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_fold9/FOXA2_multitask_profile_fold9_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/FOXA2_multitask_profile_fold9/FOXA2_multitask_profile_fold9_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/FOXA2_multitask_profile_fold9/FOXA2_multitask_profile_fold9_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%|██████████| 174/174 [01:25<00:00,  2.04it/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 1/9

8400 seqlets

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

Pattern 2/9

2707 seqlets

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

Pattern 3/9

1543 seqlets

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

Pattern 4/9

523 seqlets

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

Pattern 5/9

263 seqlets

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

Pattern 6/9

159 seqlets

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

Pattern 7/9

154 seqlets

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

Pattern 8/9

92 seqlets

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

Pattern 9/9

58 seqlets

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

Metacluster 2/2

Pattern 1/3

147 seqlets

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

Pattern 2/3

126 seqlets

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

Pattern 3/3

42 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
18400
22707
31543
4523
5263
6159
7154
892
958

Metacluster 2/2

#SeqletsForwardReverse
1147
2126
342

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 1/9

Motif IDq-valPWM
FOXA3_HUMAN.H11MO.0.B2.0920000000000002e-07
FOXA2_HUMAN.H11MO.0.A2.0920000000000002e-07
MA0032.2_FOXC12.0920000000000002e-07
FOXA1_HUMAN.H11MO.0.A2.0920000000000002e-07
MA0846.1_FOXC22.0920000000000002e-07
FOXF2_HUMAN.H11MO.0.D1.03428e-05Not shown
MA0845.1_FOXB11.03428e-05Not shown
MA0847.2_FOXD21.23274e-05Not shown
FOXD3_HUMAN.H11MO.0.D1.64366e-05Not shown
MA1607.1_Foxl22.60583e-05Not shown

Motif 2/9

Motif IDq-valPWM
FOXM1_HUMAN.H11MO.0.A0.000624718
FOXJ2_HUMAN.H11MO.0.C0.0012996
MA1487.1_FOXE10.00154717
FOXF2_HUMAN.H11MO.0.D0.00686951
FOXA1_HUMAN.H11MO.0.A0.00686951
FOXL1_HUMAN.H11MO.0.D0.00686951Not shown
MA0847.2_FOXD20.00686951Not shown
FOXF1_HUMAN.H11MO.0.D0.00686951Not shown
MA0846.1_FOXC20.00686951Not shown
MA0041.1_Foxd30.00686951Not shown

Motif 3/9

Motif IDq-valPWM
HNF4A_HUMAN.H11MO.0.A1.35178e-06
HNF4G_HUMAN.H11MO.0.B1.35178e-06
MA0677.1_Nr2f60.000385305
MA0856.1_RXRG0.000385305
MA0512.2_Rxra0.000385305
MA1537.1_NR2F1(var.2)0.000385305Not shown
MA1574.1_THRB0.000385305Not shown
MA1550.1_PPARD0.000385305Not shown
MA0115.1_NR1H2::RXRA0.000425755Not shown
MA0855.1_RXRB0.000444208Not shown

Motif 4/9

Motif IDq-valPWM
CEBPB_HUMAN.H11MO.0.A9.373e-06
CEBPD_HUMAN.H11MO.0.C9.373e-06
CEBPA_HUMAN.H11MO.0.A2.13523e-05
MA0837.1_CEBPE2.1929899999999998e-05
MA0836.2_CEBPD2.5032800000000002e-05
MA0466.2_CEBPB2.67389e-05Not shown
MA0838.1_CEBPG3.98165e-05Not shown
MA0102.4_CEBPA0.00021071Not shown
HLF_HUMAN.H11MO.0.C0.00244716Not shown
DBP_HUMAN.H11MO.0.B0.0028138Not shown

Motif 5/9

Motif IDq-valPWM
MA1138.1_FOSL2::JUNB1.51567e-05
MA1135.1_FOSB::JUNB1.51567e-05
MA0489.1_JUN(var.2)1.51567e-05
MA0099.3_FOS::JUN1.51567e-05
MA1144.1_FOSL2::JUND1.51567e-05
MA1134.1_FOS::JUNB2.48558e-05Not shown
JUN_HUMAN.H11MO.0.A2.48558e-05Not shown
FOSB_HUMAN.H11MO.0.A3.77251e-05Not shown
FOSL2_HUMAN.H11MO.0.A3.77251e-05Not shown
JUND_HUMAN.H11MO.0.A3.77251e-05Not shown

Motif 6/9

Motif IDq-valPWM
HNF4G_HUMAN.H11MO.0.B3.9064199999999996e-08
HNF4A_HUMAN.H11MO.0.A6.74417e-07
MA1550.1_PPARD0.000251071
MA0677.1_Nr2f60.000281708
MA0115.1_NR1H2::RXRA0.000403101
MA0856.1_RXRG0.00047363Not shown
MA0512.2_Rxra0.000507411Not shown
MA1537.1_NR2F1(var.2)0.00070241Not shown
MA0504.1_NR2C20.0007596339999999999Not shown
MA0855.1_RXRB0.0007596339999999999Not shown

Motif 7/9

Motif IDq-valPWM
HNF4A_HUMAN.H11MO.0.A6.52857e-07
HNF4G_HUMAN.H11MO.0.B1.08724e-06
MA0677.1_Nr2f62.21444e-05
MA1550.1_PPARD2.21444e-05
MA0856.1_RXRG2.60268e-05
MA0512.2_Rxra2.7995100000000002e-05Not shown
MA1537.1_NR2F1(var.2)3.90772e-05Not shown
MA1574.1_THRB3.90772e-05Not shown
MA0855.1_RXRB3.90772e-05Not shown
NR1H2_HUMAN.H11MO.0.D5.6821400000000005e-05Not shown

Motif 8/9

Motif IDq-valPWM
CEBPD_HUMAN.H11MO.0.C4.63853e-05
CEBPA_HUMAN.H11MO.0.A8.94989e-05
CEBPB_HUMAN.H11MO.0.A8.94989e-05
MA0466.2_CEBPB0.00046333400000000003
MA0837.1_CEBPE0.00046333400000000003
NFIL3_HUMAN.H11MO.0.D0.00046333400000000003Not shown
MA0836.2_CEBPD0.000515437Not shown
MA0838.1_CEBPG0.000515437Not shown
DBP_HUMAN.H11MO.0.B0.00459596Not shown
MA0025.2_NFIL30.00465548Not shown

Motif 9/9

Motif IDq-valPWM
CEBPA_HUMAN.H11MO.0.A0.0349487
CEBPD_HUMAN.H11MO.0.C0.0349487
NFIL3_HUMAN.H11MO.0.D0.0442134
MA0466.2_CEBPB0.0442134
MA0837.1_CEBPE0.0442134
CEBPB_HUMAN.H11MO.0.A0.0442134Not shown
MA0838.1_CEBPG0.0741807Not shown
DBP_HUMAN.H11MO.0.B0.156585Not shown
MA0836.2_CEBPD0.156585Not shown
HLF_HUMAN.H11MO.0.C0.22424699999999997Not shown

Metacluster 2/2

Motif 1/3

No TOMTOM matches passing threshold

Motif 2/3

Motif IDq-valPWM
FOXA2_HUMAN.H11MO.0.A0.00265954
FOXA1_HUMAN.H11MO.0.A0.00265954
MA0848.1_FOXO40.00265954
MA0849.1_FOXO60.00265954
FOXF2_HUMAN.H11MO.0.D0.00265954
MA0042.2_FOXI10.00265954Not shown
MA0850.1_FOXP30.00265954Not shown
MA0031.1_FOXD10.00372335Not shown
MA0157.2_FOXO30.00372335Not shown
MA1489.1_FOXN30.00372335Not shown

Motif 3/3

No TOMTOM matches passing threshold