Function reference • HistogramZoo

All functions

GTF_to_GRangesList(): Produces a GRangesList out of a GTF file, each element represents the exons of a gene or transcript

GenomicHistogram(): Generates an S3 GenomicHistogram object

Histogram(): Generates an S3 Histogram object

are_colours(): Verify if a vector contains R colours

base0_to_base1(): Converts a GRanges object from base 0 to base 1

bigWig_to_histogram(): Generates GenomicHistogram objects from the coverage of a bigWig file

bin_log_likelihood(): Computes the negative log likelihood of an underlying continuous distribution from binned data.

calculate_probability_difference(): Calculate whether ha,b > pa,b

compute_coverage_on_bins(): A helper function for coverage_to_histogram that returns a dataframe from a set of ranges

correct_fitted_value(): Correcting for Jaccard/Intersection

coverage_to_histogram(): Generates a GenomicHistogram object from coverage data

create_coverageplot(): create_coverageplot creates a histogram/coverage plot with the potential to add annotations

create_histogram(): Creates a histogram with potentially variable length bins

create_layerplot(): Stacks together multiple plots

create_residualplot(): create_residualplot creates a residual plot between fitted and observed data

create_trackplot(): create_trackplot creates a horizontal plot with rows representing separate tracks

dput_str(): Return a string representation of an object

extract_segments(): Represent a single segment as a set of intervals

extract_stats_from_models(): Extract stats from models

find_all_meaningful_gap(): Find all meaningful gaps

find_bin_width(): Finds the bin_width of Histogram or GenomicHistogram bins

find_change_points(): Find changepoints in a vector with uniform stretches of values

find_consecutive_threshold(): Returns the indices for consecutive elements of a vector that are greater than a specified threshold

find_consensus_model(): Methods for voting for a consensus model based on the metrics of fit_distributions

find_local_optima(): find_local_optima returns the local optima of histograms

find_midpoint(): Finds the midpoint of Histogram or GenomicHistogram bins

fit_distributions(): Fit the model parameters by optimizing a histogram metric

fit_distributions_helper(): A helper function for fit_distributions

fit_uniform(): Fit a uniform distribution to a histogram

fit_uniform_helper(): A helper function for the S3 method fit_uniform

format_print_intervals(): Formats print intervals with introns and one-bp bins

ftc(): Fine-to-coarse segmentation algorithm

dgamma() pgamma() rgamma() qgamma(): The Gamma Distribution

dgamma_flip() pgamma_flip() rgamma_flip() qgamma_flip(): The Flipped Gamma Distribution

generate_granges_from_parent_and_ranges(): A helper function for coverage_to_histogram that returns a dataframe from a set of ranges

generate_identifiers(): Generates region_ids for GRanges objects

generate_row_ids(): Generates row names from row data

generate_uniform_distribution(): Uniform density generation

generate_xlabels(): Generates x labels for histogram-based plots, create_coverageplot and create_residualplot

genome_BED_to_histogram(): Generate GenomicHistogram objects from BED files

grenader(): Calculate the Grenander estimator of a given density vector

histogram.chisq(): Chi-squared

histogram.intersection(): Histogram intersection

histogram.jaccard(): Jaccard index

histogram.ks(): Kolmogorov-Smirnov divergence

histogram.mse(): Mean-squared error

identify_uniform_segment(): Finds the largest uniform segment that is longer than threshold

index_to_start_end(): Convert a vector of points into a data.frame of start/end points representing disjoint intervals

is_equal_integer(): Checks if a numeric vector can be used as an integer vector

labels_helper(): A helper function that generates labels at specific Histogram bins

maximal_meaningful_interval(): Identifies the maximum meaningful interval from a set of overlapping intervals, can also be applied to find the max gap

maximum_entropy(): Computes H, the maximum H_h,p(a,b)

meaningful_gap(): Determines whether ha,b is a meaningful gap

meaningful_gaps_local(): Finds the meaningful gaps between the points in s

meaningful_interval(): Determines whether ha,b is a meaningful interval

metric.histogram.dist(): Computes a given histogram distance metric to a distribution

monotone_cost(): Compute the monotone cost,

new_GenomicHistogram(): GenomicHistogram constructor

new_HZModelFit(): Constructs a new HZModelFit object

new_Histogram(): Constructs a new Histogram object

observations_to_histogram(): Take a vector of values and get the histogram for integer breaks

reassign_region_id(): reassign_region_id for Histogram objects

relative_entropy(): Kullback-Leibler divergence (Relative Entropy)

remove_max_gaps(): Creates a new set of segments from a partition of points

reset_consecutive_intervals(): Reset consecutive intervals to starting at 1, bin_width

return_x_points(): Returns the segment_and_fit x coordinates of the identified points

return_y_points(): Returns the segment_and_fit y coordinates of the identified points

scale_model_params(): Rescale parameter statistics based on original fit bin width

segment_and_fit(): Segmentation of histograms and distribution fitting

summarize_results(): Formats results of segment_and_fit

dtgamma() ptgamma() rtgamma() qtgamma(): The Truncated Gamma Distribution

dtgamma_flip() ptgamma_flip() rtgamma_flip() qtgamma_flip(): The Truncated Flipped Gamma Distribution

transcript_BED_to_histogram(): Generate GenomicHistogram objects from BED files using coordinates defined by a GTF file

uniform_mle(): Uniform maximum likelihood estimation

validate_GenomicHistogram(): Validates GenomicHistogram objects

validate_Histogram(): Validates Histogram objects

weighted.sd() weighted.var() weighted.skewness() weighted.mean(<Histogram>): Weighted higher order moments

weighted_coverage(): A helper function for coverage_to_histogram that returns the weighted coverage of a disjoint bin