Metadata-Version: 2.1
Name: metaloci
Version: 1.0.2
Summary: METALoci: spatially auto-correlated signals in 3D genomes
Home-page: https://github.com/3DGenomes/METALoci/
Author: Leo Zuber, Iago Maceda, Juan Antonio Rodríguez and Marc Martí-Renom
Author-email: martirenom@cnag.eu
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Operating System :: POSIX :: Linux
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: bioframe ==0.5.1
Requires-Dist: cooler ==0.9.2
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Requires-Dist: esda ==2.4.3
Requires-Dist: geopandas ==0.13.2
Requires-Dist: h5py ==3.10.0
Requires-Dist: hic-straw ==1.3.1
Requires-Dist: libpysal ==4.6.2
Requires-Dist: matplotlib ==3.7.2
Requires-Dist: networkx ==3.1
Requires-Dist: numba ==0.55.1
Requires-Dist: numpy ==1.21.6
Requires-Dist: pandas ==2.0.3
Requires-Dist: Pillow ==10.0.1
Requires-Dist: pybedtools ==0.9.1
Requires-Dist: scipy ==1.9.1
Requires-Dist: seaborn ==0.11.0
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Requires-Dist: sphinx-argparse

# METALoci

#### Spatially auto-correlated signals in 3D genomes.

METALoci relies on spatial autocorrelation analysis, classically employed in geostatistics, to describe how the variation of a variable depends on space at a global and local scales (e.g., identifying contamination hotspots within a city). METALoci repurposes this type of analysis to quantify spatial genome hubs of similar epigenetic properties. Briefly, the overall flowchart of METALoci consists of four steps:

* First, a genome-wide Hi-C normalized matrix is taken as input and the top interactions selected.

* Second, the selected interactions are used to build a graph layout (equivalent to a physical map) using the Kamada-Kawai algorithm with nodes representing bins in the Hi-C matrix and the 2D distance between the nodes being inversely proportional to their normalized Hi-C interaction frequency.

* Third, epigenetic/genomic signals, measured as coverage per genomic bin (e.g., ChIP-Seq signal for H3K27ac), are next mapped into the nodes of the graph layout.

* The fourth and final step involves the use of a measure of autocorrelation (specifically, the Local Moran’s I or LMI) to identify nodes and their neighborhoods with an enrichment of similar epigenetic/genomic signals.

METALoci is compatible with .cool, .mcool and .hic Hi-C formats; and with .bed signal files. The signal used in METALoci
may be any numerical signal (as long as it is in a .bed file, with the location of such signal).

#### Have a look at the [documentation](https://metaloci.readthedocs.io)!

## Installation

METALoci requires bedtools to be installed and accesible from the conda environment you will use. You can install it
with

```bash
conda install bedtools
```

#### Install metaloci from PyPI:

```bash
conda create -n metaloci python==3.9
conda activate metaloci
pip install metaloci
```

If you are experiencing any unexpected results with METALoci, we suggest to update the version of **awk** you are using.
The recommended version is 1.3.4 or newer.

In Ubuntu, you can do this with:

```bash
sudo apt install mawk
```

## Contributors

METALoci is currently being developed at the [MarciusLab](http://www.marciuslab.org) by Iago Maceda, 
Marc A. Marti-Renom and Leo Zuber, with the contribution of other members of the lab.

