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|
%global _empty_manifest_terminate_build 0
Name: python-epitoolkit
Version: 0.2.6
Release: 1
Summary: EpiToolkit is a set of tools useful in the analysis of data from EPIC / 450K microarrays.
License: MIT
URL: https://github.com/ClinicalEpigeneticsLaboratory/EpiGenToolKit
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a0/9e/d65d7b009fe2454a0db42cd416c3d199511450af9c58745d01c3c679507b/epitoolkit-0.2.6.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-numpy
Requires: python3-seaborn
Requires: python3-matplotlib
Requires: python3-plotly
Requires: python3-scipy
Requires: python3-tqdm
Requires: python3-pathlib
Requires: python3-autopep8
Requires: python3-Sphinx
%description
# EpiGenToolKit
Is a small library created to deal with data from `EPIC / 450K` microarrays. The tool allows to:
a) Simply visualize methylation levels of specific CpG or genomic region.
b) Perform enrichment analysis of a selected subset of CpG against the whole array. In this type of analysis expected frequency [%] (based on mynorm) of genomic regions is compared to observed (based on provided cpgs set), results are comapred using chi-square test.
# How to start?
a) using env
```
python -m venv env
source env/bin/activate # Windows: env\Scripts\activate
pip install epitoolkit
```
b) using poetry
```
poetry new .
poetry add epitoolkit
```
c) or just clone the repository:
```
git clone https://github.com/ClinicalEpigeneticsLaboratory/EpiGenToolKit.git
cd EpiGenToolKit && poetry install
```
# How to use?
## Visualization
To visualize single **CpG** site or specific genomic region initialize **Visualise** object:
```
from epitoolkit.tools import Visualize
viz = Visualize(manifest=<path_to_array_manifest>, # path to manifest file
mynorm=<path_to_mynorm_file>, # path to mynorm file
poi=<path_to_poi_file>, # path to poi file
poi_col=<column_name> # name of column containing sample phenotype
skiprows=0) # many manifest contains headers, set skiprows argument to ignore them.
```
all files must have *.csv extension, mynorm must contain sample names as `columns` and cpgs as `rows`, the proper
EPIC manifest may be downloaded from [here](https://emea.support.illumina.com/downloads/infinium-methylationepic-v1-0-product-files.html),
poi file must contain sample names `rows` (only samples overlapped between poi and mynorm will be used)
and POI (phenotype of interest) column containing names of phenotype e.g. Control and Case.
To visualize single CpG:
```
viz.plot_CpG("cg07881041", # cpg ID
static=False, # plot type static / interactive [default]
height=400, # plot size [default]
width=700, # plot size [default]
title="", # plot title [default]
legend_title="", # legend title [default]
font_size=22, # font size [default]
show_legend=True, # False to hide legedn [default]
x_axis_label="CpG", # x axsis label [default]
category_order=["Cohort 1", "Cohort 2], # box order [default]
y_axis_label="beta-values") # y axis label [default]
```
> NOTE: most of those arguments are default! So you don't need to specify most of them!
To visualize specific genomic region:
```
vis.plot_Range(chr=17, start=5999, end=7000)
```
> NOTE: please note that all arguments available in `viz.plot_CpG` are also in `plot_Range`
To visualize specific CpGs in genomic order, instead of whole region, just pass collection of CpGs:
```
viz.plot_Range(cpgs=["cg04594855", "cg19812938", "cg05451842"]
```
To save plots use *export* argument, for instance:
```
viz.plot_Range(chr=17, start=5999, end=6770, export="plot.html") # if static = False only html format is supported if static = True, use png extension.
```
### Enrichment analysis
To perform enrichment analysis against any type of genomic region specified in the manifest file, the user needs to initialize **EnrichemntAnalysis** object.
```
from src.epitoolkit.tools import EnrichmentAnalysis
ea = EnrichmentAnalysis(manifest=<path_to_array_manifest>,
mynorm=<path_to_mynorm_file>)
```
or if `Visualize` object already exists use `load` method (this approach makes you not have to load the data again):
```
ea = EnrichmentAnalysis.load(<Visualize_object_name>)
```
To start analysis:
```
ea.enrichmentAnalysis(categories_to_analyse=["UCSC_RefGene_Group", "Relation_to_UCSC_CpG_Island"], # list of categories to analyse
cpgs=cpgs) # list of cpgs to analyse against background
```
%package -n python3-epitoolkit
Summary: EpiToolkit is a set of tools useful in the analysis of data from EPIC / 450K microarrays.
Provides: python-epitoolkit
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-epitoolkit
# EpiGenToolKit
Is a small library created to deal with data from `EPIC / 450K` microarrays. The tool allows to:
a) Simply visualize methylation levels of specific CpG or genomic region.
b) Perform enrichment analysis of a selected subset of CpG against the whole array. In this type of analysis expected frequency [%] (based on mynorm) of genomic regions is compared to observed (based on provided cpgs set), results are comapred using chi-square test.
# How to start?
a) using env
```
python -m venv env
source env/bin/activate # Windows: env\Scripts\activate
pip install epitoolkit
```
b) using poetry
```
poetry new .
poetry add epitoolkit
```
c) or just clone the repository:
```
git clone https://github.com/ClinicalEpigeneticsLaboratory/EpiGenToolKit.git
cd EpiGenToolKit && poetry install
```
# How to use?
## Visualization
To visualize single **CpG** site or specific genomic region initialize **Visualise** object:
```
from epitoolkit.tools import Visualize
viz = Visualize(manifest=<path_to_array_manifest>, # path to manifest file
mynorm=<path_to_mynorm_file>, # path to mynorm file
poi=<path_to_poi_file>, # path to poi file
poi_col=<column_name> # name of column containing sample phenotype
skiprows=0) # many manifest contains headers, set skiprows argument to ignore them.
```
all files must have *.csv extension, mynorm must contain sample names as `columns` and cpgs as `rows`, the proper
EPIC manifest may be downloaded from [here](https://emea.support.illumina.com/downloads/infinium-methylationepic-v1-0-product-files.html),
poi file must contain sample names `rows` (only samples overlapped between poi and mynorm will be used)
and POI (phenotype of interest) column containing names of phenotype e.g. Control and Case.
To visualize single CpG:
```
viz.plot_CpG("cg07881041", # cpg ID
static=False, # plot type static / interactive [default]
height=400, # plot size [default]
width=700, # plot size [default]
title="", # plot title [default]
legend_title="", # legend title [default]
font_size=22, # font size [default]
show_legend=True, # False to hide legedn [default]
x_axis_label="CpG", # x axsis label [default]
category_order=["Cohort 1", "Cohort 2], # box order [default]
y_axis_label="beta-values") # y axis label [default]
```
> NOTE: most of those arguments are default! So you don't need to specify most of them!
To visualize specific genomic region:
```
vis.plot_Range(chr=17, start=5999, end=7000)
```
> NOTE: please note that all arguments available in `viz.plot_CpG` are also in `plot_Range`
To visualize specific CpGs in genomic order, instead of whole region, just pass collection of CpGs:
```
viz.plot_Range(cpgs=["cg04594855", "cg19812938", "cg05451842"]
```
To save plots use *export* argument, for instance:
```
viz.plot_Range(chr=17, start=5999, end=6770, export="plot.html") # if static = False only html format is supported if static = True, use png extension.
```
### Enrichment analysis
To perform enrichment analysis against any type of genomic region specified in the manifest file, the user needs to initialize **EnrichemntAnalysis** object.
```
from src.epitoolkit.tools import EnrichmentAnalysis
ea = EnrichmentAnalysis(manifest=<path_to_array_manifest>,
mynorm=<path_to_mynorm_file>)
```
or if `Visualize` object already exists use `load` method (this approach makes you not have to load the data again):
```
ea = EnrichmentAnalysis.load(<Visualize_object_name>)
```
To start analysis:
```
ea.enrichmentAnalysis(categories_to_analyse=["UCSC_RefGene_Group", "Relation_to_UCSC_CpG_Island"], # list of categories to analyse
cpgs=cpgs) # list of cpgs to analyse against background
```
%package help
Summary: Development documents and examples for epitoolkit
Provides: python3-epitoolkit-doc
%description help
# EpiGenToolKit
Is a small library created to deal with data from `EPIC / 450K` microarrays. The tool allows to:
a) Simply visualize methylation levels of specific CpG or genomic region.
b) Perform enrichment analysis of a selected subset of CpG against the whole array. In this type of analysis expected frequency [%] (based on mynorm) of genomic regions is compared to observed (based on provided cpgs set), results are comapred using chi-square test.
# How to start?
a) using env
```
python -m venv env
source env/bin/activate # Windows: env\Scripts\activate
pip install epitoolkit
```
b) using poetry
```
poetry new .
poetry add epitoolkit
```
c) or just clone the repository:
```
git clone https://github.com/ClinicalEpigeneticsLaboratory/EpiGenToolKit.git
cd EpiGenToolKit && poetry install
```
# How to use?
## Visualization
To visualize single **CpG** site or specific genomic region initialize **Visualise** object:
```
from epitoolkit.tools import Visualize
viz = Visualize(manifest=<path_to_array_manifest>, # path to manifest file
mynorm=<path_to_mynorm_file>, # path to mynorm file
poi=<path_to_poi_file>, # path to poi file
poi_col=<column_name> # name of column containing sample phenotype
skiprows=0) # many manifest contains headers, set skiprows argument to ignore them.
```
all files must have *.csv extension, mynorm must contain sample names as `columns` and cpgs as `rows`, the proper
EPIC manifest may be downloaded from [here](https://emea.support.illumina.com/downloads/infinium-methylationepic-v1-0-product-files.html),
poi file must contain sample names `rows` (only samples overlapped between poi and mynorm will be used)
and POI (phenotype of interest) column containing names of phenotype e.g. Control and Case.
To visualize single CpG:
```
viz.plot_CpG("cg07881041", # cpg ID
static=False, # plot type static / interactive [default]
height=400, # plot size [default]
width=700, # plot size [default]
title="", # plot title [default]
legend_title="", # legend title [default]
font_size=22, # font size [default]
show_legend=True, # False to hide legedn [default]
x_axis_label="CpG", # x axsis label [default]
category_order=["Cohort 1", "Cohort 2], # box order [default]
y_axis_label="beta-values") # y axis label [default]
```
> NOTE: most of those arguments are default! So you don't need to specify most of them!
To visualize specific genomic region:
```
vis.plot_Range(chr=17, start=5999, end=7000)
```
> NOTE: please note that all arguments available in `viz.plot_CpG` are also in `plot_Range`
To visualize specific CpGs in genomic order, instead of whole region, just pass collection of CpGs:
```
viz.plot_Range(cpgs=["cg04594855", "cg19812938", "cg05451842"]
```
To save plots use *export* argument, for instance:
```
viz.plot_Range(chr=17, start=5999, end=6770, export="plot.html") # if static = False only html format is supported if static = True, use png extension.
```
### Enrichment analysis
To perform enrichment analysis against any type of genomic region specified in the manifest file, the user needs to initialize **EnrichemntAnalysis** object.
```
from src.epitoolkit.tools import EnrichmentAnalysis
ea = EnrichmentAnalysis(manifest=<path_to_array_manifest>,
mynorm=<path_to_mynorm_file>)
```
or if `Visualize` object already exists use `load` method (this approach makes you not have to load the data again):
```
ea = EnrichmentAnalysis.load(<Visualize_object_name>)
```
To start analysis:
```
ea.enrichmentAnalysis(categories_to_analyse=["UCSC_RefGene_Group", "Relation_to_UCSC_CpG_Island"], # list of categories to analyse
cpgs=cpgs) # list of cpgs to analyse against background
```
%prep
%autosetup -n epitoolkit-0.2.6
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-epitoolkit -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.6-1
- Package Spec generated
|
