alignement.py

import numpy as np
import pandas as pd
from loess.loess_1d import loess_1d

from dataloader import RT_Dataset
from msms_processing import load_data
import matplotlib.pyplot as plt


ALPHABET_UNMOD = {
    "": 0,
    "A": 1,
    "C": 2,
    "D": 3,
    "E": 4,
    "F": 5,
    "G": 6,
    "H": 7,
    "I": 8,
    "K": 9,
    "L": 10,
    "M": 11,
    "N": 12,
    "P": 13,
    "Q": 14,
    "R": 15,
    "S": 16,
    "T": 17,
    "V": 18,
    "W": 19,
    "Y": 20,
    "CaC": 21,
    "OxM": 22
}

ALPHABET_UNMOD_REV = {v: k for k, v in ALPHABET_UNMOD.items()}

def numerical_to_alphabetical(arr):
    seq = ''
    for i in range(len(arr)):
        seq+=ALPHABET_UNMOD_REV[arr[i]]
    return seq

def align(dataset, reference):
    seq_ref = reference['Sequence']
    seq_common = dataset['Sequence']
    seq_ref = seq_ref.tolist()
    seq_common = seq_common.tolist()

    seq_ref = [tuple(l) for l in seq_ref]
    seq_common = [tuple(l) for l in seq_common]

    ind_dict_ref = dict((k, i) for i, k in enumerate(seq_ref))
    inter = set(ind_dict_ref).intersection(seq_common)
    ind_dict_ref = [ind_dict_ref[x] for x in inter]

    indices_common = dict((k, i) for i, k in enumerate(seq_common))
    indices_common = [indices_common[x] for x in inter]

    rt_ref = reference['Retention time'][ind_dict_ref].reset_index()
    rt_data = dataset['Retention time'][indices_common].reset_index()

    xout, yout, wout = loess_1d(np.array(rt_data['Retention time'].tolist()), np.array(rt_ref['Retention time'].tolist()),
                                xnew=dataset['Retention time'],
                                degree=1, frac=0.5,
                                npoints=None, rotate=False, sigy=None)
    dataset['Retention time'] = yout
    return dataset


data_ori = load_data('msms/msms30_01.txt').reset_index(drop=True)
# data_ori['sequence'] = data_ori['sequence'].map(numerical_to_alphabetical)

data_train = load_data('msms/msms16_01.txt').reset_index(drop=True)
# data_train = pd.read_pickle('database/data_DIA_16_01.pkl').reset_index(drop=True)
data_align = align(data_train, data_ori)
data_align.to_pickle('database/data_DIA_16_01_aligned30_01.pkl')

data_train = load_data('msms/msms17_01.txt').reset_index(drop=True)
# data_train = pd.read_pickle('database/data_DIA_17_01.pkl').reset_index(drop=True)
data_align = align(data_train, data_ori)
data_align.to_pickle('database/data_DIA_17_01_aligned30_01.pkl')

data_train = load_data('msms/msms20_01.txt').reset_index(drop=True)
# data_train = pd.read_pickle('database/data_DIA_20_01.pkl').reset_index(drop=True)
data_align = align(data_train, data_ori)
data_align.to_pickle('database/data_DIA_20_01_aligned30_01.pkl')

data_train = load_data('msms/msms23_01.txt').reset_index(drop=True)
# data_train = pd.read_pickle('database/data_DIA_23_01.pkl').reset_index(drop=True)
data_align = align(data_train, data_ori)
data_align.to_pickle('database/data_DIA_23_01_aligned30_01.pkl')

data_train = load_data('msms/msms24_01.txt').reset_index(drop=True)
# data_train = pd.read_pickle('database/data_DIA_24_01.pkl').reset_index(drop=True)
data_align = align(data_train, data_ori)
data_align.to_pickle('database/data_DIA_24_01_aligned30_01.pkl')

data_train = load_data('msms/msms30_01.txt').reset_index(drop=True)
# data_train = pd.read_pickle('database/data_DIA_30_01.pkl').reset_index(drop=True)
# data_align = align(data_train, data_ori)
data_train.to_pickle('database/data_DIA_30_01_aligned30_01.pkl')
#
# plt.scatter(data_train['Retention time'], data_align['Retention time'], s=1)
# plt.savefig('test_align_2.png')
#
#
# dataset_ref = pd.read_pickle('database/data_01_16_DIA_ISA_55.pkl')
# data_ref = Common_Dataset(dataset_ref, 25).data
# dataset_2 = pd.read_pickle('database/data_01_20_DIA_ISA_55.pkl')
# data_2 = Common_Dataset(dataset_2, 25).data
# dataset_3 = pd.read_pickle('database/data_01_17_DIA_ISA_55.pkl')
# data_3 = Common_Dataset(dataset_3, 25).data
# dataset_4 = pd.read_pickle('database/data_01_23_DIA_ISA_55.pkl')
# data_4 = Common_Dataset(dataset_4, 25).data
# data_align_3 = align(data_3, data_ref)
# data_align_4 = align(data_4, data_ref)
#
# data = pd.concat([data_ref, data_2, data_align_3, data_align_4], ignore_index=True)
# data = data.drop(columns='index')
# data['Sequence'] = data['Sequence'].map(numerical_to_alphabetical)
# num_data = data.shape[0]
# train_num = np.floor(num_data*0.8)
# train_size=0
# list_train=[]
# list_test=[]
# groups = data.groupby('Sequence')
# for seq, gr in groups:
#
#     train_size+= gr.shape[0]
#
#     if train_size>train_num:
#         list_test.append(gr)
#     else:
#         list_train.append(gr)
#
#
# dataset_train = pd.concat(list_train, ignore_index=True)
# dataset_test = pd.concat(list_test, ignore_index=True)
# dataset_train.to_pickle('database/data_DIA_ISA_55_train.pkl')
# dataset_train.to_pickle('database/data_DIA_ISA_55_test.pkl')