Common RNAseq normalization methods
Project description
Python implementation of common RNAseq normalization methods:
CPM (Counts per million)
FPKM (Fragments per kilobase million)
TPM (Transcripts per million)
UQ (Upper quartile)
CUF (Counts adjusted with UQ factors)
TMM (Trimmed mean of M-values)
CTF (Counts adjusted with TMM factors)
Features
Pure Python implementation (no need for R, etc.)
Scikit-learn compatible
Command line interface
Verbose documentation (at least we hope so…)
Tested methods
Install
We recommend installing RNAnorm with pip:
pip install rnanorm
Quick start
Implemented methods can be used from Python or from the command line.
Normalize from Python
Most commonly normalization methods are run from Python. E.g.:
>>> from rnanorm.datasets import load_rnaseq_toy
>>> from rnanorm import FPKM
>>> dataset = load_rnaseq_toy()
>>> dataset.exp
G1 G2 G3 G4 G5
S1 200.0 300.0 500.0 2000.0 7000.0
S2 400.0 600.0 1000.0 4000.0 14000.0
S3 200.0 300.0 500.0 2000.0 17000.0
S4 200.0 300.0 500.0 2000.0 2000.0
>>> fpkm = FPKM(dataset.gtf_path).set_output(transform="pandas")
>>> fpkm.fit_transform(dataset.exp)
G1 G2 G3 G4 G5
S1 100000.0 100000.0 100000.0 200000.0 700000.0
S2 100000.0 100000.0 100000.0 200000.0 700000.0
S3 50000.0 50000.0 50000.0 100000.0 850000.0
S4 200000.0 200000.0 200000.0 400000.0 400000.0
Normalize from command line
Often it is handy to do normalization from the command line:
rnanorm fpkm exp.csv --gtf annotation.gtf --out exp_fpkm.csv
Contribute
To learn about contributing to the code base, read the Contributing section.
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