Python implementation of the geNorm algorithm for gene expression.
Project description
geNorm
A Python package for RNAseq housekeeping (or, reference) normalisation.
You can:
- Run the geNorm algorithm [1], which automatically selects reference genes by recursively eliminating genes with high $
M
$ value. - Compute the gene-stability measure $
M
$ for reference genes in a given set of samples.
Here, $M
$ is defined in terms of the average variation in log-ratio expression:
M_j = \sum_{k=1}^n\frac{V_{jk}}{n-1}
where
A_{jk}^{(i)} = \log_2 \frac{a_{ij}}{a_{jk}}; V_{jk} = \sqrt{\mathrm{Var}(A_{jk})};
with expression $a_{ij}
$ referring to gene $j
$ in sample $i
$.
Installation
You can grab geNorm
from the Python Package Index:
pip3 install rna-genorm
Example
from pandas import DataFrame
from genorm import m_measure, genorm
# Expression data for three control genes.
counts = DataFrame(
[[ 1, 2, 1],
[ 3, 6, 5],
[ 5, 10, 9],
[ 3, 6, 5]],
columns=['gene_a', 'gene_b', 'gene_c'],
index=[f'sample_{i}' for i in range(1, 5)],
)
# Compute `M` value for this set of control genes.
m_measure(counts)
# Select top 2 control genes with lowest `M`.
gene_names, m_values = genorm(counts, n_stop=2)
Acknowledgements
Made by Hylke Donker & Bram van Es and open sourced under the Apache 2 license.
References:
[1]: Vandesompele, Jo, et al. "Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes." Genome biology 3.7 (2002): 1-12.
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