TF-MoDISco results

Summary of motifs

TOMTOM matches to motifs

In [1]:
import os
import sys
sys.path.append(os.path.abspath("/users/amtseng/tfmodisco/src/"))
from tfmodisco.run_tfmodisco import import_shap_scores, import_tfmodisco_results
from motif.read_motifs import pfm_info_content, pfm_to_pwm, trim_motif_by_ic
from motif.match_motifs import match_motifs_to_database
from util import figure_to_vdom_image
import plot.viz_sequence as viz_sequence
import numpy as np
import h5py
import matplotlib.pyplot as plt
import vdom.helpers as vdomh
from IPython.display import display

Define constants and paths

In [2]:
# Define parameters/fetch arguments
tf_name = os.environ["TFM_TF_NAME"]
shap_scores_path = os.environ["TFM_SHAP_PATH"]
tfm_results_path = os.environ["TFM_TFM_PATH"]
hyp_score_key = os.environ["TFM_HYP_SCORE_KEY"]
if "TFM_MOTIF_CACHE" in os.environ:
    tfm_motifs_cache_dir = os.environ["TFM_MOTIF_CACHE"]
else:
    tfm_motifs_cache_dir = None

print("TF name: %s" % tf_name)
print("DeepSHAP scores path: %s" % shap_scores_path)
print("TF-MoDISco results path: %s" % tfm_results_path)
print("Importance score key: %s" % hyp_score_key)
print("Saved TF-MoDISco-derived motifs cache: %s" % tfm_motifs_cache_dir)

TF name: E2F6
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/E2F6_multitask_profile_fold8/E2F6_multitask_profile_fold8_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/E2F6_multitask_profile_fold8/E2F6_multitask_profile_fold8_profile_tfm.h5
Importance score key: profile_hyp_scores
Saved TF-MoDISco-derived motifs cache: /users/amtseng/tfmodisco/results/reports/tfmodisco_results//cache/multitask_profile/E2F6_multitask_profile_fold8/E2F6_multitask_profile_fold8_profile
In [3]:
# Define paths and constants
input_length = 2114
shap_score_center_size = 400
In [4]:
if tfm_motifs_cache_dir:
    os.makedirs(tfm_motifs_cache_dir, exist_ok=True)

Import SHAP scores and TF-MoDISco results

In [5]:
# Import SHAP coordinates and one-hot sequences
hyp_scores, _, one_hot_seqs, shap_coords = import_shap_scores(shap_scores_path, hyp_score_key, center_cut_size=shap_score_center_size)
# This cuts the sequences/scores off just as how TF-MoDISco saw them, but the coordinates are uncut

Importing SHAP scores: 100%|██████████| 52/52 [00:23<00:00,  2.24it/s]
In [6]:
# Import the TF-MoDISco results object
tfm_obj = import_tfmodisco_results(tfm_results_path, hyp_scores, one_hot_seqs, shap_score_center_size)

Plot some SHAP score tracks

Plot the central region of some randomly selected actual importance scores

In [7]:
plot_slice = slice(int(shap_score_center_size / 4), int(3 * shap_score_center_size / 4))
for index in np.random.choice(hyp_scores.shape[0], size=5, replace=False):
    viz_sequence.plot_weights((hyp_scores[index] * one_hot_seqs[index])[plot_slice], subticks_frequency=100)

Plot TF-MoDISco results

Plot all motifs by metacluster

In [8]:
motif_pfms, motif_hcwms, motif_cwms = [], [], []  # Save the trimmed PFMs, hCWMs, and CWMs
motif_pfms_short = []  # PFMs that are even more trimmed (for TOMTOM)
num_seqlets = []  # Number of seqlets for each motif
motif_seqlets = []  # Save seqlets of each motif
metaclusters = tfm_obj.metacluster_idx_to_submetacluster_results
num_metaclusters = len(metaclusters.keys())
if tfm_motifs_cache_dir:
    motif_hdf5 = h5py.File(os.path.join(tfm_motifs_cache_dir, "all_motifs.h5"), "w")
for metacluster_i, metacluster_key in enumerate(metaclusters.keys()):
    metacluster = metaclusters[metacluster_key]
    display(vdomh.h3("Metacluster %d/%d" % (metacluster_i + 1, num_metaclusters)))
    patterns = metacluster.seqlets_to_patterns_result.patterns
    if not patterns:
        break
    motif_pfms.append([])
    motif_hcwms.append([])
    motif_cwms.append([])
    motif_pfms_short.append([])
    num_seqlets.append([])
    motif_seqlets.append([])
    num_patterns = len(patterns)
    for pattern_i, pattern in enumerate(patterns):
        seqlets = pattern.seqlets
        display(vdomh.h4("Pattern %d/%d" % (pattern_i + 1, num_patterns)))
        display(vdomh.p("%d seqlets" % len(seqlets)))
        
        pfm = pattern["sequence"].fwd
        hcwm = pattern["task0_hypothetical_contribs"].fwd
        cwm = pattern["task0_contrib_scores"].fwd
        
        pfm_fig = viz_sequence.plot_weights(pfm, subticks_frequency=10, return_fig=True)
        hcwm_fig = viz_sequence.plot_weights(hcwm, subticks_frequency=10, return_fig=True)
        cwm_fig = viz_sequence.plot_weights(cwm, subticks_frequency=10, return_fig=True)
        pfm_fig.tight_layout()
        hcwm_fig.tight_layout()
        cwm_fig.tight_layout()
        
        motif_table = vdomh.table(
            vdomh.tr(
                vdomh.td("Sequence (PFM)"),
                vdomh.td(figure_to_vdom_image(pfm_fig))
            ),
            vdomh.tr(
                vdomh.td("Hypothetical contributions (hCWM)"),
                vdomh.td(figure_to_vdom_image(hcwm_fig))
            ),
            vdomh.tr(
                vdomh.td("Actual contributions (CWM)"),
                vdomh.td(figure_to_vdom_image(cwm_fig))
            )
        )
        display(motif_table)
        plt.close("all")  # Remove all standing figures
        
        # Trim motif based on information content
        short_trimmed_pfm = trim_motif_by_ic(pfm, pfm)
        motif_pfms_short[-1].append(short_trimmed_pfm)
        
        # Expand trimming to +/- 4bp on either side
        trimmed_pfm = trim_motif_by_ic(pfm, pfm, pad=4)
        trimmed_hcwm = trim_motif_by_ic(pfm, hcwm, pad=4)
        trimmed_cwm = trim_motif_by_ic(pfm, cwm, pad=4)
        
        motif_pfms[-1].append(trimmed_pfm)
        motif_hcwms[-1].append(trimmed_hcwm)
        motif_cwms[-1].append(trimmed_cwm)
        
        num_seqlets[-1].append(len(seqlets))
        
        if tfm_motifs_cache_dir:
            # Save results and figures
            motif_id = "%d_%d" % (metacluster_i, pattern_i)
            pfm_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_pfm_full.png"))
            hcwm_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_hcwm_full.png"))
            cwm_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_cwm_full.png"))
            motif_dset = motif_hdf5.create_group(motif_id)
            motif_dset.create_dataset("pfm_full", data=pfm, compression="gzip")
            motif_dset.create_dataset("hcwm_full", data=hcwm, compression="gzip")
            motif_dset.create_dataset("cwm_full", data=cwm, compression="gzip")
            motif_dset.create_dataset("pfm_trimmed", data=trimmed_pfm, compression="gzip")
            motif_dset.create_dataset("hcwm_trimmed", data=trimmed_hcwm, compression="gzip")
            motif_dset.create_dataset("cwm_trimmed", data=trimmed_cwm, compression="gzip")
            motif_dset.create_dataset("pfm_short_trimmed", data=short_trimmed_pfm, compression="gzip")

Metacluster 1/1

Pattern 1/13

5762 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 2/13

3458 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 3/13

1302 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 4/13

1100 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 5/13

379 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 6/13

306 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 7/13

297 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 8/13

226 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 9/13

156 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 10/13

115 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 11/13

73 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 12/13

54 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Pattern 13/13

43 seqlets

Sequence (PFM)
Hypothetical contributions (hCWM)
Actual contributions (CWM)

Summary of motifs

Motifs are trimmed based on information content, and presented in descending order by number of supporting seqlets. The motifs are separated by metacluster. The motifs are presented as hCWMs. The forward orientation is defined as the orientation that is richer in purines.

In [9]:
colgroup = vdomh.colgroup(
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "5%"}),
    vdomh.col(style={"width": "45%"}),
    vdomh.col(style={"width": "45%"})
)
header = vdomh.thead(
    vdomh.tr(
        vdomh.th("#", style={"text-align": "center"}),
        vdomh.th("Seqlets", style={"text-align": "center"}),
        vdomh.th("Forward", style={"text-align": "center"}),
        vdomh.th("Reverse", style={"text-align": "center"})
    )
)

for i in range(len(motif_hcwms)):
    display(vdomh.h3("Metacluster %d/%d" % (i + 1, num_metaclusters)))
    body = []
    for j in range(len(motif_hcwms[i])):
        motif = motif_hcwms[i][j]
        if np.sum(motif[:, [0, 2]]) > 0.5 * np.sum(motif):
            # Forward is purine-rich, reverse-complement is pyrimidine-rich
            f, rc = motif, np.flip(motif, axis=(0, 1))
        else:
            f, rc = np.flip(motif, axis=(0, 1)), motif
            
        f_fig = viz_sequence.plot_weights(f, figsize=(20, 4), return_fig=True)
        f_fig.tight_layout()
        rc_fig = viz_sequence.plot_weights(rc, figsize=(20, 4), return_fig=True)
        rc_fig.tight_layout()
        
        if tfm_motifs_cache_dir:
            # Save results and figures
            motif_id = "%d_%d" % (i, j)
            f_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_hcwm_trimmed_fwd.png"))
            rc_fig.savefig(os.path.join(tfm_motifs_cache_dir, motif_id + "_hcwm_trimmed_rev.png"))

        body.append(
            vdomh.tr(
                vdomh.td(str(j + 1)),
                vdomh.td(str(num_seqlets[i][j])),
                vdomh.td(figure_to_vdom_image(f_fig)),
                vdomh.td(figure_to_vdom_image(rc_fig))
            )
        )
    display(vdomh.table(colgroup, header, vdomh.tbody(*body)))
    plt.close("all")

Metacluster 1/1

/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)
#SeqletsForwardReverse
15762
23458
31302
41100
5379
6306
7297
8226
9156
10115
1173
1254
1343

Top TOMTOM matches for each motif

Here, the TF-MoDISco motifs are plotted as hCWMs, but the TOMTOM matches are shown as PWMs.

In [10]:
num_matches_to_keep = 10
num_matches_to_show = 5

header = vdomh.thead(
    vdomh.tr(
        vdomh.th("Motif ID", style={"text-align": "center"}),
        vdomh.th("q-val", style={"text-align": "center"}),
        vdomh.th("PWM", style={"text-align": "center"})
    )
)

for i in range(len(motif_pfms)):
    display(vdomh.h3("Metacluster %d/%d" % (i + 1, num_metaclusters)))
    
    # Compute TOMTOM matches for all motifs in the metacluster at once
    out_dir = os.path.join(tfm_motifs_cache_dir, "tomtom", "metacluster_%d" % i) if tfm_motifs_cache_dir else None
    tomtom_matches = match_motifs_to_database(motif_pfms_short[i], top_k=num_matches_to_keep, temp_dir=out_dir)
    
    for j in range(len(motif_pfms[i])):
        display(vdomh.h4("Motif %d/%d" % (j + 1, len(motif_pfms[i]))))
        viz_sequence.plot_weights(motif_hcwms[i][j])
    
        body = []
        for k, (match_name, match_pfm, match_qval) in enumerate(tomtom_matches[j]):
            fig = viz_sequence.plot_weights(pfm_to_pwm(match_pfm), return_fig=True)
            fig.tight_layout()
            if k < num_matches_to_show:
                body.append(
                    vdomh.tr(
                        vdomh.td(match_name),
                        vdomh.td(str(match_qval)),
                        vdomh.td(figure_to_vdom_image(fig))
                    )
                )
                if tfm_motifs_cache_dir:
                    # Save results and figures
                    motif_id = "%d_%d" % (i, j)
                    fig.savefig(os.path.join(out_dir, motif_id + ("_hit-%d.png" % (k + 1))))
            else:
                body.append(
                    vdomh.tr(
                        vdomh.td(match_name),
                        vdomh.td(str(match_qval)),
                        vdomh.td("Not shown")
                    )
                )
        if not body:
            display(vdomh.p("No TOMTOM matches passing threshold"))
        else:
            display(vdomh.table(header, vdomh.tbody(*body)))
        plt.close("all")

Metacluster 1/1

Motif 1/13

Motif IDq-valPWM
MA0147.3_MYC0.0003134
MAX_HUMAN.H11MO.0.A0.0003134
MXI1_HUMAN.H11MO.1.A0.000945717
MA0059.1_MAX::MYC0.00232501
MA0058.3_MAX0.00232501
MYC_HUMAN.H11MO.0.A0.00232501Not shown
MXI1_HUMAN.H11MO.0.A0.00293586Not shown
MA0825.1_MNT0.00664179Not shown
MA0004.1_Arnt0.00675479Not shown
MYCN_HUMAN.H11MO.0.A0.0116825Not shown

Motif 2/13

Motif IDq-valPWM
E2F1_HUMAN.H11MO.0.A1.99824e-06
E2F6_HUMAN.H11MO.0.A3.78012e-05
E2F4_HUMAN.H11MO.0.A6.73156e-05
MA0471.2_E2F66.73156e-05
E2F3_HUMAN.H11MO.0.A6.73156e-05
TFDP1_HUMAN.H11MO.0.C6.73156e-05Not shown
MA1122.1_TFDP10.000505974Not shown
E2F7_HUMAN.H11MO.0.B0.000505974Not shown
E2F4_HUMAN.H11MO.1.A0.019475700000000002Not shown
MA0865.1_E2F80.0208526Not shown

Motif 3/13

Motif IDq-valPWM
USF2_HUMAN.H11MO.0.A0.0204084
AP2D_HUMAN.H11MO.0.D0.0204084
MXI1_HUMAN.H11MO.0.A0.0498816
MA1650.1_ZBTB140.0585184
SP2_HUMAN.H11MO.0.A0.0666127
SP1_HUMAN.H11MO.0.A0.0666127Not shown
SP3_HUMAN.H11MO.0.B0.17440999999999998Not shown
MA1122.1_TFDP10.17440999999999998Not shown
KLF16_HUMAN.H11MO.0.D0.17440999999999998Not shown
MA1513.1_KLF150.17440999999999998Not shown

Motif 4/13

No TOMTOM matches passing threshold

Motif 5/13

Motif IDq-valPWM
BACH2_HUMAN.H11MO.0.A0.00019648099999999998
BACH1_HUMAN.H11MO.0.A0.00019648099999999998
FOSL1_HUMAN.H11MO.0.A0.00039161199999999995
MA1633.1_BACH10.00039161199999999995
NFE2_HUMAN.H11MO.0.A0.00039161199999999995
JUNB_HUMAN.H11MO.0.A0.00039161199999999995Not shown
JUND_HUMAN.H11MO.0.A0.00039161199999999995Not shown
JUN_HUMAN.H11MO.0.A0.000403372Not shown
MA0591.1_Bach1::Mafk0.00063844Not shown
MA0501.1_MAF::NFE20.0006913310000000001Not shown

Motif 6/13

Motif IDq-valPWM
CTCFL_HUMAN.H11MO.0.A8.10881e-05
SP2_HUMAN.H11MO.0.A0.000100056
MA1102.2_CTCFL0.00038572599999999997
CTCF_HUMAN.H11MO.0.A0.000562304
SP3_HUMAN.H11MO.0.B0.00129178
SP4_HUMAN.H11MO.0.A0.00211747Not shown
MA0139.1_CTCF0.00211747Not shown
KLF3_HUMAN.H11MO.0.B0.00521556Not shown
SP1_HUMAN.H11MO.1.A0.00521556Not shown
AP2B_HUMAN.H11MO.0.B0.0190705Not shown

Motif 7/13

Motif IDq-valPWM
SP3_HUMAN.H11MO.0.B0.0500379
SP2_HUMAN.H11MO.0.A0.0500379
MXI1_HUMAN.H11MO.0.A0.0500379
SP1_HUMAN.H11MO.0.A0.0500379
MA1650.1_ZBTB140.0500379
MA1099.2_HES10.061400699999999996Not shown
ZBT14_HUMAN.H11MO.0.C0.0641161Not shown
THAP1_HUMAN.H11MO.0.C0.101825Not shown
KLF16_HUMAN.H11MO.0.D0.14745999999999998Not shown
TBX15_HUMAN.H11MO.0.D0.14745999999999998Not shown

Motif 8/13

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A0.00777
SP1_HUMAN.H11MO.0.A0.0190502
MA1650.1_ZBTB140.0317258
SP3_HUMAN.H11MO.0.B0.0331986
THAP1_HUMAN.H11MO.0.C0.0427854
MXI1_HUMAN.H11MO.0.A0.0593225Not shown
USF2_HUMAN.H11MO.0.A0.0599894Not shown
AP2D_HUMAN.H11MO.0.D0.0599894Not shown
MA0506.1_NRF10.0846015Not shown
RFX1_HUMAN.H11MO.0.B0.140379Not shown

Motif 9/13

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A2.50988e-05
SP1_HUMAN.H11MO.0.A0.00165229
MXI1_HUMAN.H11MO.0.A0.00165229
SP3_HUMAN.H11MO.0.B0.00165229
USF2_HUMAN.H11MO.0.A0.00221455
MA1650.1_ZBTB140.00428674Not shown
THAP1_HUMAN.H11MO.0.C0.0157999Not shown
AP2D_HUMAN.H11MO.0.D0.0218122Not shown
MA1099.2_HES10.0555392Not shown
KLF3_HUMAN.H11MO.0.B0.0555392Not shown

Motif 10/13

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A0.0024858000000000002
SP1_HUMAN.H11MO.0.A0.00638486
AP2D_HUMAN.H11MO.0.D0.00735632
MXI1_HUMAN.H11MO.0.A0.0094119
SP3_HUMAN.H11MO.0.B0.0094119
MA1099.2_HES10.025626599999999996Not shown
USF2_HUMAN.H11MO.0.A0.025626599999999996Not shown
MA1513.1_KLF150.025626599999999996Not shown
MA1650.1_ZBTB140.025626599999999996Not shown
ZBT14_HUMAN.H11MO.0.C0.025626599999999996Not shown

Motif 11/13

Motif IDq-valPWM
MA0801.1_MGA0.000144966
MA0803.1_TBX150.000144966
MA0805.1_TBX10.000144966
MA0806.1_TBX40.0005905369999999999
MA1567.1_TBX60.0005905369999999999
MA0689.1_TBX200.0005905369999999999Not shown
MA0690.1_TBX210.0005905369999999999Not shown
MA1565.1_TBX180.0005905369999999999Not shown
MA1566.1_TBX30.0005905369999999999Not shown
MA0807.1_TBX50.0005905369999999999Not shown

Motif 12/13

Motif IDq-valPWM
SP1_HUMAN.H11MO.0.A8.8207e-09
SP2_HUMAN.H11MO.0.A8.8207e-09
SP3_HUMAN.H11MO.0.B1.1500399999999999e-08
KLF16_HUMAN.H11MO.0.D5.797450000000001e-08
TBX15_HUMAN.H11MO.0.D3.03229e-07
ZN467_HUMAN.H11MO.0.C4.2120800000000005e-07Not shown
MAZ_HUMAN.H11MO.0.A4.2120800000000005e-07Not shown
PATZ1_HUMAN.H11MO.0.C4.2120800000000005e-07Not shown
WT1_HUMAN.H11MO.0.C7.59499e-07Not shown
VEZF1_HUMAN.H11MO.0.C1.99073e-06Not shown

Motif 13/13

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D4.48587e-05
MA1125.1_ZNF3840.0191243
PRDM6_HUMAN.H11MO.0.C0.0191243
FOXL1_HUMAN.H11MO.0.D0.019412099999999998
FOXG1_HUMAN.H11MO.0.D0.0248629
MA0679.2_ONECUT10.0248629Not shown
FOXJ3_HUMAN.H11MO.0.A0.030825400000000003Not shown
HXC10_HUMAN.H11MO.0.D0.0908073Not shown
ANDR_HUMAN.H11MO.0.A0.0908073Not shown
FUBP1_HUMAN.H11MO.0.D0.0908073Not shown