Direct links to results

TF-MoDISco results

Summary of motifs

TOMTOM matches to motifs

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

# 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: CEBPB
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/CEBPB_multitask_profile_fold8/CEBPB_multitask_profile_fold8_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/CEBPB_multitask_profile_fold8/CEBPB_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/CEBPB_multitask_profile_fold8/CEBPB_multitask_profile_fold8_profile

# Define paths and constants
input_length = 2114
shap_score_center_size = 400

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

Import SHAP scores and TF-MoDISco results

# 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%|██████████| 273/273 [04:17<00:00,  1.06it/s]

# 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

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

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")
if tfm_motifs_cache_dir:
    motif_hdf5.close()

Metacluster 1/2

Pattern 1/16

10029 seqlets

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

Pattern 2/16

730 seqlets

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

Pattern 3/16

616 seqlets

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

Pattern 4/16

487 seqlets

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

Pattern 5/16

202 seqlets

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

Pattern 6/16

197 seqlets

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

Pattern 7/16

197 seqlets

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

Pattern 8/16

111 seqlets

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

Pattern 9/16

103 seqlets

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

Pattern 10/16

94 seqlets

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

Pattern 11/16

91 seqlets

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

Pattern 12/16

89 seqlets

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

Pattern 13/16

60 seqlets

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

Pattern 14/16

50 seqlets

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

Pattern 15/16

49 seqlets

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

Pattern 16/16

41 seqlets

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

Metacluster 2/2

Pattern 1/4

434 seqlets

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

Pattern 2/4

209 seqlets

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

Pattern 3/4

196 seqlets

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

Pattern 4/4

139 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.

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 1/2

/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)

#	Seqlets	Forward	Reverse
1	10029
2	730
3	616
4	487
5	202
6	197
7	197
8	111
9	103
10	94
11	91
12	89
13	60
14	50
15	49
16	41

Metacluster 2/2

#	Seqlets	Forward	Reverse
1	434
2	209
3	196
4	139

Top TOMTOM matches for each motif

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

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 1/2

Motif 1/16

Motif ID	q-val	PWM
CEBPB_HUMAN.H11MO.0.A	9.55971e-11
CEBPD_HUMAN.H11MO.0.C	2.8736699999999997e-09
CEBPA_HUMAN.H11MO.0.A	5.00949e-07
MA0836.2_CEBPD	1.86942e-05
MA0102.4_CEBPA	0.000208887
MA0466.2_CEBPB	0.000415944	Not shown
MA0837.1_CEBPE	0.000415944	Not shown
MA0838.1_CEBPG	0.0008986939999999999	Not shown
MA0025.2_NFIL3	0.00133887	Not shown
NFIL3_HUMAN.H11MO.0.D	0.00268349	Not shown

Motif 2/16

Motif ID	q-val	PWM
MA1622.1_Smad2::Smad3	2.68803e-05
MA0099.3_FOS::JUN	2.68803e-05
JUN_HUMAN.H11MO.0.A	2.68803e-05
FOSL2_HUMAN.H11MO.0.A	2.68803e-05
MA1130.1_FOSL2::JUN	2.68803e-05
MA1128.1_FOSL1::JUN	2.68803e-05	Not shown
MA1141.1_FOS::JUND	2.68803e-05	Not shown
JUND_HUMAN.H11MO.0.A	4.62796e-05	Not shown
BACH2_HUMAN.H11MO.0.A	4.62796e-05	Not shown
MA0477.2_FOSL1	5.20646e-05	Not shown

Motif 3/16

Motif ID	q-val	PWM
MA0139.1_CTCF	7.06735e-16
CTCF_HUMAN.H11MO.0.A	1.34725e-14
CTCFL_HUMAN.H11MO.0.A	9.67526e-08
MA1102.2_CTCFL	5.0348999999999995e-05
MA1568.1_TCF21(var.2)	0.126453
MA1638.1_HAND2	0.16697	Not shown
ZIC3_HUMAN.H11MO.0.B	0.202939	Not shown
SNAI1_HUMAN.H11MO.0.C	0.21935300000000002	Not shown
ZIC2_HUMAN.H11MO.0.D	0.282582	Not shown
MA0155.1_INSM1	0.296808	Not shown

Motif 4/16

Motif ID	q-val	PWM
FOXA2_HUMAN.H11MO.0.A	6.36565e-08
FOXA1_HUMAN.H11MO.0.A	8.041319999999999e-08
FOXA3_HUMAN.H11MO.0.B	3.3259500000000003e-06
MA0846.1_FOXC2	3.3259500000000003e-06
FOXM1_HUMAN.H11MO.0.A	3.3259500000000003e-06
FOXF2_HUMAN.H11MO.0.D	3.3259500000000003e-06	Not shown
FOXD3_HUMAN.H11MO.0.D	8.55243e-06	Not shown
MA0847.2_FOXD2	1.13494e-05	Not shown
FOXC1_HUMAN.H11MO.0.C	2.22121e-05	Not shown
MA0032.2_FOXC1	0.00016781	Not shown

Motif 5/16

Motif ID	q-val	PWM
CTCF_HUMAN.H11MO.0.A	0.000332483
MA0139.1_CTCF	0.000603569
MA1102.2_CTCFL	0.015472899999999998
CTCFL_HUMAN.H11MO.0.A	0.0178808
MA0116.1_Znf423	0.0396587
MXI1_HUMAN.H11MO.0.A	0.0396587	Not shown
ZFX_HUMAN.H11MO.1.A	0.11361700000000001	Not shown
SP4_HUMAN.H11MO.0.A	0.23822	Not shown
MA1628.1_Zic1::Zic2	0.305747	Not shown
SNAI1_HUMAN.H11MO.0.C	0.305747	Not shown

Motif 6/16

Motif ID	q-val	PWM
CEBPE_HUMAN.H11MO.0.A	0.0450373
MA0466.2_CEBPB	0.0450373
MA0837.1_CEBPE	0.0450373
MA0838.1_CEBPG	0.09761639999999999
CEBPB_HUMAN.H11MO.0.A	0.21624000000000002
CEBPD_HUMAN.H11MO.0.C	0.306332	Not shown
MA0843.1_TEF	0.417133	Not shown
MA0847.2_FOXD2	0.417133	Not shown
MA0639.1_DBP	0.417133	Not shown
MA0025.2_NFIL3	0.417133	Not shown

Motif 7/16

Motif ID	q-val	PWM
HNF4G_HUMAN.H11MO.0.B	1.70644e-08
HNF4A_HUMAN.H11MO.0.A	2.6713e-08
MA1494.1_HNF4A(var.2)	0.00083328
MA0677.1_Nr2f6	0.00083328
MA0856.1_RXRG	0.00083328
MA0114.4_HNF4A	0.00083328	Not shown
MA0484.2_HNF4G	0.00083328	Not shown
MA0512.2_Rxra	0.00083328	Not shown
MA1550.1_PPARD	0.00083328	Not shown
MA1574.1_THRB	0.0008668539999999999	Not shown

Motif 8/16

Motif ID	q-val	PWM
MA0836.2_CEBPD	0.162641
MA0102.4_CEBPA	0.162641
MA0863.1_MTF1	0.178833
CEBPA_HUMAN.H11MO.0.A	0.178833
CEBPB_HUMAN.H11MO.0.A	0.258815
CEBPD_HUMAN.H11MO.0.C	0.27259099999999997	Not shown
MTF1_HUMAN.H11MO.0.C	0.27259099999999997	Not shown
DBP_HUMAN.H11MO.0.B	0.27259099999999997	Not shown
NFIL3_HUMAN.H11MO.0.D	0.336976	Not shown
FOXA2_HUMAN.H11MO.0.A	0.336976	Not shown

Motif 9/16

Motif ID	q-val	PWM
TBX15_HUMAN.H11MO.0.D	4.4106999999999996e-05
KLF16_HUMAN.H11MO.0.D	4.4106999999999996e-05
MAZ_HUMAN.H11MO.0.A	4.4106999999999996e-05
SP3_HUMAN.H11MO.0.B	4.4106999999999996e-05
VEZF1_HUMAN.H11MO.0.C	4.4106999999999996e-05
PATZ1_HUMAN.H11MO.0.C	4.4106999999999996e-05	Not shown
SP2_HUMAN.H11MO.0.A	4.4106999999999996e-05	Not shown
ZN467_HUMAN.H11MO.0.C	4.4106999999999996e-05	Not shown
KLF15_HUMAN.H11MO.0.A	6.97605e-05	Not shown
WT1_HUMAN.H11MO.0.C	8.771690000000001e-05	Not shown

Motif 10/16

Motif ID	q-val	PWM
ZN770_HUMAN.H11MO.0.C	3.3607300000000002e-09
MA1596.1_ZNF460	1.43121e-07
ZN770_HUMAN.H11MO.1.C	0.000141224
MA1587.1_ZNF135	0.00146858
ZSC22_HUMAN.H11MO.0.C	0.0326108
ZFX_HUMAN.H11MO.1.A	0.11411199999999999	Not shown
MA0812.1_TFAP2B(var.2)	0.193146	Not shown
MA0003.4_TFAP2A	0.272507	Not shown
MAF_HUMAN.H11MO.0.A	0.272507	Not shown
EGR2_HUMAN.H11MO.0.A	0.272507	Not shown

Motif 11/16

Motif ID	q-val	PWM
FOXM1_HUMAN.H11MO.0.A	9.106719999999999e-05
FOXA2_HUMAN.H11MO.0.A	0.00288069
FOXA1_HUMAN.H11MO.0.A	0.00353856
FOXF2_HUMAN.H11MO.0.D	0.010812899999999999
FOXA3_HUMAN.H11MO.0.B	0.0114513
FOXD3_HUMAN.H11MO.0.D	0.0120922	Not shown
MA0846.1_FOXC2	0.0120922	Not shown
MA0847.2_FOXD2	0.012311399999999998	Not shown
FOXC1_HUMAN.H11MO.0.C	0.0195168	Not shown
MA1487.1_FOXE1	0.040330300000000006	Not shown

Motif 12/16

Motif ID	q-val	PWM
SP2_HUMAN.H11MO.0.A	3.67621e-08
SP2_HUMAN.H11MO.1.B	9.93426e-07
SP3_HUMAN.H11MO.0.B	9.93426e-07
KLF3_HUMAN.H11MO.0.B	2.8030799999999998e-06
SP4_HUMAN.H11MO.1.A	5.95663e-06
SP4_HUMAN.H11MO.0.A	7.468830000000001e-06	Not shown
MA1513.1_KLF15	7.468830000000001e-06	Not shown
KLF9_HUMAN.H11MO.0.C	2.4587199999999997e-05	Not shown
SP1_HUMAN.H11MO.1.A	7.4022e-05	Not shown
KLF12_HUMAN.H11MO.0.C	8.341159999999999e-05	Not shown

Motif 13/16

Motif ID	q-val	PWM
MA0478.1_FOSL2	6.94461e-06
NFE2_HUMAN.H11MO.0.A	0.000302208
JUND_HUMAN.H11MO.0.A	0.000302208
BACH2_HUMAN.H11MO.0.A	0.00034220699999999997
JUN_HUMAN.H11MO.0.A	0.00034220699999999997
MA0150.2_Nfe2l2	0.00037759800000000004	Not shown
JUNB_HUMAN.H11MO.0.A	0.00037759800000000004	Not shown
MA1134.1_FOS::JUNB	0.000617418	Not shown
MA1633.1_BACH1	0.000617418	Not shown
FOSL1_HUMAN.H11MO.0.A	0.000955114	Not shown

Motif 14/16

Motif ID	q-val	PWM
ZN770_HUMAN.H11MO.0.C	3.69215e-06
MA1596.1_ZNF460	0.00139722
ZN770_HUMAN.H11MO.1.C	0.00283354
MA1587.1_ZNF135	0.0333066
PITX2_HUMAN.H11MO.0.D	0.05199550000000001

Motif 15/16

Motif ID	q-val	PWM
GATA4_HUMAN.H11MO.0.A	3.5386300000000004e-05
MA0766.2_GATA5	0.000161781
GATA1_HUMAN.H11MO.1.A	0.000161781
GATA2_HUMAN.H11MO.1.A	0.000161781
GATA1_HUMAN.H11MO.0.A	0.000168659
MA0036.3_GATA2	0.000234005	Not shown
TAL1_HUMAN.H11MO.0.A	0.00168458	Not shown
GATA3_HUMAN.H11MO.0.A	0.00270906	Not shown
MA0037.3_GATA3	0.00333067	Not shown
MA0482.2_GATA4	0.00705903	Not shown

Motif 16/16

Motif ID	q-val	PWM
SP1_HUMAN.H11MO.0.A	2.3124e-06
SP2_HUMAN.H11MO.0.A	6.4759300000000005e-06
SP3_HUMAN.H11MO.0.B	1.97612e-05
SP4_HUMAN.H11MO.1.A	1.97653e-05
KLF6_HUMAN.H11MO.0.A	3.1609499999999996e-05
SP2_HUMAN.H11MO.1.B	4.1278e-05	Not shown
KLF3_HUMAN.H11MO.0.B	6.09422e-05	Not shown
MA1513.1_KLF15	0.000113575	Not shown
SP1_HUMAN.H11MO.1.A	0.0001284	Not shown
KLF9_HUMAN.H11MO.0.C	0.00030877	Not shown

Metacluster 2/2

Motif 1/4

No TOMTOM matches passing threshold

Motif 2/4

No TOMTOM matches passing threshold

Motif 3/4

Motif ID	q-val	PWM
MA0837.1_CEBPE	0.000402872
CEBPD_HUMAN.H11MO.0.C	0.000402872
MA0466.2_CEBPB	0.000585634
CEBPB_HUMAN.H11MO.0.A	0.000665675
MA0838.1_CEBPG	0.00152037
CEBPG_HUMAN.H11MO.0.B	0.0058059	Not shown
ATF4_HUMAN.H11MO.0.A	0.0058059	Not shown
CEBPE_HUMAN.H11MO.0.A	0.00698201	Not shown
DDIT3_HUMAN.H11MO.0.D	0.00698201	Not shown
CEBPA_HUMAN.H11MO.0.A	0.00706791	Not shown

Motif 4/4

No TOMTOM matches passing threshold