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: NR3C1-reddytime
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/NR3C1-reddytime_multitask_profile_fold7/NR3C1-reddytime_multitask_profile_fold7_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/NR3C1-reddytime_multitask_profile_fold7/NR3C1-reddytime_multitask_profile_fold7_count_tfm.h5
Importance score key: count_hyp_scores
Saved TF-MoDISco-derived motifs cache: /users/amtseng/tfmodisco/results/reports/tfmodisco_results//cache/multitask_profile/NR3C1-reddytime_multitask_profile_fold7/NR3C1-reddytime_multitask_profile_fold7_count
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%|██████████| 186/186 [01:30<00:00,  2.06it/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")
if tfm_motifs_cache_dir:
    motif_hdf5.close()

Metacluster 1/1

Pattern 1/13

6148 seqlets

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

Pattern 2/13

3634 seqlets

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

Pattern 3/13

1584 seqlets

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

Pattern 4/13

562 seqlets

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

Pattern 5/13

477 seqlets

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

Pattern 6/13

338 seqlets

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

Pattern 7/13

257 seqlets

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

Pattern 8/13

206 seqlets

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

Pattern 9/13

162 seqlets

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

Pattern 10/13

45 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

35 seqlets

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

Pattern 13/13

32 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
16148
23634
31584
4562
5477
6338
7257
8206
9162
1045
1136
1235
1332

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
GCR_HUMAN.H11MO.0.A6.49795e-09
PRGR_HUMAN.H11MO.0.A8.62387e-07
ANDR_HUMAN.H11MO.1.A1.3858199999999998e-06
MA0727.1_NR3C20.00012447
MA0113.3_NR3C10.00019921099999999997
PRGR_HUMAN.H11MO.1.A0.010518399999999999Not shown
MA0007.3_Ar0.013531399999999999Not shown
GCR_HUMAN.H11MO.1.A0.0379624Not shown
MA1508.1_IKZF10.39662Not shown
RARG_HUMAN.H11MO.0.B0.39662Not shown

Motif 2/13

Motif IDq-valPWM
MA0478.1_FOSL21.35672e-05
JUN_HUMAN.H11MO.0.A1.35672e-05
FOSL1_HUMAN.H11MO.0.A1.35672e-05
FOSB_HUMAN.H11MO.0.A1.51953e-05
MA1144.1_FOSL2::JUND1.70462e-05
MA0099.3_FOS::JUN1.70462e-05Not shown
FOSL2_HUMAN.H11MO.0.A1.70462e-05Not shown
JUND_HUMAN.H11MO.0.A1.70462e-05Not shown
MA1137.1_FOSL1::JUNB1.70462e-05Not shown
MA1135.1_FOSB::JUNB1.70462e-05Not shown

Motif 3/13

Motif IDq-valPWM
CEBPB_HUMAN.H11MO.0.A2.5465099999999995e-10
CEBPD_HUMAN.H11MO.0.C4.0104699999999996e-06
CEBPA_HUMAN.H11MO.0.A7.16385e-06
MA0836.2_CEBPD1.9183e-05
MA0102.4_CEBPA9.26292e-05
MA0025.2_NFIL30.00044854300000000003Not shown
MA0837.1_CEBPE0.000625162Not shown
MA0466.2_CEBPB0.000723364Not shown
MA0838.1_CEBPG0.00108285Not shown
NFIL3_HUMAN.H11MO.0.D0.0015422Not shown

Motif 4/13

Motif IDq-valPWM
MA1127.1_FOSB::JUN1.90871e-05
MA1145.1_FOSL2::JUND(var.2)1.90871e-05
MA1136.1_FOSB::JUNB(var.2)2.14058e-05
ATF2_HUMAN.H11MO.2.C2.14058e-05
ATF7_HUMAN.H11MO.0.D2.14058e-05
MA1131.1_FOSL2::JUN(var.2)2.14058e-05Not shown
MA1133.1_JUN::JUNB(var.2)2.14058e-05Not shown
JDP2_HUMAN.H11MO.0.D2.14058e-05Not shown
MA1129.1_FOSL1::JUN(var.2)3.43118e-05Not shown
CREB5_HUMAN.H11MO.0.D3.57415e-05Not shown

Motif 5/13

Motif IDq-valPWM
FOXA2_HUMAN.H11MO.0.A4.18403e-07
FOXA1_HUMAN.H11MO.0.A5.41246e-07
FOXA3_HUMAN.H11MO.0.B7.007000000000001e-06
FOXC1_HUMAN.H11MO.0.C1.38547e-05
MA0846.1_FOXC21.48004e-05
FOXD3_HUMAN.H11MO.0.D2.4667399999999997e-05Not shown
MA0847.2_FOXD23.25905e-05Not shown
FOXF2_HUMAN.H11MO.0.D4.17186e-05Not shown
MA0032.2_FOXC10.00034278Not shown
FOXD1_HUMAN.H11MO.0.D0.0004986540000000001Not shown

Motif 6/13

Motif IDq-valPWM
FOXM1_HUMAN.H11MO.0.A8.59719e-05
FOXJ2_HUMAN.H11MO.0.C0.011642399999999999
FOXA1_HUMAN.H11MO.0.A0.0123887
FOXF2_HUMAN.H11MO.0.D0.012790000000000001
MA1487.1_FOXE10.012790000000000001
FOXA2_HUMAN.H11MO.0.A0.0136762Not shown
MA0846.1_FOXC20.0144505Not shown
FOXD3_HUMAN.H11MO.0.D0.0144505Not shown
MA0847.2_FOXD20.0144505Not shown
MA0041.1_Foxd30.0144761Not shown

Motif 7/13

Motif IDq-valPWM
FOXD2_HUMAN.H11MO.0.D0.00311564
FOXB1_HUMAN.H11MO.0.D0.031753500000000004
MA0791.1_POU4F30.264911
MA0032.2_FOXC10.264911
MA0845.1_FOXB10.264911
MA0683.1_POU4F20.264911Not shown
FOXJ2_HUMAN.H11MO.0.C0.377355Not shown
MA0847.2_FOXD20.377355Not shown
FOXA1_HUMAN.H11MO.0.A0.377355Not shown
PO4F3_HUMAN.H11MO.0.D0.377355Not shown

Motif 8/13

Motif IDq-valPWM
MA0808.1_TEAD30.16733
HSF1_HUMAN.H11MO.0.A0.16733
HSF2_HUMAN.H11MO.0.A0.16733
TEAD4_HUMAN.H11MO.0.A0.22834200000000002
P63_HUMAN.H11MO.1.A0.22834200000000002
NR1I3_HUMAN.H11MO.0.C0.22834200000000002Not shown
TEAD1_HUMAN.H11MO.0.A0.22834200000000002Not shown
MA0770.1_HSF20.22834200000000002Not shown
MA0486.2_HSF10.22834200000000002Not shown
ZNF85_HUMAN.H11MO.1.C0.22834200000000002Not shown

Motif 9/13

Motif IDq-valPWM
PRGR_HUMAN.H11MO.0.A0.00259782
GCR_HUMAN.H11MO.0.A0.00773092
PRGR_HUMAN.H11MO.1.A0.0429885
ANDR_HUMAN.H11MO.1.A0.0429885
MA0727.1_NR3C20.11236600000000001
MA0113.3_NR3C10.11236600000000001Not shown
HSF1_HUMAN.H11MO.0.A0.45925900000000003Not shown
MA1508.1_IKZF10.45925900000000003Not shown
HSF2_HUMAN.H11MO.0.A0.45925900000000003Not shown
TEAD3_HUMAN.H11MO.0.D0.49145200000000006Not shown

Motif 10/13

Motif IDq-valPWM
FOXA2_HUMAN.H11MO.0.A0.000620964
FOXF2_HUMAN.H11MO.0.D0.000826404
MA0847.2_FOXD20.00176858
FOXA1_HUMAN.H11MO.0.A0.00407839
MA0042.2_FOXI10.00407839
FOXM1_HUMAN.H11MO.0.A0.00407839Not shown
FOXD3_HUMAN.H11MO.0.D0.00501987Not shown
MA0849.1_FOXO60.00626818Not shown
FOXK1_HUMAN.H11MO.0.A0.00634107Not shown
MA0848.1_FOXO40.00806499Not shown

Motif 11/13

Motif IDq-valPWM
MA0847.2_FOXD20.05401860000000001
MA0032.2_FOXC10.05401860000000001
FOXA2_HUMAN.H11MO.0.A0.05401860000000001
MA1487.1_FOXE10.05401860000000001
MA0849.1_FOXO60.0804996
MA0047.3_FOXA20.0804996Not shown
MA0042.2_FOXI10.0804996Not shown
ANDR_HUMAN.H11MO.0.A0.0804996Not shown
MA0850.1_FOXP30.0804996Not shown
FOXC1_HUMAN.H11MO.0.C0.0804996Not shown

Motif 12/13

Motif IDq-valPWM
CEBPE_HUMAN.H11MO.0.A0.00010191
CEBPB_HUMAN.H11MO.0.A0.00024819999999999996
MA0837.1_CEBPE0.00024819999999999996
MA0466.2_CEBPB0.000454344
MA0838.1_CEBPG0.000651478
CEBPA_HUMAN.H11MO.0.A0.000741746Not shown
CEBPD_HUMAN.H11MO.0.C0.000741746Not shown
MA0836.2_CEBPD0.00208043Not shown
MA0102.4_CEBPA0.00525156Not shown
MA0025.2_NFIL30.00909252Not shown

Motif 13/13

Motif IDq-valPWM
MA0837.1_CEBPE0.000203308
MA0466.2_CEBPB0.000203308
MA0838.1_CEBPG0.000203308
CEBPB_HUMAN.H11MO.0.A0.00212634
CEBPD_HUMAN.H11MO.0.C0.00321062
CEBPE_HUMAN.H11MO.0.A0.00756361Not shown
MA0836.2_CEBPD0.00756361Not shown
CEBPA_HUMAN.H11MO.0.A0.021319499999999998Not shown
DBP_HUMAN.H11MO.0.B0.0628848Not shown
MA0025.2_NFIL30.0628848Not shown