Loading deepchem/metrics/genomic_metrics.pydeleted 100644 → 0 +0 −131 Original line number Diff line number Diff line """Evaluation Metrics for Genomics Datasets.""" import numpy as np from deepchem.utils.genomics import loaded_motifs def get_motif_scores(encoded_sequences, motif_names, max_scores=None, return_positions=False, GC_fraction=0.4): """Computes pwm log odds. Parameters ---------- encoded_sequences : 4darray motif_names : list of strings max_scores : int, optional return_positions : boolean, optional GC_fraction : float, optional Returns ------- (num_samples, num_motifs, seq_length) complete score array by default. If max_scores, (num_samples, num_motifs*max_scores) max score array. If max_scores and return_positions, (num_samples, 2*num_motifs*max_scores) array with max scores and their positions. """ num_samples, _, _, seq_length = encoded_sequences.shape scores = np.ones((num_samples, len(motif_names), seq_length)) for j, motif_name in enumerate(motif_names): pwm = loaded_motifs.getPwm(motif_name).getRows().T log_pwm = np.log(pwm) gc_pwm = 0.5 * np.array( [[1 - GC_fraction, GC_fraction, GC_fraction, 1 - GC_fraction]] * len( pwm[0])).T gc_log_pwm = np.log(gc_pwm) scores[:, j, :] = get_pssm_scores(encoded_sequences, log_pwm) - get_pssm_scores( encoded_sequences, gc_log_pwm) if max_scores is not None: sorted_scores = np.sort(scores)[:, :, ::-1][:, :, :max_scores] if return_positions: sorted_positions = scores.argsort()[:, :, ::-1][:, :, :max_scores] return np.concatenate( (sorted_scores.reshape((num_samples, len(motif_names) * max_scores)), sorted_positions.reshape((num_samples, len(motif_names) * max_scores))), axis=1) else: return sorted_scores.reshape((num_samples, len(motif_names) * max_scores)) else: return scores def get_pssm_scores(encoded_sequences, pssm): """Maximum PSSM scores. Convolves pssm and its reverse complement with encoded sequences and returns the maximum score at each position of each sequence. Parameters ---------- encoded_sequences: 3darray (num_examples, 1, 4, seq_length) array pssm: 2darray (4, pssm_length) array Returns ------- scores: 2darray (num_examples, seq_length) array """ encoded_sequences = encoded_sequences.squeeze(axis=1) # initialize fwd and reverse scores to -infinity fwd_scores = np.full_like(encoded_sequences, -np.inf, float) rc_scores = np.full_like(encoded_sequences, -np.inf, float) # cross-correlate separately for each base, # for both the PSSM and its reverse complement for base_indx in range(encoded_sequences.shape[1]): base_pssm = pssm[base_indx][None] base_pssm_rc = base_pssm[:, ::-1] fwd_scores[:, base_indx, :] = correlate2d( encoded_sequences[:, base_indx, :], base_pssm, mode='same') rc_scores[:, base_indx, :] = correlate2d( encoded_sequences[:, -(base_indx + 1), :], base_pssm_rc, mode='same') # sum over the bases fwd_scores = fwd_scores.sum(axis=1) rc_scores = rc_scores.sum(axis=1) # take max of fwd and reverse scores at each position scores = np.maximum(fwd_scores, rc_scores) return scores def get_pssm_scores(encoded_sequences, pssm): """ Convolves pssm and its reverse complement with encoded sequences and returns the maximum score at each position of each sequence. Parameters ---------- encoded_sequences: 3darray #(num_examples, 1, 4, seq_length) array (N_sequences, N_letters, sequence_length, 1) array pssm: 2darray (4, pssm_length) array Returns ------- scores: 2darray (num_examples, seq_length) array """ encoded_sequences = encoded_sequences.squeeze(axis=3) # initialize fwd and reverse scores to -infinity fwd_scores = np.full_like(encoded_sequences, -np.inf, float) rc_scores = np.full_like(encoded_sequences, -np.inf, float) # cross-correlate separately for each base, # for both the PSSM and its reverse complement for base_indx in range(encoded_sequences.shape[1]): base_pssm = pssm[base_indx][None] base_pssm_rc = base_pssm[:, ::-1] fwd_scores[:, base_indx, :] = correlate2d( encoded_sequences[:, base_indx, :], base_pssm, mode='same') rc_scores[:, base_indx, :] = correlate2d( encoded_sequences[:, -(base_indx + 1), :], base_pssm_rc, mode='same') # sum over the bases fwd_scores = fwd_scores.sum(axis=1) rc_scores = rc_scores.sum(axis=1) # take max of fwd and reverse scores at each position scores = np.maximum(fwd_scores, rc_scores) return scores deepchem/metrics/tests/test_genomics.pydeleted 100644 → 0 +0 −40 Original line number Diff line number Diff line """ Test that genomic metrics work. """ from __future__ import division from __future__ import unicode_literals import unittest import os import numpy as np import deepchem as dc LETTERS = "ACGT" from deepchem.metrics.genomic_metrics import get_motif_scores from deepchem.metrics.genomic_metrics import get_pssm_scores class TestGenomicMetrics(unittest.TestCase): """ Tests that genomic metrics work as expected. """ def test_get_motif_scores(self): # Encode motif motif_name = "TAL1_known4" sequences = np.array(["ACGTA", "GATAG", "CGCGC"]) sequences = dc.utils.save.seq_one_hot_encode(sequences, letters=LETTERS) # sequences now has shape (3, 4, 5, 1) self.assertEqual(sequences.shape, (3, 4, 5, 1)) get_motif_scores(sequences, [motif_name]) def test_get_pssm_scores(self): motif_name = "TAL1_known4" sequences = np.array(["ACGTA", "GATAG", "CGCGC"]) sequences = dc.utils.save.seq_one_hot_encode(sequences, letters=LETTERS) # sequences now has shape (3, 4, 5, 1) self.assertEqual(sequences.shape, (3, 4, 5, 1)) get_pssm_scores(sequences, [motif_name]) Loading
deepchem/metrics/genomic_metrics.pydeleted 100644 → 0 +0 −131 Original line number Diff line number Diff line """Evaluation Metrics for Genomics Datasets.""" import numpy as np from deepchem.utils.genomics import loaded_motifs def get_motif_scores(encoded_sequences, motif_names, max_scores=None, return_positions=False, GC_fraction=0.4): """Computes pwm log odds. Parameters ---------- encoded_sequences : 4darray motif_names : list of strings max_scores : int, optional return_positions : boolean, optional GC_fraction : float, optional Returns ------- (num_samples, num_motifs, seq_length) complete score array by default. If max_scores, (num_samples, num_motifs*max_scores) max score array. If max_scores and return_positions, (num_samples, 2*num_motifs*max_scores) array with max scores and their positions. """ num_samples, _, _, seq_length = encoded_sequences.shape scores = np.ones((num_samples, len(motif_names), seq_length)) for j, motif_name in enumerate(motif_names): pwm = loaded_motifs.getPwm(motif_name).getRows().T log_pwm = np.log(pwm) gc_pwm = 0.5 * np.array( [[1 - GC_fraction, GC_fraction, GC_fraction, 1 - GC_fraction]] * len( pwm[0])).T gc_log_pwm = np.log(gc_pwm) scores[:, j, :] = get_pssm_scores(encoded_sequences, log_pwm) - get_pssm_scores( encoded_sequences, gc_log_pwm) if max_scores is not None: sorted_scores = np.sort(scores)[:, :, ::-1][:, :, :max_scores] if return_positions: sorted_positions = scores.argsort()[:, :, ::-1][:, :, :max_scores] return np.concatenate( (sorted_scores.reshape((num_samples, len(motif_names) * max_scores)), sorted_positions.reshape((num_samples, len(motif_names) * max_scores))), axis=1) else: return sorted_scores.reshape((num_samples, len(motif_names) * max_scores)) else: return scores def get_pssm_scores(encoded_sequences, pssm): """Maximum PSSM scores. Convolves pssm and its reverse complement with encoded sequences and returns the maximum score at each position of each sequence. Parameters ---------- encoded_sequences: 3darray (num_examples, 1, 4, seq_length) array pssm: 2darray (4, pssm_length) array Returns ------- scores: 2darray (num_examples, seq_length) array """ encoded_sequences = encoded_sequences.squeeze(axis=1) # initialize fwd and reverse scores to -infinity fwd_scores = np.full_like(encoded_sequences, -np.inf, float) rc_scores = np.full_like(encoded_sequences, -np.inf, float) # cross-correlate separately for each base, # for both the PSSM and its reverse complement for base_indx in range(encoded_sequences.shape[1]): base_pssm = pssm[base_indx][None] base_pssm_rc = base_pssm[:, ::-1] fwd_scores[:, base_indx, :] = correlate2d( encoded_sequences[:, base_indx, :], base_pssm, mode='same') rc_scores[:, base_indx, :] = correlate2d( encoded_sequences[:, -(base_indx + 1), :], base_pssm_rc, mode='same') # sum over the bases fwd_scores = fwd_scores.sum(axis=1) rc_scores = rc_scores.sum(axis=1) # take max of fwd and reverse scores at each position scores = np.maximum(fwd_scores, rc_scores) return scores def get_pssm_scores(encoded_sequences, pssm): """ Convolves pssm and its reverse complement with encoded sequences and returns the maximum score at each position of each sequence. Parameters ---------- encoded_sequences: 3darray #(num_examples, 1, 4, seq_length) array (N_sequences, N_letters, sequence_length, 1) array pssm: 2darray (4, pssm_length) array Returns ------- scores: 2darray (num_examples, seq_length) array """ encoded_sequences = encoded_sequences.squeeze(axis=3) # initialize fwd and reverse scores to -infinity fwd_scores = np.full_like(encoded_sequences, -np.inf, float) rc_scores = np.full_like(encoded_sequences, -np.inf, float) # cross-correlate separately for each base, # for both the PSSM and its reverse complement for base_indx in range(encoded_sequences.shape[1]): base_pssm = pssm[base_indx][None] base_pssm_rc = base_pssm[:, ::-1] fwd_scores[:, base_indx, :] = correlate2d( encoded_sequences[:, base_indx, :], base_pssm, mode='same') rc_scores[:, base_indx, :] = correlate2d( encoded_sequences[:, -(base_indx + 1), :], base_pssm_rc, mode='same') # sum over the bases fwd_scores = fwd_scores.sum(axis=1) rc_scores = rc_scores.sum(axis=1) # take max of fwd and reverse scores at each position scores = np.maximum(fwd_scores, rc_scores) return scores
deepchem/metrics/tests/test_genomics.pydeleted 100644 → 0 +0 −40 Original line number Diff line number Diff line """ Test that genomic metrics work. """ from __future__ import division from __future__ import unicode_literals import unittest import os import numpy as np import deepchem as dc LETTERS = "ACGT" from deepchem.metrics.genomic_metrics import get_motif_scores from deepchem.metrics.genomic_metrics import get_pssm_scores class TestGenomicMetrics(unittest.TestCase): """ Tests that genomic metrics work as expected. """ def test_get_motif_scores(self): # Encode motif motif_name = "TAL1_known4" sequences = np.array(["ACGTA", "GATAG", "CGCGC"]) sequences = dc.utils.save.seq_one_hot_encode(sequences, letters=LETTERS) # sequences now has shape (3, 4, 5, 1) self.assertEqual(sequences.shape, (3, 4, 5, 1)) get_motif_scores(sequences, [motif_name]) def test_get_pssm_scores(self): motif_name = "TAL1_known4" sequences = np.array(["ACGTA", "GATAG", "CGCGC"]) sequences = dc.utils.save.seq_one_hot_encode(sequences, letters=LETTERS) # sequences now has shape (3, 4, 5, 1) self.assertEqual(sequences.shape, (3, 4, 5, 1)) get_pssm_scores(sequences, [motif_name])
