Motifs derived from filter-activating sequences

Motifs from filter weights

In [1]:
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`
Out[1]:
0it [00:00, ?it/s]

Define constants and paths

In [2]:
# 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/singletask_profile_finetune/NR3C1-reddytime_singletask_profile_finetune_task15_fold5_filter_activations.h5
Path to filter weights: /users/amtseng/tfmodisco/results/filter_weights/singletask_profile_finetune/NR3C1-reddytime_singletask_profile_finetune_task15_fold5_filter_weights.npy
Saved activation-derived motifs cache: /users/amtseng/tfmodisco/results/reports/filter_derived_motifs/cache/NR3C1-reddytime_singletask_profile_finetune_task15_fold5_filter
In [3]:
# Constants/paths
input_length = 2114
filter_width = 21
reference_genome_path = "/users/amtseng/genomes/hg38.fasta"
In [4]:
if activation_motifs_cache_dir:
    os.makedirs(activation_motifs_cache_dir, exist_ok=True)

Helper functions

For extracting motifs

In [5]:
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))
In [6]:
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
In [7]:
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)
In [8]:
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")
In [9]:
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:

  1. Identify the top 10000 most well-predicted input sequences, ranked by cross entropy
  2. For each window in each of these sequences, compute the filter activation for each 1st-layer filter
  3. 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:

  1. Identify the top 10000 most well-predicted input sequences, ranked by cross entropy
  2. Nullify each filter by setting it to the average activation over these 10000 most well-predicted inputs
  3. A filter's influence is the average change in cross entropy before and after nullification
In [10]:
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 15...
	Computing maximum activation...

Extracting motif for filter 27...
	Computing maximum activation...

Extracting motif for filter 30...
	Computing maximum activation...

Extracting motif for filter 32...
	Computing maximum activation...

Extracting motif for filter 33...
	Computing maximum activation...

Extracting motif for filter 44...
	Computing maximum activation...

Extracting motif for filter 54...
	Computing maximum activation...

Extract motifs from filter weights

In [11]:
# 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).

In [12]:
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)
RankFilter indexInfluencePWM
1360.119
2600.118
363-0.000
422-0.000
517-0.000
618-0.000
719-0.000
820-0.000
921-0.000
1024-0.000
1123-0.000
1215-0.000
1325-0.000
1426-0.000
1527-0.000
1628-0.000
1716-0.000
1814-0.000
1930-0.000
2013-0.000
2112-0.000
2211-0.000
2310-0.000
249-0.000
258-0.000
267-0.000
276-0.000
285-0.000
294-0.000
303-0.000
312-0.000
321-0.000
3329-0.000
3431-0.000
3562-0.000
3632-0.000
3761-0.000
3859-0.000
3958-0.000
4057-0.000
4156-0.000
4255-0.000
4354-0.000
4453-0.000
4552-0.000
4651-0.000
4750-0.000
4849-0.000
4948-0.000
5047-0.000
5146-0.000
5245-0.000
5344-0.000
5443-0.000
5542-0.000
5641-0.000
5740-0.000
5839-0.000
5938-0.000
6037-0.000
6135-0.000
6234-0.000
6333-0.000
640-0.000

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).

In [13]:
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")
RankFilter indexInfluenceMean-normalized filter weights
1360.119
2600.118
363-0.000
422-0.000
517-0.000
618-0.000
719-0.000
820-0.000
921-0.000
1024-0.000
1123-0.000
1215-0.000
1325-0.000
1426-0.000
1527-0.000
1628-0.000
1716-0.000
1814-0.000
1930-0.000
2013-0.000
2112-0.000
2211-0.000
2310-0.000
249-0.000
258-0.000
267-0.000
276-0.000
285-0.000
294-0.000
303-0.000
312-0.000
321-0.000
3329-0.000
3431-0.000
3562-0.000
3632-0.000
3761-0.000
3859-0.000
3958-0.000
4057-0.000
4156-0.000
4255-0.000
4354-0.000
4453-0.000
4552-0.000
4651-0.000
4750-0.000
4849-0.000
4948-0.000
5047-0.000
5146-0.000
5245-0.000
5344-0.000
5443-0.000
5542-0.000
5641-0.000
5740-0.000
5839-0.000
5938-0.000
6037-0.000
6135-0.000
6234-0.000
6333-0.000
640-0.000