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https://github.com/gphmorin/geneo
A set of functions for pedigree analysis.
https://github.com/gphmorin/geneo
bioinformatics genealogy genetics pedigree pedigree-analysis
Last synced: about 1 month ago
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A set of functions for pedigree analysis.
- Host: GitHub
- URL: https://github.com/gphmorin/geneo
- Owner: GPhMorin
- License: mit
- Created: 2024-10-24T22:30:46.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-21T19:50:25.000Z (about 1 month ago)
- Last Synced: 2024-11-21T20:32:34.020Z (about 1 month ago)
- Topics: bioinformatics, genealogy, genetics, pedigree, pedigree-analysis
- Language: C++
- Homepage:
- Size: 8.88 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# GENEO Toolkit
A set of functions for pedigree analysis, designed for use with data from the [GÉNÉO portal](https://portailgeneo.ca). Based on the functionality of [GENLIB](https://cran.r-project.org/web/packages/GENLIB/index.html): see the article by Gauvin et al. (2015) \<[doi:10.1186/s12859-015-0581-5](https://doi.org/10.1186/s12859-015-0581-5)>.
## Documentation
The [GENLIB reference manual](https://cran.r-project.org/web/packages/GENLIB/GENLIB.pdf) and this README file are sufficient to learn how to use the GENEO toolkit.
## Aims
* Easily port R code using GENLIB into Python code using the GENEO toolkit;
* Integrate with Python libraries such as Pandas and NumPy;
* Provide speed and convenience;
* Present a modular structure for further development.
## Functions
* Create a pedigree structure from a file or `DataFrame`;
* Output a pedigree as a `DataFrame`;
* Identify individuals in a pedigree, such as probands and founders;
* Extract a subpedigree from a pedigree;
* Describe a pedigree, such as the number of individuals and its completeness;
* Compute information about a pedigree, such as the pairwise kinship coefficients of probands and the genetic contributions of ancestors;
* (Eventually) Simulate information about pedigrees and individuals.
## Installation
* Clone this repository, `cd` into it, then run `pip install .`. Alternatively, without cloning, run `pip install https://github.com/GPhMorin/geneo/archive/main.zip`. Both options install two packages, `geneo` and `cgeneo` (used by the former internally), and their dependencies. You will need a compiler that supports C++17.
* If OpenMP is found during installation, the `geneo.phi()` function will run in parallel, making it the fastest implementation of kinship computation that we know of. If you use macOS, you may need to follow [these instructions](https://www.scivision.dev/cmake-openmp/) to enable OpenMP.
* On Windows 11, the toolkit was tested using Microsoft Visual C++ 2022.
## Data
* If the pedigree is loaded from a file, using `geneo.genealogy("path/to/pedigree.csv")`, the file *must* start with an irrelevant line (such as `ind father mother sex`) and the following lines must contain, as digits, each individual's ID, their father's ID (`0` if unknown), their mother's ID (`0` if unknown), and their sex (`0` if unknown, `1` if male, `2` if female), in that order. Each information must be separated by anything but digits (tabs, spaces, commas, etc.), with one line per individual.
* Three datasets come from the GENLIB source code: `geneo.geneaJi`, `geneo.genea140` and `geneo.pop140`. They are part of the project for testing and practice. More information on these datasets is available in the [GENLIB reference manual](https://cran.r-project.org/web/packages/GENLIB/GENLIB.pdf). They may be loaded using `geneo.genealogy(geneo.geneaJi)`, etc.
* You may also load the pedigree from a Pandas DataFrame, for instance:
```python
import geneo as gen
import pandas as pd
inds = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
fathers = [0, 0, 0, 1, 1, 0, 3, 3, 6, 6]
mothers = [0, 0, 0, 2, 2, 0, 4, 4, 5, 5]
sexes = [1, 2, 1, 2, 2, 1, 2, 1, 1, 2]
df = pd.DataFrame({'ind': inds, 'father': fathers,
'mother': mothers, 'sex': sexes})
ped = gen.genealogy(df)
```
## Comparison with GENLIB
The function calls are almost verbatim copies of GENLIB's. For instance:
```r
# With GENLIB
library(GENLIB)
data(genea140)
ped <- gen.genealogy(genea140)
pro <- gen.pro(ped)
phi <- gen.phi(ped, pro=pro)
mean <- gen.phiMean(phi)
mrca <- gen.findMRCA(ped, c(802424, 868572))
dist <- gen.find.Min.Distance.MRCA(mrca)
out <- gen.genout(ped, sorted=TRUE)
```
```python
# With the GENEO toolkit
import geneo as gen
genea140 = gen.genea140
ped = gen.genealogy(genea140)
pro = gen.pro(ped)
phi = gen.phi(ped, pro=pro)
mean = gen.phiMean(phi)
mrca = gen.findMRCA(ped, [802424, 868572])
dist = gen.find_Min_Distance_MRCA(mrca)
out = gen.genout(ped, sorted=True)
```
## GENLIB Functions Not Included
| Function | Description |
| --------------------------- | ------------------------------------------------------------------------------ |
| `gen.graph` | Pedigree graphical tool |
| | |
| `gen.simuHaplo` | Gene dropping simulations - haplotypes |
| `gen.simuHaplo_convert` | Convert proband simulation results into sequence data given founder haplotypes |
| `gen.simuHaplo_IBD_compare` | Compare proband haplotypes for IBD sharing |
| `gen.simuHaplo_traceback` | Trace inheritance path for results from gene dropping simulation |
| `gen.simuProb` | Gene dropping simulations - Probabilities |
| `gen.simuSample` | Gene dropping simulations - Sample |
| `gen.simuSampleFreq` | Gene dropping simulations - Frequencies |
| `gen.simuSet` | Gene dropping simulations with specified transmission probabilities |
| | |
| `gen.fCI` | Average inbreeding coefficient confidence interval |
| `gen.phiCI` | Average kinship confidence interval |
| `gen.completenessVar` | Variance of completeness index |
| `gen.implexVar` | Variance of genealogical implex |
| `gen.meangendepthVar` | Variance of genealogical depth |