bigwig

Description

A track for bigwig files.

Parameters

Necessary:

  • file

Optional:

  • title: Put here a title which will apprear on the right.

  • height: 0.5 (default) or float above 0.

  • overlay_previous: no (default) or yes or share-y.

  • orientation: by default this option is not set but you can also put: inverted.

  • color: #33a02c (default)

  • alpha: 1 (default) or any float above 0 below 1

  • max_value: by default this option is not set but you can also put: any float

  • min_value: by default this option is not set but you can also put: any float

  • show_data_range: true (default) or false.

  • type: fill (default)

  • negative_color: by default this option is not set

  • nans_to_zeros: false (default) or true.

  • summary_method: mean (default) or average, max, min, stdev, dev, coverage, cov or sum.

  • number_of_bins: 700 (default) or any integer above 1

  • transform: no (default) or log, log1p, -log, log2 or log10.

  • log_pseudocount: 0 (default) or any float

  • y_axis_values: transformed (default) or original.

  • second_file*: by default this option is not set

  • operation*: file (default)

  • grid: false (default) or true.

* While pyGenomeTracks can convert coverage tracks on the fly, this might be a time-consuming step, especially on large files and if you want to replot many times. In this situation, we recommend using the deepTools suite to convert your files in advance. For example bamCoverage or bamCompare

Output of make_tracks_file:

# title of track (plotted on the right side)
title =
# height of track in cm (ignored if the track is overlay on top the previous track)
height = 2
# if you want to plot the track upside-down:
# orientation = inverted
# if you want to plot the track on top of the previous track. Options are 'yes' or 'share-y'.
# For the 'share-y' option the y axis values is shared between this plot and the overlay plot.
# Otherwise, each plot use its own scale
#overlay_previous = yes

color = #666666
# To use a different color for negative values
#negative_color = red
# To use transparency, you can use alpha
# default is 1
# alpha = 0.5
# the default for min_value and max_value is 'auto' which means that the scale will go
# roughly from the minimum value found in the region plotted to the maximum value found.
min_value = 0
#max_value = auto
# The number of bins takes the region to be plotted and divides it
# into the number of bins specified
# Then, at each bin the bigwig mean value is computed and plotted.
# A lower number of bins produces a coarser tracks
number_of_bins = 700
# to convert missing data (NaNs) into zeros. Otherwise, missing data is not plotted.
nans_to_zeros = true
# The possible summary methods are given by pyBigWig:
# mean/average/stdev/dev/max/min/cov/coverage/sum
# default is mean
summary_method = mean
# for type, the options are: line, points, fill. Default is fill
# to add the preferred line width or point size use:
# type = line:lw where lw (linewidth) is float
# similarly points:ms sets the point size (markersize (ms) to the given float
# type = line:0.5
# type = points:0.5
# set show_data_range to false to hide the text on the left showing the data range
show_data_range = true
# to compute operations on the fly on the file
# or between 2 bigwig files
# operation will be evaluated, it should contains file or
# file and second_file,
# we advice to use nans_to_zeros = true to avoid unexpected nan values
#operation = 0.89 * file
#operation = - file
#operation = file - second_file
#operation = log2((1 + file) / (1 + second_file))
#operation = max(file, second_file)
#second_file = path for the second file
# To log transform your data you can also use transform and log_pseudocount:
# For the transform values:
# 'log1p': transformed_values = log(1 + initial_values)
# 'log': transformed_values = log(log_pseudocount + initial_values)
# 'log2': transformed_values = log2(log_pseudocount + initial_values)
# 'log10': transformed_values = log10(log_pseudocount + initial_values)
# '-log': transformed_values = - log(log_pseudocount + initial_values)
# For example:
#tranform = log
#log_pseudocount = 2
# When a transformation is applied, by default the y axis
# gives the transformed values, if you prefer to see
# the original values:
#y_axis_values = original
# If you want to have a grid on the y-axis
#grid = true
file_type = bigwig