created
gtrack.attr.get("hic_obs_shuffle", "seed") # the seed of each chromosome
options(old_opts)