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* init get_new_training_data script and strand spec * refactor main script, fix strand-spec * debugging and testing update_seqweaver * fixed h5 file output * added class modules for training seqweaver * added validation/training strat * debugging main update seqweaver module * strand backward compatibility * further fixes to backward compatibility * val partition fix * indexing fix for backward compatibility * addressing kathy's comments * fixed relative paths in update_seqweaver * handling strand=. as None
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| """ | ||
| Seqweaver architecture (Park & Troyanskaya, 2021). | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
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| class LambdaBase(nn.Sequential): | ||
| def __init__(self, fn, *args): | ||
| super(LambdaBase, self).__init__(*args) | ||
| self.lambda_func = fn | ||
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| def forward_prepare(self, input): | ||
| output = [] | ||
| for module in self._modules.values(): | ||
| output.append(module(input)) | ||
| return output if output else input | ||
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| class Lambda(LambdaBase): | ||
| def forward(self, input): | ||
| return self.lambda_func(self.forward_prepare(input)) | ||
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| class Seqweaver(nn.Module): | ||
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| def __init__(self, n_classes): # 217 human, 43 mouse | ||
| super(Seqweaver, self).__init__() | ||
| self.model = nn.Sequential( | ||
| nn.Conv2d(4, 160, (1, 8)), | ||
| nn.ReLU(), | ||
| nn.MaxPool2d((1, 4), (1, 4)), | ||
| nn.Dropout(0.1), | ||
| nn.Conv2d(160, 320, (1, 8)), | ||
| nn.ReLU(), | ||
| nn.MaxPool2d((1, 4), (1, 4)), | ||
| nn.Dropout(0.1), | ||
| nn.Conv2d(320, 480, (1, 8)), | ||
| nn.ReLU(), | ||
| nn.Dropout(0.3)) | ||
| self.fc = nn.Sequential( | ||
| Lambda(lambda x: torch.reshape(x, (x.size(0), 25440))), | ||
| nn.Sequential( | ||
| Lambda(lambda x: x.reshape(1, -1) | ||
| if 1 == len(x.size()) else x), | ||
| nn.Linear(25440, n_classes) | ||
| ), | ||
| nn.ReLU(), | ||
| nn.Sequential( | ||
| Lambda(lambda x: x.view(1, -1) | ||
| if 1 == len(x.size()) else x), | ||
| nn.Linear(n_classes, n_classes) | ||
| ), | ||
| nn.Sigmoid(), | ||
| ) | ||
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| def forward(self, x): | ||
| x = x.unsqueeze(2) | ||
| x = self.model(x) | ||
| x = self.fc(x) | ||
| return x | ||
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| def criterion(): | ||
| return nn.BCELoss() | ||
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| def get_optimizer(lr): | ||
| return (torch.optim.SGD, | ||
| {"lr": lr, "weight_decay": 1e-6, "momentum": 0.9}) |
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| """ | ||
| This module provides the `UpdateSeqweaver` class, which wraps the master bed file | ||
| containing all of the features' binding sites parsed from CLIP-seq. | ||
| It supports new dataset construction and training for Seqweaver. | ||
| """ | ||
| import h5py | ||
| import gzip | ||
| import numpy as np | ||
| import sys | ||
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| from selene_sdk.sequences.genome import Genome | ||
| from selene_sdk.targets.genomic_features import GenomicFeatures | ||
| from selene_sdk.samplers.dataloader import H5DataLoader | ||
| from selene_sdk.train_model import TrainModel | ||
| from selene_sdk.utils.config import load_path | ||
| from selene_sdk.utils.config_utils import parse_configs_and_run | ||
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| class UpdateSeqweaver(): | ||
| """ | ||
| Stores a dataset specifying sequence regions and features. | ||
| Accepts a tabix-indexed `*.bed` file with the following columns, | ||
| in order: | ||
| [chrom, start, end, feature, strand] | ||
| Parameters | ||
| ---------- | ||
| input_path : str | ||
| Path to the tabix-indexed dataset. Note that for the file to | ||
| be tabix-indexed, it must have been compressed with `bgzip`. | ||
| Thus, `input_path` should be a `*.gz` file with a | ||
| corresponding `*.tbi` file in the same directory. | ||
| output_path : str | ||
| Path to the output constructed-training data file. | ||
| feature_path : str | ||
| Path to a '\n'-delimited .txt file containing feature names. | ||
| hg_fasta : str | ||
| Path to an indexed FASTA file -- a `*.fasta` file with | ||
| a corresponding `*.fai` file in the same directory. This file | ||
| should contain the target organism's genome sequence. | ||
| """ | ||
| def __init__(self, input_path, train_path, validate_path, feature_path, hg_fasta, yaml_path, val_prop=0.1, sequence_len=1000): | ||
| """ | ||
| Constructs a new `UpdateSeqweaver` object. | ||
| """ | ||
| self.input_path = input_path | ||
| self.train_path = train_path | ||
| self.validate_path = validate_path | ||
| self.feature_path = feature_path | ||
| self.yaml_path = yaml_path | ||
| self.val_prop = val_prop | ||
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| self.hg_fasta = hg_fasta | ||
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| self.sequence_len = sequence_len | ||
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| with open(self.feature_path, 'r') as handle: | ||
| self.feature_set = [line.split('\n')[0] for line in handle.readlines()] | ||
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| def _from_midpoint(self, start, end): | ||
| """ | ||
| Computes start and end of the sequence about the peak midpoint. | ||
| Parameters | ||
| ---------- | ||
| start : int | ||
| The 0-based first position in the region. | ||
| end : int | ||
| One past the 0-based last position in the region. | ||
| Returns | ||
| ------- | ||
| seq_start : int | ||
| Sequence start position about the peak midpoint. | ||
| seq_end : int | ||
| Sequence end position about the peak midpoint. | ||
| """ | ||
| region_len = end - start | ||
| midpoint = start + region_len // 2 | ||
| seq_start = midpoint - np.floor(self.sequence_len / 2.) | ||
| seq_end = midpoint + np.ceil(self.sequence_len / 2.) | ||
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| return int(seq_start), int(seq_end) | ||
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| def construct_training_data(self): | ||
| """ | ||
| Construct training dataset from bed file and write to output_file. | ||
| Parameters | ||
| ---------- | ||
| output_path : str | ||
| Path to the output file for the constructed training data. | ||
| colname_file : str | ||
| Path to a .txt file containing newline-delimited feature names. | ||
| """ | ||
| list_of_regions = [] | ||
| with gzip.open(self.input_path) as f: | ||
| for line in f: | ||
| line = [str(data,'utf-8') for data in line.strip().split()] | ||
| list_of_regions.append(line) | ||
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| seqs = Genome(self.hg_fasta, blacklist_regions = 'hg19') | ||
| targets = GenomicFeatures(self.input_path, | ||
| features = self.feature_set, feature_thresholds = 0.5) | ||
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| data_seqs = [] | ||
| data_labels = [] | ||
| for r in list_of_regions: | ||
| chrom, start, end, target, strand = r | ||
| start, end = int(start), int(end) | ||
| sstart, ssend = self._from_midpoint(start, end) | ||
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| # 1 x 4 x 1000 bp | ||
| # get_encoding_from_coords : Converts sequence to one-hot-encoding for each of the 4 bases | ||
| dna_seq, has_unk = seqs.get_encoding_from_coords_check_unk(chrom, sstart, ssend, strand=strand) | ||
| if has_unk: | ||
| continue | ||
| if len(dna_seq) != self.sequence_len: | ||
| continue | ||
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| # 1 x n_features | ||
| # get_feature_data: Computes which features overlap with the given region. | ||
| labels = targets.get_feature_data(chrom, start, end, strand=strand) | ||
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| data_seqs.append(dna_seq) | ||
| data_labels.append(labels) | ||
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| # partition some to validation before writing | ||
| val_count = int(np.floor(self.val_prop * len(data_seqs))) | ||
| validate_seqs = data_seqs[:val_count] | ||
| validate_labels = data_labels[:val_count] | ||
| training_seqs = data_seqs[val_count:] | ||
| training_labels = data_labels[val_count:] | ||
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| with h5py.File(self.validate_path, "w") as fh: | ||
| fh.create_dataset("valid_sequences", data=np.array(validate_seqs, dtype=np.int64)) | ||
| fh.create_dataset("valid_targets", data=np.array(validate_labels, dtype=np.int64)) | ||
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| with h5py.File(self.train_path, "w") as fh: | ||
| fh.create_dataset("train_sequences", data=np.array(training_seqs, dtype=np.int64)) | ||
| fh.create_dataset("train_targets", data=np.array(training_labels, dtype=np.int64)) | ||
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| def _load_yaml(self): | ||
| # load yaml configuration | ||
| return load_path(self.yaml_path) | ||
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| def train_model(self): | ||
| # load config file and train model | ||
| yaml_config = self._load_yaml() | ||
| parse_configs_and_run(yaml_config) |
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