qnorm 0.5.1

Quantile normalization

qnorm

Quantile normalization made easy! This tool was developed as the current (Python) implementations scattered across the web do not correctly resolve collisions/ties in the ranks. Properly resolving rank ties is important when ties happen frequently, such as when working with discrete numbers (integers) in count tables. This implementation should be relatively fast, and can use multiple cores to sort the columns and tie-resolvement is accelerated by numba.

Code example

We recreate the example of Wikipedia:

import pandas as pd
import qnorm

df = pd.DataFrame({'C1': {'A': 5, 'B': 2, 'C': 3, 'D': 4},
                   'C2': {'A': 4, 'B': 1, 'C': 4, 'D': 2},
                   'C3': {'A': 3, 'B': 4, 'C': 6, 'D': 8}})

print(qnorm.quantile_normalize(df, axis=1))

which is what we expect:

         C1        C2        C3
A  5.666667  5.166667  2.000000
B  2.000000  2.000000  3.000000
C  3.000000  5.166667  4.666667
D  4.666667  3.000000  5.666667

Qnorm accepts an (optional) axis argument, which is used to normalize along. If axis=1 (default), standardize each sample (column), if axis=0, standardize each feature (row).

NOTE: The function quantile_normalize also accepts numpy arrays.

Multicore support

To accelerate the computation you can pass a ncpus argument to the function call and qnorm will be run in parallel:

qnorm.quantile_normalize(df, ncpus=8)  

Normalize onto distribution

You can also use the quantile_normalize function to normalize "onto" a distribution, by passing a target along to the function call.

import pandas as pd
import qnorm

df = pd.DataFrame({'C1': {'A': 4, 'B': 3, 'C': 2, 'D': 1},
                   'C2': {'A': 1, 'B': 2, 'C': 3, 'D': 4}})

print(qnorm.quantile_normalize(df, target=[8, 9, 10, 11]))

With our values now transformed onto the target:

     C1    C2
A  11.0   8.0
B  10.0   9.0
C   9.0  10.0
D   8.0  11.0

How fast is it and what is its memory usage?

How better to measure this than a little benchmark / example? For this example we will consider 100 replicates, each consisting of one million integer values between 0 and 100, which should give us plenty of rank ties.

import numpy as np
import qnorm


test = np.random.randint(0, 100, size=(1_000_000, 100), dtype=np.int32)

qnorm.quantile_normalize(test, ncpus=4)

user@comp:~$ /usr/bin/time --verbose python small_qnorm_script.py |& grep -P "(wall clock|Maximum resident set size)"

Elapsed (wall clock) time (h:mm:ss or m:ss): 0:07.52
Maximum resident set size (kbytes): 2768884

It takes only 7.5 seconds to initialize our table and quantile normalize it. I think that's pretty fast!

The test array we made consists of 100 * 1_000_000 = 100_000_000 single point precision integers, so four bytes each (400_000_000 bytes, 0.4 gigabytes). The memory footprint of our script is 0.27 gigabytes, around 7 times our input. Unfortunately that makes qnorm a bit memory hungry, but that should not be a problem in 99% of the cases.

Scaling of ncpus

Using more than four cpus generally does not lead to a much bigger speedup.

Command Line Interface (CLI) example

Qnorm also contains a CLI for converting csv/tsv files. The CLI depends on pandas, but this is an optional dependency of qnorm. To make use of the CLI make sure to install pandas in your current environment as well!

user@comp:~$ qnorm --help

usage: qnorm [-h] [-v] table

Quantile normalize your table

positional arguments:
  table          input csv/tsv file which will be quantile normalized

optional arguments:
  -h, --help     show this help message and exit
  -v, --version  show program's version number and exit

And again the example of Wikipedia:

user@comp:~$ cat table.tsv
        C1      C2      C3
A       5       4       3
B       2       1       4
C       3       4       6
D       4       2       8

user@comp:~$ qnorm table.tsv
        C1      C2      C3
A       5.666666666666666       5.166666666666666       2.0
B       2.0     2.0     3.0
C       3.0     5.166666666666666       4.666666666666666
D       4.666666666666666       3.0     5.666666666666666

NOTE: the qnorm cli assumes that the first column and the first row are used as descriptors, and are "ignored" in the quantile normalization process. Lines starting with a hashtag "#" are treated as comments and ignored.

Installation

pip

user@comp:~$ pip install qnorm

conda

Installing qnorm from the conda-forge channel can be achieved by adding conda-forge to your channels with:

user@comp:~$ conda config --add channels conda-forge

Once the conda-forge channel has been enabled, qnorm can be installed with:

user@comp:~$ conda install qnorm

local

clone the repository

user@comp:~$ git clone https://github.com/Maarten-vd-Sande/qnorm

And install it

user@comp:~$ cd qnorm
user@comp:~$ pip install .