netomescalar
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A method to scale (LC-MS) metabolite profiles using reference pools and internal standards.
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Please read detailed user manual at the wiki.
Prerequisites
- Python3
- pip3
Step 1 - Install netomescalar
From Mac OS
$ sudo pip3 install netomescalar
From Windows OS open the command prompt as an administrator
$ pip3 install netomescalar
Step 2 - Acquire a LC-MS profiles and sample information
netomescalar demo -or- Use your own in this format!
Step 3 - Run from python OR command line
From Python:
import pandas as pd
from netomescalar.netomescalar import all_normalize, z_score
data = pd.read_csv('/path/data.csv', header=0)
sample_information = pd.read_csv('/path/sample_information.csv')
df = all_normalize(is_to_use='valine-d8', pref_to_use='PREFA', prefs_to_remove='', normalization= ['NN', 'IS], data= data, sample_information=sample_information, pool_missing_p=100,
fill_method =None)
From command line:
$ netomescalar -i Data_Input.csv -s Sample_Info.csv -o norm_results.csv --pref-to-use PREFA -m nn --pool-missing-p 100 -m NN IS --is-to-use 'valine-d8'
Step 3 - Run from netome cafe, the netome web server
