rnanorm 2.0.0rc2

Common RNAseq normalization methods

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.