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update requirements file
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‎LeafletSC.egg-info/PKG-INFO‎

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Metadata-Version: 2.1
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Name: LeafletSC
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Version: 0.1.6
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Version: 0.1.7
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Summary: Alternative splicing quantification in single cells with Leaflet
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Home-page: https://github.com/daklab/Leaflet
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Author: Karin Isaev, Columbia University and NYGC
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## Getting Started
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LeafletSC is implemented in Python and requires Python version 3.9 or higher. We recommend the following approach:
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LeafletSC is implemented in Python and requires Python version 3.10 (3.11 has not been tested yet). We recommend the following approach:
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```bash
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# create a conda environment with python 3.9
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conda create -n "LeafletSC" python=3.9.15 ipython
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# create a conda environment with python 3.10
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conda create -n "LeafletSC" python=3.10 ipython
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# activate environment
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conda activate LeafletSC
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# install latest version of LeafletSC into this environment
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pip install LeafletSC==0.1.2
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pip install LeafletSC==0.1.6
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```
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Once the package is installed, you can load it in python as follows:

‎LeafletSC.egg-info/SOURCES.txt‎

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LeafletSC.egg-info/requires.txt
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LeafletSC.egg-info/top_level.txt
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LeafletSC/beta_binomial_mix/__init__.py
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LeafletSC/beta_binomial_mix/cellstate_consistency.py
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LeafletSC/beta_binomial_mix/model.py
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LeafletSC/beta_binomial_mix/calculate_cellstate_consistency.py
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LeafletSC/beta_binomial_mix/modeling.py
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LeafletSC/clustering/__init__.py
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LeafletSC/clustering/find_intron_clusters.py
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LeafletSC/clustering/load_cluster_data.py
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LeafletSC/clustering/obtain_intron_clusters.py
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LeafletSC/clustering/prep_model_input.py
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LeafletSC/clustering/prepare_model_input.py
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LeafletSC/simulation/__init__.py
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LeafletSC/simulation/simulate_counts.py
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LeafletSC/utils/__init__.py

build/lib/LeafletSC/beta_binomial_mix/cellstate_consistency.py renamed to build/lib/LeafletSC/beta_binomial_mix/calculate_cellstate_consistency.py

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build/lib/LeafletSC/beta_binomial_mix/model.py renamed to build/lib/LeafletSC/beta_binomial_mix/modeling.py

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def score(a, b):
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return log_beta(a,b).sum() - log_beta(a.sum(), b.sum())
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# Add functionality for getting score q-value to measure significance of differential splicing
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build/lib/LeafletSC/clustering/obtain_intron_clusters.py renamed to build/lib/LeafletSC/clustering/find_intron_clusters.py

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build/lib/LeafletSC/clustering/prep_model_input.py renamed to build/lib/LeafletSC/clustering/prepare_model_input.py

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‎build/lib/LeafletSC/utils/visualization.py‎

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plt.title("Junction:" + str(junc) + " Gene: " + simple_data_junc["gene_id"].values[0])
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# set x axis label to "Junction Usage Ratio (PSI)"
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plt.xlabel("Junction Usage Ratio (PSI)")
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plt.show()
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plt.show()
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# Add visualization for seeing junction in a specific intron cluster relative to a gene and
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# how much each junciton is used in each cell type

‎dist/LeafletSC-0.1.6-py3-none-any.whl‎

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‎dist/LeafletSC-0.1.6.tar.gz‎

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‎dist/LeafletSC-0.1.7-py3-none-any.whl‎

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