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_fold3/FOXA2_multitask_profile_fold3_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/FOXA2_multitask_profile_fold3/FOXA2_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/FOXA2_multitask_profile_fold3/FOXA2_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%|██████████| 174/174 [02:37<00:00,  1.11it/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

7570 seqlets

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

Pattern 2/9

2375 seqlets

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

Pattern 3/9

1526 seqlets

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

Pattern 4/9

585 seqlets

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

Pattern 5/9

219 seqlets

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

Pattern 6/9

195 seqlets

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

Pattern 7/9

177 seqlets

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

Pattern 8/9

155 seqlets

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

Pattern 9/9

59 seqlets

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

Metacluster 2/2

Pattern 1/1

63 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
17570
22375
31526
4585
5219
6195
7177
8155
959

Metacluster 2/2

#SeqletsForwardReverse
163

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.B1.66641e-09
MA0846.1_FOXC22.9556400000000002e-08
FOXA1_HUMAN.H11MO.0.A2.9556400000000002e-08
FOXA2_HUMAN.H11MO.0.A2.9556400000000002e-08
MA0032.2_FOXC12.38192e-07
MA0847.2_FOXD26.86316e-06Not shown
FOXF2_HUMAN.H11MO.0.D6.86316e-06Not shown
FOXD3_HUMAN.H11MO.0.D7.802639999999998e-06Not shown
MA0845.1_FOXB11.12432e-05Not shown
MA1607.1_Foxl21.76042e-05Not shown

Motif 2/9

Motif IDq-valPWM
FOXM1_HUMAN.H11MO.0.A9.83363e-05
FOXJ2_HUMAN.H11MO.0.C0.00510714
MA1487.1_FOXE10.00510714
FOXA1_HUMAN.H11MO.0.A0.00510714
FOXF2_HUMAN.H11MO.0.D0.00618657
MA0846.1_FOXC20.00715598Not shown
FOXA2_HUMAN.H11MO.0.A0.00715598Not shown
MA0847.2_FOXD20.00816005Not shown
FOXD3_HUMAN.H11MO.0.D0.0094467Not shown
FOXA3_HUMAN.H11MO.0.B0.0094467Not shown

Motif 3/9

Motif IDq-valPWM
HNF4A_HUMAN.H11MO.0.A1.99867e-06
HNF4G_HUMAN.H11MO.0.B1.99867e-06
MA0677.1_Nr2f60.000164964
MA0856.1_RXRG0.000164964
MA1550.1_PPARD0.000164964
MA0512.2_Rxra0.000164964Not shown
MA1537.1_NR2F1(var.2)0.000164964Not shown
MA1574.1_THRB0.000164964Not shown
MA0115.1_NR1H2::RXRA0.00017313400000000003Not shown
MA0855.1_RXRB0.00017391099999999998Not shown

Motif 4/9

Motif IDq-valPWM
CEBPB_HUMAN.H11MO.0.A4.59041e-07
CEBPA_HUMAN.H11MO.0.A1.39477e-05
CEBPD_HUMAN.H11MO.0.C1.39477e-05
MA0836.2_CEBPD3.09011e-05
MA0837.1_CEBPE0.000184732
MA0466.2_CEBPB0.000199746Not shown
MA0102.4_CEBPA0.000199746Not shown
MA0838.1_CEBPG0.00042982Not shown
MA0043.3_HLF0.0009407510000000001Not shown
MA0025.2_NFIL30.00144489Not shown

Motif 5/9

Motif IDq-valPWM
MA0491.2_JUND4.3794499999999995e-05
MA1633.1_BACH14.3794499999999995e-05
NF2L2_HUMAN.H11MO.0.A4.3794499999999995e-05
MA0496.3_MAFK4.3794499999999995e-05
MA1634.1_BATF0.000295345
MA0462.2_BATF::JUN0.000295345Not shown
JUND_HUMAN.H11MO.0.A0.000295345Not shown
JUN_HUMAN.H11MO.0.A0.000295345Not shown
MA0099.3_FOS::JUN0.000295345Not shown
MA0655.1_JDP20.000295345Not shown

Motif 6/9

Motif IDq-valPWM
HNF4A_HUMAN.H11MO.0.A1.7983900000000002e-05
MA0677.1_Nr2f61.7983900000000002e-05
NR1H2_HUMAN.H11MO.0.D1.7983900000000002e-05
HNF4G_HUMAN.H11MO.0.B1.7983900000000002e-05
MA1550.1_PPARD1.7983900000000002e-05
MA0115.1_NR1H2::RXRA2.57637e-05Not shown
MA0512.2_Rxra2.7398600000000002e-05Not shown
MA0856.1_RXRG3.4869e-05Not shown
MA0855.1_RXRB3.50374e-05Not shown
MA1537.1_NR2F1(var.2)4.5955600000000004e-05Not shown

Motif 7/9

Motif IDq-valPWM
HNF4G_HUMAN.H11MO.0.B0.00011486
HNF4A_HUMAN.H11MO.0.A0.00011486
MA0856.1_RXRG0.00011486
RXRG_HUMAN.H11MO.0.B0.00011486
MA0855.1_RXRB0.00011486
MA1574.1_THRB0.00011486Not shown
MA0512.2_Rxra0.00011486Not shown
MA1550.1_PPARD0.00011486Not shown
MA0677.1_Nr2f60.00011486Not shown
MA1537.1_NR2F1(var.2)0.000183067Not shown

Motif 8/9

Motif IDq-valPWM
FOXA2_HUMAN.H11MO.0.A0.07667819999999999
MA0102.4_CEBPA0.07667819999999999
MA0847.2_FOXD20.07667819999999999
MA1607.1_Foxl20.07667819999999999
FOXC1_HUMAN.H11MO.0.C0.0859338
FOXB1_HUMAN.H11MO.0.D0.0859338Not shown
FOXD1_HUMAN.H11MO.0.D0.0909235Not shown
FOXA1_HUMAN.H11MO.0.A0.10569200000000001Not shown
FOXF2_HUMAN.H11MO.0.D0.11764000000000001Not shown
MA0032.2_FOXC10.11764000000000001Not shown

Motif 9/9

Motif IDq-valPWM
CPEB1_HUMAN.H11MO.0.D0.00012528799999999998
FOXL1_HUMAN.H11MO.0.D0.0254634
MA1125.1_ZNF3840.025600900000000003
PRDM6_HUMAN.H11MO.0.C0.025600900000000003
FOXG1_HUMAN.H11MO.0.D0.0410911
ANDR_HUMAN.H11MO.0.A0.07862960000000001Not shown
FOXJ3_HUMAN.H11MO.0.A0.100563Not shown
FUBP1_HUMAN.H11MO.0.D0.100563Not shown
ONEC2_HUMAN.H11MO.0.D0.112426Not shown
MA0679.2_ONECUT10.112426Not shown

Metacluster 2/2

Motif 1/1

Motif IDq-valPWM
FOXA2_HUMAN.H11MO.0.A1.31614e-05
MA1607.1_Foxl22.26125e-05
FOXA3_HUMAN.H11MO.0.B3.2114e-05
MA0846.1_FOXC23.2114e-05
FOXA1_HUMAN.H11MO.0.A3.2114e-05
FOXF2_HUMAN.H11MO.0.D0.000134998Not shown
MA0032.2_FOXC10.000155126Not shown
MA0852.2_FOXK10.000182124Not shown
FOXD3_HUMAN.H11MO.0.D0.000448359Not shown
MA0847.2_FOXD20.000448359Not shown