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_fold6/NR3C1-reddytime_multitask_profile_fold6_imp_scores.h5
TF-MoDISco results path: /users/amtseng/tfmodisco/results/tfmodisco/multitask_profile/NR3C1-reddytime_multitask_profile_fold6/NR3C1-reddytime_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/NR3C1-reddytime_multitask_profile_fold6/NR3C1-reddytime_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%|██████████| 186/186 [01:28<00:00,  2.09it/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 1/12

6041 seqlets

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

Pattern 2/12

2703 seqlets

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

Pattern 3/12

1620 seqlets

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

Pattern 4/12

1069 seqlets

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

Pattern 5/12

1026 seqlets

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

Pattern 6/12

652 seqlets

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

Pattern 7/12

511 seqlets

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

Pattern 8/12

155 seqlets

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

Pattern 9/12

144 seqlets

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

Pattern 10/12

103 seqlets

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

Pattern 11/12

58 seqlets

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

Pattern 12/12

51 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
16041
22703
31620
41069
51026
6652
7511
8155
9144
10103
1158
1251

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 1/12

Motif IDq-valPWM
GCR_HUMAN.H11MO.0.A4.77917e-10
PRGR_HUMAN.H11MO.0.A7.010850000000001e-07
ANDR_HUMAN.H11MO.1.A7.010850000000001e-07
MA0727.1_NR3C28.24386e-05
MA0113.3_NR3C10.00017995400000000002
PRGR_HUMAN.H11MO.1.A0.0059811000000000005Not shown
MA0007.3_Ar0.00779362Not shown
GCR_HUMAN.H11MO.1.A0.025892200000000004Not shown
RARG_HUMAN.H11MO.0.B0.267074Not shown
MA1508.1_IKZF10.31943499999999997Not shown

Motif 2/12

Motif IDq-valPWM
FOSL1_HUMAN.H11MO.0.A2.3616e-07
FOSB_HUMAN.H11MO.0.A1.0604e-06
MA0478.1_FOSL21.0604e-06
JUN_HUMAN.H11MO.0.A2.0163400000000003e-06
FOSL2_HUMAN.H11MO.0.A2.15077e-06
MA1622.1_Smad2::Smad32.68846e-06Not shown
MA0099.3_FOS::JUN2.99118e-06Not shown
JUND_HUMAN.H11MO.0.A3.92593e-06Not shown
MA0489.1_JUN(var.2)5.23457e-06Not shown
MA1144.1_FOSL2::JUND5.23457e-06Not shown

Motif 3/12

Motif IDq-valPWM
CEBPB_HUMAN.H11MO.0.A1.21786e-09
CEBPD_HUMAN.H11MO.0.C5.45933e-07
CEBPA_HUMAN.H11MO.0.A1.0779899999999998e-06
MA0836.2_CEBPD3.1596e-05
MA0102.4_CEBPA0.000160069
MA0025.2_NFIL30.00031890099999999996Not shown
MA0837.1_CEBPE0.000610944Not shown
MA0466.2_CEBPB0.000729112Not shown
DBP_HUMAN.H11MO.0.B0.0009474710000000001Not shown
MA0043.3_HLF0.000975418Not shown

Motif 4/12

Motif IDq-valPWM
FOXA1_HUMAN.H11MO.0.A1.0187599999999999e-07
FOXA2_HUMAN.H11MO.0.A1.10044e-06
FOXM1_HUMAN.H11MO.0.A1.10044e-06
FOXF2_HUMAN.H11MO.0.D6.65132e-06
FOXA3_HUMAN.H11MO.0.B1.0642100000000001e-05
FOXD3_HUMAN.H11MO.0.D1.33026e-05Not shown
MA0847.2_FOXD22.23302e-05Not shown
MA0846.1_FOXC22.23302e-05Not shown
FOXC1_HUMAN.H11MO.0.C2.8160700000000003e-05Not shown
FOXJ2_HUMAN.H11MO.0.C0.000803153Not shown

Motif 5/12

Motif IDq-valPWM
GCR_HUMAN.H11MO.0.A0.000210218
PRGR_HUMAN.H11MO.0.A0.000210218
ANDR_HUMAN.H11MO.1.A0.0044855
MA0727.1_NR3C20.0435629
MA0113.3_NR3C10.08475369999999999
HSF1_HUMAN.H11MO.0.A0.08475369999999999Not shown
MA0808.1_TEAD30.134741Not shown
HSF2_HUMAN.H11MO.0.A0.134741Not shown
ZN502_HUMAN.H11MO.0.C0.185907Not shown
ZN394_HUMAN.H11MO.1.D0.24281799999999998Not shown

Motif 6/12

Motif IDq-valPWM
MA1136.1_FOSB::JUNB(var.2)4.853100000000001e-05
MA0605.2_ATF34.853100000000001e-05
MA1140.2_JUNB(var.2)4.853100000000001e-05
MA1139.1_FOSL2::JUNB(var.2)4.853100000000001e-05
MA1133.1_JUN::JUNB(var.2)4.853100000000001e-05
JDP2_HUMAN.H11MO.0.D4.853100000000001e-05Not shown
MA1127.1_FOSB::JUN4.853100000000001e-05Not shown
ATF7_HUMAN.H11MO.0.D4.853100000000001e-05Not shown
ATF2_HUMAN.H11MO.2.C4.853100000000001e-05Not shown
MA0656.1_JDP2(var.2)4.853100000000001e-05Not shown

Motif 7/12

Motif IDq-valPWM
TEAD2_HUMAN.H11MO.0.D0.00127155
MA1121.1_TEAD20.00248646
MA0808.1_TEAD30.00248646
TEAD4_HUMAN.H11MO.0.A0.00249358
TEAD1_HUMAN.H11MO.0.A0.00249358
MA0809.2_TEAD40.00829603Not shown
MA0090.3_TEAD10.00853305Not shown

Motif 8/12

Motif IDq-valPWM
MA1132.1_JUN::JUNB0.0620617
MA1622.1_Smad2::Smad30.0620617
MA0833.2_ATF40.0620617
MA1634.1_BATF0.0620617
MA0462.2_BATF::JUN0.0620617
MA1141.1_FOS::JUND0.0620617Not shown
MA1633.1_BACH10.0620617Not shown
FOSL2_HUMAN.H11MO.0.A0.0620617Not shown
FOSL1_HUMAN.H11MO.0.A0.0620617Not shown
MA1138.1_FOSL2::JUNB0.0620617Not shown

Motif 9/12

Motif IDq-valPWM
PRGR_HUMAN.H11MO.0.A0.00184121
GCR_HUMAN.H11MO.0.A0.00435751
ANDR_HUMAN.H11MO.1.A0.023498599999999998
PRGR_HUMAN.H11MO.1.A0.036481
MA0727.1_NR3C20.055073000000000004
MA0113.3_NR3C10.0648943Not shown
HSF1_HUMAN.H11MO.0.A0.396098Not shown
MA1508.1_IKZF10.396098Not shown
HSF2_HUMAN.H11MO.0.A0.396098Not shown
GCR_HUMAN.H11MO.1.A0.396098Not shown

Motif 10/12

Motif IDq-valPWM
MA0041.1_Foxd30.0366207
FOXJ2_HUMAN.H11MO.0.C0.0890291
PRGR_HUMAN.H11MO.0.A0.160171
MA1487.1_FOXE10.160171
FOXQ1_HUMAN.H11MO.0.C0.21593
FOXF1_HUMAN.H11MO.0.D0.21593Not shown
FOXM1_HUMAN.H11MO.0.A0.21593Not shown
ANDR_HUMAN.H11MO.1.A0.21593Not shown
FOXL1_HUMAN.H11MO.0.D0.21593Not shown
MA0846.1_FOXC20.276997Not shown

Motif 11/12

Motif IDq-valPWM
FOXD2_HUMAN.H11MO.0.D0.00765307
FOXB1_HUMAN.H11MO.0.D0.0284296
MA1487.1_FOXE10.21224
FOXA1_HUMAN.H11MO.0.A0.226635
MA0847.2_FOXD20.226635
MA0032.2_FOXC10.303935Not shown
MA0845.1_FOXB10.303935Not shown
FOXA2_HUMAN.H11MO.0.A0.310812Not shown
FOXM1_HUMAN.H11MO.0.A0.370285Not shown

Motif 12/12

Motif IDq-valPWM
CEBPB_HUMAN.H11MO.0.A0.00027329599999999997
CEBPA_HUMAN.H11MO.0.A0.0009686569999999999
CEBPD_HUMAN.H11MO.0.C0.00203487
CEBPE_HUMAN.H11MO.0.A0.00203487
MA0837.1_CEBPE0.00214839
MA0836.2_CEBPD0.00220615Not shown
MA0466.2_CEBPB0.00220615Not shown
MA0838.1_CEBPG0.00307191Not shown
MA0102.4_CEBPA0.00574556Not shown
PRGR_HUMAN.H11MO.0.A0.00576718Not shown