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: MAFK
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/MAFK_multitask_profile_fold3/MAFK_multitask_profile_fold3_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/MAFK_multitask_profile_fold3/MAFK_multitask_profile_fold3_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/MAFK_multitask_profile_fold3/MAFK_multitask_profile_fold3_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%|██████████| 311/311 [05:29<00:00,  1.06s/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

9944 seqlets

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

Pattern 2/13

818 seqlets

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

Pattern 3/13

232 seqlets

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

Pattern 4/13

230 seqlets

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

Pattern 5/13

149 seqlets

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

Pattern 6/13

82 seqlets

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

Pattern 7/13

80 seqlets

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

Pattern 8/13

73 seqlets

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

Pattern 9/13

40 seqlets

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

Pattern 10/13

37 seqlets

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

Pattern 11/13

36 seqlets

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

Pattern 12/13

32 seqlets

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

Pattern 13/13

30 seqlets

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

Metacluster 2/2

Pattern 1/3

111 seqlets

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

Pattern 2/3

44 seqlets

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

Pattern 3/3

35 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
19944
2818
3232
4230
5149
682
780
873
940
1037
1136
1232
1330

Metacluster 2/2

#SeqletsForwardReverse
1111
244
335

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
MAFK_HUMAN.H11MO.0.A1.29268e-09
MAFB_HUMAN.H11MO.0.B1.29268e-09
MA0496.3_MAFK1.29268e-09
MA1520.1_MAF2.4107100000000003e-09
MAFG_HUMAN.H11MO.0.A2.4107100000000003e-09
MAFK_HUMAN.H11MO.1.A9.20292e-09Not shown
MAF_HUMAN.H11MO.0.A4.3824300000000006e-08Not shown
MA1521.1_MAFA5.9541699999999995e-08Not shown
MAFF_HUMAN.H11MO.0.B1.34927e-07Not shown
MAF_HUMAN.H11MO.1.B6.626089999999999e-06Not shown

Motif 2/13

Motif IDq-valPWM
MA0139.1_CTCF3.22209e-16
CTCF_HUMAN.H11MO.0.A2.17713e-12
CTCFL_HUMAN.H11MO.0.A8.604949999999999e-07
MA1102.2_CTCFL0.00040818
MA1568.1_TCF21(var.2)0.06861089999999999
MA1638.1_HAND20.0729468Not shown
SNAI1_HUMAN.H11MO.0.C0.142237Not shown
ZIC3_HUMAN.H11MO.0.B0.374314Not shown
MA1648.1_TCF12(var.2)0.46395299999999995Not shown
BHA15_HUMAN.H11MO.0.B0.46395299999999995Not shown

Motif 3/13

Motif IDq-valPWM
MA0117.2_Mafb0.00428404
MA0495.3_MAFF0.00428404
MAF_HUMAN.H11MO.1.B0.00428404
MA0659.2_MAFG0.00683195
NRL_HUMAN.H11MO.0.D0.0187658
MAFB_HUMAN.H11MO.0.B0.035630199999999994Not shown
MAFF_HUMAN.H11MO.0.B0.0445217Not shown
MAFG_HUMAN.H11MO.1.A0.0445217Not shown
MAFG_HUMAN.H11MO.0.A0.071422Not shown
MAFK_HUMAN.H11MO.0.A0.071422Not shown

Motif 4/13

Motif IDq-valPWM
CTCF_HUMAN.H11MO.0.A0.0101931
MA0139.1_CTCF0.0198806
MA1102.2_CTCFL0.09998439999999999
MA1629.1_Zic20.09998439999999999
THAP1_HUMAN.H11MO.0.C0.09998439999999999
MA0622.1_Mlxip0.12358599999999999Not shown
USF2_HUMAN.H11MO.0.A0.12358599999999999Not shown
MA1568.1_TCF21(var.2)0.12358599999999999Not shown
SNAI1_HUMAN.H11MO.0.C0.12358599999999999Not shown
ZIC3_HUMAN.H11MO.0.B0.12358599999999999Not shown

Motif 5/13

Motif IDq-valPWM
MAFK_HUMAN.H11MO.1.A0.00288452
MAF_HUMAN.H11MO.1.B0.00288452
MAF_HUMAN.H11MO.0.A0.00368561
MA0496.3_MAFK0.00544622
MA1520.1_MAF0.00544622
MAFF_HUMAN.H11MO.0.B0.00544622Not shown
MA1521.1_MAFA0.00544622Not shown
MAFG_HUMAN.H11MO.0.A0.00544622Not shown
CR3L2_HUMAN.H11MO.0.D0.00544622Not shown
MA0839.1_CREB3L10.00570875Not shown

Motif 6/13

Motif IDq-valPWM
MA0117.2_Mafb0.013506
MAF_HUMAN.H11MO.1.B0.013506
MA0659.2_MAFG0.013506
MA0495.3_MAFF0.013506
MAFK_HUMAN.H11MO.0.A0.018895099999999998
MAFG_HUMAN.H11MO.0.A0.018895099999999998Not shown
MA0842.2_NRL0.0490141Not shown
MA0501.1_MAF::NFE20.054383Not shown
ESX1_HUMAN.H11MO.0.D0.0585295Not shown
MAFG_HUMAN.H11MO.1.A0.058892599999999996Not shown

Motif 7/13

Motif IDq-valPWM
MAF_HUMAN.H11MO.1.B0.00111384
MAFK_HUMAN.H11MO.0.A0.049643900000000005
MAFG_HUMAN.H11MO.1.A0.132322
MAFG_HUMAN.H11MO.0.A0.132322
MA0496.3_MAFK0.132322
HME1_HUMAN.H11MO.0.D0.175482Not shown
MA1520.1_MAF0.175482Not shown
GBX1_HUMAN.H11MO.0.D0.175482Not shown
CR3L2_HUMAN.H11MO.0.D0.175482Not shown
MA1521.1_MAFA0.175482Not shown

Motif 8/13

Motif IDq-valPWM
MA0139.1_CTCF0.000508245
CTCF_HUMAN.H11MO.0.A0.00368154
CTCFL_HUMAN.H11MO.0.A0.07428610000000001
MA1568.1_TCF21(var.2)0.20667399999999997
ZBTB4_HUMAN.H11MO.0.D0.458896
MA1638.1_HAND20.458896Not shown
ZFP28_HUMAN.H11MO.0.C0.458896Not shown

Motif 9/13

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D4.33826e-05
PRDM6_HUMAN.H11MO.0.C0.0240852
MA1125.1_ZNF3840.0272901
FOXL1_HUMAN.H11MO.0.D0.03989790000000001
FOXG1_HUMAN.H11MO.0.D0.0988393
ANDR_HUMAN.H11MO.0.A0.150095Not shown
FOXJ3_HUMAN.H11MO.0.A0.150095Not shown
MA0679.2_ONECUT10.17636Not shown
ONEC2_HUMAN.H11MO.0.D0.17636Not shown
FUBP1_HUMAN.H11MO.0.D0.180198Not shown

Motif 10/13

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D4.91182e-05
MA1125.1_ZNF3840.0358277
PRDM6_HUMAN.H11MO.0.C0.0358277
FOXL1_HUMAN.H11MO.0.D0.0358277
FOXG1_HUMAN.H11MO.0.D0.0569158
MA0679.2_ONECUT10.08546820000000001Not shown
FOXJ3_HUMAN.H11MO.0.A0.08546820000000001Not shown
HXC10_HUMAN.H11MO.0.D0.08546820000000001Not shown
ANDR_HUMAN.H11MO.0.A0.08546820000000001Not shown
FUBP1_HUMAN.H11MO.0.D0.127598Not shown

Motif 11/13

No TOMTOM matches passing threshold

Motif 12/13

No TOMTOM matches passing threshold

Motif 13/13

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D2.9712900000000004e-06
MA1125.1_ZNF3840.00148756
MA0679.2_ONECUT10.00148756
HXC10_HUMAN.H11MO.0.D0.00692577
FOXL1_HUMAN.H11MO.0.D0.00908851
FOXG1_HUMAN.H11MO.0.D0.00908851Not shown
LMX1A_HUMAN.H11MO.0.D0.0106719Not shown
ARI3A_HUMAN.H11MO.0.D0.0124921Not shown
MA0757.1_ONECUT30.0190416Not shown
ONEC2_HUMAN.H11MO.0.D0.0327455Not shown

Metacluster 2/2

Motif 1/3

Motif IDq-valPWM
MAF_HUMAN.H11MO.0.A0.00044215199999999996
MAFG_HUMAN.H11MO.0.A0.000494132
MAFK_HUMAN.H11MO.0.A0.000611081
MA0117.2_Mafb0.00142399
MA0496.3_MAFK0.00142399
MA1520.1_MAF0.00168372Not shown
MAF_HUMAN.H11MO.1.B0.00292361Not shown
MA1521.1_MAFA0.00300099Not shown
MAFK_HUMAN.H11MO.1.A0.00305966Not shown
CR3L2_HUMAN.H11MO.0.D0.00336625Not shown

Motif 2/3

Motif IDq-valPWM
ZN467_HUMAN.H11MO.0.C0.07355249999999999
KLF3_HUMAN.H11MO.0.B0.0906254
FLI1_HUMAN.H11MO.0.A0.0906254
OLIG2_HUMAN.H11MO.0.B0.0906254
VEZF1_HUMAN.H11MO.0.C0.0906254
KLF6_HUMAN.H11MO.0.A0.0906254Not shown
BC11A_HUMAN.H11MO.0.A0.0906254Not shown
ZN263_HUMAN.H11MO.0.A0.0906254Not shown
MAZ_HUMAN.H11MO.0.A0.152835Not shown
ETV5_HUMAN.H11MO.0.C0.152835Not shown

Motif 3/3

No TOMTOM matches passing threshold