Direct links to results¶

Motifs derived from filter-activating sequences

import os
import sys
sys.path.append(os.path.abspath("/users/amtseng/tfmodisco/src/"))
from motif.read_motifs import pfm_info_content, pfm_to_pwm
from util import figure_to_vdom_image
import plot.viz_sequence as viz_sequence
import h5py
import numpy as np
import pyfaidx
import matplotlib.pyplot as plt
import vdom.helpers as vdomh
from IPython.display import display
import tqdm
tqdm.tqdm_notebook()

/users/amtseng/miniconda3/envs/tfmodisco-mini/lib/python3.7/site-packages/ipykernel_launcher.py:14: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`

0it [00:00, ?it/s]

Define constants and paths¶

# Define parameters/fetch arguments
filter_activations_path = os.environ["TFM_FILTER_ACTIVATIONS"]
filter_weights_path = os.environ["TFM_FILTER_WEIGHTS"]

if "TFM_MOTIF_CACHE" in os.environ:
    activation_motifs_cache_dir = os.environ["TFM_MOTIF_CACHE"]
else:
    activation_motifs_cache_dir = None

print("Path to filter activations: %s" % filter_activations_path)
print("Path to filter weights: %s" % filter_weights_path)
print("Saved activation-derived motifs cache: %s" % activation_motifs_cache_dir)

Path to filter activations: /users/amtseng/tfmodisco/results/filter_activations/multitask_profile_finetune/JUND_multitask_profile_finetune_fold7_filter_activations.h5
Path to filter weights: /users/amtseng/tfmodisco/results/filter_weights/multitask_profile_finetune/JUND_multitask_profile_finetune_fold7_filter_weights.npy
Saved activation-derived motifs cache: /users/amtseng/tfmodisco/results/reports/filter_derived_motifs/cache/JUND_multitask_profile_finetune_fold7_filter

# Constants/paths
input_length = 2114
filter_width = 21
reference_genome_path = "/users/amtseng/genomes/hg38.fasta"

if activation_motifs_cache_dir:
    os.makedirs(activation_motifs_cache_dir, exist_ok=True)

Helper functions¶

For extracting motifs

def dna_to_one_hot(seqs):
    """
    Converts a list of DNA ("ACGT") sequences to one-hot encodings, where the
    position of 1s is ordered alphabetically by "ACGT". `seqs` must be a list
    of N strings, where every string is the same length L. Returns an N x L x 4
    NumPy array of one-hot encodings, in the same order as the input sequences.
    All bases will be converted to upper-case prior to performing the encoding.
    Any bases that are not "ACGT" will be given an encoding of all 0s.
    """
    seq_len = len(seqs[0])
    assert np.all(np.array([len(s) for s in seqs]) == seq_len)

    # Join all sequences together into one long string, all uppercase
    seq_concat = "".join(seqs).upper()

    one_hot_map = np.identity(5)[:, :-1]

    # Convert string into array of ASCII character codes;
    base_vals = np.frombuffer(bytearray(seq_concat, "utf8"), dtype=np.int8)

    # Anything that's not an A, C, G, or T gets assigned a higher code
    base_vals[~np.isin(base_vals, np.array([65, 67, 71, 84]))] = 85

    # Convert the codes into indices in [0, 4], in ascending order by code
    _, base_inds = np.unique(base_vals, return_inverse=True)

    # Get the one-hot encoding for those indices, and reshape back to separate
    return one_hot_map[base_inds].reshape((len(seqs), seq_len, 4))

def extract_filter_activation_motifs(filter_activations_path, reference_genome_path):
    """
    Extracts the motifs that correspond to each filter. Returns an
    F x W x 4 array, where F is the number of filters and W is the width
    of each filter. The order of filters matches those in the saved HDF5/model.
    """
    reader = h5py.File(filter_activations_path, "r")
    activations_reader = reader["activations"]
    num_coords, two, num_windows, num_filters = activations_reader.shape
    
    assert two == 2
    assert num_windows == input_length - filter_width + 1
    
    print("Importing coordinates...")
    coords = np.empty((num_coords, 3), dtype=object)
    coords[:, 0] = reader["coords"]["coords_chrom"][:].astype(str)
    coords[:, 1] = reader["coords"]["coords_start"][:]
    coords[:, 2] = reader["coords"]["coords_end"][:]
    
    print("Fetching one-hot sequences...")
    genome_reader = pyfaidx.Fasta(reference_genome_path)
    one_hot_seqs = np.empty((num_coords, input_length, 4))
    batch_size = 128
    num_batches = int(np.ceil(num_coords / batch_size))
    for i in tqdm.notebook.trange(num_batches):
        batch_slice = slice(i * batch_size, (i + 1) * batch_size)
        one_hot_seqs[batch_slice] = dna_to_one_hot([
            genome_reader[chrom][start:end].seq for chrom, start, end in coords[batch_slice]
        ])
    
    pfms = np.empty((num_filters, filter_width, 4))
    for filter_index in range(num_filters):
        print("Extracting motif for filter %d..." % filter_index)
    
        print("\tComputing maximum activation...")
        acts = activations_reader[:, :, :, filter_index]
        max_act = np.max(acts)
        
        inds = np.where(acts >= 0.5 * max_act)
        
        windows, num_windows = np.zeros((filter_width, 4)), 0
        for coord_index, strand_index, pos_index in tqdm.notebook.tqdm(
            zip(*inds), total=len(inds[0]), desc="Extracting windows..."
        ):
            if strand_index == 0:
                window = one_hot_seqs[coord_index, pos_index : pos_index + filter_width]
            else:
                # Reverse complement; the positions are flipped
                window = np.flip(
                    one_hot_seqs[coord_index, input_length - filter_width - pos_index : input_length - pos_index],
                    axis=(0, 1)
                )
            windows = windows + window
            num_windows += 1
        
        pfms[filter_index] = windows / num_windows
    
    return pfms

def compute_filter_influence(filter_activations_path):
    """
    Extracts the influence of each filter by computing the difference
    in cross entropy when each filter is nullified.
    Returns an F-array, where F is the number of filters, containing the
    change in average cross entropy (after nullification - before
    nullification). The order of filters matches those in the saved
    HDF5/model.
    """
    reader = h5py.File(filter_activations_path, "r")
    print("Reading in cross entropies...")
    before_null_cross_ents = reader["predictions"]["cross_ents"][:]
    after_null_cross_ents = reader["nullified_predictions"]["cross_ents"][:]
    
    before_null = np.nanmean(before_null_cross_ents)
    
    num_filters = after_null_cross_ents.shape[1]
    
    influences = []
    for filter_index in tqdm.notebook.trange(num_filters):
        after_null = np.nanmean(after_null_cross_ents[:, filter_index])
        influences.append(after_null - before_null)
        
    return np.array(influences)

def save_activation_motifs(filter_pfms, filter_influences, path):
    """
    Saves the filter-activation-derived PFMs and influence values.
    """
    with h5py.File(path, "w") as f:
        f.create_dataset("pfms", data=filter_pfms, compression="gzip")
        f.create_dataset("influences", data=filter_influences, compression="gzip")

def load_activation_motifs(path):
    """
    Loads the filter-activation-derived PFMs and influence values.
    """
    with h5py.File(path, "r") as f:
        return f["pfms"][:], f["influences"][:]

Extract motifs from filter activations¶

Extract the motifs derived from each filter, ranked by filter influence.

Deriving a filter's motif:

Identify the top 10000 most well-predicted input sequences, ranked by cross entropy
For each window in each of these sequences, compute the filter activation for each 1st-layer filter
A filter's motif is the aggregation of sequence windows which activate that filter to at least half its maximum activation (over the top 10000 most well-predicted inputs)

Deriving a filter's influence:

Identify the top 10000 most well-predicted input sequences, ranked by cross entropy
Nullify each filter by setting it to the average activation over these 10000 most well-predicted inputs
A filter's influence is the average change in cross entropy before and after nullification

compute_motifs = True
if activation_motifs_cache_dir:
    # Import if it exists
    cache_path = os.path.join(activation_motifs_cache_dir, "filter_activation_motifs.h5")
    if os.path.exists(cache_path) and os.stat(cache_path).st_size:
        filter_pfms, filter_influences = load_activation_motifs(cache_path)
        compute_motifs = False

if compute_motifs:
    # Extract PFMs of highly-activating sequences
    filter_pfms = extract_filter_activation_motifs(filter_activations_path, reference_genome_path)

    # Compute influence of each filter
    filter_influences = compute_filter_influence(filter_activations_path)

    if activation_motifs_cache_dir:
        save_activation_motifs(filter_pfms, filter_influences, cache_path)

Importing coordinates...
Fetching one-hot sequences...

Extracting motif for filter 0...
	Computing maximum activation...

Extracting motif for filter 1...
	Computing maximum activation...

Extracting motif for filter 2...
	Computing maximum activation...

Extracting motif for filter 3...
	Computing maximum activation...

Extracting motif for filter 4...
	Computing maximum activation...

Extracting motif for filter 5...
	Computing maximum activation...

Extracting motif for filter 6...
	Computing maximum activation...

Extracting motif for filter 7...
	Computing maximum activation...

Extracting motif for filter 8...
	Computing maximum activation...

Extract motifs from filter weights¶

# Import the filter weights themselves
filter_weights = np.load(filter_weights_path)
assert len(filter_weights.shape) == 3
assert filter_weights.shape[:2] == (filter_width, 4)
filter_weights = np.transpose(filter_weights, axes=(2, 0, 1))  # Shape: F x W x 4

Motifs derived from filter-activating sequences¶

For each filter, its motif is constructed by averaging all of the sequences that activate it at least to half of its maximal activation. We show the PWMs. The filters are ranked by influence (i.e. the average difference in prediction cross entropy when the filter is nullified--that is, replaced with its average activation).

colgroup = vdomh.colgroup(
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "85%"})
)
header = vdomh.thead(
    vdomh.tr(
        vdomh.th("Rank", style={"text-align": "center"}),
        vdomh.th("Filter index", style={"text-align": "center"}),
        vdomh.th("Influence", style={"text-align": "center"}),
        vdomh.th("PWM", style={"text-align": "center"})
    )
)

body = []
for i, filter_index in enumerate(np.flip(np.argsort(filter_influences))):
    pwm = pfm_to_pwm(filter_pfms[filter_index])
    if np.sum(pwm[:, [0, 2]]) < 0.5 * np.sum(pwm):
        # Flip to purine-rich version
        pwm = np.flip(pwm, axis=(0, 1))
    fig = viz_sequence.plot_weights(pwm, figsize=(20, 4), return_fig=True)
    fig.tight_layout()
    
    body.append(
        vdomh.tr(
            vdomh.td(str(i + 1)),
            vdomh.td(str(filter_index)),
            vdomh.td("%.3f" % filter_influences[filter_index]),
            vdomh.td(figure_to_vdom_image(fig))
        )
    )
    
    if activation_motifs_cache_dir:
        # Save motif PWM
        fig.savefig(os.path.join(activation_motifs_cache_dir, "filter_activation_motif_%d.png" % filter_index))

display(vdomh.table(colgroup, header, vdomh.tbody(*body)))
plt.close("all")

/users/amtseng/tfmodisco/src/plot/viz_sequence.py:152: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  fig = plt.figure(figsize=figsize)

Motifs derived from filter weights¶

For each filter, we show its corresponding motif simply as the mean-normalized multiplicative weights in the filter. For consistency, we rank the filters by influence (as above).

colgroup = vdomh.colgroup(
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "85%"})
)
header = vdomh.thead(
    vdomh.tr(
        vdomh.th("Rank", style={"text-align": "center"}),
        vdomh.th("Filter index", style={"text-align": "center"}),
        vdomh.th("Influence", style={"text-align": "center"}),
        vdomh.th("Mean-normalized filter weights", style={"text-align": "center"})
    )
)

body = []
for i, filter_index in enumerate(np.flip(np.argsort(filter_influences))):
    weights = filter_weights[filter_index]
    weights = weights - np.mean(weights, axis=1, keepdims=True)
    if np.sum(weights[:, [0, 2]]) < 0.5 * np.sum(weights):
        # Flip to purine-rich version
        weights = np.flip(weights, axis=(0, 1))
    fig = viz_sequence.plot_weights(weights, figsize=(20, 4), return_fig=True)
    fig.tight_layout()
    
    body.append(
        vdomh.tr(
            vdomh.td(str(i + 1)),
            vdomh.td(str(filter_index)),
            vdomh.td("%.3f" % filter_influences[filter_index]),
            vdomh.td(figure_to_vdom_image(fig))
        )
    )
    
    if activation_motifs_cache_dir:
        # Save motif PWM
        fig.savefig(os.path.join(activation_motifs_cache_dir, "filter_weight_motif_%d.png" % filter_index))

display(vdomh.table(colgroup, header, vdomh.tbody(*body)))
plt.close("all")

Rank	Filter index	Influence
1	7	0.042
2	34	0.018
3	13	0.016
4	14	0.009
5	25	0.008
6	6	0.007
7	10	0.006
8	50	0.006
9	41	0.006
10	33	0.005
11	53	0.005
12	11	0.005
13	43	0.005
14	38	0.005
15	44	0.005
16	16	0.004
17	60	0.004
18	52	0.004
19	39	0.004
20	37	0.004
21	5	0.004
22	0	0.004
23	2	0.003
24	36	0.003
25	32	0.003
26	29	0.003
27	49	0.003
28	1	0.003
29	51	0.003
30	28	0.003
31	22	0.002
32	18	0.002
33	58	0.002
34	8	0.002
35	57	0.002
36	15	0.002
37	19	0.002
38	55	0.002
39	46	0.002
40	61	0.002
41	12	0.002
42	45	0.002
43	54	0.002
44	31	0.002
45	20	0.002
46	27	0.002
47	9	0.001
48	63	0.001
49	17	0.001
50	4	0.001
51	21	0.001
52	30	0.001
53	24	0.001
54	3	0.001
55	26	0.001
56	40	0.001
57	47	0.001
58	62	0.001
59	59	0.001
60	42	0.001
61	56	0.001
62	48	0.001
63	23	0.000
64	35	0.000

Rank	Filter index	Influence
1	7	0.042
2	34	0.018
3	13	0.016
4	14	0.009
5	25	0.008
6	6	0.007
7	10	0.006
8	50	0.006
9	41	0.006
10	33	0.005
11	53	0.005
12	11	0.005
13	43	0.005
14	38	0.005
15	44	0.005
16	16	0.004
17	60	0.004
18	52	0.004
19	39	0.004
20	37	0.004
21	5	0.004
22	0	0.004
23	2	0.003
24	36	0.003
25	32	0.003
26	29	0.003
27	49	0.003
28	1	0.003
29	51	0.003
30	28	0.003
31	22	0.002
32	18	0.002
33	58	0.002
34	8	0.002
35	57	0.002
36	15	0.002
37	19	0.002
38	55	0.002
39	46	0.002
40	61	0.002
41	12	0.002
42	45	0.002
43	54	0.002
44	31	0.002
45	20	0.002
46	27	0.002
47	9	0.001
48	63	0.001
49	17	0.001
50	4	0.001
51	21	0.001
52	30	0.001
53	24	0.001
54	3	0.001
55	26	0.001
56	40	0.001
57	47	0.001
58	62	0.001
59	59	0.001
60	42	0.001
61	56	0.001
62	48	0.001
63	23	0.000
64	35	0.000