pareidolia 0.4.5

Multi-sample change detection in Hi-C patterns

Multi-sample change detection in Hi-C patterns

Pareidolia for detects changes in intensities of a specific pattern (e.g. chromatin loops and domain borders) from Hi-C maps. It can be used to compare samples from different conditions and use multiple replicates to improve results.

This toolkit exploits Chromosight correlation maps, allowing the same method to detect changes in different Hi-C patterns (e.g. loops or borders).

Usage

Pareidolia can be used both as a python package and as a command line tool:

import pareidolia.hic_utils as pah
import chromosight.kernels as ck
pah.change_detection_pipeline(
  ["ctrl1.cool", "ctrl2.cool", "treat1.cool", "treat2.cool"],
  ["control", "control", "treatment", "treatment"],
  kernel=ck.loops,
  subsample=True,
  n_cpus=8,
)

We can also use the CLI to execute the same instruction:

pareidolia -n 8 \
           -k loops \
           ctrl1.cool,ctrl2.cool,treat1.cool,treat2.cool \
           control,control,treatment,treatment \
           output.tsv

Pareidolia can either detect changes de-novo, or compute the change intensity at a set of input positions. The input positions can be provided as a bed2d (=bedpe) file, containing a list of 2D genomic coordinates. This file can be provided with the –bed2d-file option on the CLI, or using the bed2d_file parameter in the python API.

Padeidolia accepts chromosight kernels as kernel names. A list of valid kernels can be displayed using chromosight list-kernels. Alternatively, when using the API, an arbitrary 2D numpy array can be provided as kernels.

Installation

Pareidolia is available on Pypi and can be installed using:

pip3 install --user pareidolia