sequence_attention: Learning, Visualizing and Exploring 16S rRNA Structure Using an Attention-based Deep Neural Network

sequence_attention is a deep learning model python package that trains a Read2Phenotype model on 16S rRNA data. Drexel University EESI Lab, 2020
Maintainer: Zhengqiao Zhao, zz374 at drexel dot edu
Owner: Gail Rosen, gailr at ece dot drexel dot edu

Dependencies

The following are required:

• python=3.5.4
• matplotlib=2.1.1
• pandas=0.20.3
• scipy=1.0.0
• scikit-learn=0.19.1
• Tensorflow=1.9.0
• Keras=2.2.2

Data requirement

Raw data

A raw data folder is required. It contains a comma separated file (CSV) and FASTA nucleic acid files for different samples. The directory structure of raw data is shown below:

raw_data
│   meta_data.csv  
|   SAMPLE_1.fna
|   SAMPLE_2.fna
|   SAMPLE_3.fna
|   ...

Examples of the required files is shown below:

1. meta_data.csv example:

sample_id	label
SAMPLE_1	feces
SAMPLE_2	tongue
SAMPLE_3	feces
SAMPLE_4	skin
...	...

2. SAMPLE_1.fna

>SAMPLE_1_1
GCGAGCGAAGTTCGGAATTACTGGGCGTAAAGGGTGTGTA
>SAMPLE_1_2
GCGAGCGTTGTTCGGAACCACTGGGCGTAAAGGGTGTGTA
>SAMPLE_1_3
GCGAGCGTTGTTCGGAATTACTGGGCGTAGAGGGTGTGTA

Processed data

Here is an example of processed data directory structure:

processed_data
│   meta_data.pkl 
|   train_test_split.pkl
|   label_dict.pkl
│
└───SAMPLE_1
│   │   SAMPLE_1_1.fna
│   │   SAMPLE_1_2.fna
│   │   ...
└───SAMPLE_2
|   │   SAMPLE_2_1.fna
|   │   SAMPLE_2_2.fna
|   │   ...
|   ...

Tutorial

Data Preprocessing

First, please review the settings in config.py file especially the data path. Then, run the following to preprocessing the data. There are two ways to prepare the data: 1). split a fna file into files that contain one read per file. Then the generator can quickly load reads and construct a batch of data. However, the splitting data part can be slow; 2). convert a fna file to a dictionary. Then the generator can load a whole dictionary file (saved as pickle file) and look up for a read and construct a batch of data. It is fast in data preparation step.

import keras
import numpy as np
from sequence_attention import SeqAttModel, preprocess_data, DataGenerator, DataGeneratorUnlabeled
from config import Config

opt = Config()
###===== method 1 =====###
preprocess_data(opt)
###===== method 2 =====###
preprocess_data_pickle(opt)

Model Initialization

Once the data is preprocessed, run the following to load metadata, initialize the model and the data generator.

import pickle
label_dict = pickle.load(open('{}/label_dict.pkl'.format(opt.out_dir), 'rb')) 
sample_to_label, read_meta_data = pickle.load(open('{}/meta_data.pkl'.format(opt.out_dir), 'rb'))
partition = pickle.load(open('{}/train_test_split.pkl'.format(opt.out_dir), 'rb')) 
seq_att_model = SeqAttModel(opt)
###===== method 1 =====###
training_generator = DataGenerator(partition['train'], sample_to_label, label_dict, 
                                   dim=(opt.SEQLEN,opt.BASENUM), batch_size=opt.batch_size, shuffle=opt.shuffle)
testing_generator = DataGenerator(partition['test'], sample_to_label, label_dict, 
                                   dim=(opt.SEQLEN,opt.BASENUM), batch_size=opt.batch_size, shuffle=opt.shuffle)
###===== method 2 =====###
training_generator = DataGeneratorPickle(partition['train'], sample_to_label, label_dict, 
                                   dim=(opt.SEQLEN,opt.BASENUM), batch_size=opt.batch_size, shuffle=opt.shuffle)
testing_generator = DataGeneratorPickle(partition['test'], sample_to_label, label_dict, 
                                   dim=(opt.SEQLEN,opt.BASENUM), batch_size=opt.batch_size, shuffle=opt.shuffle)

Model training and evaluation

Once the model is trained, you can evaluate the performance of the model using the training data and testing data. The following command will return accuracy.

seq_att_model.train_generator(training_generator, n_workers=opt.n_workers)
seq_att_model.evaluate_generator(testing_generator, n_workers=opt.n_workers)

Model interpretation and sequence visualization

Prepare the X_visual (N by SEQ_LEN by NUMBASE) in numpy array, y_visual (phenotypic labels in integers) in numpy array and a list of taxonomic labels of those sequences (e.g., genus level labels). We also need a list of phenotypic labels of those sequences. Then run the following commands to plot embedding and attention weights visualization figures.

prediction, attention_weights, sequence_embedding = seq_att_model.extract_weigths(X_visual)
from sequence_attention import SeqVisualUnit
idx_to_label = {label_dict[label]: label for label in label_dict}
seq_visual_unit = SeqVisualUnit(X_visual, y_visual, idx_to_label, taxa_label_list, 
                                prediction, attention_weights, sequence_embedding, 'Figures')
seq_visual_unit.plot_embedding()
seq_visual_unit.plot_attention('Prevotella')

In the code snippet above, we also need , label_dict (phenotypic labels to integer dictionary saved in $opt.out_dir/label_dict.pkl by the previous steps.