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: CEBPB
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/CEBPB_multitask_profile_fold10/CEBPB_multitask_profile_fold10_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/CEBPB_multitask_profile_fold10/CEBPB_multitask_profile_fold10_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_fold10/CEBPB_multitask_profile_fold10_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%|██████████| 273/273 [05:50<00:00,  1.28s/it]
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")
if tfm_motifs_cache_dir:
    motif_hdf5.close()

Metacluster 1/2

Pattern 1/13

10503 seqlets

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

Pattern 2/13

675 seqlets

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

Pattern 3/13

516 seqlets

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

Pattern 4/13

440 seqlets

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

Pattern 5/13

430 seqlets

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

Pattern 6/13

323 seqlets

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

Pattern 7/13

109 seqlets

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

Pattern 8/13

81 seqlets

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

Pattern 9/13

73 seqlets

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

Pattern 10/13

68 seqlets

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

Pattern 11/13

67 seqlets

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

Pattern 12/13

53 seqlets

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

Pattern 13/13

43 seqlets

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

Metacluster 2/2

Pattern 1/16

468 seqlets

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

Pattern 2/16

445 seqlets

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

Pattern 3/16

300 seqlets

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

Pattern 4/16

270 seqlets

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

Pattern 5/16

253 seqlets

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

Pattern 6/16

250 seqlets

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

Pattern 7/16

213 seqlets

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

Pattern 8/16

208 seqlets

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

Pattern 9/16

207 seqlets

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

Pattern 10/16

200 seqlets

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

Pattern 11/16

183 seqlets

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

Pattern 12/16

182 seqlets

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

Pattern 13/16

101 seqlets

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

Pattern 14/16

86 seqlets

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

Pattern 15/16

81 seqlets

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

Pattern 16/16

65 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 1/2

/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)
#SeqletsForwardReverse
110503
2675
3516
4440
5430
6323
7109
881
973
1068
1167
1253
1343

Metacluster 2/2

#SeqletsForwardReverse
1468
2445
3300
4270
5253
6250
7213
8208
9207
10200
11183
12182
13101
1486
1581
1665

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 1/2

Motif 1/13

Motif IDq-valPWM
CEBPB_HUMAN.H11MO.0.A2.2864400000000002e-09
CEBPD_HUMAN.H11MO.0.C8.63992e-09
CEBPA_HUMAN.H11MO.0.A5.00871e-07
MA0836.2_CEBPD4.0728e-05
MA0102.4_CEBPA0.000154821
MA0837.1_CEBPE0.00048495300000000004Not shown
MA0466.2_CEBPB0.000490867Not shown
MA0838.1_CEBPG0.000901331Not shown
MA0025.2_NFIL30.00155945Not shown
DBP_HUMAN.H11MO.0.B0.00238864Not shown

Motif 2/13

Motif IDq-valPWM
FOSL1_HUMAN.H11MO.0.A1.79937e-07
FOSB_HUMAN.H11MO.0.A4.4383200000000003e-07
JUND_HUMAN.H11MO.0.A1.51283e-06
JUN_HUMAN.H11MO.0.A1.51283e-06
FOS_HUMAN.H11MO.0.A1.51283e-06
FOSL2_HUMAN.H11MO.0.A1.51283e-06Not shown
MA1622.1_Smad2::Smad31.35175e-05Not shown
MA0099.3_FOS::JUN1.93636e-05Not shown
JUNB_HUMAN.H11MO.0.A1.93636e-05Not shown
MA0478.1_FOSL21.93636e-05Not shown

Motif 3/13

Motif IDq-valPWM
MA0139.1_CTCF1.8087600000000002e-17
CTCF_HUMAN.H11MO.0.A1.3682899999999999e-13
CTCFL_HUMAN.H11MO.0.A1.31313e-07
MA1102.2_CTCFL7.23136e-05
MA1568.1_TCF21(var.2)0.09827029999999999
MA1638.1_HAND20.116461Not shown
ZIC3_HUMAN.H11MO.0.B0.21855500000000003Not shown
SNAI1_HUMAN.H11MO.0.C0.21855500000000003Not shown
ZIC2_HUMAN.H11MO.0.D0.342079Not shown
MA0155.1_INSM10.408335Not shown

Motif 4/13

Motif IDq-valPWM
MA0837.1_CEBPE0.000665325
MA0466.2_CEBPB0.000665325
MA0838.1_CEBPG0.000665325
CEBPE_HUMAN.H11MO.0.A0.013226
CEBPD_HUMAN.H11MO.0.C0.021484299999999998
CEBPB_HUMAN.H11MO.0.A0.043653500000000005Not shown
HLF_HUMAN.H11MO.0.C0.0752409Not shown
MA0862.1_GMEB20.076723Not shown
CEBPA_HUMAN.H11MO.0.A0.0951692Not shown
MA0836.2_CEBPD0.0951692Not shown

Motif 5/13

Motif IDq-valPWM
FOXA2_HUMAN.H11MO.0.A3.53476e-07
FOXA1_HUMAN.H11MO.0.A3.53476e-07
FOXF2_HUMAN.H11MO.0.D2.32077e-06
FOXA3_HUMAN.H11MO.0.B2.32077e-06
FOXD3_HUMAN.H11MO.0.D6.08429e-06
FOXM1_HUMAN.H11MO.0.A6.08429e-06Not shown
MA0846.1_FOXC26.08429e-06Not shown
MA0847.2_FOXD22.04504e-05Not shown
FOXC1_HUMAN.H11MO.0.C2.04504e-05Not shown
FOXD1_HUMAN.H11MO.0.D0.000216126Not shown

Motif 6/13

Motif IDq-valPWM
MA1648.1_TCF12(var.2)0.0748511
MA0522.3_TCF30.0756107
MA1114.1_PBX30.0756107
ZBT7A_HUMAN.H11MO.0.A0.0756107
PTF1A_HUMAN.H11MO.1.B0.0756107
MA0103.3_ZEB10.0756107Not shown
ZN263_HUMAN.H11MO.0.A0.0756107Not shown
SP2_HUMAN.H11MO.0.A0.0756107Not shown
MA1102.2_CTCFL0.0756107Not shown
ITF2_HUMAN.H11MO.0.C0.0756107Not shown

Motif 7/13

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D7.804960000000001e-06
MA1125.1_ZNF3840.00760659
FOXL1_HUMAN.H11MO.0.D0.00760659
MA0679.2_ONECUT10.021735
FOXG1_HUMAN.H11MO.0.D0.021735
PRDM6_HUMAN.H11MO.0.C0.032319400000000005Not shown
FOXJ3_HUMAN.H11MO.0.A0.035299000000000004Not shown
HXC10_HUMAN.H11MO.0.D0.035299000000000004Not shown
ANDR_HUMAN.H11MO.0.A0.042638199999999994Not shown
ONEC2_HUMAN.H11MO.0.D0.077999Not shown

Motif 8/13

Motif IDq-valPWM
GATA1_HUMAN.H11MO.1.A1.7558799999999999e-06
GATA2_HUMAN.H11MO.1.A1.7558799999999999e-06
GATA4_HUMAN.H11MO.0.A1.7558799999999999e-06
GATA1_HUMAN.H11MO.0.A7.24298e-06
GATA2_HUMAN.H11MO.0.A1.1258e-05
GATA6_HUMAN.H11MO.0.A2.85317e-05Not shown
TAL1_HUMAN.H11MO.0.A0.00015639700000000001Not shown
MA0037.3_GATA30.000328589Not shown
MA0766.2_GATA50.000633634Not shown
MA0036.3_GATA20.000633634Not shown

Motif 9/13

Motif IDq-valPWM
HNF4A_HUMAN.H11MO.0.A0.00207572
HNF4G_HUMAN.H11MO.0.B0.00217971
MA1111.1_NR2F20.00475911
COT2_HUMAN.H11MO.0.A0.00475911
MA0114.4_HNF4A0.00494868
MA0484.2_HNF4G0.00634207Not shown
MA0115.1_NR1H2::RXRA0.0102554Not shown
NR1H2_HUMAN.H11MO.0.D0.0102554Not shown
NR6A1_HUMAN.H11MO.0.B0.0102554Not shown
COT2_HUMAN.H11MO.1.A0.0102554Not shown

Motif 10/13

Motif IDq-valPWM
FOXB1_HUMAN.H11MO.0.D9.646459999999999e-05
FOXD2_HUMAN.H11MO.0.D0.00321879
MA0845.1_FOXB10.00435353
FOXA2_HUMAN.H11MO.0.A0.00660694
MA0032.2_FOXC10.00660694
FOXA1_HUMAN.H11MO.0.A0.00912756Not shown
MA0847.2_FOXD20.0204767Not shown
FOXM1_HUMAN.H11MO.0.A0.0207523Not shown
MA0846.1_FOXC20.026030599999999997Not shown
MA0148.4_FOXA10.026030599999999997Not shown

Motif 11/13

Motif IDq-valPWM
HNF4A_HUMAN.H11MO.0.A0.00027763099999999997
HNF4G_HUMAN.H11MO.0.B0.00027763099999999997
MA1537.1_NR2F1(var.2)0.0153636
MA0484.2_HNF4G0.0153636
MA0512.2_Rxra0.0153636
MA1574.1_THRB0.0153636Not shown
MA1550.1_PPARD0.0153636Not shown
NR2F6_HUMAN.H11MO.0.D0.0153636Not shown
MA0856.1_RXRG0.0153636Not shown
MA0677.1_Nr2f60.0153636Not shown

Motif 12/13

No TOMTOM matches passing threshold

Motif 13/13

Motif IDq-valPWM
ATF4_HUMAN.H11MO.0.A2.65137e-05
CEBPG_HUMAN.H11MO.0.B2.65137e-05
DDIT3_HUMAN.H11MO.0.D0.000883907
MA0833.2_ATF40.00566823
MA1636.1_CEBPG(var.2)0.045848900000000005
BATF_HUMAN.H11MO.1.A0.064317Not shown
CEBPB_HUMAN.H11MO.0.A0.11515999999999998Not shown
CEBPA_HUMAN.H11MO.0.A0.11515999999999998Not shown
MA0003.4_TFAP2A0.21401900000000001Not shown
CEBPD_HUMAN.H11MO.0.C0.21401900000000001Not shown

Metacluster 2/2

Motif 1/16

No TOMTOM matches passing threshold

Motif 2/16

Motif IDq-valPWM
CEBPD_HUMAN.H11MO.0.C1.77615e-05
CEBPB_HUMAN.H11MO.0.A2.58875e-05
MA0837.1_CEBPE0.000139689
MA0466.2_CEBPB0.00018769099999999998
CEBPA_HUMAN.H11MO.0.A0.000986368
MA0838.1_CEBPG0.000986368Not shown
MA0043.3_HLF0.00426959Not shown
CEBPE_HUMAN.H11MO.0.A0.00546327Not shown
DBP_HUMAN.H11MO.0.B0.00690808Not shown
MA0836.2_CEBPD0.00690808Not shown

Motif 3/16

No TOMTOM matches passing threshold

Motif 4/16

No TOMTOM matches passing threshold

Motif 5/16

Motif IDq-valPWM
SP2_HUMAN.H11MO.0.A0.191296
SP1_HUMAN.H11MO.0.A0.314627
MA1653.1_ZNF1480.450433
SP1_HUMAN.H11MO.1.A0.450433
GABPA_HUMAN.H11MO.0.A0.450433
MA1584.1_ZIC50.450433Not shown
MA1102.2_CTCFL0.450433Not shown
KLF12_HUMAN.H11MO.0.C0.450433Not shown
KLF15_HUMAN.H11MO.0.A0.450433Not shown
ZIC4_HUMAN.H11MO.0.D0.450433Not shown

Motif 6/16

Motif IDq-valPWM
EGR4_HUMAN.H11MO.0.D0.30396799999999996
TBX1_HUMAN.H11MO.0.D0.30396799999999996
PURA_HUMAN.H11MO.0.D0.30396799999999996
ESR2_HUMAN.H11MO.0.A0.30396799999999996
RARA_HUMAN.H11MO.1.A0.30396799999999996
NFIC_HUMAN.H11MO.0.A0.30396799999999996Not shown
GATA1_HUMAN.H11MO.0.A0.30396799999999996Not shown
KLF12_HUMAN.H11MO.0.C0.30396799999999996Not shown
MA1513.1_KLF150.30396799999999996Not shown
SP4_HUMAN.H11MO.0.A0.305998Not shown

Motif 7/16

No TOMTOM matches passing threshold

Motif 8/16

No TOMTOM matches passing threshold

Motif 9/16

No TOMTOM matches passing threshold

Motif 10/16

No TOMTOM matches passing threshold

Motif 11/16

Motif IDq-valPWM
KLF13_HUMAN.H11MO.0.D0.0256709
MA1517.1_KLF60.0256709
MA0741.1_KLF160.0256709
MA0746.2_SP30.0256709
MA0657.1_KLF130.0256709
MA0742.1_Klf120.0256709Not shown
MA0079.4_SP10.0256709Not shown
MA0747.1_SP80.0256709Not shown
MA0740.1_KLF140.0256709Not shown
MA1516.1_KLF30.0256709Not shown

Motif 12/16

No TOMTOM matches passing threshold

Motif 13/16

No TOMTOM matches passing threshold

Motif 14/16

No TOMTOM matches passing threshold

Motif 15/16

Motif IDq-valPWM
CEBPD_HUMAN.H11MO.0.C8.296519999999999e-06
MA0837.1_CEBPE0.000131821
MA0838.1_CEBPG0.000131821
CEBPB_HUMAN.H11MO.0.A0.000131821
MA0466.2_CEBPB0.000131821
CEBPA_HUMAN.H11MO.0.A0.00111985Not shown
CEBPE_HUMAN.H11MO.0.A0.00417922Not shown
HLF_HUMAN.H11MO.0.C0.00468023Not shown
NFIL3_HUMAN.H11MO.0.D0.00607967Not shown
BATF_HUMAN.H11MO.1.A0.0062508Not shown

Motif 16/16

Motif IDq-valPWM
MA1489.1_FOXN30.339819
MA0157.2_FOXO30.339819
FOXC2_HUMAN.H11MO.0.D0.339819
MA0031.1_FOXD10.339819
MA0852.2_FOXK10.339819
MA0084.1_SRY0.339819Not shown
PRDM6_HUMAN.H11MO.0.C0.339819Not shown
MA1103.2_FOXK20.339819Not shown
MA0613.1_FOXG10.339819Not shown
BPTF_HUMAN.H11MO.0.D0.386802Not shown