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_fold2/NR3C1-reddytime_multitask_profile_fold2_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/NR3C1-reddytime_multitask_profile_fold2/NR3C1-reddytime_multitask_profile_fold2_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/NR3C1-reddytime_multitask_profile_fold2/NR3C1-reddytime_multitask_profile_fold2_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%|██████████| 186/186 [01:30<00:00,  2.05it/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

5305 seqlets

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

Pattern 2/13

3112 seqlets

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

Pattern 3/13

1623 seqlets

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

Pattern 4/13

1134 seqlets

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

Pattern 5/13

723 seqlets

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

Pattern 6/13

343 seqlets

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

Pattern 7/13

243 seqlets

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

Pattern 8/13

228 seqlets

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

Pattern 9/13

223 seqlets

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

Pattern 10/13

177 seqlets

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

Pattern 11/13

45 seqlets

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

Pattern 12/13

34 seqlets

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

Pattern 13/13

33 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
15305
23112
31623
41134
5723
6343
7243
8228
9223
10177
1145
1234
1333

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.A1.13018e-08
PRGR_HUMAN.H11MO.0.A1.7771e-06
ANDR_HUMAN.H11MO.1.A1.7771e-06
MA0727.1_NR3C20.000103144
MA0113.3_NR3C10.00018994900000000002
PRGR_HUMAN.H11MO.1.A0.00770794Not shown
MA0007.3_Ar0.011324299999999999Not shown
GCR_HUMAN.H11MO.1.A0.0347141Not shown
MA1508.1_IKZF10.401725Not shown
MA1623.1_Stat20.459354Not shown

Motif 2/13

Motif IDq-valPWM
FOSB_HUMAN.H11MO.0.A2.8589e-05
JUND_HUMAN.H11MO.0.A2.8589e-05
JUN_HUMAN.H11MO.0.A2.8589e-05
FOSL1_HUMAN.H11MO.0.A2.8589e-05
FOSL2_HUMAN.H11MO.0.A2.8589e-05
MA1130.1_FOSL2::JUN2.8589e-05Not shown
MA0099.3_FOS::JUN7.18975e-05Not shown
MA1128.1_FOSL1::JUN0.00010065700000000001Not shown
NFE2_HUMAN.H11MO.0.A0.00010065700000000001Not shown
MA1141.1_FOS::JUND0.00010065700000000001Not shown

Motif 3/13

Motif IDq-valPWM
CEBPB_HUMAN.H11MO.0.A3.96678e-11
CEBPA_HUMAN.H11MO.0.A1.0421e-06
MA0836.2_CEBPD1.6562599999999997e-06
CEBPD_HUMAN.H11MO.0.C1.6562599999999997e-06
MA0102.4_CEBPA3.30268e-05
MA0837.1_CEBPE0.00122175Not shown
MA0466.2_CEBPB0.00134345Not shown
MA0025.2_NFIL30.00153212Not shown
MA0838.1_CEBPG0.00153212Not shown
DBP_HUMAN.H11MO.0.B0.00301932Not shown

Motif 4/13

Motif IDq-valPWM
FOXA1_HUMAN.H11MO.0.A8.07543e-07
FOXA2_HUMAN.H11MO.0.A8.535930000000001e-07
FOXM1_HUMAN.H11MO.0.A3.1195999999999997e-06
FOXF2_HUMAN.H11MO.0.D1.2023800000000002e-05
FOXA3_HUMAN.H11MO.0.B2.7799e-05
FOXD3_HUMAN.H11MO.0.D3.47488e-05Not shown
MA0846.1_FOXC23.7054899999999997e-05Not shown
FOXC1_HUMAN.H11MO.0.C3.7054899999999997e-05Not shown
MA0847.2_FOXD25.68443e-05Not shown
MA0032.2_FOXC10.00133525Not shown

Motif 5/13

Motif IDq-valPWM
MA1129.1_FOSL1::JUN(var.2)1.23155e-05
MA1136.1_FOSB::JUNB(var.2)1.23155e-05
MA1127.1_FOSB::JUN1.23155e-05
JDP2_HUMAN.H11MO.0.D1.23155e-05
MA1139.1_FOSL2::JUNB(var.2)1.23155e-05
MA0656.1_JDP2(var.2)1.23155e-05Not shown
MA0840.1_Creb51.23155e-05Not shown
MA1126.1_FOS::JUN(var.2)2.01526e-05Not shown
MA1475.1_CREB3L4(var.2)6.09271e-05Not shown
MA1133.1_JUN::JUNB(var.2)6.09271e-05Not shown

Motif 6/13

Motif IDq-valPWM
TEAD2_HUMAN.H11MO.0.D0.000239692
MA0808.1_TEAD30.00166577
MA1121.1_TEAD20.00166577
TEAD4_HUMAN.H11MO.0.A0.00166577
TEAD1_HUMAN.H11MO.0.A0.00166577
MA0809.2_TEAD40.0109164Not shown
MA0090.3_TEAD10.0139858Not shown

Motif 7/13

Motif IDq-valPWM
MA0107.1_RELA0.22534400000000002
NFKB1_HUMAN.H11MO.1.B0.22534400000000002
MA0808.1_TEAD30.22534400000000002
TF65_HUMAN.H11MO.0.A0.281681
MA0101.1_REL0.281681
P53_HUMAN.H11MO.1.A0.281681Not shown
NFKB2_HUMAN.H11MO.0.B0.281681Not shown
REL_HUMAN.H11MO.0.B0.281681Not shown
P73_HUMAN.H11MO.1.A0.341605Not shown
HSF2_HUMAN.H11MO.0.A0.41081599999999996Not shown

Motif 8/13

Motif IDq-valPWM
GCR_HUMAN.H11MO.0.A0.015933100000000002
PRGR_HUMAN.H11MO.0.A0.015933100000000002
MA0808.1_TEAD30.0496546
ZN394_HUMAN.H11MO.1.D0.0496546
ANDR_HUMAN.H11MO.1.A0.0731671
HSF1_HUMAN.H11MO.0.A0.0731671Not shown
SMCA5_HUMAN.H11MO.0.C0.191601Not shown
ZN502_HUMAN.H11MO.0.C0.191601Not shown
HSF4_HUMAN.H11MO.0.D0.191601Not shown
HSF2_HUMAN.H11MO.0.A0.275306Not shown

Motif 9/13

Motif IDq-valPWM
FOXD2_HUMAN.H11MO.0.D0.00548641
FOXB1_HUMAN.H11MO.0.D0.0685047
MA0032.2_FOXC10.365859
MA0791.1_POU4F30.365859
MA0845.1_FOXB10.365859
PO4F3_HUMAN.H11MO.0.D0.365859Not shown
MA0683.1_POU4F20.365859Not shown
FOXJ2_HUMAN.H11MO.0.C0.37198400000000004Not shown
FOXA2_HUMAN.H11MO.0.A0.392486Not shown
FOXA1_HUMAN.H11MO.0.A0.392486Not shown

Motif 10/13

Motif IDq-valPWM
NFIC_HUMAN.H11MO.0.A3.5738800000000005e-06
MA0119.1_NFIC::TLX14.67466e-06
NFIA_HUMAN.H11MO.0.C4.67466e-06
MA1528.1_NFIX(var.2)1.4578699999999998e-05
MA1527.1_NFIC(var.2)2.6370599999999998e-05
MA1643.1_NFIB6.0903400000000004e-05Not shown
NFIC_HUMAN.H11MO.1.A0.00703066Not shown
TLX1_HUMAN.H11MO.0.D0.00831432Not shown
NFIB_HUMAN.H11MO.0.D0.0126859Not shown
NFIA_HUMAN.H11MO.1.D0.028102999999999996Not shown

Motif 11/13

Motif IDq-valPWM
ZN281_HUMAN.H11MO.0.A0.013559799999999999
MA1653.1_ZNF1480.013559799999999999
KLF16_HUMAN.H11MO.0.D0.013559799999999999
ZN341_HUMAN.H11MO.0.C0.013559799999999999
E2F4_HUMAN.H11MO.0.A0.013559799999999999
MA1630.1_Znf2810.013559799999999999Not shown
TBX15_HUMAN.H11MO.0.D0.013559799999999999Not shown
ZBT17_HUMAN.H11MO.0.A0.013559799999999999Not shown
ZN148_HUMAN.H11MO.0.D0.013559799999999999Not shown
WT1_HUMAN.H11MO.0.C0.013559799999999999Not shown

Motif 12/13

Motif IDq-valPWM
CREM_HUMAN.H11MO.0.C0.00018842200000000002
MA0834.1_ATF70.00018842200000000002
MA1139.1_FOSL2::JUNB(var.2)0.00108429
ATF1_HUMAN.H11MO.0.B0.00141629
MA0609.2_CREM0.00158177
ATF2_HUMAN.H11MO.0.B0.00158177Not shown
MA1127.1_FOSB::JUN0.00181071Not shown
CREB1_HUMAN.H11MO.0.A0.00181071Not shown
MA1145.1_FOSL2::JUND(var.2)0.00181071Not shown
MA1131.1_FOSL2::JUN(var.2)0.00181071Not shown

Motif 13/13

Motif IDq-valPWM
NFIC_HUMAN.H11MO.0.A2.1863800000000002e-05
NFIA_HUMAN.H11MO.0.C0.000251254
MA1528.1_NFIX(var.2)0.00055477
MA1527.1_NFIC(var.2)0.000622234
MA0119.1_NFIC::TLX10.0006864060000000001
MA1643.1_NFIB0.0027300000000000002Not shown
NFIB_HUMAN.H11MO.0.D0.0027300000000000002Not shown
TLX1_HUMAN.H11MO.0.D0.006958Not shown
NFIC_HUMAN.H11MO.1.A0.013556799999999999Not shown
NFIA_HUMAN.H11MO.1.D0.0152947Not shown