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: FOXA2
DeepSHAP scores path: /users/amtseng/tfmodisco/results/importance_scores/multitask_profile/FOXA2_multitask_profile_fold6/FOXA2_multitask_profile_fold6_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/FOXA2_multitask_profile_fold6/FOXA2_multitask_profile_fold6_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/FOXA2_multitask_profile_fold6/FOXA2_multitask_profile_fold6_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%|██████████| 174/174 [01:46<00:00,  1.63it/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")

Metacluster 1/2

Pattern 1/8

5112 seqlets

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

Pattern 2/8

2849 seqlets

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

Pattern 3/8

2398 seqlets

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

Pattern 4/8

1686 seqlets

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

Pattern 5/8

995 seqlets

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

Pattern 6/8

313 seqlets

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

Pattern 7/8

50 seqlets

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

Pattern 8/8

39 seqlets

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

Metacluster 2/2

Pattern 1/1

113 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

#SeqletsForwardReverse
15112
22849
32398
41686
5995
6313
750
839

Metacluster 2/2

#SeqletsForwardReverse
1113

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 1/8

Motif IDq-valPWM
FOXA2_HUMAN.H11MO.0.A4.11877e-07
FOXA3_HUMAN.H11MO.0.B6.794889999999999e-07
FOXA1_HUMAN.H11MO.0.A7.409749999999999e-07
MA0846.1_FOXC23.4707400000000003e-06
FOXD3_HUMAN.H11MO.0.D5.55318e-06
FOXF2_HUMAN.H11MO.0.D1.64963e-05Not shown
MA1607.1_Foxl21.75668e-05Not shown
MA0852.2_FOXK12.19413e-05Not shown
FOXC1_HUMAN.H11MO.0.C2.3404e-05Not shown
MA0032.2_FOXC13.48177e-05Not shown

Motif 2/8

Motif IDq-valPWM
MA0845.1_FOXB16.34607e-05
MA0032.2_FOXC10.00010093700000000001
FOXD2_HUMAN.H11MO.0.D0.000336456
FOXB1_HUMAN.H11MO.0.D0.000901479
MA0846.1_FOXC20.00214425
FOXA3_HUMAN.H11MO.0.B0.00214425Not shown
FOXA1_HUMAN.H11MO.0.A0.00214425Not shown
FOXA2_HUMAN.H11MO.0.A0.00214425Not shown
FOXF2_HUMAN.H11MO.0.D0.00492053Not shown
MA0847.2_FOXD20.00492053Not shown

Motif 3/8

Motif IDq-valPWM
FOXM1_HUMAN.H11MO.0.A0.000118076
FOXJ2_HUMAN.H11MO.0.C0.00345693
MA1487.1_FOXE10.00570831
FOXA1_HUMAN.H11MO.0.A0.00649857
FOXF2_HUMAN.H11MO.0.D0.00850629
FOXA2_HUMAN.H11MO.0.A0.00887586Not shown
MA0846.1_FOXC20.00887586Not shown
MA0847.2_FOXD20.00887586Not shown
FOXD3_HUMAN.H11MO.0.D0.00902552Not shown
MA0041.1_Foxd30.00902552Not shown

Motif 4/8

Motif IDq-valPWM
HNF4G_HUMAN.H11MO.0.B1.38772e-05
HNF4A_HUMAN.H11MO.0.A1.60199e-05
MA0677.1_Nr2f69.14878e-05
MA0856.1_RXRG9.14878e-05
MA1550.1_PPARD9.14878e-05
MA1574.1_THRB9.14878e-05Not shown
MA0512.2_Rxra9.14878e-05Not shown
MA1537.1_NR2F1(var.2)9.14878e-05Not shown
MA0115.1_NR1H2::RXRA0.00010423600000000001Not shown
MA0855.1_RXRB0.00010423600000000001Not shown

Motif 5/8

Motif IDq-valPWM
MA0837.1_CEBPE3.3654199999999997e-06
MA0466.2_CEBPB3.3654199999999997e-06
MA0838.1_CEBPG2.21323e-05
CEBPB_HUMAN.H11MO.0.A6.88019e-05
CEBPD_HUMAN.H11MO.0.C0.00011710799999999999
HLF_HUMAN.H11MO.0.C0.00136126Not shown
CEBPA_HUMAN.H11MO.0.A0.00174124Not shown
DBP_HUMAN.H11MO.0.B0.00332994Not shown
MA0639.1_DBP0.00332994Not shown
MA0043.3_HLF0.0034213Not shown

Motif 6/8

Motif IDq-valPWM
MA1135.1_FOSB::JUNB6.36858e-05
MA1138.1_FOSL2::JUNB6.36858e-05
MA0489.1_JUN(var.2)6.36858e-05
MA1144.1_FOSL2::JUND6.36858e-05
MA0099.3_FOS::JUN6.36858e-05
JUN_HUMAN.H11MO.0.A6.36858e-05Not shown
FOSB_HUMAN.H11MO.0.A6.36858e-05Not shown
FOSL2_HUMAN.H11MO.0.A9.3866e-05Not shown
MA1622.1_Smad2::Smad30.00011263899999999999Not shown
MA1134.1_FOS::JUNB0.00011263899999999999Not shown

Motif 7/8

Motif IDq-valPWM
HNF1B_HUMAN.H11MO.0.A2.55715e-10
HNF1A_HUMAN.H11MO.0.C4.0187900000000003e-08
MA0046.2_HNF1A6.37554e-07
MA0153.2_HNF1B9.69367e-07
HNF1B_HUMAN.H11MO.1.A2.03039e-05
ZFHX3_HUMAN.H11MO.0.D0.0421217Not shown
MEOX2_HUMAN.H11MO.0.D0.11027999999999999Not shown
PAX4_HUMAN.H11MO.0.D0.11027999999999999Not shown
MA0706.1_MEOX20.172018Not shown
FOXJ3_HUMAN.H11MO.1.B0.21269699999999997Not shown

Motif 8/8

Motif IDq-valPWM
MA1550.1_PPARD1.10725e-05
MA1537.1_NR2F1(var.2)1.2346199999999999e-05
RXRG_HUMAN.H11MO.0.B3.99492e-05
MA0677.1_Nr2f63.99492e-05
MA1574.1_THRB3.99492e-05
MA0512.2_Rxra6.43493e-05Not shown
MA0856.1_RXRG6.43493e-05Not shown
MA0855.1_RXRB8.54815e-05Not shown
NR2F6_HUMAN.H11MO.0.D0.000187215Not shown
MA0504.1_NR2C20.00034495699999999996Not shown

Metacluster 2/2

Motif 1/1

Motif IDq-valPWM
MA0846.1_FOXC21.78003e-07
FOXA1_HUMAN.H11MO.0.A2.5228000000000003e-07
FOXA3_HUMAN.H11MO.0.B2.5228000000000003e-07
FOXA2_HUMAN.H11MO.0.A3.81731e-07
MA0847.2_FOXD23.95497e-06
MA0032.2_FOXC15.16889e-06Not shown
FOXF2_HUMAN.H11MO.0.D5.9977499999999995e-06Not shown
FOXC1_HUMAN.H11MO.0.C5.9977499999999995e-06Not shown
FOXD3_HUMAN.H11MO.0.D5.9977499999999995e-06Not shown
FOXD1_HUMAN.H11MO.0.D5.33593e-05Not shown